Theory Wiki - User contributions [en]

PSLC GradStudents

2010-08-27T17:29:21Z

Juffs: /* Who are the PSLC grads? */

The purpose of this page is to serve as a repository of information relevant for grad students. We hope to maintain this page as a repository of current and relevant information for graduate students currently affiliated with the PSLC, as well as grad students who hope to be in the PSLC.

== Announcements==

1) PSLC grads are now responsible for keeping the [http://www.learnlab.org/research/wiki/index.php/PSLC_People#Graduate_Students List of PSLC Grads] up to date.

* If you know of someone who should be added (or deleted) from this list please e-mail the webmaster at bef25@pitt.edu. Alternatively, feel free to go in and update the list yourself!

== Meeting Notes==

== FAQs==

'''1. What does it take to be a PSLC grad student?'''

Well, there are basically three ways you can be considered a PSLC grad student.
a. You work on a project that receives funding from the PSLC.
b. Your advisor or collaborator receives funding from the PSLC and asks you to be involved.
c. You want to be a PSLC grad student.

'''2. What types of opportunities does the PSLC have for a grad student like me?'''

There are a variety of different levels of involvement and types of activities that the PSLC offers.

For the casual grad student, the PSLC organizes a speaker series with talks that may be of interest to students interested in the learning sciences. These are open to whomever wishes to go. There are also monthly lunch meetings where people associated with the PSLC can give a talk on their work.

The grad student community also hopes to organize events catered toward grad students, with topics like applying for grants, finding jobs, collaboration with people at other universities, etc. These are also open to the public.

For those who wish to get more involved, the grad student community also has monthly meetings to discuss center-wide issues, read and discuss articles we believe are relevant, plan future events, etc. Again, these are open to the public.

Finally, each thrust has regular or semi-regular meetings to discuss the thrust's theoretical framework, set the research agenda, and discuss the progress of projects within that thrust. While these are open to anyone, they're probably of limited interest unless you currently have or have had a project affiliated with the thrust.

'''3. What is expected of me as a PSLC grad student?'''

If you receive funding from the PSLC, you are expected, to the extent it is possible, to attend the thrust meetings for your relevant thrust, and attend the monthly PSLC lunches. The grad student community also encourages you to come to the grad student monthly meetings, of course.

If you don't receive funding from the PSLC, but still wish to be a part of the grad student community, your level of involvement is up to you.

'''How do I find out about upcoming talks/meetings/events?'''

One option is to check the Announcements section of this page. A possibly better option would be to get on our mailing list. To do that, e-mail Jo Bodnar at jobodnar AT cs.cmu.edu and ask to be put on the PSLC general mailing list and grad student mailing list.

There is also a regularly updated calendar at our main webpage (learnlab.org) that is updated regularly and gives a fairly complete account of most PSLC events.

4. '''I already consider myself a PSLC grad, and want to be included on this page! What do I have to do?'''

Well the great thing about the wiki page is that anybody can update it whenever they want! So, if you have an account here, and you know how to edit tables, you can just log in and add yourself!

If you don't have an account already, you can easily request one (NOTE: I forget how to do it- I'll need to add that). Once you have an account, you can just click "Edit" above the table, and you can add yourself.

5. '''But that's such a pain! Isn't there an easier way?!'''

There sure is! If you don't want to make all that effort just to have your name and e-mail address on a page, just send your info to our Wikimaster (yep, we made that word up!), Ben Friedline, at bef25 AT pitt.edu, and he'll put it on here.

== Who are the PSLC grads? ==

{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
|-
! Grad Student Name
! University/Department
! Advisor
! E-mail
! Bio
! Personal Webpage
! PSLC Projects
|-
| Colleen Davy || Carnegie Mellon/Psychology || Brian MacWhinney || cdavy1@andrew.cmu.edu || I am interested in how adult second language learners develop fluent speaking skills in their second language. || N/A || [http://www.learnlab.org/research/wiki/index.php/Davy_%26_MacWhinney_-_Spanish_Sentence_Production Spanish Sentence Production]
|-
| Benjamin Friedline || University of Pittsburgh || Alan Juffs || bef25@pitt.edu || I am interested in how adult second language learners acquire morphology in a second language. || N/A || [http://www.learnlab.org/research/wiki/index.php/Juffs_-_Feature_Focus_in_Word_Learning Feature Focus in Word Learning]
|}

== Science of Learning Relevant Courses ==
The PIER program offers three courses -- see the [www.cmu.edu/pier/ PIER web site].

See also the courses taught be any of the PSLC faculty.

(Please add the names of relevant courses and web pointers if possible!)

<pre>
05832 / 05432 Cognitive Modeling & Intelligent Tutoring Systems
3:00pm-4:20pm, Tuesdays and Thursdays, Fall 2010
Room 3002, Newell-Simon Hall, Carnegie Mellon University
9 units
Dr. Vincent Aleven, aleven@cs.cmu.edu
</pre>

Students in this course will learn about the Cognitive Tutor technology that has been demonstrated to dramatically enhance student learning in domains like math, science, and computer programming. This type of tutoring software is currently in use in 2,700 schools around the country and is used extensively as platform for learning sciences research. The technology is grounded in artificial intelligence, cognitive psychology, and cognitive task analysis. Students will learn data-driven and theoretical methods for analyzing human problem solving and will learn to use such data to inform the design of intelligent tutoring systems. Course projects will focus on the development of an intelligent tutor using CTAT, the Cognitive Tutor Authoring Tools (see http://ctat.pact.cs.cmu.edu). Some assignments will focus on creating cognitive models in the Jess production rule modeling language.

Students should either have programming skills, or experience in the cognitive psychology of human problem solving, or HCI / design skills, or permission from the instructor.

PSLC People

2010-08-27T17:18:33Z

Juffs: /* Graduate Students */

== '''The Executive Committee''' ==

=== Directors ===
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| '''Ken Koedinger''' || Carnegie Mellon University || Human-Computer Interaction Institute
|-
| '''Charles Perfetti''' || University of Pittsburgh || Psychology, LRDC Director
|}

=== Managing Director ===
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| '''Michael Bett''' || Carnegie Mellon University || Human-Computer Interaction Institute
|}

=== Members ===
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Aleven, Vincent || Carnegie Mellon University || Human-Computer Interaction
|-
| Eskenazi, Maxine || Carnegie Mellon University || Language Technologies Institute
|-
| Fiez, Julie || University of Pittsburgh || Psychology
|-
| Gordon, Geoff || Carnegie Mellon University || Machine Learning
|-
| Klahr, David || Carnegie Mellon University || Psychology
|-
| Lovett, Marsha || Carnegie Mellon University || Psychology
|-
| Nokes, Tim || University of Pittsburgh || LRDC
|-
| Resnick, Lauren || University of Pittsburgh || Learning Research and Development Center
|-
| Rose, Carolyn || Carnegie Mellon University || Human-Computer Interaction Institute/Language Technologies Institute
|}

== Advisory Board ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Aronson, Joshua || New York University || Applied Psychology
|-
| Atkinson, Robert || Arizona State University || Division of Psychology in Education
|-
| Azevedo, Roger || University of Memphis || Psychology
|-
| Biswas, Gautam || Vanderbilt University || Computer Science and Computer Engineering
|-
| Collins, Allan || Northwestern University || Education and Social Policy
|-
| Dede, Christopher || Harvard University || Technology in Education
|-
| Goldman, Susan || University of Illinois || Psychology
|-
| Goldstone, Rob || Indiana University || Psychology
|-
| Griffiths, Tom || Berkeley || Psychology
|-
| Lesgold, Alan || University of Pittsburgh || School of Education
|-
| Li, Ping || Penn State University || Psychology
|-
| Minstrell, Jim || FACET Innovations, LLC Seattle, WA ||
|-
| Schauble, Leona || Vanderbilt University || Teaching & Learning
|}

== Graduate Students ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"

| Adam Skory || Carnegie Mellon ||
|-
| Benjamin Friedline || University of Pittsburgh || Linguistics
|-
| Colleen Davy || Carnegie Mellon ||
|-
| Garbiel Parent || Carnegie Mellon ||
|-
| (Derek) Ho Leung Chan || University of Pittsburgh ||
|-
| Leida Tolentino || University of Pittsburgh ||
|-
| Nora Presson || Carnegie Mellon ||
|-
| Ruth Wylie || Carnegie Mellon ||
|-
| Susan Dunlap || University of Pittsburgh ||
|-
| Yun Zhao || Carnegie Mellon ||
|-
| Benjamin Shih || Carnegie Mellon ||
|-
| Collin Lynch || University of Pittsburgh ||
|-
| Erik Zawadzki || Carnegie Mellon ||
|-
| Nan Li || Carnegie Mellon ||
|-
| Amy Ogan || Carnegie Mellon ||
|-
| Dan Belenky || University of Pittsburgh ||
|-
| Matthew Easterday || Carnegie Mellon ||
|-
| Soniya Gadgil || University of Pittsburgh ||
|-
| Yanhui Zhang || Carnegie Mellon ||
|-
| Dejana Diziol || Freiburg ||
|-
| Elizabeth Ayers || Carnegie Mellon ||
|-
| Elsa Golden || Carnegie Mellon ||
|-
| April Galyardt || Carnegie Mellon ||
|-
| Jamie Jirout || Carnegie Mellon ||
|-
| Martina Rau || Carnegie Mellon ||
|-
| Tom Lauwers || Carnegie Mellon ||
|-
| Tracy Sweet || Carnegie Mellon ||
|-
| Kevin Del Rosa || Carnegie Mellon ||
|-
| Turadg Aleahmad || Carnegie Mellon ||
|-
| Gahgene Gweon || Carnegie Mellon ||
|-
| Anagha Kulkarni (Joshi) || Carnegie Mellon ||
|-
| Bryan Matlen || Carnegie Mellon ||
|-
| Sung-Young Jung || University of Pittsburgh ||
|-
| Gustavo Santos || Carnegie Mellon ||
|-
| Hao-Chuan Wang || Carnegie Mellon ||
|-
| Indrayana Rustandi || Carnegie Mellon ||
|-
| Jessica Nelson || University of Pittsburgh ||
|-
| Rohit Kumar || Carnegie Mellon ||
|-
| Roxana Gheorghiu || University of Pittsburgh ||
|-
| Tamar Degani || University of Pittsburgh ||
|-
| Yan Mu || Carnegie Mellon ||
|-
| Elijah Mayfield || Carnegie Mellon ||
|-
| Erin Walker || Carnegie Mellon ||
|-
| Iris Howley || Carnegie Mellon ||
|-
| Sherice Clark || University of Ediborough ||
|-
| Tracy Clark || Univeristy of Pennslyvania ||
|-
| Laurens Feestra || Netherlands ||
|-
| Maaike Waalkens || Netherlands ||
|-
| Mary Lou Vercelotti || University of Pittsburgh || Linguistics
|-
| Katherine Martin || University of Pittsburgh || Linguistics
|}

== Post Docs ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"

| Laura Halderman || University of Pittsburgh ||
|-
| Hua Ai || Carnegie Mellon || LTI
|-
| Seiji Isotani || Carnegie Mellon University || HCII
|-
| John Connelly || University of Pittsburgh ||
|-
| Amy Crosson || University of Pittsburgh ||
|-
| Min Chi || Carnegie Mellon || HCII
|-
| Alicia Chang || University of Pittsburgh ||
|-
| Ido Roll || University of British Columbia ||
|-
| Connie Guan Qun || University of Pittsburgh ||
|-
| Stephanie Siler || Carnegie Mellon || Psychology
|-
| Zelha Tunc-Pekkan || Carnegie Mellon || HCII
|-
| Fan Cao || University of Pittsburgh ||
|-
| Suzanne Adlof || University of Pittsburgh ||
|-
| Chin-LungYang || University of Pittsburgh ||
|-
| Candace Walkington || University of Texas ||
|-
| Matthew Bernacki || University of Pittsburgh ||
|-
| Gregory Dyke || University of Pittsburgh ||
|-
| Sherrice Clarke || University of Pittsburgh ||

|}

== Faculty ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Al Corbett || Carnegie Mellon || HCII
|-
| Alan Juffs || University of Pittsburgh || Linguistics
|-
| Brian Junker || Carnegie Mellon || Statisics
|-
| Brian MacWhinney || Carnegie Mellon || Psychology
|-
| Bruce McLaren || Carnegie Mellon || HCII
|-
| Carolyn Rosé || Carnegie Mellon || LTI/HCII
|-
| Charles Perfetti || University of Pittsburgh || LRDC
|-
| Christa Asterhan || Hebrew University ||
|-
| David Klahr || Carnegie Mellon || Psychology
|-
| David Yaron || Carnegie Mellon || Chemistry
|-
| Geoff Gordon || Carnegie Mellon || Machine Learning
|-
| Jack Mostow || Carnegie Mellon || Robotics
|-
| Jim Greeno || University of Pittsburgh || Instruction and Learning
|-
| John Stamper || Carnegie Mellon || HCII
|-
| Ken Koedinger || Carnegie Mellon || HCII
|-
| Kirsten Butcher || University of Utah || Instructional Design & Educational Technology
|-
| Kurt VanLehn || Arizona State University || Computer Science and Engineering
|-
| Lauren Resnick || University of Pittsburgh || LRDC
|-
| Louis Gomez || University of Pittsburgh || School of Education
|-
| Marsha Lovett || Carnegie Mellon || Eberly Center
|-
| Mary Catherine O'Connor || Boston University || School of Education
|-
| Matthew Kam || Carnegie Mellon || HCII
|-
| Maxine Eskenazi || Carnegie Mellon || LTI
|-
| Nel de Jong || Vrije Universiteit Amsterdam ||
|-
| Niels Pinkwart || Clausthal University of Technology ||
|-
| Nikol Rummel || Ruhr-Universität Bochum || Psychology
|-
| Noboru Matsuda || Carnegie Mellon || HCII
|-
| Phil Pavlik || Carnegie Mellon || HCII
|-
| Richard Scheines || Carnegie Mellon || Philosphy
|-
| Ryan Baker || WPI ||
|-
| Sandy Katz || University of Pittsburgh || LRDC
|-
| Sarah Michaels || Clark University || Education
|-
| Teruko Matamura || Carnegie Mellon || LTI
|-
| Tim Nokes || University of Pittsburgh ||
|-
| Vincent Aleven || Carnegie Mellon || LTI
|-
| William Cohen || Carnegie Mellon || ML

|}

== Staff ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"

| Bob Hausman || Carnegie Learning ||
|-
| Brett Leber || Carnegie Mellon ||
|-
| Cressida Magaro || Carnegie Mellon ||
|-
| Cressida Magaro || Carnegie Mellon ||
|-
| Demi, Sandy || Carnegie Mellon University ||
|-
| Gail Kusbit || Carnegie Mellon || Research Manager
|-
| Haney, Mark || University of Pittsburgh ||
|-
| Jo Bodnar || Carnegie Mellon ||
|-
| Karabinos, Michael || Carnegie Mellon University ||
|-
| Kevin Willows || Carnegie Mellon ||
|-
| Kowalski, John || Carnegie Mellon University ||
|-
| Martin van Velsen || Carnegie Mellon ||
|-
| McGuire, Christy || Edalytics ||
|-
| Michael Bett || Carnegie Mellon || Managing Director
|-
| Mike Karabinos || Carnegie Mellon ||
|-
| Sewell, Jonathan || Carnegie Mellon University ||
|-
| Silliman, Scott || University of Pittsburgh ||
|-
| Skogsholm, Alida || Carnegie Mellon University || DataShop Manager
|-
| Smith, Dorolyn || University of Pittsburgh ||
|-
| Steve Ritter || Carnegie Learning || Founder
|-
| Strader, Ross || Carnegie Mellon University ||
|-
| Thomas Harris || Edalytics ||

|}

PSLC People

2010-08-27T17:18:00Z

Juffs: /* Graduate Students */

== '''The Executive Committee''' ==

=== Directors ===
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| '''Ken Koedinger''' || Carnegie Mellon University || Human-Computer Interaction Institute
|-
| '''Charles Perfetti''' || University of Pittsburgh || Psychology, LRDC Director
|}

=== Managing Director ===
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| '''Michael Bett''' || Carnegie Mellon University || Human-Computer Interaction Institute
|}

=== Members ===
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Aleven, Vincent || Carnegie Mellon University || Human-Computer Interaction
|-
| Eskenazi, Maxine || Carnegie Mellon University || Language Technologies Institute
|-
| Fiez, Julie || University of Pittsburgh || Psychology
|-
| Gordon, Geoff || Carnegie Mellon University || Machine Learning
|-
| Klahr, David || Carnegie Mellon University || Psychology
|-
| Lovett, Marsha || Carnegie Mellon University || Psychology
|-
| Nokes, Tim || University of Pittsburgh || LRDC
|-
| Resnick, Lauren || University of Pittsburgh || Learning Research and Development Center
|-
| Rose, Carolyn || Carnegie Mellon University || Human-Computer Interaction Institute/Language Technologies Institute
|}

== Advisory Board ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Aronson, Joshua || New York University || Applied Psychology
|-
| Atkinson, Robert || Arizona State University || Division of Psychology in Education
|-
| Azevedo, Roger || University of Memphis || Psychology
|-
| Biswas, Gautam || Vanderbilt University || Computer Science and Computer Engineering
|-
| Collins, Allan || Northwestern University || Education and Social Policy
|-
| Dede, Christopher || Harvard University || Technology in Education
|-
| Goldman, Susan || University of Illinois || Psychology
|-
| Goldstone, Rob || Indiana University || Psychology
|-
| Griffiths, Tom || Berkeley || Psychology
|-
| Lesgold, Alan || University of Pittsburgh || School of Education
|-
| Li, Ping || Penn State University || Psychology
|-
| Minstrell, Jim || FACET Innovations, LLC Seattle, WA ||
|-
| Schauble, Leona || Vanderbilt University || Teaching & Learning
|}

== Graduate Students ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"

| Adam Skory || Carnegie Mellon ||
|-
| Benjamin Friedline || University of Pittsburgh ||
|-
| Colleen Davy || Carnegie Mellon ||
|-
| Garbiel Parent || Carnegie Mellon ||
|-
| (Derek) Ho Leung Chan || University of Pittsburgh ||
|-
| Leida Tolentino || University of Pittsburgh ||
|-
| Nora Presson || Carnegie Mellon ||
|-
| Ruth Wylie || Carnegie Mellon ||
|-
| Susan Dunlap || University of Pittsburgh ||
|-
| Yun Zhao || Carnegie Mellon ||
|-
| Benjamin Shih || Carnegie Mellon ||
|-
| Collin Lynch || University of Pittsburgh ||
|-
| Erik Zawadzki || Carnegie Mellon ||
|-
| Nan Li || Carnegie Mellon ||
|-
| Amy Ogan || Carnegie Mellon ||
|-
| Dan Belenky || University of Pittsburgh ||
|-
| Matthew Easterday || Carnegie Mellon ||
|-
| Soniya Gadgil || University of Pittsburgh ||
|-
| Yanhui Zhang || Carnegie Mellon ||
|-
| Dejana Diziol || Freiburg ||
|-
| Elizabeth Ayers || Carnegie Mellon ||
|-
| Elsa Golden || Carnegie Mellon ||
|-
| April Galyardt || Carnegie Mellon ||
|-
| Jamie Jirout || Carnegie Mellon ||
|-
| Martina Rau || Carnegie Mellon ||
|-
| Tom Lauwers || Carnegie Mellon ||
|-
| Tracy Sweet || Carnegie Mellon ||
|-
| Kevin Del Rosa || Carnegie Mellon ||
|-
| Turadg Aleahmad || Carnegie Mellon ||
|-
| Gahgene Gweon || Carnegie Mellon ||
|-
| Anagha Kulkarni (Joshi) || Carnegie Mellon ||
|-
| Bryan Matlen || Carnegie Mellon ||
|-
| Sung-Young Jung || University of Pittsburgh ||
|-
| Gustavo Santos || Carnegie Mellon ||
|-
| Hao-Chuan Wang || Carnegie Mellon ||
|-
| Indrayana Rustandi || Carnegie Mellon ||
|-
| Jessica Nelson || University of Pittsburgh ||
|-
| Rohit Kumar || Carnegie Mellon ||
|-
| Roxana Gheorghiu || University of Pittsburgh ||
|-
| Tamar Degani || University of Pittsburgh ||
|-
| Yan Mu || Carnegie Mellon ||
|-
| Elijah Mayfield || Carnegie Mellon ||
|-
| Erin Walker || Carnegie Mellon ||
|-
| Iris Howley || Carnegie Mellon ||
|-
| Sherice Clark || University of Ediborough ||
|-
| Tracy Clark || Univeristy of Pennslyvania ||
|-
| Laurens Feestra || Netherlands ||
|-
| Maaike Waalkens || Netherlands ||
|-
| Mary Lou Vercelotti || University of Pittsburgh || Linguistics
|-
| Katherine Martin || University of Pittsburgh || Linguistics
|}

== Post Docs ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"

| Laura Halderman || University of Pittsburgh ||
|-
| Hua Ai || Carnegie Mellon || LTI
|-
| Seiji Isotani || Carnegie Mellon University || HCII
|-
| John Connelly || University of Pittsburgh ||
|-
| Amy Crosson || University of Pittsburgh ||
|-
| Min Chi || Carnegie Mellon || HCII
|-
| Alicia Chang || University of Pittsburgh ||
|-
| Ido Roll || University of British Columbia ||
|-
| Connie Guan Qun || University of Pittsburgh ||
|-
| Stephanie Siler || Carnegie Mellon || Psychology
|-
| Zelha Tunc-Pekkan || Carnegie Mellon || HCII
|-
| Fan Cao || University of Pittsburgh ||
|-
| Suzanne Adlof || University of Pittsburgh ||
|-
| Chin-LungYang || University of Pittsburgh ||
|-
| Candace Walkington || University of Texas ||
|-
| Matthew Bernacki || University of Pittsburgh ||
|-
| Gregory Dyke || University of Pittsburgh ||
|-
| Sherrice Clarke || University of Pittsburgh ||

|}

== Faculty ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"
| Al Corbett || Carnegie Mellon || HCII
|-
| Alan Juffs || University of Pittsburgh || Linguistics
|-
| Brian Junker || Carnegie Mellon || Statisics
|-
| Brian MacWhinney || Carnegie Mellon || Psychology
|-
| Bruce McLaren || Carnegie Mellon || HCII
|-
| Carolyn Rosé || Carnegie Mellon || LTI/HCII
|-
| Charles Perfetti || University of Pittsburgh || LRDC
|-
| Christa Asterhan || Hebrew University ||
|-
| David Klahr || Carnegie Mellon || Psychology
|-
| David Yaron || Carnegie Mellon || Chemistry
|-
| Geoff Gordon || Carnegie Mellon || Machine Learning
|-
| Jack Mostow || Carnegie Mellon || Robotics
|-
| Jim Greeno || University of Pittsburgh || Instruction and Learning
|-
| John Stamper || Carnegie Mellon || HCII
|-
| Ken Koedinger || Carnegie Mellon || HCII
|-
| Kirsten Butcher || University of Utah || Instructional Design & Educational Technology
|-
| Kurt VanLehn || Arizona State University || Computer Science and Engineering
|-
| Lauren Resnick || University of Pittsburgh || LRDC
|-
| Louis Gomez || University of Pittsburgh || School of Education
|-
| Marsha Lovett || Carnegie Mellon || Eberly Center
|-
| Mary Catherine O'Connor || Boston University || School of Education
|-
| Matthew Kam || Carnegie Mellon || HCII
|-
| Maxine Eskenazi || Carnegie Mellon || LTI
|-
| Nel de Jong || Vrije Universiteit Amsterdam ||
|-
| Niels Pinkwart || Clausthal University of Technology ||
|-
| Nikol Rummel || Ruhr-Universität Bochum || Psychology
|-
| Noboru Matsuda || Carnegie Mellon || HCII
|-
| Phil Pavlik || Carnegie Mellon || HCII
|-
| Richard Scheines || Carnegie Mellon || Philosphy
|-
| Ryan Baker || WPI ||
|-
| Sandy Katz || University of Pittsburgh || LRDC
|-
| Sarah Michaels || Clark University || Education
|-
| Teruko Matamura || Carnegie Mellon || LTI
|-
| Tim Nokes || University of Pittsburgh ||
|-
| Vincent Aleven || Carnegie Mellon || LTI
|-
| William Cohen || Carnegie Mellon || ML

|}

== Staff ==
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"

| Bob Hausman || Carnegie Learning ||
|-
| Brett Leber || Carnegie Mellon ||
|-
| Cressida Magaro || Carnegie Mellon ||
|-
| Cressida Magaro || Carnegie Mellon ||
|-
| Demi, Sandy || Carnegie Mellon University ||
|-
| Gail Kusbit || Carnegie Mellon || Research Manager
|-
| Haney, Mark || University of Pittsburgh ||
|-
| Jo Bodnar || Carnegie Mellon ||
|-
| Karabinos, Michael || Carnegie Mellon University ||
|-
| Kevin Willows || Carnegie Mellon ||
|-
| Kowalski, John || Carnegie Mellon University ||
|-
| Martin van Velsen || Carnegie Mellon ||
|-
| McGuire, Christy || Edalytics ||
|-
| Michael Bett || Carnegie Mellon || Managing Director
|-
| Mike Karabinos || Carnegie Mellon ||
|-
| Sewell, Jonathan || Carnegie Mellon University ||
|-
| Silliman, Scott || University of Pittsburgh ||
|-
| Skogsholm, Alida || Carnegie Mellon University || DataShop Manager
|-
| Smith, Dorolyn || University of Pittsburgh ||
|-
| Steve Ritter || Carnegie Learning || Founder
|-
| Strader, Ross || Carnegie Mellon University ||
|-
| Thomas Harris || Edalytics ||

|}

PSLC GradStudents

2010-08-27T17:17:21Z

Juffs: /* Announcements */

The purpose of this page is to serve as a repository of information relevant for grad students. We hope to maintain this page as a repository of current and relevant information for graduate students currently affiliated with the PSLC, as well as grad students who hope to be in the PSLC.

== Announcements==

1) PSLC grads are now responsible for keeping the [http://www.learnlab.org/research/wiki/index.php/PSLC_People#Graduate_Students List of PSLC Grads] up to date.

* If you know of someone who should be added (or deleted) from this list please e-mail the webmaster at bef25@pitt.edu. Alternatively, feel free to go in and update the list yourself!

== Meeting Notes==

== FAQs==

'''1. What does it take to be a PSLC grad student?'''

Well, there are basically three ways you can be considered a PSLC grad student.
a. You work on a project that receives funding from the PSLC.
b. Your advisor or collaborator receives funding from the PSLC and asks you to be involved.
c. You want to be a PSLC grad student.

'''2. What types of opportunities does the PSLC have for a grad student like me?'''

There are a variety of different levels of involvement and types of activities that the PSLC offers.

For the casual grad student, the PSLC organizes a speaker series with talks that may be of interest to students interested in the learning sciences. These are open to whomever wishes to go. There are also monthly lunch meetings where people associated with the PSLC can give a talk on their work.

The grad student community also hopes to organize events catered toward grad students, with topics like applying for grants, finding jobs, collaboration with people at other universities, etc. These are also open to the public.

For those who wish to get more involved, the grad student community also has monthly meetings to discuss center-wide issues, read and discuss articles we believe are relevant, plan future events, etc. Again, these are open to the public.

Finally, each thrust has regular or semi-regular meetings to discuss the thrust's theoretical framework, set the research agenda, and discuss the progress of projects within that thrust. While these are open to anyone, they're probably of limited interest unless you currently have or have had a project affiliated with the thrust.

'''3. What is expected of me as a PSLC grad student?'''

If you receive funding from the PSLC, you are expected, to the extent it is possible, to attend the thrust meetings for your relevant thrust, and attend the monthly PSLC lunches. The grad student community also encourages you to come to the grad student monthly meetings, of course.

If you don't receive funding from the PSLC, but still wish to be a part of the grad student community, your level of involvement is up to you.

'''How do I find out about upcoming talks/meetings/events?'''

One option is to check the Announcements section of this page. A possibly better option would be to get on our mailing list. To do that, e-mail Jo Bodnar at jobodnar AT cs.cmu.edu and ask to be put on the PSLC general mailing list and grad student mailing list.

There is also a regularly updated calendar at our main webpage (learnlab.org) that is updated regularly and gives a fairly complete account of most PSLC events.

4. '''I already consider myself a PSLC grad, and want to be included on this page! What do I have to do?'''

Well the great thing about the wiki page is that anybody can update it whenever they want! So, if you have an account here, and you know how to edit tables, you can just log in and add yourself!

If you don't have an account already, you can easily request one (NOTE: I forget how to do it- I'll need to add that). Once you have an account, you can just click "Edit" above the table, and you can add yourself.

5. '''But that's such a pain! Isn't there an easier way?!'''

There sure is! If you don't want to make all that effort just to have your name and e-mail address on a page, just send your info to our Wikimaster (yep, we made that word up!), Ben Friedline, at bef25 AT pitt.edu, and he'll put it on here.

== Who are the PSLC grads? ==

{| border = "1"
|-
! Grad Student Name
! University/Department
! Advisor
! E-mail
! Bio
! Personal Webpage
! PSLC Projects
! Other
|-
| Colleen Davy
| Carnegie Mellon/Psychology
| Brian MacWhinney
| cdavy1@andrew.cmu.edu
| I am interested in how adult second language learners develop fluent speaking skills in their second language.
|
| [http://www.learnlab.org/research/wiki/index.php/Davy_%26_MacWhinney_-_Spanish_Sentence_Production Spanish Sentence Production]
|

|}
{| border = "1"
|-
! Grad Student Name
! University/Department
! Advisor
! E-mail
! Bio
! Personal Webpage
! PSLC Projects
! Other
|-
| Benjamin Friedline
| University of Pittsburgh/Linguistics
| Alan Juffs
| bef25@pitt.edu
| I am interested in how adult second language learners acquire morphology in a second language.
|
| [http://www.learnlab.org/research/wiki/index.php/Juffs_-_Feature_Focus_in_Word_Learning Feature Focus in Word Learning]
|

|}

== Science of Learning Relevant Courses ==
The PIER program offers three courses -- see the [www.cmu.edu/pier/ PIER web site].

See also the courses taught be any of the PSLC faculty.

(Please add the names of relevant courses and web pointers if possible!)

<pre>
05832 / 05432 Cognitive Modeling & Intelligent Tutoring Systems
3:00pm-4:20pm, Tuesdays and Thursdays, Fall 2010
Room 3002, Newell-Simon Hall, Carnegie Mellon University
9 units
Dr. Vincent Aleven, aleven@cs.cmu.edu
</pre>

Students in this course will learn about the Cognitive Tutor technology that has been demonstrated to dramatically enhance student learning in domains like math, science, and computer programming. This type of tutoring software is currently in use in 2,700 schools around the country and is used extensively as platform for learning sciences research. The technology is grounded in artificial intelligence, cognitive psychology, and cognitive task analysis. Students will learn data-driven and theoretical methods for analyzing human problem solving and will learn to use such data to inform the design of intelligent tutoring systems. Course projects will focus on the development of an intelligent tutor using CTAT, the Cognitive Tutor Authoring Tools (see http://ctat.pact.cs.cmu.edu). Some assignments will focus on creating cognitive models in the Jess production rule modeling language.

Students should either have programming skills, or experience in the cognitive psychology of human problem solving, or HCI / design skills, or permission from the instructor.

PSLC GradStudents

2010-08-27T17:10:38Z

Juffs: /* Who are the PSLC grads? */

The purpose of this page is to serve as a repository of information relevant for grad students. We hope to maintain this page as a repository of current and relevant information for graduate students currently affiliated with the PSLC, as well as grad students who hope to be in the PSLC.

== Announcements==

== Meeting Notes==

== FAQs==

'''1. What does it take to be a PSLC grad student?'''

Well, there are basically three ways you can be considered a PSLC grad student.
a. You work on a project that receives funding from the PSLC.
b. Your advisor or collaborator receives funding from the PSLC and asks you to be involved.
c. You want to be a PSLC grad student.

'''2. What types of opportunities does the PSLC have for a grad student like me?'''

There are a variety of different levels of involvement and types of activities that the PSLC offers.

For the casual grad student, the PSLC organizes a speaker series with talks that may be of interest to students interested in the learning sciences. These are open to whomever wishes to go. There are also monthly lunch meetings where people associated with the PSLC can give a talk on their work.

The grad student community also hopes to organize events catered toward grad students, with topics like applying for grants, finding jobs, collaboration with people at other universities, etc. These are also open to the public.

For those who wish to get more involved, the grad student community also has monthly meetings to discuss center-wide issues, read and discuss articles we believe are relevant, plan future events, etc. Again, these are open to the public.

Finally, each thrust has regular or semi-regular meetings to discuss the thrust's theoretical framework, set the research agenda, and discuss the progress of projects within that thrust. While these are open to anyone, they're probably of limited interest unless you currently have or have had a project affiliated with the thrust.

'''3. What is expected of me as a PSLC grad student?'''

If you receive funding from the PSLC, you are expected, to the extent it is possible, to attend the thrust meetings for your relevant thrust, and attend the monthly PSLC lunches. The grad student community also encourages you to come to the grad student monthly meetings, of course.

If you don't receive funding from the PSLC, but still wish to be a part of the grad student community, your level of involvement is up to you.

'''How do I find out about upcoming talks/meetings/events?'''

One option is to check the Announcements section of this page. A possibly better option would be to get on our mailing list. To do that, e-mail Jo Bodnar at jobodnar AT cs.cmu.edu and ask to be put on the PSLC general mailing list and grad student mailing list.

There is also a regularly updated calendar at our main webpage (learnlab.org) that is updated regularly and gives a fairly complete account of most PSLC events.

4. '''I already consider myself a PSLC grad, and want to be included on this page! What do I have to do?'''

Well the great thing about the wiki page is that anybody can update it whenever they want! So, if you have an account here, and you know how to edit tables, you can just log in and add yourself!

If you don't have an account already, you can easily request one (NOTE: I forget how to do it- I'll need to add that). Once you have an account, you can just click "Edit" above the table, and you can add yourself.

5. '''But that's such a pain! Isn't there an easier way?!'''

There sure is! If you don't want to make all that effort just to have your name and e-mail address on a page, just send your info to our Wikimaster (yep, we made that word up!), Ben Friedline, at bef25 AT pitt.edu, and he'll put it on here.

== Who are the PSLC grads? ==

{| border = "1"
|-
! Grad Student Name
! University/Department
! Advisor
! E-mail
! Bio
! Personal Webpage
! PSLC Projects
! Other
|-
| Colleen Davy
| Carnegie Mellon/Psychology
| Brian MacWhinney
| cdavy1@andrew.cmu.edu
| I am interested in how adult second language learners develop fluent speaking skills in their second language.
|
| [http://www.learnlab.org/research/wiki/index.php/Davy_%26_MacWhinney_-_Spanish_Sentence_Production Spanish Sentence Production]
|

|}
{| border = "1"
|-
! Grad Student Name
! University/Department
! Advisor
! E-mail
! Bio
! Personal Webpage
! PSLC Projects
! Other
|-
| Benjamin Friedline
| University of Pittsburgh/Linguistics
| Alan Juffs
| bef25@pitt.edu
| I am interested in how adult second language learners acquire morphology in a second language.
|
| [http://www.learnlab.org/research/wiki/index.php/Juffs_-_Feature_Focus_in_Word_Learning Feature Focus in Word Learning]
|

|}

== Science of Learning Relevant Courses ==
The PIER program offers three courses -- see the [www.cmu.edu/pier/ PIER web site].

See also the courses taught be any of the PSLC faculty.

(Please add the names of relevant courses and web pointers if possible!)

<pre>
05832 / 05432 Cognitive Modeling & Intelligent Tutoring Systems
3:00pm-4:20pm, Tuesdays and Thursdays, Fall 2010
Room 3002, Newell-Simon Hall, Carnegie Mellon University
9 units
Dr. Vincent Aleven, aleven@cs.cmu.edu
</pre>

Students in this course will learn about the Cognitive Tutor technology that has been demonstrated to dramatically enhance student learning in domains like math, science, and computer programming. This type of tutoring software is currently in use in 2,700 schools around the country and is used extensively as platform for learning sciences research. The technology is grounded in artificial intelligence, cognitive psychology, and cognitive task analysis. Students will learn data-driven and theoretical methods for analyzing human problem solving and will learn to use such data to inform the design of intelligent tutoring systems. Course projects will focus on the development of an intelligent tutor using CTAT, the Cognitive Tutor Authoring Tools (see http://ctat.pact.cs.cmu.edu). Some assignments will focus on creating cognitive models in the Jess production rule modeling language.

Students should either have programming skills, or experience in the cognitive psychology of human problem solving, or HCI / design skills, or permission from the instructor.

Juffs - Feature Focus in Word Learning

2010-05-19T16:44:49Z

Juffs: /* L2 learning of derived words */

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! PI
| Ben Friedline, Alan Juffs
|-
! Start date
| September 2009
|-
! End date
| July 2010
|-
! Learnlab
| English
|}

== L2 learning of derived words ==
Benjamin Friedline and Alan Juffs

'''Background'''

Inflected and derived words occur frequently in the English language and serve important functions in everyday communication. The term derived word refers to the combination of a base word with a derivational affix. For instance, if the derivational affix –ness is added to the base kind (adjective), the word kindness (noun) is derived. The affix –ness is very productive and can be added to many words to derive novel words such as darkness, awareness, and illness. Other derivational affixes such as –ity are not as productive as –ness and can be used to form a limited number of words, such as ¬purity and scarcity. The term inflected word refers to the combination of a base word and an inflectional affix. For example, if the inflectional affix –ed is added to the base word walk (verb), the resulting word is walked (verb). The addition of the –ed affix is very productive in the formation of the past tense even though it does not apply to a number of irregular past tense forms, such as drove, ate, and sat.

Importantly, the differences in productivity of each type of affix have led some researchers to conclude that there are differences in how they are processed by native speakers. In Words and Rules (WR) theory, Pinker and Ullman (2002) argue that irregular inflected forms (e.g., drove) are stored in the lexicon (or mental dictionary) as whole words, whereas regular forms (e.g., walked) are generated by a regular rule. This theory has also been applied to the processing of derived words in two recent psychological investigations (cf. Alegre & Gordon, 1999; Hagiwara et al., 1999). The main point behind both of these studies is to illustrate that derived words can be either rule-governed (e.g., words with –ness) or stored as whole words in the lexicon (e.g., words with –ity as in purity).

In the area of second language acquisition, adult second language (L2) learners often fail to attain native-like proficiency when producing derived and inflected words in an L2. Lardiere (1998), for instance, showed that second language learners still make errors with inflectional morphology even after many years of exposure to English. In Lardiere’s (1998) study, she recorded and analyzed naturalistic conversations from a Chinese learner of American English. The results of this study indicated that the learner supplied the inflectional affix –ed correctly in only 34% of obligatory contexts even after 18 years of exposure to English. Additionally, in terms of derived words, a recent study by Juffs and Friedline (2010) revealed that intermediate L2 learners often made errors in the production of derived words such as those in examples (1) and (2).
(1) We have one different [difference].
(2) I like doing something music [musical].
In example (1) the learner uses the adjective form different instead of the grammatically correct form difference, which is a noun. In example (2) the learner uses the noun form music in a position that requires the adjective form musical.

The preponderance of such errors in L2 speech has led some researchers to conclude that L2 learners are permanently impaired on the production of derived and inflected words because they do not have access to the same rule-based mechanisms that are present during L1 acquisition (Jiang, 2004; Felser & Clahsen, 2009; Silva & Clahsen, 2008). This hypothesis is formally known as the Fundamental Difference Hypothesis (FDH; Bley-Vroman, 1989), and it has received support from a number of recent studies in the field of second language acquisition. Silva and Clahsen (2008), for instance, use evidence from a masked-priming experiment to compare native speakers to adult L2 learners on a series of morphological priming tasks. The results from this study indicated full priming effects (e.g., darkness primes dark) for native speakers on both inflections and derivations, but only partial priming effects for L2 learners on derivations and no priming effects for L2 learners on inflections. Silva and Clahsen (2008) argue that the limited priming effects (or complete lack thereof) indicate that L2 learners lack rule-based mechanisms and do not know that ¬–ness can be affixed to many adjectives to derive nouns such as darkness, awareness, and illness. This lack of rule-based mechanisms may mean that adult L2 learners memorize all words as unanalyzed chunks of language, without realizing that dark and darkness or walk and walked are intimately related in both form and meaning.

The research by Silva and Clahsen (2008) makes an important contribution to a theory of second language acquisition because it provides a possible explanation for why L2 learners have difficulties with derived and inflected words (i.e., they cannot access rule-based mechanisms). However, this research is limited because it assumes that L2 learners are permanently impaired when compared to native-speakers on all types of rule-based inflectional and derivational morphological processes. This assumption is at odds with findings from studies such as the morpheme order studies (e.g., Bailey, Madden, & Krashen, 1974), critical period studies (e.g., Johnson & Newport, 1989), and studies on the role of morpheme salience in L2 acquisition (e.g., Ellis, 2006) in that these past studies indicate that certain morphemes, such as progressive, may be more easily acquired than others. Johnson and Newport (1989), for instance, claim that the progressive morpheme may not be subject to critical period effects.

Additionally, the role of a learner’s first language may also influence the relative difficulty of a particular morpheme. Potential L1 effects have been discussed in a number of recent SLA studies (e.g., Juffs & Friedline, 2010; White, 2003). Before we conclude that L2 learners are equally impaired in all areas of morphological knowledge, further research is needed to identify if certain morphological structures are easier to acquire than others, how exactly L2 morphological knowledge diverges from native-speaker knowledge, and how a learner’s first language might influence L2 morphological knowledge. The goal of the present research is to answer these questions as they relate to derivational morphology.

'''Research Questions'''

Why are ESL learners so poor in their knowledge of English morphology? What are the knowledge components that are the most challenging for learning through normal language exposure? Do learners have a representational problem or a processing problem? Specifically, what instructional interventions can be designed to overcome observed processing differences in L1 and L2 morphology?

'''Research plan'''

For year 1, the goal of the research is to analyze the knowledge components of ESL learners to lay the groundwork for a hypothesis-based intervention. The research will systematically investigate the components of L2 learners’ knowledge of English derivational morphology to address the following questions:

1) What are the components of L2 derivational knowledge?
2) Are these components different from L1 derivational knowledge?
3) Does L1 matter for the acquisition of derived words in an L2?

'''Methodology'''

To answer these questions, Friedline has developed a series of tasks that will be used to assess what native English speakers and second language learners know about derived words. These tasks included lexical decision, semantic relatedness, and morphological decomposition. Each of these tasks contained several conditions that tested different components of morphological knowledge. Studies on the acquisition of L1 morphological knowledge (e.g., Carlisle, 2000; Carlisle & Fleming, 2003) were consulted in order to develop these conditions. Each condition is outlined below.

''Lexical decision task''

Explanation: In this task, students were asked to rate words from 1 (not a word) or 6 (definitely a word). All words were morphologically complex (e.g., base + affix). Some of the words were real words in English, while other words were not real words in English. The purpose of this task was to assess if native-speakers were sensitive to the effects of semantic blocking and affix ordering. There were four conditions in this task. The conditions are listed below along with an example to illustrate the types of words that were presented in each condition.

Condition 1: Real words
Example: The suffix –able is added to verbs to derive adjectives such as workable or comfortable. A response of 4, 5, or 6 would be counted as accurate.

Condition 2: Semantic blocking
Example: Even though you can add the affix –able to many verbs to derive adjectives, there are some verbs like arrivable and departable look that do not normally take the suffix –able to form adjectives. A response of 1, 2, or 3 would be counted as accurate.

Condition 3: Correct affix ordering
Example: There are some bases that can take two affixes. You can add the affix –able to the verb respect to derive the adjective respectable. Then, you can add the affix –ity to respectable to derive the noun respectability. A response of 4, 5, or 6 would be counted as accurate.

Condition 4: Incorrect affix ordering
Example: In a word like respectability, the word is correct because the affixes are added in the correct order. However, if I add the affix -ity before I add the affix –able, I derive a word like respectitiable. This word is not correct because the affixes are not added in the correct order. A response of 1, 2, or 3 would be counted as accurate.

''Word relatedness task''

Explanation: In this task, students were asked to rate words based on their meaning from 1 (not related) to 6 (definitely related). There were five conditions in this exercise.

Condition 1: No relationship in meaning
Some words are not related in meaning in any way. The words cat and bus are not related in meaning in any way. A response of 1, 2, or 3 would be counted as accurate.

Condition 2: Relationship in meaning
Other words are related in meaning. For instance, bank and money are related in that a bank is a place where you deposit your money. A response of 4, 5, or 6 would be counted as accurate.

Condition 3: Relationship in meaning with different affixes.
This condition contained words with suffixes that were related in meaning. For example, productive (adj.) and production (n.) both share the base produce (v.). A response of 4, 5, or 6 would be counted as accurate.

Condition 4: Relationship in orthography only, not meaning
There are some words that may look like they are related in meaning because they share the same initial letters. In this condition, students saw words like permanence and permission. These words share the letters p-e-r-m, but are unrelated in meaning. A response of 1, 2, or 3 would be counted as accurate.

Condition 5: Relationship in affix only, not meaning
In the final condition, students were presented with words that shared the same affix, but were unrelated in meaning. For example, the words reality and curiosity are unrelated in meaning, but share the affix –ity. A response of 1, 2, or 3 would be counted as accurate.

''Word Analysis Task''

Explanation: On the Word Analysis Task, students were asked to provide the base word of the word provided. Some of these words consisted of a base and an affix such as musician, which has music as a base. Other words, however, could not be broken down into a base and a affix. For instance, dollar cannot be broken down into doll + ar because dollar is a base form. Accuracy was computed for decomposable and non-decomposable words.

Native speakers piloted these tassk in the fall of 2009, and preliminary results are reported for each task in the tables below. A pull out from the ELI in Spring 2010 will collect learner data.

''Participants''

These tasks were administered to native speakers and L2 learners during the fall 2009 and spring 2010 semesters. A total of 23 native-English speakers participated in the study. All of the native speakers were undergraduates at the University of Pittsburgh. Ninety ESL learners participated in this study from three different levels of language proficiency: beginner (n=26), intermediate (n=36), and advanced (n=28). These learners were enrolled in an intensive English program at the University of Pittsburgh.

'''Descriptive Results'''

''Lexical Decision Task''

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''NS Accuracy'''
|-
! Condition 1: Real words
| 93%
|-
! Condition 2: Semantic blocking
| 81%
|-
! Condition 3: Correct affix ordering
| 93%
|-
! Condition 4: Incorrect Affix ordering
| 95%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Beginner Accuracy'''
|-
! Condition 1: Real words
| 89%
|-
! Condition 2: Semantic blocking
| 48%
|-
! Condition 3: Correct affix ordering
| 66%
|-
! Condition 4: Incorrect Affix ordering
| 53%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Intermediate Accuracy'''
|-
! Condition 1: Real words
| 90%
|-
! Condition 2: Semantic blocking
| 59%
|-
! Condition 3: Correct affix ordering
| 69%
|-
! Condition 4: Incorrect Affix ordering
| 70%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Advanced Accuracy'''
|-
! Condition 1: Real words
| 96%
|-
! Condition 2: Semantic blocking
| 53%
|-
! Condition 3: Correct affix ordering
| 77%
|-
! Condition 4: Incorrect Affix ordering
| 81%
|}

''Word relatedness task''
{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''NS Accuracy'''
|-
! Condition 1: No relationship in meaning
| 92%
|-
! Condition 2: Relationship in meaning
| 92%
|-
! Condition 3: Relationship in meaning with different affixes
| 97%
|-
! Condition 4: Relationship in orthography only
| 89%
|-
! Condition 5: Relationship in affix only
| 90%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Beginner Accuracy'''
|-
! Condition 1: No relationship in meaning
| 91%
|-
! Condition 2: Relationship in meaning
| 84%
|-
! Condition 3: Relationship in meaning with different affixes
| 89%
|-
! Condition 4: Relationship in orthography only
| 73%
|-
! Condition 5: Relationship in affix only
| 74%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Intermediate Accuracy'''
|-
! Condition 1: No relationship in meaning
| 89%
|-
! Condition 2: Relationship in meaning
| 87%
|-
! Condition 3: Relationship in meaning with different affixes
| 89%
|-
! Condition 4: Relationship in orthography only
| 70%
|-
! Condition 5: Relationship in affix only
| 69%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Advanced Accuracy'''
|-
! Condition 1: No relationship in meaning
| 95%
|-
! Condition 2: Relationship in meaning
| 79%
|-
! Condition 3: Relationship in meaning with different affixes
| 96%
|-
! Condition 4: Relationship in orthography only
| 81%
|-
! Condition 5: Relationship in affix only
| 89%
|}

''Word Analysis Task''
{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''NS Accuracy'''
|-
! Condition 1: Decomposable
| 85%
|-
! Condition 2: Non-decomposable
| 92%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Beginner Accuracy'''
|-
! Condition 1: Decomposable
| 59%
|-
! Condition 2: Non-decomposable
| 73%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Intermediate Accuracy'''
|-
! Condition 1: Decomposable
| 61%
|-
! Condition 2: Non-decomposable
| 84%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Advanced Accuracy'''
|-
! Condition 1: Decomposable
| 65%
|-
! Condition 2: Non-decomposable
| 81%
|}

'''Discussion'''

''Beginning L2 Learners (Level 3)''

The results of this study indicate that level 3 learners from the ELI at the University of Pittsburgh often have problems when processing derived words. Firstly, the lexical decision task may indicate that beginning learners are not sensitive to constraints on the use of morphemes such as –able and –ness or constraints on the ordering of affixes. For instance, the learners in the present study often judge words such as departable and hopenessful to be real English words in spite of the fact that most native speakers rarely (if ever) consider these words to be real English words. Sixteen of 26 level 3 learners said that smileable was a real word in English, while only 1 of 23 native speakers said that this was a real word in English. Likewise, for hopenessful, 18 of 26 learners said that this was a real word, but only 1 of 23 natives considered hopenessful to be a real English word. Second, the word relatedness task seems to indicate that learners rely heavily on orthographic/phonological overlap when processing the meaning of words. Put another way, the learners in this study said that word pairs that were related in form only were also related in meaning. For instance, 12 out of 26 level 3 learners said that the word pair majority-activity were related in meaning, while native speakers (N=23) never say that these words are related in meaning. Finally, the results from the word analysis task may suggest that learners at this level have significant difficulties with derived words that involve phonological and/or orthographic changes to the base. In the present study, almost all of the level 3 learners (22 of 26) incorrectly provided the base word for extension. Native speakers, on the other hand, provided the incorrect base for extension only 4 times out of 23 subjects.

''Intermediate L2 Learners (Level 4)''

For Level 4 L2 learners, the lexical decision task may indicate that even intermediate-level learners are not sensitive to constraints on the use of morphemes such as –able and –ness or constraints on the ordering of affixes. For instance, the learners in the present study often judge words such as departable and hopenessful to be real English words in spite of the fact that most native speakers rarely (if ever) consider these words to be real English words. Seventeen of 36 level 4 learners said that smileable was a real word in English, while only 1 of 23 native speakers said that this was a real word in English. Likewise, for hopenessful, 21 of 36 learners said that this was a real word, but only 1 of 23 natives considered hopenessful to be a real English word. Second, the word relatedness task seems to indicate that learners rely heavily on orthographic/phonological overlap when processing the meaning of words. Put another way, the learners in this study said that word pairs that were related in form only were also related in meaning. For instance, 14 out of 36 level 3 learners said that the word pair majority-activity were related in meaning, while native speakers (N=23) never say that these words are related in meaning. Finally, the results from the word analysis task may suggest that learners at this level have significant difficulties with derived words that involve phonological and/or orthographic changes to the base. In the present study, more than two-thirds of the level 4 learners (25 of 36) incorrectly provided the base word for extension. Native speakers, on the other hand, provided the incorrect base for extension only 4 times out of 23 subjects.

''Advanced L2 Learners (Level 5)''

For level 5 L2 learners, the lexical decision task may indicate that even advanced learners are not sensitive to constraints on the use of morphemes such as –able and –ness. For instance, the learners in the present study often judge words such as smileable and leavable to be real English words in spite of the fact that most native speakers rarely (if ever) consider these words to be real English words. Fourteen of 28 level 5 learners said that smileable was a real word in English, while only 1 of 23 native speakers said that this was a real word in English. Likewise, for leavable, 19 of 28 learners said that this was a real word, but only 4 of 23 natives considered leavable to be a real English word. Second, the word relatedness task seems to indicate that advanced learners still rely to some degree on orthographic/phonological overlap when processing the meaning of words. Put another way, some advanced learners in this study said that word pairs that were related in form only were also related in meaning. For instance, 11 out of 28 level 3 learners said that the word pair constantly-conservative were related in meaning, while native speakers (N=23) never say that these words are related in meaning. Finally, the results from the word analysis task may suggest that learners at level 5 have significant difficulties with derived words that involve phonological and/or orthographic changes to the base. In the present study, many of the errors on the word analysis task were errors on words that involved a significant orthographic and sometimes phonological change to the base. For instance, 19 of 28 level 5 students incorrectly provided the base word for extension. Native speakers, on the other hand, provided the incorrect base for extension only 4 times out of 23 subjects.

'''General Discussion'''
Overall, the results indicate that second language learners are not sensitive to the constraints on the appropriate usage and production of derivational morphology as are native English speakers. Learners from all proficiency levels consistently judged words with inappropriate derivational affixes to be real words, whereas NS did not. Data from the word relatedness task also seem to suggest that orthographic/phonological overlap between two unrelated words may be used by L2 learners at all proficiency levels when searching for a words' meaning. Finally, the results from the word analysis task may suggest that learners from all levels of proficiency have significant difficulties with derived words that involve phonological and/or orthographic changes to the base.

**Additional statistical results and key theoretical discussion is forthcoming in the first author's doctoral dissertation.**

'''Next steps'''
The next step in this project is to investigate how different types of instruction influence L2 sensitivity to constraints on the usage of derivational morphemes.

'''Selected References'''

Alegre, M., & Gordon, P. (1999). Rule-based versus associative processes in derivational morphology. Brain and Language, 68, 347-354.

Bailey, N., Madden, C., & Krashen, S. (1974). Is there a "natural sequence" in adult second language learning? Language Learning, 24(2), 234-243.

Bley-Vroman, R. (1989). The logical problem of second language learning. In S. Gass & J. Schachter (Eds.), Linguistic Perspectives on Second Language Acquisition. Cambridge: Cambridge University Press.

Carlisle, J.F. (2000). Awareness of the structure and meaning of morphologically complex words: Impact on reading. Reading and Writing, 12(3-4), 169-190.

Carlisle, J. F., & Fleming, J. (2003). Lexical processing of morphologically complex words in the elementary years. Scientific Studies of Reading, 7(3), 239-253.

Ellis, N. C. (2006). Selective attention and transfer phenomena in L2 acquisition: Contingency, cue competition, salience, interference, overshadowing, blocking, and perceptual learning. Applied Linguistics, 27(2), 164-194.

Felser, C., & Clahsen, H. (2009). Grammatical processing of spoken language in child and adult language learners. Journal of Psycholinguistic Research, 38(3), 305-319.

Gonnerman, L. M., Seidenberg, M. S., & Andersen, E. S. (2007). Graded semantic and phonological similarity effect in priming: Evidence for a distributed connectionist approach to morphology. Journal of Experimental Psychology, 136(2), 323-345.

Hagiwara, H., Sugioka, Y., Ito, T., Kawamura, M., & Shiota, J.-i. (1999). Neurolinguistic evidence for rule-based nominal suffixation. Language, 75(4), 739-763.

Hay, J. (2002). From speech perception to morphology: Affix ordering revisited. Language, 72(3), 527-555.

Hay, J. B., & Baayen, R. H. (2005). Shifting paradigms: gradient structure in morphology. TRENDS in Cognitive Science, 9(7), 342-348.

Jiang, N. (2004). Morphological insensitivity in second language processing. Applied Psycholinguistics, 25, 603-634.

Johnson, J. S., & Newport, E. L. (1989). Critical period effects in secon dlanguage learning: The influence of maturational state on the acquisition of English as a second language. Cognitive Psychology, 21, 60-99.

Friedline, B., & Juffs, A. (2010). L1 influence, morphological (in)sensitivity and L2 lexical development: Evidence from production data. Unpublished manuscript, University of Pittsburgh, PA.

Lardiere, D. (1998). Dissociating syntax from morphology in a divergent L2 end-state grammar. Second Language Research, 14(4), 359-375.

Lardiere, D. (2006). Ultimate attainment in second language acquisition: a case study. New York: Routledge

Marslen-Wilson, W. D., Bozic, M., & Randall, B. (2008). Early decomposition in visual word recognition: Dissociating morphology, form, and meaning. Language and Cognitive Processes, 23(3), 394-421.

Nation, I. S. P. (2001). Learning vocabulary in another language. New York: Cambridge University Press.

Pinker, S., & Ullman, M. T. (2002). The past and future of the past tense. TRENDS in Cognitive Science, 6(11), 456-463.

Silva, R., & Clahsen, H. (2008). Morphologically complex words in L1 and L2 processing: Evidence from masked priming experiments in English. Bilingualism: Language and Cognition, 11(2), 245-260.

White, L. (2003). Fossilization in steady state L2 grammars: Persistent problems with inflectional morphology. Bilingualism: Language and Cognition, 6(2), 129-141.

Juffs - Feature Focus in Word Learning

2010-05-19T16:40:49Z

Juffs: /* L2 learning of derived words */

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! PI
| Ben Friedline, Alan Juffs
|-
! Start date
| September 2009
|-
! End date
| July 2010
|-
! Learnlab
| English
|}

== L2 learning of derived words ==
Benjamin Friedline and Alan Juffs

'''Background'''

Inflected and derived words occur frequently in the English language and serve important functions in everyday communication. The term derived word refers to the combination of a base word with a derivational affix. For instance, if the derivational affix –ness is added to the base kind (adjective), the word kindness (noun) is derived. The affix –ness is very productive and can be added to many words to derive novel words such as darkness, awareness, and illness. Other derivational affixes such as –ity are not as productive as –ness and can be used to form a limited number of words, such as ¬purity and scarcity. The term inflected word refers to the combination of a base word and an inflectional affix. For example, if the inflectional affix –ed is added to the base word walk (verb), the resulting word is walked (verb). The addition of the –ed affix is very productive in the formation of the past tense even though it does not apply to a number of irregular past tense forms, such as drove, ate, and sat.

Importantly, the differences in productivity of each type of affix have led some researchers to conclude that there are differences in how they are processed by native speakers. In Words and Rules (WR) theory, Pinker and Ullman (2002) argue that irregular inflected forms (e.g., drove) are stored in the lexicon (or mental dictionary) as whole words, whereas regular forms (e.g., walked) are generated by a regular rule. This theory has also been applied to the processing of derived words in two recent psychological investigations (cf. Alegre & Gordon, 1999; Hagiwara et al., 1999). The main point behind both of these studies is to illustrate that derived words can be either rule-governed (e.g., words with –ness) or stored as whole words in the lexicon (e.g., words with –ity as in purity).

In the area of second language acquisition, adult second language (L2) learners often fail to attain native-like proficiency when producing derived and inflected words in an L2. Lardiere (1998), for instance, showed that second language learners still make errors with inflectional morphology even after many years of exposure to English. In Lardiere’s (1998) study, she recorded and analyzed naturalistic conversations from a Chinese learner of American English. The results of this study indicated that the learner supplied the inflectional affix –ed correctly in only 34% of obligatory contexts even after 18 years of exposure to English. Additionally, in terms of derived words, a recent study by Juffs and Friedline (2010) revealed that intermediate L2 learners often made errors in the production of derived words such as those in examples (1) and (2).
(1) We have one different [difference].
(2) I like doing something music [musical].
In example (1) the learner uses the adjective form different instead of the grammatically correct form difference, which is a noun. In example (2) the learner uses the noun form music in a position that requires the adjective form musical.

The preponderance of such errors in L2 speech has led some researchers to conclude that L2 learners are permanently impaired on the production of derived and inflected words because they do not have access to the same rule-based mechanisms that are present during L1 acquisition (Jiang, 2004; Felser & Clahsen, 2009; Silva & Clahsen, 2008). This hypothesis is formally known as the Fundamental Difference Hypothesis (FDH; Bley-Vroman, 1989), and it has received support from a number of recent studies in the field of second language acquisition. Silva and Clahsen (2008), for instance, use evidence from a masked-priming experiment to compare native speakers to adult L2 learners on a series of morphological priming tasks. The results from this study indicated full priming effects (e.g., darkness primes dark) for native speakers on both inflections and derivations, but only partial priming effects for L2 learners on derivations and no priming effects for L2 learners on inflections. Silva and Clahsen (2008) argue that the limited priming effects (or complete lack thereof) indicate that L2 learners lack rule-based mechanisms and do not know that ¬–ness can be affixed to many adjectives to derive nouns such as darkness, awareness, and illness. This lack of rule-based mechanisms may mean that adult L2 learners memorize all words as unanalyzed chunks of language, without realizing that dark and darkness or walk and walked are intimately related in both form and meaning.

The research by Silva and Clahsen (2008) makes an important contribution to a theory of second language acquisition because it provides a possible explanation for why L2 learners have difficulties with derived and inflected words (i.e., they cannot access rule-based mechanisms). However, this research is limited because it assumes that L2 learners are permanently impaired when compared to native-speakers on all types of rule-based inflectional and derivational morphological processes. This assumption is at odds with findings from studies such as the morpheme order studies (e.g., Bailey, Madden, & Krashen, 1974), critical period studies (e.g., Johnson & Newport, 1989), and studies on the role of morpheme salience in L2 acquisition (e.g., Ellis, 2006) in that these past studies indicate that certain morphemes, such as progressive, may be more easily acquired than others. Johnson and Newport (1989), for instance, claim that the progressive morpheme may not be subject to critical period effects.

Additionally, the role of a learner’s first language may also influence the relative difficulty of a particular morpheme. Potential L1 effects have been discussed in a number of recent SLA studies (e.g., Juffs & Friedline, 2010; White, 2003). Before we conclude that L2 learners are equally impaired in all areas of morphological knowledge, further research is needed to identify if certain morphological structures are easier to acquire than others, how exactly L2 morphological knowledge diverges from native-speaker knowledge, and how a learner’s first language might influence L2 morphological knowledge. The goal of the present research is to answer these questions as they relate to derivational morphology.

'''Research Questions'''

Why are ESL learners so poor in their knowledge of English morphology? What are the knowledge components that are the most challenging for learning through normal language exposure? Do learners have a representational problem or a processing problem? Specifically, what instructional interventions can be designed to overcome observed processing differences in L1 and L2 morphology?

'''Research plan'''

For year 1, the goal of the research is to analyze the knowledge components of ESL learners to lay the groundwork for a hypothesis-based intervention. The research will systematically investigate the components of L2 learners’ knowledge of English derivational morphology to address the following questions:

1) What are the components of L2 derivational knowledge?
2) Are these components different from L1 derivational knowledge?
3) Does L1 matter for the acquisition of derived words in an L2?

'''Methodology'''

To answer these questions, Friedline has developed a series of tasks that will be used to assess what native English speakers and second language learners know about derived words. These tasks included lexical decision, semantic relatedness, and morphological decomposition. Each of these tasks contained several conditions that tested different components of morphological knowledge. Studies on the acquisition of L1 morphological knowledge (e.g., Carlisle, 2000; Carlisle & Fleming, 2003) were consulted in order to develop these conditions. Each condition is outlined below.

''Lexical decision task''

Explanation: In this task, students were asked to rate words from 1 (not a word) or 6 (definitely a word). All words were morphologically complex (e.g., base + affix). Some of the words were real words in English, while other words were not real words in English. The purpose of this task was to assess if native-speakers were sensitive to the effects of semantic blocking and affix ordering. There were four conditions in this task. The conditions are listed below along with an example to illustrate the types of words that were presented in each condition.

Condition 1: Real words
Example: The suffix –able is added to verbs to derive adjectives such as workable or comfortable. A response of 4, 5, or 6 would be counted as accurate.

Condition 2: Semantic blocking
Example: Even though you can add the affix –able to many verbs to derive adjectives, there are some verbs like arrivable and departable look that do not normally take the suffix –able to form adjectives. A response of 1, 2, or 3 would be counted as accurate.

Condition 3: Correct affix ordering
Example: There are some bases that can take two affixes. You can add the affix –able to the verb respect to derive the adjective respectable. Then, you can add the affix –ity to respectable to derive the noun respectability. A response of 4, 5, or 6 would be counted as accurate.

Condition 4: Incorrect affix ordering
Example: In a word like respectability, the word is correct because the affixes are added in the correct order. However, if I add the affix -ity before I add the affix –able, I derive a word like respectitiable. This word is not correct because the affixes are not added in the correct order. A response of 1, 2, or 3 would be counted as accurate.

''Word relatedness task''

Explanation: In this task, students were asked to rate words based on their meaning from 1 (not related) to 6 (definitely related). There were five conditions in this exercise.

Condition 1: No relationship in meaning
Some words are not related in meaning in any way. The words cat and bus are not related in meaning in any way. A response of 1, 2, or 3 would be counted as accurate.

Condition 2: Relationship in meaning
Other words are related in meaning. For instance, bank and money are related in that a bank is a place where you deposit your money. A response of 4, 5, or 6 would be counted as accurate.

Condition 3: Relationship in meaning with different affixes.
This condition contained words with suffixes that were related in meaning. For example, productive (adj.) and production (n.) both share the base produce (v.). A response of 4, 5, or 6 would be counted as accurate.

Condition 4: Relationship in orthography only, not meaning
There are some words that may look like they are related in meaning because they share the same initial letters. In this condition, students saw words like permanence and permission. These words share the letters p-e-r-m, but are unrelated in meaning. A response of 1, 2, or 3 would be counted as accurate.

Condition 5: Relationship in affix only, not meaning
In the final condition, students were presented with words that shared the same affix, but were unrelated in meaning. For example, the words reality and curiosity are unrelated in meaning, but share the affix –ity. A response of 1, 2, or 3 would be counted as accurate.

''Word Analysis Task''

Explanation: On the Word Analysis Task, students were asked to provide the base word of the word provided. Some of these words consisted of a base and an affix such as musician, which has music as a base. Other words, however, could not be broken down into a base and a affix. For instance, dollar cannot be broken down into doll + ar because dollar is a base form. Accuracy was computed for decomposable and non-decomposable words.

Native speakers piloted these tassk in the fall of 2009, and preliminary results are reported for each task in the tables below. A pull out from the ELI in Spring 2010 will collect learner data.

''Participants''

These tasks were administered to native speakers and L2 learners during the fall 2009 and spring 2010 semesters. A total of 23 native-English speakers participated in the study. All of the native speakers were undergraduates at the University of Pittsburgh. Ninety ESL learners participated in this study from three different levels of language proficiency: beginner (n=26), intermediate (n=36), and advanced (n=28). These learners were enrolled in an intensive English program at the University of Pittsburgh.

'''Descriptive Results'''

''Lexical Decision Task''

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''NS Accuracy'''
|-
! Condition 1: Real words
| 93%
|-
! Condition 2: Semantic blocking
| 81%
|-
! Condition 3: Correct affix ordering
| 93%
|-
! Condition 4: Incorrect Affix ordering
| 95%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Beginner Accuracy'''
|-
! Condition 1: Real words
| 89%
|-
! Condition 2: Semantic blocking
| 48%
|-
! Condition 3: Correct affix ordering
| 66%
|-
! Condition 4: Incorrect Affix ordering
| 53%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Intermediate Accuracy'''
|-
! Condition 1: Real words
| 90%
|-
! Condition 2: Semantic blocking
| 59%
|-
! Condition 3: Correct affix ordering
| 69%
|-
! Condition 4: Incorrect Affix ordering
| 70%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Advanced Accuracy'''
|-
! Condition 1: Real words
| 96%
|-
! Condition 2: Semantic blocking
| 53%
|-
! Condition 3: Correct affix ordering
| 77%
|-
! Condition 4: Incorrect Affix ordering
| 81%
|}

''Word relatedness task''
{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''NS Accuracy'''
|-
! Condition 1: No relationship in meaning
| 92%
|-
! Condition 2: Relationship in meaning
| 92%
|-
! Condition 3: Relationship in meaning with different affixes
| 97%
|-
! Condition 4: Relationship in orthography only
| 89%
|-
! Condition 5: Relationship in affix only
| 90%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Beginner Accuracy'''
|-
! Condition 1: No relationship in meaning
| 91%
|-
! Condition 2: Relationship in meaning
| 84%
|-
! Condition 3: Relationship in meaning with different affixes
| 89%
|-
! Condition 4: Relationship in orthography only
| 73%
|-
! Condition 5: Relationship in affix only
| 74%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Intermediate Accuracy'''
|-
! Condition 1: No relationship in meaning
| 89%
|-
! Condition 2: Relationship in meaning
| 87%
|-
! Condition 3: Relationship in meaning with different affixes
| 89%
|-
! Condition 4: Relationship in orthography only
| 70%
|-
! Condition 5: Relationship in affix only
| 69%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Advanced Accuracy'''
|-
! Condition 1: No relationship in meaning
| 95%
|-
! Condition 2: Relationship in meaning
| 79%
|-
! Condition 3: Relationship in meaning with different affixes
| 96%
|-
! Condition 4: Relationship in orthography only
| 81%
|-
! Condition 5: Relationship in affix only
| 89%
|}

''Word Analysis Task''
{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''NS Accuracy'''
|-
! Condition 1: Decomposable
| 85%
|-
! Condition 2: Non-decomposable
| 92%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Beginner Accuracy'''
|-
! Condition 1: Decomposable
| 59%
|-
! Condition 2: Non-decomposable
| 73%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Intermediate Accuracy'''
|-
! Condition 1: Decomposable
| 61%
|-
! Condition 2: Non-decomposable
| 84%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Advanced Accuracy'''
|-
! Condition 1: Decomposable
| 65%
|-
! Condition 2: Non-decomposable
| 81%
|}

'''Discussion'''

''Beginning L2 Learners (Level 3)''

The results of this study indicate that level 3 learners from the ELI at the University of Pittsburgh often have problems when processing derived words. Firstly, the lexical decision task may indicate that beginning learners are not sensitive to constraints on the use of morphemes such as –able and –ness or constraints on the ordering of affixes. For instance, the learners in the present study often judge words such as departable and hopenessful to be real English words in spite of the fact that most native speakers rarely (if ever) consider these words to be real English words. Sixteen of 26 level 3 learners said that smileable was a real word in English, while only 1 of 23 native speakers said that this was a real word in English. Likewise, for hopenessful, 18 of 26 learners said that this was a real word, but only 1 of 23 natives considered hopenessful to be a real English word. Second, the word relatedness task seems to indicate that learners rely heavily on orthographic/phonological overlap when processing the meaning of words. Put another way, the learners in this study said that word pairs that were related in form only were also related in meaning. For instance, 12 out of 26 level 3 learners said that the word pair majority-activity were related in meaning, while native speakers (N=23) never say that these words are related in meaning. Finally, the results from the word analysis task may suggest that learners at this level have significant difficulties with derived words that involve phonological and/or orthographic changes to the base. In the present study, almost all of the level 3 learners (22 of 26) incorrectly provided the base word for extension. Native speakers, on the other hand, provided the incorrect base for extension only 4 times out of 23 subjects.

''Intermediate L2 Learners (Level 4)''

For Level 4 L2 learners, the lexical decision task may indicate that even intermediate-level learners are not sensitive to constraints on the use of morphemes such as –able and –ness or constraints on the ordering of affixes. For instance, the learners in the present study often judge words such as departable and hopenessful to be real English words in spite of the fact that most native speakers rarely (if ever) consider these words to be real English words. Seventeen of 36 level 4 learners said that smileable was a real word in English, while only 1 of 23 native speakers said that this was a real word in English. Likewise, for hopenessful, 21 of 36 learners said that this was a real word, but only 1 of 23 natives considered hopenessful to be a real English word. Second, the word relatedness task seems to indicate that learners rely heavily on orthographic/phonological overlap when processing the meaning of words. Put another way, the learners in this study said that word pairs that were related in form only were also related in meaning. For instance, 14 out of 36 level 3 learners said that the word pair majority-activity were related in meaning, while native speakers (N=23) never say that these words are related in meaning. Finally, the results from the word analysis task may suggest that learners at this level have significant difficulties with derived words that involve phonological and/or orthographic changes to the base. In the present study, more than two-thirds of the level 4 learners (25 of 36) incorrectly provided the base word for extension. Native speakers, on the other hand, provided the incorrect base for extension only 4 times out of 23 subjects.

''Advanced L2 Learners (Level 5)''

For level 5 L2 learners, the lexical decision task may indicate that even advanced learners are not sensitive to constraints on the use of morphemes such as –able and –ness. For instance, the learners in the present study often judge words such as smileable and leavable to be real English words in spite of the fact that most native speakers rarely (if ever) consider these words to be real English words. Fourteen of 28 level 5 learners said that smileable was a real word in English, while only 1 of 23 native speakers said that this was a real word in English. Likewise, for leavable, 19 of 28 learners said that this was a real word, but only 4 of 23 natives considered leavable to be a real English word. Second, the word relatedness task seems to indicate that advanced learners still rely to some degree on orthographic/phonological overlap when processing the meaning of words. Put another way, some advanced learners in this study said that word pairs that were related in form only were also related in meaning. For instance, 11 out of 28 level 3 learners said that the word pair constantly-conservative were related in meaning, while native speakers (N=23) never say that these words are related in meaning. Finally, the results from the word analysis task may suggest that learners at level 5 have significant difficulties with derived words that involve phonological and/or orthographic changes to the base. In the present study, many of the errors on the word analysis task were errors on words that involved a significant orthographic and sometimes phonological change to the base. For instance, 19 of 28 level 5 students incorrectly provided the base word for extension. Native speakers, on the other hand, provided the incorrect base for extension only 4 times out of 23 subjects.

'''General Discussion'''
Overall, the results indicate that second language learners are not sensitive to the constraints on the appropriate usage and production of derivational morphology as are native English speakers. Learners from all proficiency levels consistently judged words with inappropriate derivational affixes to be real words, whereas NS did not. Data from the word relatedness task also seem to suggest that orthographic/phonological overlap between two unrelated words may be used by L2 learners at all proficiency levels when searching for a words' meaning. Finally, the results from the word analysis task may suggest that learners from all levels of proficiency have significant difficulties with derived words that involve phonological and/or orthographic changes to the base.

**Additional statistical results and key theoretical discussion is forthcoming in the first author's doctoral dissertation.**

'''Selected References'''

Alegre, M., & Gordon, P. (1999). Rule-based versus associative processes in derivational morphology. Brain and Language, 68, 347-354.

Bailey, N., Madden, C., & Krashen, S. (1974). Is there a "natural sequence" in adult second language learning? Language Learning, 24(2), 234-243.

Bley-Vroman, R. (1989). The logical problem of second language learning. In S. Gass & J. Schachter (Eds.), Linguistic Perspectives on Second Language Acquisition. Cambridge: Cambridge University Press.

Carlisle, J.F. (2000). Awareness of the structure and meaning of morphologically complex words: Impact on reading. Reading and Writing, 12(3-4), 169-190.

Carlisle, J. F., & Fleming, J. (2003). Lexical processing of morphologically complex words in the elementary years. Scientific Studies of Reading, 7(3), 239-253.

Ellis, N. C. (2006). Selective attention and transfer phenomena in L2 acquisition: Contingency, cue competition, salience, interference, overshadowing, blocking, and perceptual learning. Applied Linguistics, 27(2), 164-194.

Felser, C., & Clahsen, H. (2009). Grammatical processing of spoken language in child and adult language learners. Journal of Psycholinguistic Research, 38(3), 305-319.

Gonnerman, L. M., Seidenberg, M. S., & Andersen, E. S. (2007). Graded semantic and phonological similarity effect in priming: Evidence for a distributed connectionist approach to morphology. Journal of Experimental Psychology, 136(2), 323-345.

Hagiwara, H., Sugioka, Y., Ito, T., Kawamura, M., & Shiota, J.-i. (1999). Neurolinguistic evidence for rule-based nominal suffixation. Language, 75(4), 739-763.

Hay, J. (2002). From speech perception to morphology: Affix ordering revisited. Language, 72(3), 527-555.

Hay, J. B., & Baayen, R. H. (2005). Shifting paradigms: gradient structure in morphology. TRENDS in Cognitive Science, 9(7), 342-348.

Jiang, N. (2004). Morphological insensitivity in second language processing. Applied Psycholinguistics, 25, 603-634.

Johnson, J. S., & Newport, E. L. (1989). Critical period effects in secon dlanguage learning: The influence of maturational state on the acquisition of English as a second language. Cognitive Psychology, 21, 60-99.

Friedline, B., & Juffs, A. (2010). L1 influence, morphological (in)sensitivity and L2 lexical development: Evidence from production data. Unpublished manuscript, University of Pittsburgh, PA.

Lardiere, D. (1998). Dissociating syntax from morphology in a divergent L2 end-state grammar. Second Language Research, 14(4), 359-375.

Lardiere, D. (2006). Ultimate attainment in second language acquisition: a case study. New York: Routledge

Marslen-Wilson, W. D., Bozic, M., & Randall, B. (2008). Early decomposition in visual word recognition: Dissociating morphology, form, and meaning. Language and Cognitive Processes, 23(3), 394-421.

Nation, I. S. P. (2001). Learning vocabulary in another language. New York: Cambridge University Press.

Pinker, S., & Ullman, M. T. (2002). The past and future of the past tense. TRENDS in Cognitive Science, 6(11), 456-463.

Silva, R., & Clahsen, H. (2008). Morphologically complex words in L1 and L2 processing: Evidence from masked priming experiments in English. Bilingualism: Language and Cognition, 11(2), 245-260.

White, L. (2003). Fossilization in steady state L2 grammars: Persistent problems with inflectional morphology. Bilingualism: Language and Cognition, 6(2), 129-141.

Juffs - Feature Focus in Word Learning

2010-05-19T16:11:51Z

Juffs: /* L2 learning of derived words */

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! PI
| Ben Friedline, Alan Juffs
|-
! Start date
| September 2009
|-
! End date
| July 2010
|-
! Learnlab
| English
|}

== L2 learning of derived words ==
Benjamin Friedline and Alan Juffs

'''Background'''

Inflected and derived words occur frequently in the English language and serve important functions in everyday communication. The term derived word refers to the combination of a base word with a derivational affix. For instance, if the derivational affix –ness is added to the base kind (adjective), the word kindness (noun) is derived. The affix –ness is very productive and can be added to many words to derive novel words such as darkness, awareness, and illness. Other derivational affixes such as –ity are not as productive as –ness and can be used to form a limited number of words, such as ¬purity and scarcity. The term inflected word refers to the combination of a base word and an inflectional affix. For example, if the inflectional affix –ed is added to the base word walk (verb), the resulting word is walked (verb). The addition of the –ed affix is very productive in the formation of the past tense even though it does not apply to a number of irregular past tense forms, such as drove, ate, and sat.

Importantly, the differences in productivity of each type of affix have led some researchers to conclude that there are differences in how they are processed by native speakers. In Words and Rules (WR) theory, Pinker and Ullman (2002) argue that irregular inflected forms (e.g., drove) are stored in the lexicon (or mental dictionary) as whole words, whereas regular forms (e.g., walked) are generated by a regular rule. This theory has also been applied to the processing of derived words in two recent psychological investigations (cf. Alegre & Gordon, 1999; Hagiwara et al., 1999). The main point behind both of these studies is to illustrate that derived words can be either rule-governed (e.g., words with –ness) or stored as whole words in the lexicon (e.g., words with –ity as in purity).

In the area of second language acquisition, adult second language (L2) learners often fail to attain native-like proficiency when producing derived and inflected words in an L2. Lardiere (1998), for instance, showed that second language learners still make errors with inflectional morphology even after many years of exposure to English. In Lardiere’s (1998) study, she recorded and analyzed naturalistic conversations from a Chinese learner of American English. The results of this study indicated that the learner supplied the inflectional affix –ed correctly in only 34% of obligatory contexts even after 18 years of exposure to English. Additionally, in terms of derived words, a recent study by Juffs and Friedline (2010) revealed that intermediate L2 learners often made errors in the production of derived words such as those in examples (1) and (2).
(1) We have one different [difference].
(2) I like doing something music [musical].
In example (1) the learner uses the adjective form different instead of the grammatically correct form difference, which is a noun. In example (2) the learner uses the noun form music in a position that requires the adjective form musical.

The preponderance of such errors in L2 speech has led some researchers to conclude that L2 learners are permanently impaired on the production of derived and inflected words because they do not have access to the same rule-based mechanisms that are present during L1 acquisition (Jiang, 2004; Felser & Clahsen, 2009; Silva & Clahsen, 2008). This hypothesis is formally known as the Fundamental Difference Hypothesis (FDH; Bley-Vroman, 1989), and it has received support from a number of recent studies in the field of second language acquisition. Silva and Clahsen (2008), for instance, use evidence from a masked-priming experiment to compare native speakers to adult L2 learners on a series of morphological priming tasks. The results from this study indicated full priming effects (e.g., darkness primes dark) for native speakers on both inflections and derivations, but only partial priming effects for L2 learners on derivations and no priming effects for L2 learners on inflections. Silva and Clahsen (2008) argue that the limited priming effects (or complete lack thereof) indicate that L2 learners lack rule-based mechanisms and do not know that ¬–ness can be affixed to many adjectives to derive nouns such as darkness, awareness, and illness. This lack of rule-based mechanisms may mean that adult L2 learners memorize all words as unanalyzed chunks of language, without realizing that dark and darkness or walk and walked are intimately related in both form and meaning.

The research by Silva and Clahsen (2008) makes an important contribution to a theory of second language acquisition because it provides a possible explanation for why L2 learners have difficulties with derived and inflected words (i.e., they cannot access rule-based mechanisms). However, this research is limited because it assumes that L2 learners are permanently impaired when compared to native-speakers on all types of rule-based inflectional and derivational morphological processes. This assumption is at odds with findings from studies such as the morpheme order studies (e.g., Bailey, Madden, & Krashen, 1974), critical period studies (e.g., Johnson & Newport, 1989), and studies on the role of morpheme salience in L2 acquisition (e.g., Ellis, 2006) in that these past studies indicate that certain morphemes, such as progressive, may be more easily acquired than others. Johnson and Newport (1989), for instance, claim that the progressive morpheme may not be subject to critical period effects.

Additionally, the role of a learner’s first language may also influence the relative difficulty of a particular morpheme. Potential L1 effects have been discussed in a number of recent SLA studies (e.g., Juffs & Friedline, 2010; White, 2003). Before we conclude that L2 learners are equally impaired in all areas of morphological knowledge, further research is needed to identify if certain morphological structures are easier to acquire than others, how exactly L2 morphological knowledge diverges from native-speaker knowledge, and how a learner’s first language might influence L2 morphological knowledge. The goal of the present research is to answer these questions as they relate to derivational morphology.

'''Research Questions'''

Why are ESL learners so poor in their knowledge of English morphology? What are the knowledge components that are the most challenging for learning through normal language exposure? Do learners have a representational problem or a processing problem? Specifically, what instructional interventions can be designed to overcome observed processing differences in L1 and L2 morphology?

'''Research plan'''

For year 1, the goal of the research is to analyze the knowledge components of ESL learners to lay the groundwork for a hypothesis-based intervention. The research will systematically investigate the components of L2 learners’ knowledge of English derivational morphology to address the following questions:

1) What are the components of L2 derivational knowledge?
2) Are these components different from L1 derivational knowledge?
3) Does L1 matter for the acquisition of derived words in an L2?

'''Methodology'''

To answer these questions, Friedline has developed a series of tasks that will be used to assess what native English speakers and second language learners know about derived words. These tasks included lexical decision, semantic relatedness, and morphological decomposition. Each of these tasks contained several conditions that tested different components of morphological knowledge. Studies on the acquisition of L1 morphological knowledge (e.g., Carlisle, 2000; Carlisle & Fleming, 2003) were consulted in order to develop these conditions. Each condition is outlined below.

''Lexical decision task''

Explanation: In this task, students were asked to rate words from 1 (not a word) or 6 (definitely a word). All words were morphologically complex (e.g., base + affix). Some of the words were real words in English, while other words were not real words in English. The purpose of this task was to assess if native-speakers were sensitive to the effects of semantic blocking and affix ordering. There were four conditions in this task. The conditions are listed below along with an example to illustrate the types of words that were presented in each condition.

Condition 1: Real words
Example: The suffix –able is added to verbs to derive adjectives such as workable or comfortable. A response of 4, 5, or 6 would be counted as accurate.

Condition 2: Semantic blocking
Example: Even though you can add the affix –able to many verbs to derive adjectives, there are some verbs like arrivable and departable look that do not normally take the suffix –able to form adjectives. A response of 1, 2, or 3 would be counted as accurate.

Condition 3: Correct affix ordering
Example: There are some bases that can take two affixes. You can add the affix –able to the verb respect to derive the adjective respectable. Then, you can add the affix –ity to respectable to derive the noun respectability. A response of 4, 5, or 6 would be counted as accurate.

Condition 4: Incorrect affix ordering
Example: In a word like respectability, the word is correct because the affixes are added in the correct order. However, if I add the affix -ity before I add the affix –able, I derive a word like respectitiable. This word is not correct because the affixes are not added in the correct order. A response of 1, 2, or 3 would be counted as accurate.

''Word relatedness task''

Explanation: In this task, students were asked to rate words based on their meaning from 1 (not related) to 6 (definitely related). There were five conditions in this exercise.

Condition 1: No relationship in meaning
Some words are not related in meaning in any way. The words cat and bus are not related in meaning in any way. A response of 1, 2, or 3 would be counted as accurate.

Condition 2: Relationship in meaning
Other words are related in meaning. For instance, bank and money are related in that a bank is a place where you deposit your money. A response of 4, 5, or 6 would be counted as accurate.

Condition 3: Relationship in meaning with different affixes.
This condition contained words with suffixes that were related in meaning. For example, productive (adj.) and production (n.) both share the base produce (v.). A response of 4, 5, or 6 would be counted as accurate.

Condition 4: Relationship in orthography only, not meaning
There are some words that may look like they are related in meaning because they share the same initial letters. In this condition, students saw words like permanence and permission. These words share the letters p-e-r-m, but are unrelated in meaning. A response of 1, 2, or 3 would be counted as accurate.

Condition 5: Relationship in affix only, not meaning
In the final condition, students were presented with words that shared the same affix, but were unrelated in meaning. For example, the words reality and curiosity are unrelated in meaning, but share the affix –ity. A response of 1, 2, or 3 would be counted as accurate.

''Word Analysis Task''

Explanation: On the Word Analysis Task, students were asked to provide the base word of the word provided. Some of these words consisted of a base and an affix such as musician, which has music as a base. Other words, however, could not be broken down into a base and a affix. For instance, dollar cannot be broken down into doll + ar because dollar is a base form. Accuracy was computed for decomposable and non-decomposable words.

Native speakers piloted these tassk in the fall of 2009, and preliminary results are reported for each task in the tables below. A pull out from the ELI in Spring 2010 will collect learner data.

''Participants''

These tasks were administered to native speakers and L2 learners during the fall 2009 and spring 2010 semesters. A total of 23 native-English speakers participated in the study. All of the native speakers were undergraduates at the University of Pittsburgh. Ninety ESL learners participated in this study from three different levels of language proficiency: beginner (n=26), intermediate (n=36), and advanced (n=28). These learners were enrolled in an intensive English program at the University of Pittsburgh.

'''Descriptive Results'''

''Lexical Decision Task''

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''NS Accuracy'''
|-
! Condition 1: Real words
| 93%
|-
! Condition 2: Semantic blocking
| 81%
|-
! Condition 3: Correct affix ordering
| 93%
|-
! Condition 4: Incorrect Affix ordering
| 95%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Beginner Accuracy'''
|-
! Condition 1: Real words
| 89%
|-
! Condition 2: Semantic blocking
| 48%
|-
! Condition 3: Correct affix ordering
| 66%
|-
! Condition 4: Incorrect Affix ordering
| 53%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Intermediate Accuracy'''
|-
! Condition 1: Real words
| 90%
|-
! Condition 2: Semantic blocking
| 59%
|-
! Condition 3: Correct affix ordering
| 69%
|-
! Condition 4: Incorrect Affix ordering
| 70%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Advanced Accuracy'''
|-
! Condition 1: Real words
| 96%
|-
! Condition 2: Semantic blocking
| 53%
|-
! Condition 3: Correct affix ordering
| 77%
|-
! Condition 4: Incorrect Affix ordering
| 81%
|}

''Word relatedness task''
{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''NS Accuracy'''
|-
! Condition 1: No relationship in meaning
| 92%
|-
! Condition 2: Relationship in meaning
| 92%
|-
! Condition 3: Relationship in meaning with different affixes
| 97%
|-
! Condition 4: Relationship in orthography only
| 89%
|-
! Condition 5: Relationship in affix only
| 90%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Beginner Accuracy'''
|-
! Condition 1: No relationship in meaning
| 91%
|-
! Condition 2: Relationship in meaning
| 84%
|-
! Condition 3: Relationship in meaning with different affixes
| 89%
|-
! Condition 4: Relationship in orthography only
| 73%
|-
! Condition 5: Relationship in affix only
| 74%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Intermediate Accuracy'''
|-
! Condition 1: No relationship in meaning
| 89%
|-
! Condition 2: Relationship in meaning
| 87%
|-
! Condition 3: Relationship in meaning with different affixes
| 89%
|-
! Condition 4: Relationship in orthography only
| 70%
|-
! Condition 5: Relationship in affix only
| 69%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Advanced Accuracy'''
|-
! Condition 1: No relationship in meaning
| 95%
|-
! Condition 2: Relationship in meaning
| 79%
|-
! Condition 3: Relationship in meaning with different affixes
| 96%
|-
! Condition 4: Relationship in orthography only
| 81%
|-
! Condition 5: Relationship in affix only
| 89%
|}

''Word Analysis Task''
{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''NS Accuracy'''
|-
! Condition 1: Decomposable
| 85%
|-
! Condition 2: Non-decomposable
| 92%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Beginner Accuracy'''
|-
! Condition 1: Decomposable
| 59%
|-
! Condition 2: Non-decomposable
| 73%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Intermediate Accuracy'''
|-
! Condition 1: Decomposable
| 61%
|-
! Condition 2: Non-decomposable
| 84%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Advanced Accuracy'''
|-
! Condition 1: Decomposable
| 65%
|-
! Condition 2: Non-decomposable
| 81%
|}

'''Discussion'''

''Beginning L2 Learners (Level 3)''

The results of this study indicate that level 3 learners from the ELI at the University of Pittsburgh often have problems when processing derived words. Firstly, the lexical decision task may indicate that beginning learners are not sensitive to constraints on the use of morphemes such as –able and –ness or constraints on the ordering of affixes. For instance, the learners in the present study often judge words such as departable and hopenessful to be real English words in spite of the fact that most native speakers rarely (if ever) consider these words to be real English words. Sixteen of 26 level 3 learners said that smileable was a real word in English, while only 1 of 23 native speakers said that this was a real word in English. Likewise, for hopenessful, 18 of 26 learners said that this was a real word, but only 1 of 23 natives considered hopenessful to be a real English word. Second, the word relatedness task seems to indicate that learners rely heavily on orthographic/phonological overlap when processing the meaning of words. Put another way, the learners in this study said that word pairs that were related in form only were also related in meaning. For instance, 12 out of 26 level 3 learners said that the word pair majority-activity were related in meaning, while native speakers (N=23) never say that these words are related in meaning. Finally, the results from the word analysis task may suggest that learners at this level have significant difficulties with derived words that involve phonological and/or orthographic changes to the base. In the present study, almost all of the level 3 learners (22 of 26) incorrectly provided the base word for extension. Native speakers, on the other hand, provided the incorrect base for extension only 4 times out of 23 subjects.

''Intermediate L2 Learners (Level 4)''

For Level 4 L2 learners, the lexical decision task may indicate that even intermediate-level learners are not sensitive to constraints on the use of morphemes such as –able and –ness or constraints on the ordering of affixes. For instance, the learners in the present study often judge words such as departable and hopenessful to be real English words in spite of the fact that most native speakers rarely (if ever) consider these words to be real English words. Seventeen of 36 level 4 learners said that smileable was a real word in English, while only 1 of 23 native speakers said that this was a real word in English. Likewise, for hopenessful, 21 of 36 learners said that this was a real word, but only 1 of 23 natives considered hopenessful to be a real English word. Second, the word relatedness task seems to indicate that learners rely heavily on orthographic/phonological overlap when processing the meaning of words. Put another way, the learners in this study said that word pairs that were related in form only were also related in meaning. For instance, 14 out of 36 level 3 learners said that the word pair majority-activity were related in meaning, while native speakers (N=23) never say that these words are related in meaning. Finally, the results from the word analysis task may suggest that learners at this level have significant difficulties with derived words that involve phonological and/or orthographic changes to the base. In the present study, more than two-thirds of the level 4 learners (25 of 36) incorrectly provided the base word for extension. Native speakers, on the other hand, provided the incorrect base for extension only 4 times out of 23 subjects.

''Advanced L2 Learners (Level 5)''

For level 5 L2 learners, the lexical decision task may indicate that even advanced learners are not sensitive to constraints on the use of morphemes such as –able and –ness. For instance, the learners in the present study often judge words such as smileable and leavable to be real English words in spite of the fact that most native speakers rarely (if ever) consider these words to be real English words. Fourteen of 28 level 5 learners said that smileable was a real word in English, while only 1 of 23 native speakers said that this was a real word in English. Likewise, for leavable, 19 of 28 learners said that this was a real word, but only 4 of 23 natives considered leavable to be a real English word. Second, the word relatedness task seems to indicate that advanced learners still rely to some degree on orthographic/phonological overlap when processing the meaning of words. Put another way, some advanced learners in this study said that word pairs that were related in form only were also related in meaning. For instance, 11 out of 28 level 3 learners said that the word pair constantly-conservative were related in meaning, while native speakers (N=23) never say that these words are related in meaning. Finally, the results from the word analysis task may suggest that learners at level 5 have significant difficulties with derived words that involve phonological and/or orthographic changes to the base. In the present study, many of the errors on the word analysis task were errors on words that involved a significant orthographic and sometimes phonological change to the base. For instance, 19 of 28 level 5 students incorrectly provided the base word for extension. Native speakers, on the other hand, provided the incorrect base for extension only 4 times out of 23 subjects.

'''General Discussion'''
**Forthcoming in the first author's doctoral dissertation**

'''Selected References'''

Alegre, M., & Gordon, P. (1999). Rule-based versus associative processes in derivational morphology. Brain and Language, 68, 347-354.

Bailey, N., Madden, C., & Krashen, S. (1974). Is there a "natural sequence" in adult second language learning? Language Learning, 24(2), 234-243.

Bley-Vroman, R. (1989). The logical problem of second language learning. In S. Gass & J. Schachter (Eds.), Linguistic Perspectives on Second Language Acquisition. Cambridge: Cambridge University Press.

Carlisle, J.F. (2000). Awareness of the structure and meaning of morphologically complex words: Impact on reading. Reading and Writing, 12(3-4), 169-190.

Carlisle, J. F., & Fleming, J. (2003). Lexical processing of morphologically complex words in the elementary years. Scientific Studies of Reading, 7(3), 239-253.

Ellis, N. C. (2006). Selective attention and transfer phenomena in L2 acquisition: Contingency, cue competition, salience, interference, overshadowing, blocking, and perceptual learning. Applied Linguistics, 27(2), 164-194.

Felser, C., & Clahsen, H. (2009). Grammatical processing of spoken language in child and adult language learners. Journal of Psycholinguistic Research, 38(3), 305-319.

Gonnerman, L. M., Seidenberg, M. S., & Andersen, E. S. (2007). Graded semantic and phonological similarity effect in priming: Evidence for a distributed connectionist approach to morphology. Journal of Experimental Psychology, 136(2), 323-345.

Hagiwara, H., Sugioka, Y., Ito, T., Kawamura, M., & Shiota, J.-i. (1999). Neurolinguistic evidence for rule-based nominal suffixation. Language, 75(4), 739-763.

Hay, J. (2002). From speech perception to morphology: Affix ordering revisited. Language, 72(3), 527-555.

Hay, J. B., & Baayen, R. H. (2005). Shifting paradigms: gradient structure in morphology. TRENDS in Cognitive Science, 9(7), 342-348.

Jiang, N. (2004). Morphological insensitivity in second language processing. Applied Psycholinguistics, 25, 603-634.

Johnson, J. S., & Newport, E. L. (1989). Critical period effects in secon dlanguage learning: The influence of maturational state on the acquisition of English as a second language. Cognitive Psychology, 21, 60-99.

Juffs, A. & Friedline, B. (2010). L1 influence, morphological (in)sensitivity and L2 lexical development: Evidence from production data. Unpublished manuscript, University of Pittsburgh, PA.

Lardiere, D. (1998). Dissociating syntax from morphology in a divergent L2 end-state grammar. Second Language Research, 14(4), 359-375.

Lardiere, D. (2006). Ultimate attainment in second language acquisition: a case study. New York: Routledge

Marslen-Wilson, W. D., Bozic, M., & Randall, B. (2008). Early decomposition in visual word recognition: Dissociating morphology, form, and meaning. Language and Cognitive Processes, 23(3), 394-421.

Nation, I. S. P. (2001). Learning vocabulary in another language. New York: Cambridge University Press.

Pinker, S., & Ullman, M. T. (2002). The past and future of the past tense. TRENDS in Cognitive Science, 6(11), 456-463.

Silva, R., & Clahsen, H. (2008). Morphologically complex words in L1 and L2 processing: Evidence from masked priming experiments in English. Bilingualism: Language and Cognition, 11(2), 245-260.

White, L. (2003). Fossilization in steady state L2 grammars: Persistent problems with inflectional morphology. Bilingualism: Language and Cognition, 6(2), 129-141.

Juffs - Feature Focus in Word Learning

2010-05-19T16:10:36Z

Juffs: /* L2 learning of derived words */

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! PI
| Ben Friedline, Alan Juffs
|-
! Start date
| September 2009
|-
! End date
| July 2010
|-
! Learnlab
| English
|}

== L2 learning of derived words ==
Benjamin Friedline and Alan Juffs

'''Background'''

Inflected and derived words occur frequently in the English language and serve important functions in everyday communication. The term derived word refers to the combination of a base word with a derivational affix. For instance, if the derivational affix –ness is added to the base kind (adjective), the word kindness (noun) is derived. The affix –ness is very productive and can be added to many words to derive novel words such as darkness, awareness, and illness. Other derivational affixes such as –ity are not as productive as –ness and can be used to form a limited number of words, such as ¬purity and scarcity. The term inflected word refers to the combination of a base word and an inflectional affix. For example, if the inflectional affix –ed is added to the base word walk (verb), the resulting word is walked (verb). The addition of the –ed affix is very productive in the formation of the past tense even though it does not apply to a number of irregular past tense forms, such as drove, ate, and sat.

Importantly, the differences in productivity of each type of affix have led some researchers to conclude that there are differences in how they are processed by native speakers. In Words and Rules (WR) theory, Pinker and Ullman (2002) argue that irregular inflected forms (e.g., drove) are stored in the lexicon (or mental dictionary) as whole words, whereas regular forms (e.g., walked) are generated by a regular rule. This theory has also been applied to the processing of derived words in two recent psychological investigations (cf. Alegre & Gordon, 1999; Hagiwara et al., 1999). The main point behind both of these studies is to illustrate that derived words can be either rule-governed (e.g., words with –ness) or stored as whole words in the lexicon (e.g., words with –ity as in purity).

In the area of second language acquisition, adult second language (L2) learners often fail to attain native-like proficiency when producing derived and inflected words in an L2. Lardiere (1998), for instance, showed that second language learners still make errors with inflectional morphology even after many years of exposure to English. In Lardiere’s (1998) study, she recorded and analyzed naturalistic conversations from a Chinese learner of American English. The results of this study indicated that the learner supplied the inflectional affix –ed correctly in only 34% of obligatory contexts even after 18 years of exposure to English. Additionally, in terms of derived words, a recent study by Juffs and Friedline (2010) revealed that intermediate L2 learners often made errors in the production of derived words such as those in examples (1) and (2).
(1) We have one different [difference].
(2) I like doing something music [musical].
In example (1) the learner uses the adjective form different instead of the grammatically correct form difference, which is a noun. In example (2) the learner uses the noun form music in a position that requires the adjective form musical.

The preponderance of such errors in L2 speech has led some researchers to conclude that L2 learners are permanently impaired on the production of derived and inflected words because they do not have access to the same rule-based mechanisms that are present during L1 acquisition (Jiang, 2004; Felser & Clahsen, 2009; Silva & Clahsen, 2008). This hypothesis is formally known as the Fundamental Difference Hypothesis (FDH; Bley-Vroman, 1989), and it has received support from a number of recent studies in the field of second language acquisition. Silva and Clahsen (2008), for instance, use evidence from a masked-priming experiment to compare native speakers to adult L2 learners on a series of morphological priming tasks. The results from this study indicated full priming effects (e.g., darkness primes dark) for native speakers on both inflections and derivations, but only partial priming effects for L2 learners on derivations and no priming effects for L2 learners on inflections. Silva and Clahsen (2008) argue that the limited priming effects (or complete lack thereof) indicate that L2 learners lack rule-based mechanisms and do not know that ¬–ness can be affixed to many adjectives to derive nouns such as darkness, awareness, and illness. This lack of rule-based mechanisms may mean that adult L2 learners memorize all words as unanalyzed chunks of language, without realizing that dark and darkness or walk and walked are intimately related in both form and meaning.

The research by Silva and Clahsen (2008) makes an important contribution to a theory of second language acquisition because it provides a possible explanation for why L2 learners have difficulties with derived and inflected words (i.e., they cannot access rule-based mechanisms). However, this research is limited because it assumes that L2 learners are permanently impaired when compared to native-speakers on all types of rule-based inflectional and derivational morphological processes. This assumption is at odds with findings from studies such as the morpheme order studies (e.g., Bailey, Madden, & Krashen, 1974), critical period studies (e.g., Johnson & Newport, 1989), and studies on the role of morpheme salience in L2 acquisition (e.g., Ellis, 2006) in that these past studies indicate that certain morphemes, such as progressive, may be more easily acquired than others. Johnson and Newport (1989), for instance, claim that the progressive morpheme may not be subject to critical period effects.

Additionally, the role of a learner’s first language may also influence the relative difficulty of a particular morpheme. Potential L1 effects have been discussed in a number of recent SLA studies (e.g., Juffs & Friedline, 2010; White, 2003). Before we conclude that L2 learners are equally impaired in all areas of morphological knowledge, further research is needed to identify if certain morphological structures are easier to acquire than others, how exactly L2 morphological knowledge diverges from native-speaker knowledge, and how a learner’s first language might influence L2 morphological knowledge. The goal of the present research is to answer these questions as they relate to derivational morphology.

'''Research Questions'''

Why are ESL learners so poor in their knowledge of English morphology? What are the knowledge components that are the most challenging for learning through normal language exposure? Do learners have a representational problem or a processing problem? Specifically, what instructional interventions can be designed to overcome observed processing differences in L1 and L2 morphology?

'''Research plan'''

For year 1, the goal of the research is to analyze the knowledge components of ESL learners to lay the groundwork for a hypothesis-based intervention. The research will systematically investigate the components of L2 learners’ knowledge of English derivational morphology to address the following questions:

1) What are the components of L2 derivational knowledge?
2) Are these components different from L1 derivational knowledge?
3) Does L1 matter for the acquisition of derived words in an L2?

'''Methodology'''

To answer these questions, Friedline has developed a series of tasks that will be used to assess what native English speakers and second language learners know about derived words. These tasks included lexical decision, semantic relatedness, and morphological decomposition. Each of these tasks contained several conditions that tested different components of morphological knowledge. Studies on the acquisition of L1 morphological knowledge (e.g., Carlisle, 2000; Carlisle & Fleming, 2003) were consulted in order to develop these conditions. Each condition is outlined below.

''Lexical decision task''

Explanation: In this task, students were asked to rate words from 1 (not a word) or 6 (definitely a word). All words were morphologically complex (e.g., base + affix). Some of the words were real words in English, while other words were not real words in English. The purpose of this task was to assess if native-speakers were sensitive to the effects of semantic blocking and affix ordering. There were four conditions in this task. The conditions are listed below along with an example to illustrate the types of words that were presented in each condition.

Condition 1: Real words
Example: The suffix –able is added to verbs to derive adjectives such as workable or comfortable. A response of 4, 5, or 6 would be counted as accurate.

Condition 2: Semantic blocking
Example: Even though you can add the affix –able to many verbs to derive adjectives, there are some verbs like arrivable and departable look that do not normally take the suffix –able to form adjectives. A response of 1, 2, or 3 would be counted as accurate.

Condition 3: Correct affix ordering
Example: There are some bases that can take two affixes. You can add the affix –able to the verb respect to derive the adjective respectable. Then, you can add the affix –ity to respectable to derive the noun respectability. A response of 4, 5, or 6 would be counted as accurate.

Condition 4: Incorrect affix ordering
Example: In a word like respectability, the word is correct because the affixes are added in the correct order. However, if I add the affix -ity before I add the affix –able, I derive a word like respectitiable. This word is not correct because the affixes are not added in the correct order. A response of 1, 2, or 3 would be counted as accurate.

''Word relatedness task''

Explanation: In this task, students were asked to rate words based on their meaning from 1 (not related) to 6 (definitely related). There were five conditions in this exercise.

Condition 1: No relationship in meaning
Some words are not related in meaning in any way. The words cat and bus are not related in meaning in any way. A response of 1, 2, or 3 would be counted as accurate.

Condition 2: Relationship in meaning
Other words are related in meaning. For instance, bank and money are related in that a bank is a place where you deposit your money. A response of 4, 5, or 6 would be counted as accurate.

Condition 3: Relationship in meaning with different affixes.
This condition contained words with suffixes that were related in meaning. For example, productive (adj.) and production (n.) both share the base produce (v.). A response of 4, 5, or 6 would be counted as accurate.

Condition 4: Relationship in orthography only, not meaning
There are some words that may look like they are related in meaning because they share the same initial letters. In this condition, students saw words like permanence and permission. These words share the letters p-e-r-m, but are unrelated in meaning. A response of 1, 2, or 3 would be counted as accurate.

Condition 5: Relationship in affix only, not meaning
In the final condition, students were presented with words that shared the same affix, but were unrelated in meaning. For example, the words reality and curiosity are unrelated in meaning, but share the affix –ity. A response of 1, 2, or 3 would be counted as accurate.

''Word Analysis Task''

Explanation: On the Word Analysis Task, students were asked to provide the base word of the word provided. Some of these words consisted of a base and an affix such as musician, which has music as a base. Other words, however, could not be broken down into a base and a affix. For instance, dollar cannot be broken down into doll + ar because dollar is a base form. Accuracy was computed for decomposable and non-decomposable words.

Native speakers piloted these tassk in the fall of 2009, and preliminary results are reported for each task in the tables below. A pull out from the ELI in Spring 2010 will collect learner data.

''Participants''

These tasks were administered to native speakers and L2 learners during the fall 2009 and spring 2010 semesters. A total of 23 native-English speakers participated in the study. All of the native speakers were undergraduates at the University of Pittsburgh. Ninety ESL learners participated in this study from three different levels of language proficiency: beginner (n=26), intermediate (n=36), and advanced (n=28). These learners were enrolled in an intensive English program at the University of Pittsburgh.

'''Descriptive Results'''

''Lexical Decision Task''

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''NS Accuracy'''
|-
! Condition 1: Real words
| 93%
|-
! Condition 2: Semantic blocking
| 81%
|-
! Condition 3: Correct affix ordering
| 93%
|-
! Condition 4: Incorrect Affix ordering
| 95%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Beginner Accuracy'''
|-
! Condition 1: Real words
| 89%
|-
! Condition 2: Semantic blocking
| 48%
|-
! Condition 3: Correct affix ordering
| 66%
|-
! Condition 4: Incorrect Affix ordering
| 53%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Intermediate Accuracy'''
|-
! Condition 1: Real words
| 90%
|-
! Condition 2: Semantic blocking
| 59%
|-
! Condition 3: Correct affix ordering
| 69%
|-
! Condition 4: Incorrect Affix ordering
| 70%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Advanced Accuracy'''
|-
! Condition 1: Real words
| 96%
|-
! Condition 2: Semantic blocking
| 53%
|-
! Condition 3: Correct affix ordering
| 77%
|-
! Condition 4: Incorrect Affix ordering
| 81%
|}

''Word relatedness task''
{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''NS Accuracy'''
|-
! Condition 1: No relationship in meaning
| 92%
|-
! Condition 2: Relationship in meaning
| 92%
|-
! Condition 3: Relationship in meaning with different affixes
| 97%
|-
! Condition 4: Relationship in orthography only
| 89%
|-
! Condition 5: Relationship in affix only
| 90%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Beginner Accuracy'''
|-
! Condition 1: No relationship in meaning
| 91%
|-
! Condition 2: Relationship in meaning
| 84%
|-
! Condition 3: Relationship in meaning with different affixes
| 89%
|-
! Condition 4: Relationship in orthography only
| 73%
|-
! Condition 5: Relationship in affix only
| 74%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Intermediate Accuracy'''
|-
! Condition 1: No relationship in meaning
| 89%
|-
! Condition 2: Relationship in meaning
| 87%
|-
! Condition 3: Relationship in meaning with different affixes
| 89%
|-
! Condition 4: Relationship in orthography only
| 70%
|-
! Condition 5: Relationship in affix only
| 69%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Advanced Accuracy'''
|-
! Condition 1: No relationship in meaning
| 95%
|-
! Condition 2: Relationship in meaning
| 79%
|-
! Condition 3: Relationship in meaning with different affixes
| 96%
|-
! Condition 4: Relationship in orthography only
| 81%
|-
! Condition 5: Relationship in affix only
| 89%
|}

''Word Analysis Task''
{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''NS Accuracy'''
|-
! Condition 1: Decomposable
| 85%
|-
! Condition 2: Non-decomposable
| 92%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Beginner Accuracy'''
|-
! Condition 1: Decomposable
| 59%
|-
! Condition 2: Non-decomposable
| 73%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Intermediate Accuracy'''
|-
! Condition 1: Decomposable
| 61%
|-
! Condition 2: Non-decomposable
| 84%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Advanced Accuracy'''
|-
! Condition 1: Decomposable
| 65%
|-
! Condition 2: Non-decomposable
| 81%
|}

'''Discussion'''

''Beginning L2 Learners (Level 3)''

The results of this study indicate that level 3 learners from the ELI at the University of Pittsburgh often have problems when processing derived words. Firstly, the lexical decision task may indicate that beginning learners are not sensitive to constraints on the use of morphemes such as –able and –ness or constraints on the ordering of affixes. For instance, the learners in the present study often judge words such as departable and hopenessful to be real English words in spite of the fact that most native speakers rarely (if ever) consider these words to be real English words. Sixteen of 26 level 3 learners said that smileable was a real word in English, while only 1 of 23 native speakers said that this was a real word in English. Likewise, for hopenessful, 18 of 26 learners said that this was a real word, but only 1 of 23 natives considered hopenessful to be a real English word. Second, the word relatedness task seems to indicate that learners rely heavily on orthographic/phonological overlap when processing the meaning of words. Put another way, the learners in this study said that word pairs that were related in form only were also related in meaning. For instance, 12 out of 26 level 3 learners said that the word pair majority-activity were related in meaning, while native speakers (N=23) never say that these words are related in meaning. Finally, the results from the word analysis task may suggest that learners at this level have significant difficulties with derived words that involve phonological and/or orthographic changes to the base. In the present study, almost all of the level 3 learners (22 of 26) incorrectly provided the base word for extension. Native speakers, on the other hand, provided the incorrect base for extension only 4 times out of 23 subjects.

''Intermediate L2 Learners (Level 4)''

For Level 4 L2 learners, the lexical decision task may indicate that even intermediate-level learners are not sensitive to constraints on the use of morphemes such as –able and –ness or constraints on the ordering of affixes. For instance, the learners in the present study often judge words such as departable and hopenessful to be real English words in spite of the fact that most native speakers rarely (if ever) consider these words to be real English words. Seventeen of 36 level 4 learners said that smileable was a real word in English, while only 1 of 23 native speakers said that this was a real word in English. Likewise, for hopenessful, 21 of 36 learners said that this was a real word, but only 1 of 23 natives considered hopenessful to be a real English word. Second, the word relatedness task seems to indicate that learners rely heavily on orthographic/phonological overlap when processing the meaning of words. Put another way, the learners in this study said that word pairs that were related in form only were also related in meaning. For instance, 14 out of 36 level 3 learners said that the word pair majority-activity were related in meaning, while native speakers (N=23) never say that these words are related in meaning. Finally, the results from the word analysis task may suggest that learners at this level have significant difficulties with derived words that involve phonological and/or orthographic changes to the base. In the present study, more than two-thirds of the level 4 learners (25 of 36) incorrectly provided the base word for extension. Native speakers, on the other hand, provided the incorrect base for extension only 4 times out of 23 subjects.

''Advanced L2 Learners (Level 5)''

For level 5 L2 learners, the lexical decision task may indicate that even advanced learners are not sensitive to constraints on the use of morphemes such as –able and –ness. For instance, the learners in the present study often judge words such as smileable and leavable to be real English words in spite of the fact that most native speakers rarely (if ever) consider these words to be real English words. Fourteen of 28 level 5 learners said that smileable was a real word in English, while only 1 of 23 native speakers said that this was a real word in English. Likewise, for leavable, 19 of 28 learners said that this was a real word, but only 4 of 23 natives considered leavable to be a real English word. Second, the word relatedness task seems to indicate that advanced learners still rely to some degree on orthographic/phonological overlap when processing the meaning of words. Put another way, some advanced learners in this study said that word pairs that were related in form only were also related in meaning. For instance, 11 out of 28 level 3 learners said that the word pair constantly-conservative were related in meaning, while native speakers (N=23) never say that these words are related in meaning. Finally, the results from the word analysis task may suggest that learners at level 5 have significant difficulties with derived words that involve phonological and/or orthographic changes to the base. In the present study, many of the errors on the word analysis task were errors on words that involved a significant orthographic and sometimes phonological change to the base. For instance, 19 of 28 level 5 students incorrectly provided the base word for extension. Native speakers, on the other hand, provided the incorrect base for extension only 4 times out of 23 subjects.

'''General Discussion'''
**Forthcoming in the first author's doctoral dissertation**

'''Selected References'''

Alegre, M., & Gordon, P. (1999). Rule-based versus associative processes in derivational morphology. Brain and Language, 68, 347-354.
Bailey, N., Madden, C., & Krashen, S. (1974). Is there a "natural sequence" in adult second language learning? Language Learning, 24(2), 234-243.
Bley-Vroman, R. (1989). The logical problem of second language learning. In S. Gass & J. Schachter (Eds.), Linguistic Perspectives on Second Language Acquisition. Cambridge: Cambridge University Press.
Carlisle, J.F. (2000). Awareness of the structure and meaning of morphologically complex words: Impact on reading. Reading and Writing, 12(3-4), 169-190.
Carlisle, J. F., & Fleming, J. (2003). Lexical processing of morphologically complex words in the elementary years. Scientific Studies of Reading, 7(3), 239-253.
Ellis, N. C. (2006). Selective attention and transfer phenomena in L2 acquisition: Contingency, cue competition, salience, interference, overshadowing, blocking, and perceptual learning. Applied Linguistics, 27(2), 164-194.
Felser, C., & Clahsen, H. (2009). Grammatical processing of spoken language in child and adult language learners. Journal of Psycholinguistic Research, 38(3), 305-319.
Gonnerman, L. M., Seidenberg, M. S., & Andersen, E. S. (2007). Graded semantic and phonological similarity effect in priming: Evidence for a distributed connectionist approach to morphology. Journal of Experimental Psychology, 136(2), 323-345.
Hagiwara, H., Sugioka, Y., Ito, T., Kawamura, M., & Shiota, J.-i. (1999). Neurolinguistic evidence for rule-based nominal suffixation. Language, 75(4), 739-763.
Hay, J. (2002). From speech perception to morphology: Affix ordering revisited. Language, 72(3), 527-555.
Hay, J. B., & Baayen, R. H. (2005). Shifting paradigms: gradient structure in morphology. TRENDS in Cognitive Science, 9(7), 342-348.
Jiang, N. (2004). Morphological insensitivity in second language processing. Applied Psycholinguistics, 25, 603-634.
Johnson, J. S., & Newport, E. L. (1989). Critical period effects in secon dlanguage learning: The influence of maturational state on the acquisition of English as a second language. Cognitive Psychology, 21, 60-99.
Juffs, A. & Friedline, B. (2010). L1 influence, morphological (in)sensitivity and L2 lexical development: Evidence from production data. Unpublished manuscript, University of Pittsburgh, PA.
Lardiere, D. (1998). Dissociating syntax from morphology in a divergent L2 end-state grammar. Second Language Research, 14(4), 359-375.
Lardiere, D. (2006). Ultimate attainment in second language acquisition: a case study. New York: Routledge
Marslen-Wilson, W. D., Bozic, M., & Randall, B. (2008). Early decomposition in visual word recognition: Dissociating morphology, form, and meaning. Language and Cognitive Processes, 23(3), 394-421.
Nation, I. S. P. (2001). Learning vocabulary in another language. New York: Cambridge University Press.
Pinker, S., & Ullman, M. T. (2002). The past and future of the past tense. TRENDS in Cognitive Science, 6(11), 456-463.
Silva, R., & Clahsen, H. (2008). Morphologically complex words in L1 and L2 processing: Evidence from masked priming experiments in English. Bilingualism: Language and Cognition, 11(2), 245-260.
White, L. (2003). Fossilization in steady state L2 grammars: Persistent problems with inflectional morphology. Bilingualism: Language and Cognition, 6(2), 129-141.

Juffs - Feature Focus in Word Learning

2010-05-19T16:10:19Z

Juffs: /* L2 learning of derived words */

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! PI
| Ben Friedline, Alan Juffs
|-
! Start date
| September 2009
|-
! End date
| July 2010
|-
! Learnlab
| English
|}

== L2 learning of derived words ==
Benjamin Friedline and Alan Juffs

'''Background'''
Inflected and derived words occur frequently in the English language and serve important functions in everyday communication. The term derived word refers to the combination of a base word with a derivational affix. For instance, if the derivational affix –ness is added to the base kind (adjective), the word kindness (noun) is derived. The affix –ness is very productive and can be added to many words to derive novel words such as darkness, awareness, and illness. Other derivational affixes such as –ity are not as productive as –ness and can be used to form a limited number of words, such as ¬purity and scarcity. The term inflected word refers to the combination of a base word and an inflectional affix. For example, if the inflectional affix –ed is added to the base word walk (verb), the resulting word is walked (verb). The addition of the –ed affix is very productive in the formation of the past tense even though it does not apply to a number of irregular past tense forms, such as drove, ate, and sat.

Importantly, the differences in productivity of each type of affix have led some researchers to conclude that there are differences in how they are processed by native speakers. In Words and Rules (WR) theory, Pinker and Ullman (2002) argue that irregular inflected forms (e.g., drove) are stored in the lexicon (or mental dictionary) as whole words, whereas regular forms (e.g., walked) are generated by a regular rule. This theory has also been applied to the processing of derived words in two recent psychological investigations (cf. Alegre & Gordon, 1999; Hagiwara et al., 1999). The main point behind both of these studies is to illustrate that derived words can be either rule-governed (e.g., words with –ness) or stored as whole words in the lexicon (e.g., words with –ity as in purity).

In the area of second language acquisition, adult second language (L2) learners often fail to attain native-like proficiency when producing derived and inflected words in an L2. Lardiere (1998), for instance, showed that second language learners still make errors with inflectional morphology even after many years of exposure to English. In Lardiere’s (1998) study, she recorded and analyzed naturalistic conversations from a Chinese learner of American English. The results of this study indicated that the learner supplied the inflectional affix –ed correctly in only 34% of obligatory contexts even after 18 years of exposure to English. Additionally, in terms of derived words, a recent study by Juffs and Friedline (2010) revealed that intermediate L2 learners often made errors in the production of derived words such as those in examples (1) and (2).
(1) We have one different [difference].
(2) I like doing something music [musical].
In example (1) the learner uses the adjective form different instead of the grammatically correct form difference, which is a noun. In example (2) the learner uses the noun form music in a position that requires the adjective form musical.

The preponderance of such errors in L2 speech has led some researchers to conclude that L2 learners are permanently impaired on the production of derived and inflected words because they do not have access to the same rule-based mechanisms that are present during L1 acquisition (Jiang, 2004; Felser & Clahsen, 2009; Silva & Clahsen, 2008). This hypothesis is formally known as the Fundamental Difference Hypothesis (FDH; Bley-Vroman, 1989), and it has received support from a number of recent studies in the field of second language acquisition. Silva and Clahsen (2008), for instance, use evidence from a masked-priming experiment to compare native speakers to adult L2 learners on a series of morphological priming tasks. The results from this study indicated full priming effects (e.g., darkness primes dark) for native speakers on both inflections and derivations, but only partial priming effects for L2 learners on derivations and no priming effects for L2 learners on inflections. Silva and Clahsen (2008) argue that the limited priming effects (or complete lack thereof) indicate that L2 learners lack rule-based mechanisms and do not know that ¬–ness can be affixed to many adjectives to derive nouns such as darkness, awareness, and illness. This lack of rule-based mechanisms may mean that adult L2 learners memorize all words as unanalyzed chunks of language, without realizing that dark and darkness or walk and walked are intimately related in both form and meaning.

The research by Silva and Clahsen (2008) makes an important contribution to a theory of second language acquisition because it provides a possible explanation for why L2 learners have difficulties with derived and inflected words (i.e., they cannot access rule-based mechanisms). However, this research is limited because it assumes that L2 learners are permanently impaired when compared to native-speakers on all types of rule-based inflectional and derivational morphological processes. This assumption is at odds with findings from studies such as the morpheme order studies (e.g., Bailey, Madden, & Krashen, 1974), critical period studies (e.g., Johnson & Newport, 1989), and studies on the role of morpheme salience in L2 acquisition (e.g., Ellis, 2006) in that these past studies indicate that certain morphemes, such as progressive, may be more easily acquired than others. Johnson and Newport (1989), for instance, claim that the progressive morpheme may not be subject to critical period effects.

Additionally, the role of a learner’s first language may also influence the relative difficulty of a particular morpheme. Potential L1 effects have been discussed in a number of recent SLA studies (e.g., Juffs & Friedline, 2010; White, 2003). Before we conclude that L2 learners are equally impaired in all areas of morphological knowledge, further research is needed to identify if certain morphological structures are easier to acquire than others, how exactly L2 morphological knowledge diverges from native-speaker knowledge, and how a learner’s first language might influence L2 morphological knowledge. The goal of the present research is to answer these questions as they relate to derivational morphology.

'''Research Questions'''

Why are ESL learners so poor in their knowledge of English morphology? What are the knowledge components that are the most challenging for learning through normal language exposure? Do learners have a representational problem or a processing problem? Specifically, what instructional interventions can be designed to overcome observed processing differences in L1 and L2 morphology?

'''Research plan'''

For year 1, the goal of the research is to analyze the knowledge components of ESL learners to lay the groundwork for a hypothesis-based intervention. The research will systematically investigate the components of L2 learners’ knowledge of English derivational morphology to address the following questions:

1) What are the components of L2 derivational knowledge?
2) Are these components different from L1 derivational knowledge?
3) Does L1 matter for the acquisition of derived words in an L2?

'''Methodology'''

To answer these questions, Friedline has developed a series of tasks that will be used to assess what native English speakers and second language learners know about derived words. These tasks included lexical decision, semantic relatedness, and morphological decomposition. Each of these tasks contained several conditions that tested different components of morphological knowledge. Studies on the acquisition of L1 morphological knowledge (e.g., Carlisle, 2000; Carlisle & Fleming, 2003) were consulted in order to develop these conditions. Each condition is outlined below.

''Lexical decision task''

Explanation: In this task, students were asked to rate words from 1 (not a word) or 6 (definitely a word). All words were morphologically complex (e.g., base + affix). Some of the words were real words in English, while other words were not real words in English. The purpose of this task was to assess if native-speakers were sensitive to the effects of semantic blocking and affix ordering. There were four conditions in this task. The conditions are listed below along with an example to illustrate the types of words that were presented in each condition.

Condition 1: Real words
Example: The suffix –able is added to verbs to derive adjectives such as workable or comfortable. A response of 4, 5, or 6 would be counted as accurate.

Condition 2: Semantic blocking
Example: Even though you can add the affix –able to many verbs to derive adjectives, there are some verbs like arrivable and departable look that do not normally take the suffix –able to form adjectives. A response of 1, 2, or 3 would be counted as accurate.

Condition 3: Correct affix ordering
Example: There are some bases that can take two affixes. You can add the affix –able to the verb respect to derive the adjective respectable. Then, you can add the affix –ity to respectable to derive the noun respectability. A response of 4, 5, or 6 would be counted as accurate.

Condition 4: Incorrect affix ordering
Example: In a word like respectability, the word is correct because the affixes are added in the correct order. However, if I add the affix -ity before I add the affix –able, I derive a word like respectitiable. This word is not correct because the affixes are not added in the correct order. A response of 1, 2, or 3 would be counted as accurate.

''Word relatedness task''

Explanation: In this task, students were asked to rate words based on their meaning from 1 (not related) to 6 (definitely related). There were five conditions in this exercise.

Condition 1: No relationship in meaning
Some words are not related in meaning in any way. The words cat and bus are not related in meaning in any way. A response of 1, 2, or 3 would be counted as accurate.

Condition 2: Relationship in meaning
Other words are related in meaning. For instance, bank and money are related in that a bank is a place where you deposit your money. A response of 4, 5, or 6 would be counted as accurate.

Condition 3: Relationship in meaning with different affixes.
This condition contained words with suffixes that were related in meaning. For example, productive (adj.) and production (n.) both share the base produce (v.). A response of 4, 5, or 6 would be counted as accurate.

Condition 4: Relationship in orthography only, not meaning
There are some words that may look like they are related in meaning because they share the same initial letters. In this condition, students saw words like permanence and permission. These words share the letters p-e-r-m, but are unrelated in meaning. A response of 1, 2, or 3 would be counted as accurate.

Condition 5: Relationship in affix only, not meaning
In the final condition, students were presented with words that shared the same affix, but were unrelated in meaning. For example, the words reality and curiosity are unrelated in meaning, but share the affix –ity. A response of 1, 2, or 3 would be counted as accurate.

''Word Analysis Task''

Explanation: On the Word Analysis Task, students were asked to provide the base word of the word provided. Some of these words consisted of a base and an affix such as musician, which has music as a base. Other words, however, could not be broken down into a base and a affix. For instance, dollar cannot be broken down into doll + ar because dollar is a base form. Accuracy was computed for decomposable and non-decomposable words.

Native speakers piloted these tassk in the fall of 2009, and preliminary results are reported for each task in the tables below. A pull out from the ELI in Spring 2010 will collect learner data.

''Participants''

These tasks were administered to native speakers and L2 learners during the fall 2009 and spring 2010 semesters. A total of 23 native-English speakers participated in the study. All of the native speakers were undergraduates at the University of Pittsburgh. Ninety ESL learners participated in this study from three different levels of language proficiency: beginner (n=26), intermediate (n=36), and advanced (n=28). These learners were enrolled in an intensive English program at the University of Pittsburgh.

'''Descriptive Results'''

''Lexical Decision Task''

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''NS Accuracy'''
|-
! Condition 1: Real words
| 93%
|-
! Condition 2: Semantic blocking
| 81%
|-
! Condition 3: Correct affix ordering
| 93%
|-
! Condition 4: Incorrect Affix ordering
| 95%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Beginner Accuracy'''
|-
! Condition 1: Real words
| 89%
|-
! Condition 2: Semantic blocking
| 48%
|-
! Condition 3: Correct affix ordering
| 66%
|-
! Condition 4: Incorrect Affix ordering
| 53%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Intermediate Accuracy'''
|-
! Condition 1: Real words
| 90%
|-
! Condition 2: Semantic blocking
| 59%
|-
! Condition 3: Correct affix ordering
| 69%
|-
! Condition 4: Incorrect Affix ordering
| 70%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Advanced Accuracy'''
|-
! Condition 1: Real words
| 96%
|-
! Condition 2: Semantic blocking
| 53%
|-
! Condition 3: Correct affix ordering
| 77%
|-
! Condition 4: Incorrect Affix ordering
| 81%
|}

''Word relatedness task''
{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''NS Accuracy'''
|-
! Condition 1: No relationship in meaning
| 92%
|-
! Condition 2: Relationship in meaning
| 92%
|-
! Condition 3: Relationship in meaning with different affixes
| 97%
|-
! Condition 4: Relationship in orthography only
| 89%
|-
! Condition 5: Relationship in affix only
| 90%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Beginner Accuracy'''
|-
! Condition 1: No relationship in meaning
| 91%
|-
! Condition 2: Relationship in meaning
| 84%
|-
! Condition 3: Relationship in meaning with different affixes
| 89%
|-
! Condition 4: Relationship in orthography only
| 73%
|-
! Condition 5: Relationship in affix only
| 74%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Intermediate Accuracy'''
|-
! Condition 1: No relationship in meaning
| 89%
|-
! Condition 2: Relationship in meaning
| 87%
|-
! Condition 3: Relationship in meaning with different affixes
| 89%
|-
! Condition 4: Relationship in orthography only
| 70%
|-
! Condition 5: Relationship in affix only
| 69%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Advanced Accuracy'''
|-
! Condition 1: No relationship in meaning
| 95%
|-
! Condition 2: Relationship in meaning
| 79%
|-
! Condition 3: Relationship in meaning with different affixes
| 96%
|-
! Condition 4: Relationship in orthography only
| 81%
|-
! Condition 5: Relationship in affix only
| 89%
|}

''Word Analysis Task''
{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''NS Accuracy'''
|-
! Condition 1: Decomposable
| 85%
|-
! Condition 2: Non-decomposable
| 92%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Beginner Accuracy'''
|-
! Condition 1: Decomposable
| 59%
|-
! Condition 2: Non-decomposable
| 73%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Intermediate Accuracy'''
|-
! Condition 1: Decomposable
| 61%
|-
! Condition 2: Non-decomposable
| 84%
|}

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! '''Condition'''
| '''L2 Advanced Accuracy'''
|-
! Condition 1: Decomposable
| 65%
|-
! Condition 2: Non-decomposable
| 81%
|}

'''Discussion'''

''Beginning L2 Learners (Level 3)''

The results of this study indicate that level 3 learners from the ELI at the University of Pittsburgh often have problems when processing derived words. Firstly, the lexical decision task may indicate that beginning learners are not sensitive to constraints on the use of morphemes such as –able and –ness or constraints on the ordering of affixes. For instance, the learners in the present study often judge words such as departable and hopenessful to be real English words in spite of the fact that most native speakers rarely (if ever) consider these words to be real English words. Sixteen of 26 level 3 learners said that smileable was a real word in English, while only 1 of 23 native speakers said that this was a real word in English. Likewise, for hopenessful, 18 of 26 learners said that this was a real word, but only 1 of 23 natives considered hopenessful to be a real English word. Second, the word relatedness task seems to indicate that learners rely heavily on orthographic/phonological overlap when processing the meaning of words. Put another way, the learners in this study said that word pairs that were related in form only were also related in meaning. For instance, 12 out of 26 level 3 learners said that the word pair majority-activity were related in meaning, while native speakers (N=23) never say that these words are related in meaning. Finally, the results from the word analysis task may suggest that learners at this level have significant difficulties with derived words that involve phonological and/or orthographic changes to the base. In the present study, almost all of the level 3 learners (22 of 26) incorrectly provided the base word for extension. Native speakers, on the other hand, provided the incorrect base for extension only 4 times out of 23 subjects.

''Intermediate L2 Learners (Level 4)''

For Level 4 L2 learners, the lexical decision task may indicate that even intermediate-level learners are not sensitive to constraints on the use of morphemes such as –able and –ness or constraints on the ordering of affixes. For instance, the learners in the present study often judge words such as departable and hopenessful to be real English words in spite of the fact that most native speakers rarely (if ever) consider these words to be real English words. Seventeen of 36 level 4 learners said that smileable was a real word in English, while only 1 of 23 native speakers said that this was a real word in English. Likewise, for hopenessful, 21 of 36 learners said that this was a real word, but only 1 of 23 natives considered hopenessful to be a real English word. Second, the word relatedness task seems to indicate that learners rely heavily on orthographic/phonological overlap when processing the meaning of words. Put another way, the learners in this study said that word pairs that were related in form only were also related in meaning. For instance, 14 out of 36 level 3 learners said that the word pair majority-activity were related in meaning, while native speakers (N=23) never say that these words are related in meaning. Finally, the results from the word analysis task may suggest that learners at this level have significant difficulties with derived words that involve phonological and/or orthographic changes to the base. In the present study, more than two-thirds of the level 4 learners (25 of 36) incorrectly provided the base word for extension. Native speakers, on the other hand, provided the incorrect base for extension only 4 times out of 23 subjects.

''Advanced L2 Learners (Level 5)''

For level 5 L2 learners, the lexical decision task may indicate that even advanced learners are not sensitive to constraints on the use of morphemes such as –able and –ness. For instance, the learners in the present study often judge words such as smileable and leavable to be real English words in spite of the fact that most native speakers rarely (if ever) consider these words to be real English words. Fourteen of 28 level 5 learners said that smileable was a real word in English, while only 1 of 23 native speakers said that this was a real word in English. Likewise, for leavable, 19 of 28 learners said that this was a real word, but only 4 of 23 natives considered leavable to be a real English word. Second, the word relatedness task seems to indicate that advanced learners still rely to some degree on orthographic/phonological overlap when processing the meaning of words. Put another way, some advanced learners in this study said that word pairs that were related in form only were also related in meaning. For instance, 11 out of 28 level 3 learners said that the word pair constantly-conservative were related in meaning, while native speakers (N=23) never say that these words are related in meaning. Finally, the results from the word analysis task may suggest that learners at level 5 have significant difficulties with derived words that involve phonological and/or orthographic changes to the base. In the present study, many of the errors on the word analysis task were errors on words that involved a significant orthographic and sometimes phonological change to the base. For instance, 19 of 28 level 5 students incorrectly provided the base word for extension. Native speakers, on the other hand, provided the incorrect base for extension only 4 times out of 23 subjects.

'''General Discussion'''
**Forthcoming in the first author's doctoral dissertation**

'''Selected References'''

Alegre, M., & Gordon, P. (1999). Rule-based versus associative processes in derivational morphology. Brain and Language, 68, 347-354.
Bailey, N., Madden, C., & Krashen, S. (1974). Is there a "natural sequence" in adult second language learning? Language Learning, 24(2), 234-243.
Bley-Vroman, R. (1989). The logical problem of second language learning. In S. Gass & J. Schachter (Eds.), Linguistic Perspectives on Second Language Acquisition. Cambridge: Cambridge University Press.
Carlisle, J.F. (2000). Awareness of the structure and meaning of morphologically complex words: Impact on reading. Reading and Writing, 12(3-4), 169-190.
Carlisle, J. F., & Fleming, J. (2003). Lexical processing of morphologically complex words in the elementary years. Scientific Studies of Reading, 7(3), 239-253.
Ellis, N. C. (2006). Selective attention and transfer phenomena in L2 acquisition: Contingency, cue competition, salience, interference, overshadowing, blocking, and perceptual learning. Applied Linguistics, 27(2), 164-194.
Felser, C., & Clahsen, H. (2009). Grammatical processing of spoken language in child and adult language learners. Journal of Psycholinguistic Research, 38(3), 305-319.
Gonnerman, L. M., Seidenberg, M. S., & Andersen, E. S. (2007). Graded semantic and phonological similarity effect in priming: Evidence for a distributed connectionist approach to morphology. Journal of Experimental Psychology, 136(2), 323-345.
Hagiwara, H., Sugioka, Y., Ito, T., Kawamura, M., & Shiota, J.-i. (1999). Neurolinguistic evidence for rule-based nominal suffixation. Language, 75(4), 739-763.
Hay, J. (2002). From speech perception to morphology: Affix ordering revisited. Language, 72(3), 527-555.
Hay, J. B., & Baayen, R. H. (2005). Shifting paradigms: gradient structure in morphology. TRENDS in Cognitive Science, 9(7), 342-348.
Jiang, N. (2004). Morphological insensitivity in second language processing. Applied Psycholinguistics, 25, 603-634.
Johnson, J. S., & Newport, E. L. (1989). Critical period effects in secon dlanguage learning: The influence of maturational state on the acquisition of English as a second language. Cognitive Psychology, 21, 60-99.
Juffs, A. & Friedline, B. (2010). L1 influence, morphological (in)sensitivity and L2 lexical development: Evidence from production data. Unpublished manuscript, University of Pittsburgh, PA.
Lardiere, D. (1998). Dissociating syntax from morphology in a divergent L2 end-state grammar. Second Language Research, 14(4), 359-375.
Lardiere, D. (2006). Ultimate attainment in second language acquisition: a case study. New York: Routledge
Marslen-Wilson, W. D., Bozic, M., & Randall, B. (2008). Early decomposition in visual word recognition: Dissociating morphology, form, and meaning. Language and Cognitive Processes, 23(3), 394-421.
Nation, I. S. P. (2001). Learning vocabulary in another language. New York: Cambridge University Press.
Pinker, S., & Ullman, M. T. (2002). The past and future of the past tense. TRENDS in Cognitive Science, 6(11), 456-463.
Silva, R., & Clahsen, H. (2008). Morphologically complex words in L1 and L2 processing: Evidence from masked priming experiments in English. Bilingualism: Language and Cognition, 11(2), 245-260.
White, L. (2003). Fossilization in steady state L2 grammars: Persistent problems with inflectional morphology. Bilingualism: Language and Cognition, 6(2), 129-141.

Juffs - Feature Focus in Word Learning

2010-05-19T16:03:23Z

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Juffs - Feature Focus in Word Learning

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2010-01-14T00:55:59Z

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Juffs - Feature Focus in Word Learning

2010-01-14T00:51:28Z

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Juffs - Feature Focus in Word Learning

2010-01-10T19:05:42Z

Juffs:

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! PI
| Ben Friedline, Alan Juffs
|-
! Start date
| September 2009
|-
! End date
| July 2010
|-
! Learnlab
| English
|}

== L2 learning of derived words ==
Ben Friedline and Alan Juffs

Research Questions

Why are ESL learners so poor in their knowledge of English morphology? What are the knowledge components that are the most challenging for learning through normal language exposure? Do learners have a representational problem or a processing problem? Specifically, what instructional interventions can be designed to overcome observed processing differences in L1 and L2 morphology?

Research plan
For year 1, the goal of the research is to analyze the knowledge components of ESL learners to lay the groundwork for a hypothesis-based intervention. The research will systematically investigate the components of L2 learners’ knowledge of English derivational morphology to address the following questions:

1) What are the components of L2 derivational knowledge?
2) Are these components different from L1 derivational knowledge?
3) Does L1 matter for the acquisition of derived words in an L2?

To answer these questions, Friedline has developed a series of tasks based on previous L1 research. The first task is a fill-in-the-blank type of activity in which students are provided with a base form and a meaningful context and asked to fill-in-the-blank with an appropriate (i.e., morphologically complex) form. This task, similar to the Test of Morphological Structure (Rubin, 1988) that has been used in L1 acquisition research (e.g., Carlisle, 2000; 2003), is designed to assess learners’ productive morphological knowledge in obligatory contexts. An L2 learner would see the base form “friend” and a meaningful context that required the adjective form of “friend” to elicit “friendly” from an L2 learner. Each of the four tasks will test different components of L2 derivational knowledge. Native speakers piloted the task in the fall of 2009, and results are being tabulated. A pull out from the ELI in Spring 2010 will collected learner data.

Selected References

Hay, J., & Baayen, R. H. (2005). Shifting paradigms: gradient structure in morphology. Trends in Cognitive Science, 9, 342-348.

Jiang, N. (2004). Morphological insenstivity in second language processing. Applied Psycholinguistics, 25, 603-634.

Lardiere, D. (2006). Ultimate attainment in second language acquisition: a case study. New York: Routledge

Silva, R., & Clahsen, H. (2008). Morphologically complex words in L1 and L2 processing: evidence from masked priming experiments in English. Bilingualism: Language and cognition, 11, 245-260.

Juffs - Feature Focus in Word Learning

2010-01-10T19:03:19Z

Juffs:

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! PI
| Ben Friedline, Alan Juffs
|-
! Start date
| September 2009
|-
! End date
| July 2010
|-
! Learnlab
| English
|}

== L2 learning of derived words ==
Ben Friedline and Alan Juffs

Research Questions

Why are ESL learners so poor in their knowledge of English morphology? What are the knowledge components that are the most challenging for learning through normal language exposure? Do learners have a representational problem or a processing problem? Specifically, what instructional interventions can be designed to overcome observed processing differences in L1 and L2 morphology?

Research plan
For year 1, the goal of the research is to analyze the knowledge components of ESL learners to lay the groundwork for a hypothesis-based intervention. The research will systematically investigate the components of L2 learners’ knowledge of English derivational morphology to address the following questions:

1) What are the components of L2 derivational knowledge?
2) Are these components different from L1 derivational knowledge?
3) Does L1 matter for the acquisition of derived words in an L2?

To answer these questions, Friedline has developed a series of tasks based on previous L1 research. The first task is a fill-in-the-blank type of activity in which students are provided with a base form and a meaningful context and asked to fill-in-the-blank with an appropriate (i.e., morphologically complex) form. This task, similar to the Test of Morphological Structure (Rubin, 1988) that has been used in L1 acquisition research (e.g., Carlisle, 2000; 2003), is designed to assess learners’ productive morphological knowledge in obligatory contexts. An L2 learner would see the base form “friend” and a meaningful context that required the adjective form of “friend” to elicit “friendly” from an L2 learner. Each of the four tasks will test different components of L2 derivational knowledge. Native speakers will pilot the task in the fall of 2009, with a pull out from the ELI in Spring 2010.

Selected References

Hay, J., & Baayen, R. H. (2005). Shifting paradigms: gradient structure in morphology. Trends in Cognitive Science, 9, 342-348.
Jiang, N. (2004). Morphological insenstivity in second language processing. Applied Psycholinguistics, 25, 603-634.
Lardiere, D. (2006). Ultimate attainment in second language acquisition: a case study. New York: Routledge
Silva, R., & Clahsen, H. (2008). Morphologically complex words in L1 and L2 processing: evidence from masked priming experiments in English. Bilingualism: Language and cognition, 11, 245-260.

Juffs - Feature Focus in Word Learning

2010-01-10T19:02:11Z

Juffs:

{| class="wikitable" border="1" style="margin: 2em auto 2em auto"
|-
! PI
| Ben Friedline, Alan Juffs
|-
! Start date
| September 2009
|-
! End date
| July 2010
|-
! Learnlab
| English
|}

== L2 learning of derived words ==
Ben Friedline and Alan Juffs

Research Questions
Why are ESL learners so poor in their knowledge of English morphology? What are the knowledge components that are the most challenging for learning through normal language exposure? Do learners have a representational problem or a processing problem? Specifically, what instructional interventions can be designed to overcome observed processing differences in L1 and L2 morphology?

Research plan
For year 1, the goal of the research is to analyze the knowledge components of ESL learners to lay the groundwork for a hypothesis-based intervention. The research will systematically investigate the components of L2 learners’ knowledge of English derivational morphology to address the following questions:

1) What are the components of L2 derivational knowledge?
2) Are these components different from L1 derivational knowledge?
3) Does L1 matter for the acquisition of derived words in an L2?

To answer these questions, Friedline has developed a series of tasks based on previous L1 research. The first task is a fill-in-the-blank type of activity in which students are provided with a base form and a meaningful context and asked to fill-in-the-blank with an appropriate (i.e., morphologically complex) form. This task, similar to the Test of Morphological Structure (Rubin, 1988) that has been used in L1 acquisition research (e.g., Carlisle, 2000; 2003), is designed to assess learners’ productive morphological knowledge in obligatory contexts. An L2 learner would see the base form “friend” and a meaningful context that required the adjective form of “friend” to elicit “friendly” from an L2 learner. Each of the four tasks will test different components of L2 derivational knowledge. Native speakers will pilot the task in the fall of 2009, with a pull out from the ELI in Spring 2010.

Selected References

Hay, J., & Baayen, R. H. (2005). Shifting paradigms: gradient structure in morphology. Trends in Cognitive Science, 9, 342-348.
Jiang, N. (2004). Morphological insenstivity in second language processing. Applied Psycholinguistics, 25, 603-634.
Lardiere, D. (2006). Ultimate attainment in second language acquisition: a case study. New York: Routledge
Silva, R., & Clahsen, H. (2008). Morphologically complex words in L1 and L2 processing: evidence from masked priming experiments in English. Bilingualism: Language and cognition, 11, 245-260.

Juffs - Feature Focus in Word Learning

2010-01-10T18:59:10Z

Juffs: New page: L2 learning of derived words Research Questions Why are ESL learners so poor in their knowledge of English morphology? What are the knowledge components that are the most challenging for l...

L2 learning of derived words
Research Questions
Why are ESL learners so poor in their knowledge of English morphology? What are the knowledge components that are the most challenging for learning through normal language exposure? Do learners have a representational problem or a processing problem? Specifically, what instructional interventions can be designed to overcome observed processing differences in L1 and L2 morphology?

Research plan
For year 1, the goal of the research is to analyze the knowledge components of ESL learners to lay the groundwork for a hypothesis-based intervention. The research will systematically investigate the components of L2 learners’ knowledge of English derivational morphology to address the following questions:

1) What are the components of L2 derivational knowledge?
2) Are these components different from L1 derivational knowledge?
3) Does L1 matter for the acquisition of derived words in an L2?

To answer these questions, Friedline has developed a series of tasks based on previous L1 research. The first task is a fill-in-the-blank type of activity in which students are provided with a base form and a meaningful context and asked to fill-in-the-blank with an appropriate (i.e., morphologically complex) form. This task, similar to the Test of Morphological Structure (Rubin, 1988) that has been used in L1 acquisition research (e.g., Carlisle, 2000; 2003), is designed to assess learners’ productive morphological knowledge in obligatory contexts. An L2 learner would see the base form “friend” and a meaningful context that required the adjective form of “friend” to elicit “friendly” from an L2 learner. Each of the four tasks will test different components of L2 derivational knowledge. Native speakers will pilot the task in the fall of 2009, with a pull out from the ELI in Spring 2010.

Selected References

Hay, J., & Baayen, R. H. (2005). Shifting paradigms: gradient structure in morphology. Trends in Cognitive Science, 9, 342-348.
Jiang, N. (2004). Morphological insenstivity in second language processing. Applied Psycholinguistics, 25, 603-634.
Lardiere, D. (2006). Ultimate attainment in second language acquisition: a case study. New York: Routledge
Silva, R., & Clahsen, H. (2008). Morphologically complex words in L1 and L2 processing: evidence from masked priming experiments in English. Bilingualism: Language and cognition, 11, 245-260.

REAP Comparison to Classroom Instruction (Fall 2006)

2008-05-13T20:30:43Z

Juffs:

=== Logistical Information ===

{| border="1"
|+
|-
| '''Contributors''' ||Alan Juffs, Lois Wilson, Maxine Eskenazi, Michael Heilman
|-
| '''Study Start Date''' || September 2006
|-
| '''Study End Date''' || April, 2007
|-
| '''Learnlab Courses''' || English Language Institute Reading 4 (ESL LearnLab)
|-
| '''Number of Students''' || ~72
|-
| '''Total Participant Hours (est.)''' || approximately 360
|-
| '''Data in Datashop''' || no
|}

[[ABSTRACT]]

THIS WIKI ENTRY IS UNDER CONSTRUCTION.

This paper focuses on the long-term retention and production of vocabulary in an intensive English program (IEP). The study compares the learning of words through a computer assisted learning program with an 'ecological' control of standard classroom practice. The study seeks to elucidiate the strengths of both approaches. The paper draws on the practical framework of Coxhead (2001) and Nation (2005) and the theoretical perspectives of Laufer & Hulstijn (2001). The project collected data from two separate classes of ESL learners. Hence the study was run twice with different participants.The vocabulary instruction occurred in intermediate level reading class (intermediate =TOEFL 450 or iBT TOEFL 45, average MTELP score, 58). In the CALL condition, all learners spent 40 minutes per week for 9 weeks reading texts containing words from the Academic Word List. In fall 2006, topic interest was manipulated in the CALL condition. In spring 2007, all of the students received texts that were in line with their interests. In contrast, in classroom instruction, a subset of the learners' normal in-class vocabulary instruction was tracked. Pre-, post and delayed post-test data were collected for the CALL vocabulary learning and the in-class learning conditions. In addition, during this period, all of the students' writing assignments were collected on-line. From this database of written output, each student’s texts were analyzed to determine which words seen during computer training and regular reading class had transferred to their spontaneous output in compositions in their writing class. Results indicate that although the CALL practice led to recognition one semester later, only the words which were practiced during regular reading class vocabulary instruction transferred to their spontaneous writing. This transfer effect is attributed to the deeper processing from spoken and written output that occurred during the regular vocabulary instruction. The data also showed that the production of words seen in the CALL condition alone suffered from more errors in word recognition (‘clang’ associations) and morphological form errors (Schmitt & Meara, 1997). We conclude that these data suggest that some negative views on output practice by Folse (2006) and Mason and Krashen (2005) must be modified to accommodate these data. As a result of this comparision, we are amending REAP to create more interactive production opportunities.

[[INTRODUCTION]]

In the fall of 2006 and the spring of 2007, vocabulary learning using REAP [http://reap.cs.cmu.edu/]and in regular class vocabulary instruction was studied. This research therefore focused on a comparison of REAP with the vocabulary instruction that usually occurs in classrooms. In that sense, it is not very tightly controlled study, but it is ecologically valid in the sense that the study reflects what actually happens in classes.

It is important to note that the REAP treatments in the fall of 2006 and spring 2007 were slightly different. In the fall of 2006, participants were introduced to the personalization of texts that they read. Some students received texts that they were interested by topic, while others received random texts that contained their focus words. In the fall of 2006, all of the students had their focus words highlighted, but not all students received texts that were of interest to them all the time. Details can be read here.[http://learnlab.org/research/wiki/index.php/REAP_Study_on_Personalization_of_Readings_by_Topic_%28Fall_2006%29] Students were all able to select their topics of interest in the spring of 2007 and also had their focus words highlighted. [See the study in level 5 for a comparison of highlighted versus non lighted words,[http://www.learnlab.org/research/wiki/index.php/REAP_Study_on_Focusing_of_Attention_%28Spring_2007%29] ]

In the classroom conditions, the Reading 4 curriculum supervisor decided on a list of Academic Word List vocabulary [http://language.massey.ac.nz/staff/awl/] items that had been excluded from the tests that the students took to establish their focus word lists. This list included 58 items that in her view the students should know.

[[BRIEF LITERATURE REVIEW]]

Recent research in vocabulary acquisition has suggested that the time taken for written output practice may not be well spent (Folse, 2006). Instead of using new words to create new meaningful texts during practice, Folse (2006) has suggested that fill-in-the-blank type exercises are more efficient. Moreover, Krashen and colleagues suggest that form-focussed vocabulary instruction is not efficient (Mason & Krashen, 2004).

In contrast, Hulstijn and Laufer (2001) (H&L) have proposed a model that emphasizes output practice.They call this model the 'involvement load hypothesis' for vocabulary acquisition. The involvement load hypothesis suggests that deeper processing leads to better learning. However, definitions of 'depth of processing' during learning have not been made. Hulstijn and Laufer (2001) suggest a definition that contains three parts. Each part is assigned a processing load weight, from 0 to 2. Although H&L do not fully flesh out their model, one can infer the different processing weights for each component of processing. The first is 'need', which they controversially label 'non-cognitive'. A level of 'no need' would be '0'; an externally imposed need would be a weight of 1, and a self-generated need would be a '2'. The second component of processing is 'search'. No search would have a weight of '0', teacher or class provided information would be '1', and self-look up in a dictionary or on line would be '2'. Finally, there is the evaluation stage. A zero involvement would be a non-linguistic response such as choosing from a multiple choice list, i.e. no output at all. A level of '1' would be an activity such as 'fill-in-the-blank'. Finally, a level 2 load would be free production such as in class writing activities.

The problem with both Folse (2006) and H&L is that they selected for in class study a very limited number of words (15 and 10) respectively. In that sense, they are 'bench laboratory studies' conducted in a classroom and lack the validity of a study in which the students know they will be accountable for the learning outcomes. Moreover, the post-test results in both studies showed surprisingly low retention scores. For example, H&L showed that an average of delayed post-test gain for practice with reading and fill-in the blank of 1.6 or 1.7 out of 10, and production only 2.6 or 3.7 out of ten.

However, we know that learners need to master many more than 10-15 words to be able to effectively comprehend and produce academic discourse. Recent proposals have suggested that learners need to know between 5000 and 9000 words to function in an academic environment in English (Nation, 2006, p.60). What is needed is a much closer focus how time can be effectively used to make a 50-150 word (family) gain over time. This goal has been the focus in REAP. However, the time on task for each word in REAP is limited, and we have observed limited gains.

Learning a lexical entry is more than a form-meaning correspondence. Indeed, lexical items are highly complex.[[http://www.learnlab.org/research/papers.php]]. While reading practice in REAP may be sufficient for learning form-meaning links in Levelt's model, the acquisition of syntax and morphology may require deeper processing. In PSLC terminology, robust learning [http://www.learnlab.org/research/wiki/index.php/Robust_learning ]requires learning all knowledge components [http://www.learnlab.org/research/wiki/index.php/Knowledge_component] in the lexeme and lemma.

[[RESEARCH QUESTIONS]]

1. How does REAP vocabulary learning differ from the ecological control 'normal' in class instruction?

2. How do the learning outcomes differ in the immediate number of words learned, and in the number of words the learners transfer to other contexts?

3. If there are differences, what might the source of those differences be in terms of Hulstijn and Laufer's involvement load hypothesis.

4. Can 'deeper processing' through writing be ‘skipped’ by using a CALL program?

5. What can teachers and computer scientists learn from each other?

The two treatments in REAP and in class are summarized in the following table. The major differences from the point of view of the involvement load hypothesis are that in REAP the need is created by the student in his or her self-generated list, whereas in class the teacher decides on the words to be learned. Second, in terms of search, the student is responsible for looking up words in the computer program, whereas in class the teacher assigns group activities for discovery of words. Finally, in terms of 'evaluation', REAP only requires a non-linguistic response to a multiple choice question, whereas in class activities require evaluation where meaningful output is created.

The hypothesis then is that the in class learning gains will be far greater than for REAP. The question is how much better the in class activities will be. We explore the involvement load hypothesis scores for REAP and in class activities in the methodology section.

[[METHODOLOGY]]

[[Participants]]

The participants in the two semesters are typical of the heterogeneous first language (L2) classes in Intensive English Programs in general in the United States. Recently, the ELI at the University of Pittsburgh has had larger numbers of Arabic-speaking and Korean speaking learners. Overall, the Arabic-speaking students score lower on overall proficiency tests than the Korean speakers. However, the Arabic speakers are placed in level 4 classes because they have passed level 3 adequately.

[[Image:REAPMTELP0607.jpg|700px]]

The variation in MTELP scores is important. This point will be raised in the discussion section.

[[Procedure]]

The table below summarizes the differences between REAP and the in class treatment of vocabulary. The REAP method is described elsewhere.

[[Image:REAPinClass.jpg|700px]]

For the vocabulary practice in the classroom and as homework, some more details are necessary. The teachers in the ELI focus a great deal on form, meaning, and collocation through a variety of receptive and productive exercises.

1. For homework or in class students are given a list of 7-9 “focus words”. They find the word in the reading and copy the sentence with the word. They determine the part of speech in the context and then find the corresponding definition of the word in an ESL dictionary.
Students follow the same procedure with 3-5 words of their choice.
Students choose 3 words from their total list and write an original sentence with each word.

2. Teachers may do a variety of vocabulary exercises to practice the words in class including games in pairs/groups, word-definition matching, guided speaking activities requiring the focus words, etc.

3. Vocabulary tests are given on words from two readings as these readings are finished. (1 test every 1 _ -2 weeks). Tests are cloze with a word bank and answering questions which use the words. In the latter type of question, students must show in their answer that they understand the focus word, for example, by using a synonym.

Ex: What skills are [[essential]] if you want to become a lawyer?

These activities took perhaps 1.25 hours of class time in addition to homework according to the in class teachers. Hence, in class and homework represents more time on task than REAP. This factor has been accepted by H&L as inevitable in this kind of research, because production just takes longer.

In terms of Hulstijn and Laufer's involvement load hypothesis, we can estimate their level of depth of processing 'quantitatively'.

{| border="1"
|+
|-
| '''Condition''' || NEED || SEARCH || EVALUATION || TOTAL LOAD
|-
| '''REAP''' || 2 || 2|| 0 || 4
|-
| '''IN CLASS''' || 1 || 1|| 2|| 4
|}

Hence, both sets of activities have the same involvement load. However, REAP'S involvement load is divided strongly into need and search, whereas the in class activities are spread more over need, and search , plus a very strong evaluation component. Hulstijn and Laufer (2001) make no predictions about where the involvement load will fall, but common sense dictates that involvement needs to be spread across the different factors. For this reason, we anticipate, based on H&Ls results, that the learners in class will do much better in retaining their words.

[[RESULTS]]

The descriptive results are provided below. We discuss the classroom results first. As can be observed, the supervisor chose some words that were already known to the students. In fall 2006, students knew an average of 13.17 out of 58 words, and in the spring they knew an average of 17.6 out of 58 words. In the post-test, they scored 32.17 and 35.57 out of 40 words tested. This test was a fill in the blank test. Hence, students learned an average of 18 words in each semester in the classroom. In the written production task at the end of term, students scored 73% and 78% on a test of 30 words.

{| border="1"
|+
|-
| '''Term''' || Fall 2006 || Spring 2007 ||
|-
| '''Results''' || [[Image:Fall2006.jpg|500px]] || [[Image:spring2007.jpg|500px]]
|}

For the REAP sessions, it will be recalled that in fall 2006, the students were divided into those who received texts of interest and those who did not. These students' pretest was of course 0. The target look-ups were relatively low, averaging only 22.27 per student over the semester. The average gain for REAP, even under the experimental conditions that didn't favour learning for some students was 9.24 in the cloze test. However, the dramatic difference between in class and in REAP is in the production test. In the production test in fall 2006, the students scored only 29% with REAP words compared to 73% from in class.

A fairer comparison is in the spring of 2007. In this case, all the learners received texts that they were interested in. Of note here, is that the target look-ups in REAP increased to an average of 57 per student. In addition, the composition of the class was more balanced, with fewer Arabic speaking students, who were known to have 'gamed' the system (Juffs et al, in review). In spring, the average gain with REAP increased from 9.24 words in the cloze test to 14.00. Note that this result, 14, is only 3 words less than the cloze gains for the in class vocabulary. Bear in mind also that the time on task was much higher for the in class and homework vocabulary learning. In the spring, therefore, we can conclude that REAP was at least as good as in class work for recognition.

STILL NEED PRODUCTION DATA FOR SPRING 2007.

[[DISCUSSION]]

Robust learning measures.

Learning
These results support our claim that processing load cannot be established on a simple numerical basis. Recall that according Hulstijn and Laufer. For robust learning of all knowledge components of lexeme/lemma, output production (evaluation) is necessary. Written output practice, combined with involvement in class, may produce more learning than CALL programs that provide exposure only even if students are ‘internally motivated’ by self-selecting lists, and self-motivated in their search behaviors. Although evaulation (writing) is time-consuming, students need to use these words in order to master them.

This may seem like an obvious statement. However, given the continued suggestions in the literature that fill in the blank activities (Folse, 2006) and simply listening (Mason and Krashen) is 'more efficient' for lexical learning, we would like to suggest that for robust learning, evaluation in H&L's model is a vital part of mastering all of the knowledge components that make up the lexeme/lemma.

Transfer[http://www.learnlab.org/research/wiki/index.php/Transfer]

We also examined the transfer of words from REAP and in class instruction in the online data base of students' writing. In the fall of 2006, students used 10 words that they had seen REAP in free production in their writing classes. These words are listed by the BNC corpus frequency below:

Focus words seen in REAP: 10 uses
BNC-1,000 [ fams 2 : types 2 : tokens 2 ] assume produce

BNC-2,000 [ fams 1 : types 1 : tokens 1 ] distinction

BNC-3,000 [ fams 1 : types 1 : tokens 1 ] conceive

BNC-4,000 [ fams 1 : types 1 : tokens 1 ] abandon

BNC-5,000 [ fams 1 : types 1 : tokens 1 ] derive

BNC-6,000 [ fams 2 : types 2 : tokens 2 ] cite (x3) prohibit

In contrast, students used words on their in class vocabulary list 840 times. Even allowing for the words that they knew (average 14 out of 58), this represents a large difference in transfer between the REAP and in class treatments.

A final note is in order regarding the MTELP. As with any set of learners in an IEP, learners in the same class may enter with different overall proficiency scores. This cannot be avoided because almost all learners have some kind of exposure to Englush prior to their arrival in the US. As Stanovich (1986) would predict, those students who have a higher proficiency entering the class also learn more. This effect is known as the Matthew effect. Correlations between MTELP scores and vocabulary test scores in fall 2006 are as follows:

MTELP Predicts in class scores:

Pretest: r= 0.52, p ≤.01
Post-test: r= 0.59, p ≤ .01
Production: r= 0.58, p ≤.01

Predicts REAP
Cloze: r= 0.53, p ≤.01
Production: r= 0.59, p ≤.01

The MTELP scores were not related to look ups or the number of texts that the students read.

In the spring of 2007, MTELP scores again predict performance, with the exception of the post-test. This finding is significant because here the Matthew effect is NOT seen. In other words vocabulary gains during the class were not related to their overall proficiency. This result suggests that the classroom instruction was able to overcome this powerful effect.

Predicts in class
Pretest: r= 0.63, p ≤.05
Post-test: r= 0.36, p ≤ .06, ns
Production: r= 0.44, p ≤.02

Predicts REAP
Cloze: r= 0.58, p ≤.001

[[Selected References]]

Allum, P. (2002). CALL and the classroom: the case for comparative research. ReCALL, 14, 146-166.

Barcroft, J. (2004). Effects of sentence writing in second language lexical acquisition. Second Language Research, 20, 303-334.

Barcroft, J. (2006). Negative Effects of forced output on vocabulary learning. Second Language Research, 22, 487-497.

Folse, K. S. (2006). The effect of type of written exercise on L2 vocabulary retention. TESOL Quarterly, 40, 273-293.

Hulstijn, J., & Laufer, B. (2001). Some empirical evidence for the involvement load hypothesis in vocabulary acquisition. Language Learning, 51, 539-558.

Juffs, A., Friedline, B. F., Eskenazi, M., Wilson, L., & Heilman, M. (in review). Activity theory and computer-assisted learning of English vocabulary. Applied Linguistics.

Mason, B. and Krashen, S. (2004). Is form-focused vocabulary instruction worthwhile? RELC Journal, 35, 175-189.

Nation, I. S. P. (2006). How large a vocabulary is needed for listening and reading? Canadian Modern Language Review, 63, 59-82.

Paribakht, T.S., and M. Wesche (1997). Vocabulary Enhancement Activities and Reading for Meaning in Second Language Vocabulary Acquisition. In J. Coady and T. Huckin (eds.), Second Language Vocabulary Acquisition (Cambridge: Cambridge University Press): 174-200.

Stanowicz, K. E. (1986). Matthew effects in reading: some consequences of individual differences in the acquisition of literacy. Reading Research Quarterly, 21, 360-407.

REAP Comparison to Classroom Instruction (Fall 2006)

2008-05-13T19:58:32Z

Juffs:

=== Logistical Information ===

{| border="1"
|+
|-
| '''Contributors''' ||Alan Juffs, Lois Wilson, Maxine Eskenazi, Michael Heilman
|-
| '''Study Start Date''' || September 2006
|-
| '''Study End Date''' || April, 2007
|-
| '''Learnlab Courses''' || English Language Institute Reading 4 (ESL LearnLab)
|-
| '''Number of Students''' || ~72
|-
| '''Total Participant Hours (est.)''' || approximately 360
|-
| '''Data in Datashop''' || no
|}

[[ABSTRACT]]

THIS WIKI ENTRY IS UNDER CONSTRUCTION.

This paper focuses on the long-term retention and production of vocabulary in an intensive English program (IEP). The study compares the learning of words through a computer assisted learning program with an 'ecological' control of standard classroom practice. The study seeks to elucidiate the strengths of both approaches. The paper draws on the practical framework of Coxhead (2001) and Nation (2005) and the theoretical perspectives of Laufer & Hulstijn (2001). The project collected data from two separate classes of ESL learners. Hence the study was run twice with different participants.The vocabulary instruction occurred in intermediate level reading class (intermediate =TOEFL 450 or iBT TOEFL 45, average MTELP score, 58). In the CALL condition, all learners spent 40 minutes per week for 9 weeks reading texts containing words from the Academic Word List. In fall 2006, topic interest was manipulated in the CALL condition. In spring 2007, all of the students received texts that were in line with their interests. In contrast, in classroom instruction, a subset of the learners' normal in-class vocabulary instruction was tracked. Pre-, post and delayed post-test data were collected for the CALL vocabulary learning and the in-class learning conditions. In addition, during this period, all of the students' writing assignments were collected on-line. From this database of written output, each student’s texts were analyzed to determine which words seen during computer training and regular reading class had transferred to their spontaneous output in compositions in their writing class. Results indicate that although the CALL practice led to recognition one semester later, only the words which were practiced during regular reading class vocabulary instruction transferred to their spontaneous writing. This transfer effect is attributed to the deeper processing from spoken and written output that occurred during the regular vocabulary instruction. The data also showed that the production of words seen in the CALL condition alone suffered from more errors in word recognition (‘clang’ associations) and morphological form errors (Schmitt & Meara, 1997). We conclude that these data suggest that some negative views on output practice by Folse (2006) and Mason and Krashen (2005) must be modified to accommodate these data. As a result of this comparision, we are amending REAP to create more interactive production opportunities.

[[INTRODUCTION]]

In the fall of 2006 and the spring of 2007, vocabulary learning using REAP [http://reap.cs.cmu.edu/]and in regular class vocabulary instruction was studied. This research therefore focused on a comparison of REAP with what normally happens in classrooms. In that sense, it is not very tightly controlled study, but it is ecologically valid in the sense that the study reflects what actually happens in classes.

It is important to note that the REAP treatments in the fall of 2006 and spring 2007 were slightly different. In the fall of 2006, participants were introduced to the personalization of texts that they read. Some students received texts that they were interested by topic, while others received random texts that contained their focus words. In the fall of 2006, all of the students had their focus words highlighted, but not all students received texts that were of interest to them all the time. Details can be read here.[http://learnlab.org/research/wiki/index.php/REAP_Study_on_Personalization_of_Readings_by_Topic_%28Fall_2006%29] Students were all able to select their topics of interest in the spring of 2007 and also had their focus words highlighted. [See the study in level 5 for a comparison of highlighted versus non lighted words,[http://www.learnlab.org/research/wiki/index.php/REAP_Study_on_Focusing_of_Attention_%28Spring_2007%29] ]

In the classroom conditions, the Reading 4 curriculum supervisor decided on a list of Academic Word List vocabulary [http://language.massey.ac.nz/staff/awl/] items that had been excluded from the tests that the students took to establish their focus word lists. This list included 58 items that in her view the students should know.

[[BRIEF LITERATURE REVIEW]]

Recent research in vocabulary acquisition has suggested that the time taken for written output practice may not be well spent (Folse, 2006). Instead of using new words to create new meaningful texts during practice, Folse (2006) has suggested that fill-in-the-blank type exercises are more efficient. Moreover, Krashen and colleagues continue to suggest that form-focussed vocabulary instruction is not efficient (Mason & Krashen, 2004).

However, Hulstijn and Laufer (2001) (H&L) have suggested the involvement load hypothesis for vocabulary acquistion. The involvement load hypothesis suggests that deeper processing leads to better learning. However, definitions of 'depth of processing' during learning have not been made. Hulstijn and Laufer (2001) suggest a definition that contains three parts. Each part is assigned a processing load weight, from 0 to 2. Although H&L do not fully flesh out their model, one can infer the different processing weights for each component of processing. The first is 'need', which they controversially label 'non-cognitive'. A level of no need would be '0'; an externally imposed need would be a weight of 1, and a self-generated need would be a '2'. The second component of processing is 'search'. No search would have a weight of '0', teacher or class provided information would be '1', and self-look up in a dictionary or on line would be '2'. Finally, there is the evaluation stage. A zero involvement would be a non-linguistic response such as choosing from a multiple choice list, i.e. no output at all. A level of '1' would be an activity such as 'fill-in-the-blank'. Finally, a level 2 load would be free production such as in class writing activities.

The problem with both Folse (2006) and H&L is that they selected for in class study a very limited number of words (15 and 10) respectively. Moreover, the post-test results in both studies showed surprisingly low retention scores. For example, H&L showed that an average of delayed post-test gain for practice with reading and fill-in the blank of 1.6 or 1.7 out of 10, and production only 2.6 or 3.7 out of ten.

However, we know that learners need to master many more than 10-15 words to be able to effectively comprehend and produce academic discourse. What is needed is a much closer focus how time can be effectively used to make a 50-150 word (family) gain over time. This goal has been the focus in REAP. However, the time on task for each word in REAP is limited, and we have observed limited gains.

Learning a lexical entry is more than a form-meaning correspondence. Indeed, lexical items are highly complex.[[http://www.learnlab.org/research/papers.php]]. While reading practice in REAP may be sufficient for learning form-meaning links in Levelt's model, the acquisition of syntax and morphology may require deeper processing. In PSLC terminology, robust learning [http://www.learnlab.org/research/wiki/index.php/Robust_learning ]requires learning all knowledge components [http://www.learnlab.org/research/wiki/index.php/Knowledge_component] in the lexeme and lemma.

[[RESEARCH QUESTIONS]]

1. How does REAP vocabulary learning differ from the ecological control 'normal' in class instruction?

2. How do the learning outcomes differ in the immediate number of words learned, and in the number of words the learners transfer to other contexts?

3. If there are differences, what might the source of those differences be in terms of Hulstijn and Laufer's involvement load hypothesis.

4. Can 'deeper processing' through writing be ‘skipped’ by using a CALL program?

5. What can teachers and computer scientists learn from each other?

The two treatments in REAP and in class are summarized in the following table. The major differences from the point of view of the involvement load hypothesis are that in REAP the need is created by the student in his or her self-generated list, whereas in class the teacher decides on the words to be learned. Second, in terms of search, the student is responsible for looking up words in the computer program, whereas in class the teacher assigns group activities for discovery of words. Finally, in terms of 'evaluation', REAP only requires a non-linguistic response to a multiple choice question, whereas in class activities require evaluation where meaningful output is created.

The hypothesis then is that the in class learning gains will be far greater than for REAP. The question is how much better the in class activities will be. We explore the involvement load hypothesis scores for REAP and in class activities in the methodology section.

[[METHODOLOGY]]

[[Participants]]

The participants in the two semesters are typical of the heterogeneous first language (L2) classes in Intensive English Programs in general in the United States. Recently, the ELI at the University of Pittsburgh has had larger numbers of Arabic-speaking and Korean speaking learners. Overall, the Arabic-speaking students score lower on overall proficiency tests than the Korean speakers. However, the Arabic speakers are placed in level 4 classes because they have passed level 3 adequately.

[[Image:REAPMTELP0607.jpg|700px]]

The variation in MTELP scores is important. This point will be raised in the discussion section.

[[Procedure]]

The table below summarizes the differences between REAP and the in class treatment of vocabulary. The REAP method is described elsewhere.

[[Image:REAPinClass.jpg|700px]]

For the vocabulary practice in the classroom and as homework, some more details are necessary. The teachers in the ELI focus a great deal on form, meaning, and collocation through a variety of receptive and productive exercises.

1. For homework or in class students are given a list of 7-9 “focus words”. They find the word in the reading and copy the sentence with the word. They determine the part of speech in the context and then find the corresponding definition of the word in an ESL dictionary.
Students follow the same procedure with 3-5 words of their choice.
Students choose 3 words from their total list and write an original sentence with each word.

2. Teachers may do a variety of vocabulary exercises to practice the words in class including games in pairs/groups, word-definition matching, guided speaking activities requiring the focus words, etc.

3. Vocabulary tests are given on words from two readings as these readings are finished. (1 test every 1 _ -2 weeks). Tests are cloze with a word bank and answering questions which use the words. In the latter type of question, students must show in their answer that they understand the focus word, for example, by using a synonym.

Ex: What skills are [[essential]] if you want to become a lawyer?

These activities took perhaps 1.25 hours of class time in addition to homework according to the in class teachers. Hence, in class and homework represents more time on task than REAP. This factor has been accepted by H&L as inevitable in this kind of research, because production just takes longer.

In terms of Hulstijn and Laufer's involvement load hypothesis, we can estimate their level of depth of processing 'quantitatively'.

{| border="1"
|+
|-
| '''Condition''' || NEED || SEARCH || EVALUATION || TOTAL LOAD
|-
| '''REAP''' || 2 || 2|| 0 || 4
|-
| '''IN CLASS''' || 1 || 1|| 2|| 4
|}

Hence, both sets of activities have the same involvement load. However, REAP'S involvement load is divided strongly into need and search, whereas the in class activities are spread more over need, and search , plus a very strong evaluation component. Hulstijn and Laufer (2001) make no predictions about where the involvement load will fall, but common sense dictates that involvement needs to be spread across the different factors. For this reason, we anticipate, based on H&Ls results, that the learners in class will do much better in retaining their words.

[[RESULTS]]

The descriptive results are provided below. We discuss the classroom results first. As can be observed, the supervisor chose some words that were already known to the students. In fall 2006, students knew an average of 13.17 out of 58 words, and in the spring they knew an average of 17.6 out of 58 words. In the post-test, they scored 32.17 and 35.57 out of 40 words tested. This test was a fill in the blank test. Hence, students learned an average of 18 words in each semester in the classroom. In the written production task at the end of term, students scored 73% and 78% on a test of 30 words.

{| border="1"
|+
|-
| '''Term''' || Fall 2006 || Spring 2007 ||
|-
| '''Results''' || [[Image:Fall2006.jpg|500px]] || [[Image:spring2007.jpg|500px]]
|}

For the REAP sessions, it will be recalled that in fall 2006, the students were divided into those who received texts of interest and those who did not. These students' pretest was of course 0. The target look-ups were relatively low, averaging only 22.27 per student over the semester. The average gain for REAP, even under the experimental conditions that didn't favour learning for some students was 9.24 in the cloze test. However, the dramatic difference between in class and in REAP is in the production test. In the production test in fall 2006, the students scored only 29% with REAP words compared to 73% from in class.

A fairer comparison is in the spring of 2007. In this case, all the learners received texts that they were interested in. Of note here, is that the target look-ups in REAP increased to an average of 57 per student. In addition, the composition of the class was more balanced, with fewer Arabic speaking students, who were known to have 'gamed' the system (Juffs et al, in review). In spring, the average gain with REAP increased from 9.24 words in the cloze test to 14.00. Note that this result, 14, is only 3 words less than the cloze gains for the in class vocabulary. Bear in mind also that the time on task was much higher for the in class and homework vocabulary learning. In the spring, therefore, we can conclude that REAP was at least as good as in class work for recognition.

STILL NEED PRODUCTION DATA FOR SPRING 2007.

[[DISCUSSION]]

Robust learning measures.

Learning
These results support our claim that processing load cannot be established on a simple numerical basis. Recall that according Hulstijn and Laufer. For robust learning of all knowledge components of lexeme/lemma, output production (evaluation) is necessary. Written output practice, combined with involvement in class, may produce more learning than CALL programs that provide exposure only even if students are ‘internally motivated’ by self-selecting lists, and self-motivated in their search behaviors. Although evaulation (writing) is time-consuming, students need to use these words in order to master them.

This may seem like an obvious statement. However, given the continued suggestions in the literature that fill in the blank activities (Folse, 2006) and simply listening (Mason and Krashen) is 'more efficient' for lexical learning, we would like to suggest that for robust learning, evaluation in H&L's model is a vital part of mastering all of the knowledge components that make up the lexeme/lemma.

Transfer[http://www.learnlab.org/research/wiki/index.php/Transfer]

We also examined the transfer of words from REAP and in class instruction in the online data base of students' writing. In the fall of 2006, students used 10 words that they had seen REAP in free production in their writing classes. These words are listed by the BNC corpus frequency below:

Focus words seen in REAP: 10 uses
BNC-1,000 [ fams 2 : types 2 : tokens 2 ] assume produce

BNC-2,000 [ fams 1 : types 1 : tokens 1 ] distinction

BNC-3,000 [ fams 1 : types 1 : tokens 1 ] conceive

BNC-4,000 [ fams 1 : types 1 : tokens 1 ] abandon

BNC-5,000 [ fams 1 : types 1 : tokens 1 ] derive

BNC-6,000 [ fams 2 : types 2 : tokens 2 ] cite (x3) prohibit

In contrast, students used words on their in class vocabulary list 840 times. Even allowing for the words that they knew (average 14 out of 58), this represents a large difference in transfer between the REAP and in class treatments.

A final note is in order regarding the MTELP. As with any set of learners in an IEP, learners in the same class may enter with different overall proficiency scores. This cannot be avoided because almost all learners have some kind of exposure to Englush prior to their arrival in the US. As Stanovich (1986) would predict, those students who have a higher proficiency entering the class also learn more. This effect is known as the Matthew effect. Correlations between MTELP scores and vocabulary test scores in fall 2006 are as follows:

MTELP Predicts in class scores:

Pretest: r= 0.52, p ≤.01
Post-test: r= 0.59, p ≤ .01
Production: r= 0.58, p ≤.01

Predicts REAP
Cloze: r= 0.53, p ≤.01
Production: r= 0.59, p ≤.01

The MTELP scores were not related to look ups or the number of texts that the students read.

In the spring of 2007, MTELP scores again predict performance, with the exception of the post-test. This finding is significant because here the Matthew effect is NOT seen. In other words vocabulary gains during the class were not related to their overall proficiency. This result suggests that the classroom instruction was able to overcome this powerful effect.

Predicts in class
Pretest: r= 0.63, p ≤.05
Post-test: r= 0.36, p ≤ .06, ns
Production: r= 0.44, p ≤.02

Predicts REAP
Cloze: r= 0.58, p ≤.001

[[Selected References]]

Allum, P. (2002). CALL and the classroom: the case for comparative research. ReCALL, 14, 146-166.

Barcroft, J. (2004). Effects of sentence writing in second language lexical acquisition. Second Language Research, 20, 303-334.

Barcroft, J. (2006). Negative Effects of forced output on vocabulary learning. Second Language Research, 22, 487-497.

Folse, K. S. (2006). The effect of type of written exercise on L2 vocabulary retention. TESOL Quarterly, 40, 273-293.

Hulstijn, J., & Laufer, B. (2001). Some empirical evidence for the involvement load hypothesis in vocabulary acquisition. Language Learning, 51, 539-558.

Juffs, A., Friedline, B. F., Eskenazi, M., Wilson, L., & Heilman, M. (in review). Activity theory and computer-assisted learning of English vocabulary. Applied Linguistics.

Mason, B. and Krashen, S. (2004). Is form-focused vocabulary instruction worthwhile? RELC Journal, 35, 175-189.

Paribakht, T.S., and M. Wesche (1997). Vocabulary Enhancement Activities and Reading for Meaning in Second Language Vocabulary Acquisition. In J. Coady and T. Huckin (eds.), Second Language Vocabulary Acquisition (Cambridge: Cambridge University Press): 174-200.

Stanowicz, K. E. (1986). Matthew effects in reading: some consequences of individual differences in the acquisition of literacy. Reading Research Quarterly, 21, 360-407.

REAP Comparison to Classroom Instruction (Fall 2006)

2008-05-13T19:12:27Z

Juffs:

=== Logistical Information ===

{| border="1"
|+
|-
| '''Contributors''' ||Alan Juffs, Lois Wilson, Maxine Eskenazi, Michael Heilman
|-
| '''Study Start Date''' || September 2006
|-
| '''Study End Date''' || April, 2007
|-
| '''Learnlab Courses''' || English Language Institute Reading 4 (ESL LearnLab)
|-
| '''Number of Students''' || ~72
|-
| '''Total Participant Hours (est.)''' || approximately 360
|-
| '''Data in Datashop''' || no
|}

[[ABSTRACT]]

THIS WIKI ENTRY IS UNDER CONSTRUCTION.

This paper focuses on the long-term retention and production of vocabulary in an intensive English program (IEP). The study compares the learning of words through a computer assisted learning program with an 'ecological' control of standard classroom practice. The study seeks to elucidiate the strengths of both approaches. The paper draws on the practical framework of Coxhead (2001) and Nation (2005) and the theoretical perspectives of Laufer & Hulstijn (2001). The project collected data from over two separate classes of ESL learners. Hence the study was run twice with different participants.The vocabulary instruction occurred in intermediate level reading class (intermediate =TOEFL 450 or iBT TOEFL 45, average MTELP score, 58). All learners spent 40 minutes per week for 9 weeks reading texts containing words from the Academic Word List. In fall 2006, topic interest was manipulated in the CALL condition. In spring 2007, all of the students received texts that were in line with their interests. In contrast, in classroom instruction, a subset of the learners' normal in-class vocabulary instruction was tracked for two semesters. Pre-, post and delayed post-test data were collected for the CALL vocabulary learning and the in-class learning. In addition, during this period, all of the students' writing assignments were collected on-line. From this database of written output, each student’s texts were analyzed to determine which words seen during computer training and regular reading class had transferred to their spontaneous output in compositions in their writing class. Results indicate that although the CALL practice led to recognition one semester later, only the words which were practiced during regular reading class vocabulary instruction transferred to their spontaneous writing. This transfer effect is attributed to the output practice and deeper processing that occurred during the regular vocabulary instruction. The data also showed that the production of words seen in the CALL condition alone suffered from errors in word recognition (‘clang’ associations) and morphological form errors (Schmitt & Meara, 1997). We conclude that these data suggest that some negative views on output practice by Folse (2006) and Barcroft (2005) must be modified to accommodate these data. As a result of this comparision, we are amending REAP to create more interactive production opportunities.

[[INTRODUCTION]]

In the fall of 2006 and the spring of 2007, we tracked learners using REAP [http://reap.cs.cmu.edu/]and also tracked the in class vocabulary instruction. This study therefore focused on a comparison of REAP with what normally happens in classrooms. In that sense, it is not very tightly controlled study, but it is ecologically valid in the sense that the study reflects what actually happens in classes.

It is important to note that the REAP treatments in the fall of 2006 and spring 2007 were slightly different. In the fall of 2006, participants were introduced to the personalization of texts that they read. Some students received texts that they were interested by topic, while others received random texts that contained their focus words. In the fall of 2006, all of the students had their focus words highlighted, but not all students received texts that were of interest to them all the time. Details can be read here.[http://learnlab.org/research/wiki/index.php/REAP_Study_on_Personalization_of_Readings_by_Topic_%28Fall_2006%29] Students were all able to select their topics of interest in the spring of 2007 and also had their focus words highlighted. [See the study in level 5 for a comparison of highlighted versus non lighted words,

In the classroom conditions, the Reading 4 curriculum supervisor decided on a list of Academic Word List vocabulary [http://language.massey.ac.nz/staff/awl/] items that had been excluded from the tests that the students took to establish their focus word lists. This list included 58 items that in her view the students should know.

In this report, we compare learning gains in REAP in the fall of 2006 with in class learning. In the spring of 2007, we again compare learning gains with REAP and in class interaction.

[[BRIEF LITERATURE REVIEW]]

Recent research in vocabulary acquisition has suggested that the time taken for written output practice may not be well spent (Folse, 2006). Instead of using new words to create new meaningful texts during practice, Folse (2006) has suggested that fill-in-the-blank type exercises are more efficient. Moreover, Krashen and colleagues continue to suggest that form-focussed vocabulary instruction is not efficient (Mason & Krashen, 2004).

However, Hulstijn and Laufer (2001) (H&L) have suggested the involvement load hypothesis for vocabulary acquistion. The involvement load hypothesis suggests that deeper processing leads to better learning. However, definitions of 'depth of processing' during learning have not been made. Hulstijn and Laufer (2001) suggest a definition that contains three parts. Each part is assigned a processing load weight, from 0 to 2. Although H&L do not fully flesh out their model, one can infer the different processing weights for each component of processing. The first is 'need', which they controversially label 'non-cognitive'. A level of no need would be '0'; an externally imposed need would be a weight of 1, and a self-generated need would be a '2'. The second component of processing is 'search'. No search would have a weight of '0', teacher or class provided information would be '1', and self-look up in a dictionary or on line would be '2'. Finally, there is the evaluation stage. A zero involvement would be a non-linguistic response such as choosing from a multiple choice list, i.e. no output at all. A level of '1' would be an activity such as 'fill-in-the-blank'. Finally, a level 2 load would be free production such as in class writing activities.

The problem with both Folse (2006) and H&L is that they selected for in class study a very limited number of words (15 and 10) respectively. Moreover, the post-test results in both studies showed surprisingly low retention scores. For example, H&L showed that an average of delayed post-test gain for practice with reading and fill-in the blank of 1.6 or 1.7 out of 10, and production only 2.6 or 3.7 out of ten.

However, we know that learners need to master many more than 10-15 words to be able to effectively comprehend and produce academic discourse. What is needed is a much closer focus how time can be effectively used to make a 50-150 word (family) gain over time. This goal has been the focus in REAP. However, the time on task for each word in REAP is limited, and we have observed limited gains.

Learning a lexical entry is more than a form-meaning correspondence. Indeed, lexical items are highly complex.[[http://www.learnlab.org/research/papers.php]]. While reading practice in REAP may be sufficient for learning form-meaning links in Levelt's model, the acquisition of syntax and morphology may require deeper processing. In PSLC terminology, robust learning [http://www.learnlab.org/research/wiki/index.php/Robust_learning ]requires learning all knowledge components [http://www.learnlab.org/research/wiki/index.php/Knowledge_component] in the lexeme and lemma.

[[RESEARCH QUESTIONS]]

1. How does REAP vocabulary learning differ from the ecological control 'normal' in class instruction?

2. How do the learning outcomes differ in the immediate number of words learned, and in the number of words the learners transfer to other contexts?

3. If there are differences, what might the source of those differences be in terms of Hulstijn and Laufer's involvement load hypothesis.

4. Can 'deeper processing' through writing be ‘skipped’ by using a CALL program?

5. What can teachers and computer scientists learn from each other?

The two treatments in REAP and in class are summarized in the following table. The major differences from the point of view of the involvement load hypothesis are that in REAP the need is created by the student in his or her self-generated list, whereas in class the teacher decides on the words to be learned. Second, in terms of search, the student is responsible for looking up words in the computer program, whereas in class the teacher assigns group activities for discovery of words. Finally, in terms of 'evaluation', REAP only requires a non-linguistic response to a multiple choice question, whereas in class activities require evaluation where meaningful output is created.

The hypothesis then is that the in class learning gains will be far greater than for REAP. The question is how much better the in class activities will be. We explore the involvement load hypothesis scores for REAP and in class activities in the methodology section.

[[METHODOLOGY]]

[[Participants]]

The participants in the two semesters are typical of the heterogeneous first language (L2) classes in Intensive English Programs in general in the United States. Recently, the ELI at the University of Pittsburgh has had larger numbers of Arabic-speaking and Korean speaking learners. Overall, the Arabic-speaking students score lower on overall proficiency tests than the Korean speakers. However, the Arabic speakers are placed in level 4 classes because they have passed level 3 adequately.

[[Image:REAPMTELP0607.jpg|700px]]

The variation in MTELP scores is important. This point will be raised in the discussion section.

[[Procedure]]

The table below summarizes the differences between REAP and the in class treatment of vocabulary. The REAP method is described elsewhere.

[[Image:REAPinClass.jpg|700px]]

For the vocabulary practice in the classroom and as homework, some more details are necessary. The teachers in the ELI focus a great deal on form, meaning, and collocation through a variety of receptive and productive exercises.

1. For homework or in class students are given a list of 7-9 “focus words”. They find the word in the reading and copy the sentence with the word. They determine the part of speech in the context and then find the corresponding definition of the word in an ESL dictionary.
Students follow the same procedure with 3-5 words of their choice.
Students choose 3 words from their total list and write an original sentence with each word.

2. Teachers may do a variety of vocabulary exercises to practice the words in class including games in pairs/groups, word-definition matching, guided speaking activities requiring the focus words, etc.

3. Vocabulary tests are given on words from two readings as these readings are finished. (1 test every 1 _ -2 weeks). Tests are cloze with a word bank and answering questions which use the words. In the latter type of question, students must show in their answer that they understand the focus word, for example, by using a synonym.

Ex: What skills are [[essential]] if you want to become a lawyer?

These activities took perhaps 1.25 hours of class time in addition to homework according to the in class teachers. Hence, in class and homework represents more time on task than REAP. This factor has been accepted by H&L as inevitable in this kind of research, because production just takes longer.

In terms of Hulstijn and Laufer's involvement load hypothesis, we can estimate their level of depth of processing 'quantitatively'.

{| border="1"
|+
|-
| '''Condition''' || NEED || SEARCH || EVALUATION || TOTAL LOAD
|-
| '''REAP''' || 2 || 2|| 0 || 4
|-
| '''IN CLASS''' || 1 || 1|| 2|| 4
|}

Hence, both sets of activities have the same involvement load. However, REAP'S involvement load is divided strongly into need and search, whereas the in class activities are spread more over need, and search , plus a very strong evaluation component. Hulstijn and Laufer (2001) make no predictions about where the involvement load will fall, but common sense dictates that involvement needs to be spread across the different factors. For this reason, we anticipate, based on H&Ls results, that the learners in class will do much better in retaining their words.

[[RESULTS]]

The descriptive results are provided below. We discuss the classroom results first. As can be observed, the supervisor chose some words that were already known to the students. In fall 2006, students knew an average of 13.17 out of 58 words, and in the spring they knew an average of 17.6 out of 58 words. In the post-test, they scored 32.17 and 35.57 out of 40 words tested. This test was a fill in the blank test. Hence, students learned an average of 18 words in each semester in the classroom. In the written production task at the end of term, students scored 73% and 78% on a test of 30 words.

{| border="1"
|+
|-
| '''Term''' || Fall 2006 || Spring 2007 ||
|-
| '''Results''' || [[Image:Fall2006.jpg|500px]] || [[Image:spring2007.jpg|500px]]
|}

For the REAP sessions, it will be recalled that in fall 2006, the students were divided into those who received texts of interest and those who did not. These students' pretest was of course 0. The target look-ups were relatively low, averaging only 22.27 per student over the semester. The average gain for REAP, even under the experimental conditions that didn't favour learning for some students was 9.24 in the cloze test. However, the dramatic difference between in class and in REAP is in the production test. In the production test in fall 2006, the students scored only 29% with REAP words compared to 73% from in class.

A fairer comparison is in the spring of 2007. In this case, all the learners received texts that they were interested in. Of note here, is that the target look-ups in REAP increased to an average of 57 per student. In addition, the composition of the class was more balanced, with fewer Arabic speaking students, who were known to have 'gamed' the system (Juffs et al, in review). In spring, the average gain with REAP increased from 9.24 words in the cloze test to 14.00. Note that this result, 14, is only 3 words less than the cloze gains for the in class vocabulary. Bear in mind also that the time on task was much higher for the in class and homework vocabulary learning. In the spring, therefore, we can conclude that REAP was at least as good as in class work for recognition.

STILL NEED PRODUCTION DATA FOR SPRING 2007.

[[DISCUSSION]]

Robust learning measures.

Learning
These results support our claim that processing load cannot be established on a simple numerical basis. Recall that according Hulstijn and Laufer. For robust learning of all knowledge components of lexeme/lemma, output production (evaluation) is necessary. Written output practice, combined with involvement in class, may produce more learning than CALL programs that provide exposure only even if students are ‘internally motivated’ by self-selecting lists, and self-motivated in their search behaviors. Although evaulation (writing) is time-consuming, students need to use these words in order to master them.

This may seem like an obvious statement. However, given the continued suggestions in the literature that fill in the blank activities (Folse, 2006) and simply listening (Mason and Krashen) is 'more efficient' for lexical learning, we would like to suggest that for robust learning, evaluation in H&L's model is a vital part of mastering all of the knowledge components that make up the lexeme/lemma.

Transfer[http://www.learnlab.org/research/wiki/index.php/Transfer]

We also examined the transfer of words from REAP and in class instruction in the online data base of students' writing. In the fall of 2006, students used 10 words that they had seen REAP in free production in their writing classes. These words are listed by the BNC corpus frequency below:

Focus words seen in REAP: 10 uses
BNC-1,000 [ fams 2 : types 2 : tokens 2 ] assume produce

BNC-2,000 [ fams 1 : types 1 : tokens 1 ] distinction

BNC-3,000 [ fams 1 : types 1 : tokens 1 ] conceive

BNC-4,000 [ fams 1 : types 1 : tokens 1 ] abandon

BNC-5,000 [ fams 1 : types 1 : tokens 1 ] derive

BNC-6,000 [ fams 2 : types 2 : tokens 2 ] cite (x3) prohibit

In contrast, students used words on their in class vocabulary list 840 times. Even allowing for the words that they knew (average 14 out of 58), this represents a large difference in transfer between the REAP and in class treatments.

A final note is in order regarding the MTELP. As with any set of learners in an IEP, learners in the same class may enter with different overall proficiency scores. This cannot be avoided because almost all learners have some kind of exposure to Englush prior to their arrival in the US. As Stanovich (1986) would predict, those students who have a higher proficiency entering the class also learn more. This effect is known as the Matthew effect. Correlations between MTELP scores and vocabulary test scores in fall 2006 are as follows:

MTELP Predicts in class scores:

Pretest: r= 0.52, p ≤.01
Post-test: r= 0.59, p ≤ .01
Production: r= 0.58, p ≤.01

Predicts REAP
Cloze: r= 0.53, p ≤.01
Production: r= 0.59, p ≤.01

The MTELP scores were not related to look ups or the number of texts that the students read.

In the spring of 2007, MTELP scores again predict performance, with the exception of the post-test. This finding is significant because here the Matthew effect is NOT seen. In other words vocabulary gains during the class were not related to their overall proficiency. This result suggests that the classroom instruction was able to overcome this powerful effect.

Predicts in class
Pretest: r= 0.63, p ≤.05
Post-test: r= 0.36, p ≤ .06, ns
Production: r= 0.44, p ≤.02

Predicts REAP
Cloze: r= 0.58, p ≤.001

[[Selected References]]

Allum, P. (2002). CALL and the classroom: the case for comparative research. ReCALL, 14, 146-166.

Barcroft, J. (2004). Effects of sentence writing in second language lexical acquisition. Second Language Research, 20, 303-334.

Barcroft, J. (2006). Negative Effects of forced output on vocabulary learning. Second Language Research, 22, 487-497.

Folse, K. S. (2006). The effect of type of written exercise on L2 vocabulary retention. TESOL Quarterly, 40, 273-293.

Hulstijn, J., & Laufer, B. (2001). Some empirical evidence for the involvement load hypothesis in vocabulary acquisition. Language Learning, 51, 539-558.

Juffs, A., Friedline, B. F., Eskenazi, M., Wilson, L., & Heilman, M. (in review). Activity theory and computer-assisted learning of English vocabulary. Applied Linguistics.

Mason, B. and Krashen, S. (2004). Is form-focused vocabulary instruction worthwhile? RELC Journal, 35, 175-189.

Paribakht, T.S., and M. Wesche (1997). Vocabulary Enhancement Activities and Reading for Meaning in Second Language Vocabulary Acquisition. In J. Coady and T. Huckin (eds.), Second Language Vocabulary Acquisition (Cambridge: Cambridge University Press): 174-200.

Stanowicz, K. E. (1986). Matthew effects in reading: some consequences of individual differences in the acquisition of literacy. Reading Research Quarterly, 21, 360-407.

REAP Comparison to Classroom Instruction (Fall 2006)

2008-04-24T20:12:03Z

Juffs:

=== Logistical Information ===

{| border="1"
|+
|-
| '''Contributors''' ||Alan Juffs, Lois Wilson, Maxine Eskenazi, Michael Heilman
|-
| '''Study Start Date''' || September 2006
|-
| '''Study End Date''' || April, 2007
|-
| '''Learnlab Courses''' || English Language Institute Reading 4 (ESL LearnLab)
|-
| '''Number of Students''' || ~72
|-
| '''Total Participant Hours (est.)''' || approximately 360
|-
| '''Data in Datashop''' || no
|}

[[ABSTRACT]]

THIS WIKI ENTRY IS UNDER CONSTRUCTION.

This paper focuses on the long-term retention and production of instructed vocabulary in an intensive English program (IEP). The paper draws on the practical framework of Coxhead (2001) and Nation (2005) and the theoretical perspectives of Laufer & Hulstijn (2001). The project collected data from over 72 ESL learners over a period of two semesters. The vocabulary instruction occurred in intermediate level reading class (intermediate =TOEFL 450 or iBT TOEFL 45, average MTELP score, 58). All learners spent 40 minutes per week for 9 weeks reading texts containing words from the Academic Word List. In fall 2006, topic interest was manipulated in the CALL condition. In spring 2007, all of the students received texts that were in line with their interests. In contrast, in classroom instruction, a subset of the learners' normal in-class vocabulary instruction was tracked for two semesters. Pre-, post and delayed post-test data were collected for the CALL vocabulary learning and the in-class learning. In addition, during this period, all of the students' writing assignments were collected on-line. From this database of written output, each student’s texts were analyzed to determine which words seen during computer training and regular reading class had transferred to their spontaneous output in compositions in their writing class. Results indicate that although the CALL practice led to recognition one semester later, only the words which were practiced during regular reading class vocabulary instruction transferred to their spontaneous writing. This transfer effect is attributed to the output practice and deeper processing that occurred during the regular vocabulary instruction. The data also showed that the production of words seen in the CALL condition alone suffered from errors in word recognition (‘clang’ associations) and morphological form errors (Schmitt & Meara, 1997). We conclude that these data suggest that some negative views on output practice by Folse (2006) and Barcroft (2005) must be modified to accommodate these data. As a result of this comparision, we are amending REAP to create more interactive production opportunities.

[[INTRODUCTION]]

In the fall of 2006 and the spring of 2007, we tracked learners using REAP [http://reap.cs.cmu.edu/]and also tracked the in class vocabulary instruction. This study therefore focused on a comparison of REAP with what normally happens in classrooms. In that sense, it is not very tightly controlled study, but it is ecologically valid in the sense that the study reflects what actually happens in classes.

It is important to note that the REAP treatments in the fall of 2006 and spring 2007 were slightly different. In the fall of 2006, participants were introduced to the personalization of texts that they read. Some students received texts that they were interested by topic, while others received random texts that contained their focus words. In the fall of 2006, all of the students had their focus words highlighted, but not all students received texts that were of interest to them all the time. Details can be read here.[http://learnlab.org/research/wiki/index.php/REAP_Study_on_Personalization_of_Readings_by_Topic_%28Fall_2006%29] Students were all able to select their topics of interest in the spring of 2007 and also had their focus words highlighted. [See the study in level 5 for a comparison of highlighted versus non lighted words,

In the classroom conditions, the Reading 4 curriculum supervisor decided on a list of Academic Word List vocabulary [http://language.massey.ac.nz/staff/awl/] items that had been excluded from the tests that the students took to establish their focus word lists. This list included 58 items that in her view the students should know.

In this report, we compare learning gains in REAP in the fall of 2006 with in class learning. In the spring of 2007, we again compare learning gains with REAP and in class interaction.

[[BRIEF LITERATURE REVIEW]]

Recent research in vocabulary acquisition has suggested that the time taken for written output practice may not be well spent (Folse, 2006). Instead of using new words to create new meaningful texts during practice, Folse (2006) has suggested that fill-in-the-blank type exercises are more efficient. Moreover, Krashen and colleagues continue to suggest that form-focussed vocabulary instruction is not efficient (Mason & Krashen, 2004).

However, Hulstijn and Laufer (2001) (H&L) have suggested the involvement load hypothesis for vocabulary acquistion. The involvement load hypothesis suggests that deeper processing leads to better learning. However, definitions of 'depth of processing' during learning have not been made. Hulstijn and Laufer (2001) suggest a definition that contains three parts. Each part is assigned a processing load weight, from 0 to 2. Although H&L do not fully flesh out their model, one can infer the different processing weights for each component of processing. The first is 'need', which they controversially label 'non-cognitive'. A level of no need would be '0'; an externally imposed need would be a weight of 1, and a self-generated need would be a '2'. The second component of processing is 'search'. No search would have a weight of '0', teacher or class provided information would be '1', and self-look up in a dictionary or on line would be '2'. Finally, there is the evaluation stage. A zero involvement would be a non-linguistic response such as choosing from a multiple choice list, i.e. no output at all. A level of '1' would be an activity such as 'fill-in-the-blank'. Finally, a level 2 load would be free production such as in class writing activities.

The problem with both Folse (2006) and H&L is that they selected for in class study a very limited number of words (15 and 10) respectively. Moreover, the post-test results in both studies showed surprisingly low retention scores. For example, H&L showed that an average of delayed post-test gain for practice with reading and fill-in the blank of 1.6 or 1.7 out of 10, and production only 2.6 or 3.7 out of ten.

However, we know that learners need to master many more than 10-15 words to be able to effectively comprehend and produce academic discourse. What is needed is a much closer focus how time can be effectively used to make a 50-150 word (family) gain over time. This goal has been the focus in REAP. However, the time on task for each word in REAP is limited, and we have observed limited gains.

Learning a lexical entry is more than a form-meaning correspondence. Indeed, lexical items are highly complex.[[http://www.learnlab.org/research/papers.php]]. While reading practice in REAP may be sufficient for learning form-meaning links in Levelt's model, the acquisition of syntax and morphology may require deeper processing. In PSLC terminology, robust learning [http://www.learnlab.org/research/wiki/index.php/Robust_learning ]requires learning all knowledge components [http://www.learnlab.org/research/wiki/index.php/Knowledge_component] in the lexeme and lemma.

[[RESEARCH QUESTIONS]]

1. How does REAP vocabulary learning differ from the ecological control 'normal' in class instruction?

2. How do the learning outcomes differ in the immediate number of words learned, and in the number of words the learners transfer to other contexts?

3. If there are differences, what might the source of those differences be in terms of Hulstijn and Laufer's involvement load hypothesis.

4. Can 'deeper processing' through writing be ‘skipped’ by using a CALL program?

5. What can teachers and computer scientists learn from each other?

The two treatments in REAP and in class are summarized in the following table. The major differences from the point of view of the involvement load hypothesis are that in REAP the need is created by the student in his or her self-generated list, whereas in class the teacher decides on the words to be learned. Second, in terms of search, the student is responsible for looking up words in the computer program, whereas in class the teacher assigns group activities for discovery of words. Finally, in terms of 'evaluation', REAP only requires a non-linguistic response to a multiple choice question, whereas in class activities require evaluation where meaningful output is created.

The hypothesis then is that the in class learning gains will be far greater than for REAP. The question is how much better the in class activities will be. We explore the involvement load hypothesis scores for REAP and in class activities in the methodology section.

[[METHODOLOGY]]

[[Participants]]

The participants in the two semesters are typical of the heterogeneous first language (L2) classes in Intensive English Programs in general in the United States. Recently, the ELI at the University of Pittsburgh has had larger numbers of Arabic-speaking and Korean speaking learners. Overall, the Arabic-speaking students score lower on overall proficiency tests than the Korean speakers. However, the Arabic speakers are placed in level 4 classes because they have passed level 3 adequately.

[[Image:REAPMTELP0607.jpg|700px]]

The variation in MTELP scores is important. This point will be raised in the discussion section.

[[Procedure]]

The table below summarizes the differences between REAP and the in class treatment of vocabulary. The REAP method is described elsewhere.

[[Image:REAPinClass.jpg|700px]]

For the vocabulary practice in the classroom and as homework, some more details are necessary. The teachers in the ELI focus a great deal on form, meaning, and collocation through a variety of receptive and productive exercises.

1. For homework or in class students are given a list of 7-9 “focus words”. They find the word in the reading and copy the sentence with the word. They determine the part of speech in the context and then find the corresponding definition of the word in an ESL dictionary.
Students follow the same procedure with 3-5 words of their choice.
Students choose 3 words from their total list and write an original sentence with each word.

2. Teachers may do a variety of vocabulary exercises to practice the words in class including games in pairs/groups, word-definition matching, guided speaking activities requiring the focus words, etc.

3. Vocabulary tests are given on words from two readings as these readings are finished. (1 test every 1 _ -2 weeks). Tests are cloze with a word bank and answering questions which use the words. In the latter type of question, students must show in their answer that they understand the focus word, for example, by using a synonym.

Ex: What skills are [[essential]] if you want to become a lawyer?

These activities took perhaps 1.25 hours of class time in addition to homework according to the in class teachers. Hence, in class and homework represents more time on task than REAP. This factor has been accepted by H&L as inevitable in this kind of research, because production just takes longer.

In terms of Hulstijn and Laufer's involvement load hypothesis, we can estimate their level of depth of processing 'quantitatively'.

{| border="1"
|+
|-
| '''Condition''' || NEED || SEARCH || EVALUATION || TOTAL LOAD
|-
| '''REAP''' || 2 || 2|| 0 || 4
|-
| '''IN CLASS''' || 1 || 1|| 2|| 4
|}

Hence, both sets of activities have the same involvement load. However, REAP'S involvement load is divided strongly into need and search, whereas the in class activities are spread more over need, and search , plus a very strong evaluation component. Hulstijn and Laufer (2001) make no predictions about where the involvement load will fall, but common sense dictates that involvement needs to be spread across the different factors. For this reason, we anticipate, based on H&Ls results, that the learners in class will do much better in retaining their words.

[[RESULTS]]

The descriptive results are provided below. We discuss the classroom results first. As can be observed, the supervisor chose some words that were already known to the students. In fall 2006, students knew an average of 13.17 out of 58 words, and in the spring they knew an average of 17.6 out of 58 words. In the post-test, they scored 32.17 and 35.57 out of 40 words tested. This test was a fill in the blank test. Hence, students learned an average of 18 words in each semester in the classroom. In the written production task at the end of term, students scored 73% and 78% on a test of 30 words.

{| border="1"
|+
|-
| '''Term''' || Fall 2006 || Spring 2007 ||
|-
| '''Results''' || [[Image:Fall2006.jpg|500px]] || [[Image:spring2007.jpg|500px]]
|}

For the REAP sessions, it will be recalled that in fall 2006, the students were divided into those who received texts of interest and those who did not. These students' pretest was of course 0. The target look-ups were relatively low, averaging only 22.27 per student over the semester. The average gain for REAP, even under the experimental conditions that didn't favour learning for some students was 9.24 in the cloze test. However, the dramatic difference between in class and in REAP is in the production test. In the production test in fall 2006, the students scored only 29% with REAP words compared to 73% from in class.

A fairer comparison is in the spring of 2007. In this case, all the learners received texts that they were interested in. Of note here, is that the target look-ups in REAP increased to an average of 57 per student. In addition, the composition of the class was more balanced, with fewer Arabic speaking students, who were known to have 'gamed' the system (Juffs et al, in review). In spring, the average gain with REAP increased from 9.24 words in the cloze test to 14.00. Note that this result, 14, is only 3 words less than the cloze gains for the in class vocabulary. Bear in mind also that the time on task was much higher for the in class and homework vocabulary learning. In the spring, therefore, we can conclude that REAP was at least as good as in class work for recognition.

STILL NEED PRODUCTION DATA FOR SPRING 2007.

[[DISCUSSION]]

Robust learning measures.

Learning
These results support our claim that processing load cannot be established on a simple numerical basis. Recall that according Hulstijn and Laufer. For robust learning of all knowledge components of lexeme/lemma, output production (evaluation) is necessary. Written output practice, combined with involvement in class, may produce more learning than CALL programs that provide exposure only even if students are ‘internally motivated’ by self-selecting lists, and self-motivated in their search behaviors. Although evaulation (writing) is time-consuming, students need to use these words in order to master them.

This may seem like an obvious statement. However, given the continued suggestions in the literature that fill in the blank activities (Folse, 2006) and simply listening (Mason and Krashen) is 'more efficient' for lexical learning, we would like to suggest that for robust learning, evaluation in H&L's model is a vital part of mastering all of the knowledge components that make up the lexeme/lemma.

Transfer[http://www.learnlab.org/research/wiki/index.php/Transfer]

We also examined the transfer of words from REAP and in class instruction in the online data base of students' writing. In the fall of 2006, students used 10 words that they had seen REAP in free production in their writing classes. These words are listed by the BNC corpus frequency below:

Focus words seen in REAP: 10 uses
BNC-1,000 [ fams 2 : types 2 : tokens 2 ] assume produce

BNC-2,000 [ fams 1 : types 1 : tokens 1 ] distinction

BNC-3,000 [ fams 1 : types 1 : tokens 1 ] conceive

BNC-4,000 [ fams 1 : types 1 : tokens 1 ] abandon

BNC-5,000 [ fams 1 : types 1 : tokens 1 ] derive

BNC-6,000 [ fams 2 : types 2 : tokens 2 ] cite (x3) prohibit

In contrast, students used words on their in class vocabulary list 840 times. Even allowing for the words that they knew (average 14 out of 58), this represents a large difference in transfer between the REAP and in class treatments.

A final note is in order regarding the MTELP. As with any set of learners in an IEP, learners in the same class may enter with different overall proficiency scores. This cannot be avoided because almost all learners have some kind of exposure to Englush prior to their arrival in the US. As Stanovich (1986) would predict, those students who have a higher proficiency entering the class also learn more. This effect is known as the Matthew effect. Correlations between MTELP scores and vocabulary test scores in fall 2006 are as follows:

MTELP Predicts in class scores:

Pretest: r= 0.52, p ≤.01
Post-test: r= 0.59, p ≤ .01
Production: r= 0.58, p ≤.01

Predicts REAP
Cloze: r= 0.53, p ≤.01
Production: r= 0.59, p ≤.01

The MTELP scores were not related to look ups or the number of texts that the students read.

In the spring of 2007, MTELP scores again predict performance, with the exception of the post-test. This finding is significant because here the Matthew effect is NOT seen. In other words vocabulary gains during the class were not related to their overall proficiency. This result suggests that the classroom instruction was able to overcome this powerful effect.

Predicts in class
Pretest: r= 0.63, p ≤.05
Post-test: r= 0.36, p ≤ .06, ns
Production: r= 0.44, p ≤.02

Predicts REAP
Cloze: r= 0.58, p ≤.001

[[Selected References]]

Allum, P. (2002). CALL and the classroom: the case for comparative research. ReCALL, 14, 146-166.

Barcroft, J. (2004). Effects of sentence writing in second language lexical acquisition. Second Language Research, 20, 303-334.

Barcroft, J. (2006). Negative Effects of forced output on vocabulary learning. Second Language Research, 22, 487-497.

Folse, K. S. (2006). The effect of type of written exercise on L2 vocabulary retention. TESOL Quarterly, 40, 273-293.

Hulstijn, J., & Laufer, B. (2001). Some empirical evidence for the involvement load hypothesis in vocabulary acquisition. Language Learning, 51, 539-558.

Juffs, A., Friedline, B. F., Eskenazi, M., Wilson, L., & Heilman, M. (in review). Activity theory and computer-assisted learning of English vocabulary. Applied Linguistics.

Mason, B. and Krashen, S. (2004). Is form-focused vocabulary instruction worthwhile? RELC Journal, 35, 175-189.

Paribakht, T.S., and M. Wesche (1997). Vocabulary Enhancement Activities and Reading for Meaning in Second Language Vocabulary Acquisition. In J. Coady and T. Huckin (eds.), Second Language Vocabulary Acquisition (Cambridge: Cambridge University Press): 174-200.

Stanowicz, K. E. (1986). Matthew effects in reading: some consequences of individual differences in the acquisition of literacy. Reading Research Quarterly, 21, 360-407.

REAP Comparison to Classroom Instruction (Fall 2006)

2008-04-24T19:22:33Z

Juffs:

=== Logistical Information ===

{| border="1"
|+
|-
| '''Contributors''' ||Alan Juffs, Lois Wilson, Maxine Eskenazi, Michael Heilman
|-
| '''Study Start Date''' || September 2006
|-
| '''Study End Date''' || April, 2007
|-
| '''Learnlab Courses''' || English Language Institute Reading 4 (ESL LearnLab)
|-
| '''Number of Students''' || ~72
|-
| '''Total Participant Hours (est.)''' || approximately 360
|-
| '''Data in Datashop''' || no
|}

[[ABSTRACT]]

THIS WIKI ENTRY IS UNDER CONSTRUCTION.

This paper focuses on the long-term retention and production of instructed vocabulary in an intensive English program (IEP). The paper draws on the practical framework of Coxhead (2001) and Nation (2005) and the theoretical perspectives of Laufer & Hulstijn (2001). The project collected data from over 72 ESL learners over a period of two semesters. The vocabulary instruction occurred in intermediate level reading class (intermediate =TOEFL 450 or iBT TOEFL 45, average MTELP score, 58). All learners spent 40 minutes per week for 9 weeks reading texts containing words from the Academic Word List. In fall 2006, topic interest was manipulated in the CALL condition. In spring 2007, all of the students received texts that were in line with their interests. In contrast, in classroom instruction, a subset of the learners' normal in-class vocabulary instruction was tracked for two semesters. Pre-, post and delayed post-test data were collected for the CALL vocabulary learning and the in-class learning. In addition, during this period, all of the students' writing assignments were collected on-line. From this database of written output, each student’s texts were analyzed to determine which words seen during computer training and regular reading class had transferred to their spontaneous output in compositions in their writing class. Results indicate that although the CALL practice led to recognition one semester later, only the words which were practiced during regular reading class vocabulary instruction transferred to their spontaneous writing. This transfer effect is attributed to the output practice and deeper processing that occurred during the regular vocabulary instruction. The data also showed that the production of words seen in the CALL condition alone suffered from errors in word recognition (‘clang’ associations) and morphological form errors (Schmitt & Meara, 1997). We conclude that these data suggest that some negative views on output practice by Folse (2006) and Barcroft (2005) must be modified to accommodate these data. As a result of this comparision, we are amending REAP to create more interactive production opportunities.

[[INTRODUCTION]]

In the fall of 2006 and the spring of 2007, we tracked learners using REAP [http://reap.cs.cmu.edu/]and also tracked the in class vocabulary instruction. This study therefore focused on a comparison of REAP with what normally happens in classrooms. In that sense, it is not very tightly controlled study, but it is ecologically valid in the sense that the study reflects what actually happens in classes.

It is important to note that the REAP treatments in the fall of 2006 and spring 2007 were slightly different. In the fall of 2006, participants were introduced to the personalization of texts that they read. Some students received texts that they were interested by topic, while others received random texts that contained their focus words. In the fall of 2006, all of the students had their focus words highlighted, but not all students received texts that were of interest to them all the time. Details can be read here.[http://learnlab.org/research/wiki/index.php/REAP_Study_on_Personalization_of_Readings_by_Topic_%28Fall_2006%29] Students were all able to select their topics of interest in the spring of 2007 and also had their focus words highlighted. [See the study in level 5 for a comparison of highlighted versus non lighted words,

In the classroom conditions, the Reading 4 curriculum supervisor decided on a list of Academic Word List vocabulary [http://language.massey.ac.nz/staff/awl/] items that had been excluded from the tests that the students took to establish their focus word lists. This list included 58 items that in her view the students should know.

In this report, we compare learning gains in REAP in the fall of 2006 with in class learning. In the spring of 2007, we again compare learning gains with REAP and in class interaction.

[[BRIEF LITERATURE REVIEW]]

Recent research in vocabulary acquisition has suggested that the time taken for written output practice may not be well spent (Folse, 2006). Instead of using new words to create new meaningful texts during practice, Folse (2006) has suggested that fill-in-the-blank type exercises are more efficient. Moreover, Krashen and colleagues continue to suggest that form-focussed vocabulary instruction is not efficient (Mason & Krashen, 2004).

However, Hulstijn and Laufer (2001) (H&L) have suggested the involvement load hypothesis for vocabulary acquistion. The involvement load hypothesis suggests that deeper processing leads to better learning. However, definitions of 'depth of processing' during learning have not been made. Hulstijn and Laufer (2001) suggest a definition that contains three parts. Each part is assigned a processing load weight, from 0 to 2. Although H&L do not fully flesh out their model, one can infer the different processing weights for each component of processing. The first is 'need', which they controversially label 'non-cognitive'. A level of no need would be '0'; an externally imposed need would be a weight of 1, and a self-generated need would be a '2'. The second component of processing is 'search'. No search would have a weight of '0', teacher or class provided information would be '1', and self-look up in a dictionary or on line would be '2'. Finally, there is the evaluation stage. A zero involvement would be a non-linguistic response such as choosing from a multiple choice list, i.e. no output at all. A level of '1' would be an activity such as 'fill-in-the-blank'. Finally, a level 2 load would be free production such as in class writing activities.

The problem with both Folse (2006) and H&L is that they selected for in class study a very limited number of words (15 and 10) respectively. Moreover, the post-test results in both studies showed surprisingly low retention scores. For example, H&L showed that an average of delayed post-test gain for practice with reading and fill-in the blank of 1.6 or 1.7 out of 10, and production only 2.6 or 3.7 out of ten.

However, we know that learners need to master many more than 10-15 words to be able to effectively comprehend and produce academic discourse. What is needed is a much closer focus how time can be effectively used to make a 50-150 word (family) gain over time. This goal has been the focus in REAP. However, the time on task for each word in REAP is limited, and we have observed limited gains.

Learning a lexical entry is more than a form-meaning correspondence. Indeed, lexical items are highly complex.[[http://www.learnlab.org/research/papers.php]]. While reading practice in REAP may be sufficient for learning form-meaning links in Levelt's model, the acquisition of syntax and morphology may require deeper processing. In PSLC terminology, robust learning [http://www.learnlab.org/research/wiki/index.php/Robust_learning ]requires learning all knowledge components [http://www.learnlab.org/research/wiki/index.php/Knowledge_component] in the lexeme and lemma.

[[RESEARCH QUESTIONS]]

1. How does REAP vocabulary learning differ from the ecological control 'normal' in class instruction?

2. How do the learning outcomes differ in the immediate number of words learned, and in the number of words the learners transfer to other contexts?

3. If there are differences, what might the source of those differences be in terms of Hulstijn and Laufer's involvement load hypothesis.

4. Can 'deeper processing' through writing be ‘skipped’ by using a CALL program?

5. What can teachers and computer scientists learn from each other?

The two treatments in REAP and in class are summarized in the following table. The major differences from the point of view of the involvement load hypothesis are that in REAP the need is created by the student in his or her self-generated list, whereas in class the teacher decides on the words to be learned. Second, in terms of search, the student is responsible for looking up words in the computer program, whereas in class the teacher assigns group activities for discovery of words. Finally, in terms of 'evaluation', REAP only requires a non-linguistic response to a multiple choice question, whereas in class activities require evaluation where meaningful output is created.

The hypothesis then is that the in class learning gains will be far greater than for REAP. The question is how much better the in class activities will be. We explore the involvement load hypothesis scores for REAP and in class activities in the methodology section.

[[METHODOLOGY]]

[[Participants]]

The participants in the two semesters are typical of the heterogeneous first language (L2) classes in Intensive English Programs in general in the United States. Recently, the ELI at the University of Pittsburgh has had larger numbers of Arabic-speaking and Korean speaking learners. Overall, the Arabic-speaking students score lower on overall proficiency tests than the Korean speakers. However, the Arabic speakers are placed in level 4 classes because they have passed level 3 adequately.

[[Image:REAPMTELP0607.jpg|700px]]

The variation in MTELP scores is important. This point will be raised in the discussion section.

[[Procedure]]

The table below summarizes the differences between REAP and the in class treatment of vocabulary. The REAP method is described elsewhere.

[[Image:REAPinClass.jpg|700px]]

For the vocabulary practice in the classroom and as homework, some more details are necessary. The teachers in the ELI focus a great deal on form, meaning, and collocation through a variety of receptive and productive exercises.

1. For homework or in class students are given a list of 7-9 “focus words”. They find the word in the reading and copy the sentence with the word. They determine the part of speech in the context and then find the corresponding definition of the word in an ESL dictionary.
Students follow the same procedure with 3-5 words of their choice.
Students choose 3 words from their total list and write an original sentence with each word.

2. Teachers may do a variety of vocabulary exercises to practice the words in class including games in pairs/groups, word-definition matching, guided speaking activities requiring the focus words, etc.

3. Vocabulary tests are given on words from two readings as these readings are finished. (1 test every 1 _ -2 weeks). Tests are cloze with a word bank and answering questions which use the words. In the latter type of question, students must show in their answer that they understand the focus word, for example, by using a synonym.

Ex: What skills are [[essential]] if you want to become a lawyer?

In terms of Hulstijn and Laufer's involvement load hypothesis, we can estimate their level of depth of processing 'quantitatively'.

{| border="1"
|+
|-
| '''Condition''' || NEED || SEARCH || EVALUATION || TOTAL LOAD
|-
| '''REAP''' || 2 || 2|| 0 || 4
|-
| '''IN CLASS''' || 1 || 1|| 2|| 4
|}

Hence, both sets of activities have the same involvement load. However, REAP'S involvement load is divided strongly into need and search, whereas the in class activities are spread more over need, and search , plus a very strong evaluation component. Hulstijn and Laufer (2001) make no predictions about where the involvement load will fall, but common sense dictates that involvement needs to be spread across the different factors. For this reason, we anticipate, based on H&Ls results, that the learners in class will do much better in retaining their words.

[[RESULTS]]

The descriptive results are provided below. We discuss the classroom results first. As can be observed, the supervisor chose some words that were already known to the students. In fall 2006, students knew an average of 13.17 out of 58 words, and in the spring they knew an average of 17.6 out of 58 words. In the post-test, they scored 32.17 and 35.57 out of 40 words tested. This test was a fill in the blank test. Hence, students learned an average of 18 words in each semester in the classroom. In the written production task at the end of term, students scored 73% and 78% on a test of 30 words.

{| border="1"
|+
|-
| '''Term''' || Fall 2006 || Spring 2007 ||
|-
| '''Results''' || [[Image:Fall2006.jpg|500px]] || [[Image:spring2007.jpg|500px]]
|}

For the REAP sessions, it will be recalled that in fall 2006, the students were divided into those who received texts of interest and those who did not. These students' pretest was of course 0. The target look-ups were relatively low, averaging only 22.27 per student over the semester. The average gain for REAP, even under the experimental conditions that didn't favour learning for some students was 9.24 in the cloze test. However, the dramatic difference between in class and in REAP is in the production test. In the production test in fall 2006, the students scored only 29% with REAP words compared to 73% from in class.
A fairer comparison is in the spring of 2007. In this case, all the learners received texts that they were interested in. Of note here, is that the target look-ups in REAP increased to an average of 57 per student.

[[DISCUSSION]]

These results support our claim that processing load cannot be established on a simple numerical basis. Recall that according Hulstijn and Laufer,

[[Selected References]]

Allum, P. (2002). CALL and the classroom: the case for comparative research. ReCALL, 14, 146-166.

Barcroft, J. (2004). Effects of sentence writing in second language lexical acquisition. Second Language Research, 20, 303-334.

Barcroft, J. (2006). Negative Effects of forced output on vocabulary learning. Second Language Research, 22, 487-497.

Folse, K. S. (2006). The effect of type of written exercise on L2 vocabulary retention. TESOL Quarterly, 40, 273-293.

Hulstijn, J., & Laufer, B. (2001). Some empirical evidence for the involvement load hypothesis in vocabulary acquisition. Language Learning, 51, 539-558.

Juffs, A., Friedline, B. F., Eskenazi, M., Wilson, L., & Heilman, M. (in review). Activity theory and computer-assisted learning of English vocabulary. Applied Linguistics.

Mason, B. and Krashen, S. (2004). Is form-focused vocabulary instruction worthwhile? RELC Journal, 35, 175-189.

Paribakht, T.S., and M. Wesche (1997). Vocabulary Enhancement Activities and Reading for Meaning in Second Language Vocabulary Acquisition. In J. Coady and T. Huckin (eds.), Second Language Vocabulary Acquisition (Cambridge: Cambridge University Press): 174-200.

Stanowicz, K. E. (1986). Matthew effects in reading: some consequences of individual differences in the acquisition of literacy. Reading Research Quarterly, 21, 360-407.

REAP Comparison to Classroom Instruction (Fall 2006)

2008-04-24T19:07:31Z

Juffs:

=== Logistical Information ===

{| border="1"
|+
|-
| '''Contributors''' ||Alan Juffs, Lois Wilson, Maxine Eskenazi, Michael Heilman
|-
| '''Study Start Date''' || September 2006
|-
| '''Study End Date''' || April, 2007
|-
| '''Learnlab Courses''' || English Language Institute Reading 4 (ESL LearnLab)
|-
| '''Number of Students''' || ~72
|-
| '''Total Participant Hours (est.)''' || approximately 360
|-
| '''Data in Datashop''' || no
|}

[[ABSTRACT]]

THIS WIKI ENTRY IS UNDER CONSTRUCTION.

This paper focuses on the long-term retention and production of instructed vocabulary in an intensive English program (IEP). The paper draws on the practical framework of Coxhead (2001) and Nation (2005) and the theoretical perspectives of Laufer & Hulstijn (2001). The project collected data from over 72 ESL learners over a period of two semesters. The vocabulary instruction occurred in intermediate level reading class (intermediate =TOEFL 450 or iBT TOEFL 45, average MTELP score, 58). All learners spent 40 minutes per week for 9 weeks reading texts containing words from the Academic Word List. In fall 2006, topic interest was manipulated in the CALL condition. In spring 2007, all of the students received texts that were in line with their interests. In contrast, in classroom instruction, a subset of the learners' normal in-class vocabulary instruction was tracked for two semesters. Pre-, post and delayed post-test data were collected for the CALL vocabulary learning and the in-class learning. In addition, during this period, all of the students' writing assignments were collected on-line. From this database of written output, each student’s texts were analyzed to determine which words seen during computer training and regular reading class had transferred to their spontaneous output in compositions in their writing class. Results indicate that although the CALL practice led to recognition one semester later, only the words which were practiced during regular reading class vocabulary instruction transferred to their spontaneous writing. This transfer effect is attributed to the output practice and deeper processing that occurred during the regular vocabulary instruction. The data also showed that the production of words seen in the CALL condition alone suffered from errors in word recognition (‘clang’ associations) and morphological form errors (Schmitt & Meara, 1997). We conclude that these data suggest that some negative views on output practice by Folse (2006) and Barcroft (2005) must be modified to accommodate these data. As a result of this comparision, we are amending REAP to create more interactive production opportunities.

[[INTRODUCTION]]

In the fall of 2006 and the spring of 2007, we tracked learners using REAP [http://reap.cs.cmu.edu/]and also tracked the in class vocabulary instruction. This study therefore focused on a comparison of REAP with what normally happens in classrooms. In that sense, it is not very tightly controlled study, but it is ecologically valid in the sense that the study reflects what actually happens in classes.

It is important to note that the REAP treatments in the fall of 2006 and spring 2007 were slightly different. In the fall of 2006, participants were introduced to the personalization of texts that they read. Some students received texts that they were interested by topic, while others received random texts that contained their focus words. In the fall of 2006, all of the students had their focus words highlighted, but not all students received texts that were of interest to them all the time. Details can be read here.[http://learnlab.org/research/wiki/index.php/REAP_Study_on_Personalization_of_Readings_by_Topic_%28Fall_2006%29] Students were all able to select their topics of interest in the spring of 2007 and also had their focus words highlighted. [See the study in level 5 for a comparison of highlighted versus non lighted words,

In the classroom conditions, the Reading 4 curriculum supervisor decided on a list of Academic Word List vocabulary [http://language.massey.ac.nz/staff/awl/] items that had been excluded from the tests that the students took to establish their focus word lists. This list included 58 items that in her view the students should know.

In this report, we compare learning gains in REAP in the fall of 2006 with in class learning. In the spring of 2007, we again compare learning gains with REAP and in class interaction.

[[BRIEF LITERATURE REVIEW]]

Recent research in vocabulary acquisition has suggested that the time taken for written output practice may not be well spent (Folse, 2006). Instead of using new words to create new meaningful texts during practice, Folse (2006) has suggested that fill-in-the-blank type exercises are more efficient.

However, Hulstijn and Laufer (2001) (H&L) have suggested the involvement load hypothesis for vocabulary acquistion. The involvement load hypothesis suggests that deeper processing leads to better learning. However, definitions of 'depth of processing' during learning have not been made. Hulstijn and Laufer (2001) suggest a definition that contains three parts. Each part is assigned a processing load weight, from 0 to 2. Although H&L do not fully flesh out their model, one can infer the different processing weights for each component of processing. The first is 'need', which they controversially label 'non-cognitive'. A level of no need would be '0'; an externally imposed need would be a weight of 1, and a self-generated need would be a '2'. The second component of processing is 'search'. No search would have a weight of '0', teacher or class provided information would be '1', and self-look up in a dictionary or on line would be '2'. Finally, there is the evaluation stage. A zero involvement would be a non-linguistic response such as choosing from a multiple choice list, i.e. no output at all. A level of '1' would be an activity such as 'fill-in-the-blank'. Finally, a level 2 load would be free production such as in class writing activities.

The problem with both Folse (2006) and H&L is that they selected for in class study a very limited number of words (15 and 10) respectively. Moreover, the post-test results in both studies showed surprisingly low retention scores. For example, H&L showed that an average of delayed post-test gain for practice with reading and fill-in the blank of 1.6 or 1.7 out of 10, and production only 2.6 or 3.7 out of ten.

However, we know that learners need to master many more than 10-15 words to be able to effectively comprehend and produce academic discourse. What is needed is a much closer focus how time can be effectively used to make a 50-150 word (family) gain over time. This goal has been the focus in REAP. However, the time on task for each word in REAP is limited, and we have observed limited gains.

Learning a lexical entry is more than a form-meaning correspondence. Indeed, lexical items are highly complex.[[http://www.learnlab.org/research/papers.php]]. While reading practice in REAP may be sufficient for learning form-meaning links in Levelt's model, the acquisition of syntax and morphology may require deeper processing. In PSLC terminology, robust learning [http://www.learnlab.org/research/wiki/index.php/Robust_learning ]requires learning all knowledge components [http://www.learnlab.org/research/wiki/index.php/Knowledge_component] in the lexeme and lemma.

[[RESEARCH QUESTIONS]]

1. How does REAP vocabulary learning differ from the ecological control 'normal' in class instruction?

2. How do the learning outcomes differ in the immediate number of words learned, and in the number of words the learners transfer to other contexts?

3. If there are differences, what might the source of those differences be in terms of Hulstijn and Laufer's involvement load hypothesis.

4. Can 'deeper processing' through writing be ‘skipped’ by using a CALL program?

5. What can teachers and computer scientists learn from each other?

The two treatments in REAP and in class are summarized in the following table. The major differences from the point of view of the involvement load hypothesis are that in REAP the need is created by the student in his or her self-generated list, whereas in class the teacher decides on the words to be learned. Second, in terms of search, the student is responsible for looking up words in the computer program, whereas in class the teacher assigns group activities for discovery of words. Finally, in terms of 'evaluation', REAP only requires a non-linguistic response to a multiple choice question, whereas in class activities require evaluation where meaningful output is created.

The hypothesis then is that the in class learning gains will be far greater than for REAP. The question is how much better the in class activities will be. We explore the involvement load hypothesis scores for REAP and in class activities in the methodology section.

[[METHODOLOGY]]

[[Participants]]

The participants in the two semesters are typical of the heterogeneous first language (L2) classes in Intensive English Programs in general in the United States. Recently, the ELI at the University of Pittsburgh has had larger numbers of Arabic-speaking and Korean speaking learners. Overall, the Arabic-speaking students score lower on overall proficiency tests than the Korean speakers. However, the Arabic speakers are placed in level 4 classes because they have passed level 3 adequately.

[[Image:REAPMTELP0607.jpg|700px]]

The variation in MTELP scores is important. This point will be raised in the discussion section.

[[Procedure]]

The table below summarizes the differences between REAP and the in class treatment of vocabulary. The REAP method is described elsewhere.

[[Image:REAPinClass.jpg|700px]]

For the vocabulary practice in the classroom and as homework, some more details are necessary.

1. For homework or in class students are given a list of 7-9 “focus words”. They find the word in the reading and copy the sentence with the word. They determine the part of speech in the context and then find the corresponding definition of the word in an ESL dictionary.
Students follow the same procedure with 3-5 words of their choice.
Students choose 3 words from their total list and write an original sentence with each word.

2. Teachers may do a variety of vocabulary exercises to practice the words in class including games in pairs/groups, word-definition matching, guided speaking activities requiring the focus words, etc.

3. Vocabulary tests are given on words from two readings as these readings are finished. (1 test every 1 _ -2 weeks). Tests are cloze with a word bank and answering questions which use the words. In the latter type of question, students must show in their answer that they understand the focus word, for example, by using a synonym.

Ex: What skills are [[essential]] if you want to become a lawyer?

In terms of Hulstijn and Laufer's involvement load hypothesis, we can estimate their level of depth of processing 'quantitatively'.

{| border="1"
|+
|-
| '''Condition''' || NEED || SEARCH || EVALUATION || TOTAL LOAD
|-
| '''REAP''' || 2 || 2|| 0 || 4
|-
| '''IN CLASS''' || 1 || 1|| 2|| 4
|}

Hence, both sets of activities have the same involvement load. However, REAP'S involvement load is divided strongly into need and search, whereas the in class activities are spread more over need, and search , plus a very strong evaluation component. Hulstijn and Laufer (2001) make no predictions about where the involvement load will fall, but common sense dictates that involvement needs to be spread across the different factors. For this reason, we anticipate, based on H&Ls results, that the learners in class will do much better in retaining their words.

[[RESULTS]]

The descriptive results are provided below. We discuss the classroom results first. As can be observed, the supervisor chose some words that were already known to the students. In fall 2006, students knew an average of 13.17 out of 58 words, and in the spring they knew an average of 17.6 out of 58 words. In the post-test, they scored 32.17 and 35.57 out of 40 words tested. This test was a fill in the blank test. Hence, students learned an average of 18 words in each semester in the classroom. In the written production task at the end of term, students scored 73% and 78% on a test of 30 words.

{| border="1"
|+
|-
| '''Term''' || Fall 2006 || Spring 2007 ||
|-
| '''Results''' || [[Image:Fall2006.jpg|500px]] || [[Image:spring2007.jpg|500px]]
|}

For the REAP sessions, it will be recalled that in fall 2006, the students were divided into those who received texts of interest and those who did not. These students' pretest was of course 0. The target look-ups were relatively low, averaging only 22.27 per student over the semester. The average gain for REAP, even under the experimental conditions that didn't favour learning for some students was 9.24 in the cloze test. However, the dramatic difference between in class and in REAP is in the production test. In the production test in fall 2006, the students scored only 29% with REAP words compared to

[[DISCUSSION]]

[[Selected References]]

Allum, P. (2002). CALL and the classroom: the case for comparative research. ReCALL, 14, 146-166.

Barcroft, J. (2004). Effects of sentence writing in second language lexical acquisition. Second Language Research, 20, 303-334.

Barcroft, J. (2006). Negative Effects of forced output on vocabulary learning. Second Language Research, 22, 487-497.

Folse, K. S. (2006). The effect of type of written exercise on L2 vocabulary retention. TESOL Quarterly, 40, 273-293.

Hulstijn, J., & Laufer, B. (2001). Some empirical evidence for the involvement load hypothesis in vocabulary acquisition. Language Learning, 51, 539-558.

Juffs, A., Friedline, B. F., Eskenazi, M., Wilson, L., & Heilman, M. (in review). Activity theory and computer-assisted learning of English vocabulary. Applied Linguistics.

Stanowicz, K. E. (1986). Matthew effects in reading: some consequences of individual differences in the acquisition of literacy. Reading Research Quarterly, 21, 360-407.

REAP Comparison to Classroom Instruction (Fall 2006)

2008-04-24T18:57:32Z

Juffs:

REAP Comparison to Classroom Instruction (Fall 2006)

2008-04-24T18:42:21Z

Juffs:

REAP Comparison to Classroom Instruction (Fall 2006)

2008-04-24T17:50:47Z

Juffs:

REAP Comparison to Classroom Instruction (Fall 2006)

2008-04-24T17:46:53Z

Juffs:

REAP Comparison to Classroom Instruction (Fall 2006)

2008-04-24T17:45:11Z

Juffs:

File:Spring2007.jpg

2008-04-24T17:40:55Z

Juffs: Spring 2007 REAP results

Spring 2007 REAP results

REAP Comparison to Classroom Instruction (Fall 2006)

2008-04-23T19:25:44Z

Juffs:

REAP Comparison to Classroom Instruction (Fall 2006)

2008-04-23T19:21:45Z

Juffs:

REAP Comparison to Classroom Instruction (Fall 2006)

2008-04-23T19:16:56Z

Juffs:

REAP Comparison to Classroom Instruction (Fall 2006)

2008-04-23T19:07:00Z

Juffs:

REAP Comparison to Classroom Instruction (Fall 2006)

2008-04-23T18:54:28Z

Juffs:

REAP Comparison to Classroom Instruction (Fall 2006)

2008-04-23T18:50:33Z

Juffs:

REAP Comparison to Classroom Instruction (Fall 2006)

2008-04-23T16:59:33Z

Juffs:

REAP Comparison to Classroom Instruction (Fall 2006)

2008-04-23T16:51:53Z

Juffs:

REAP Comparison to Classroom Instruction (Fall 2006)

2008-04-22T15:06:52Z

Juffs:

REAP Comparison to Classroom Instruction (Fall 2006)

2008-04-22T14:46:55Z

Juffs:

REAP Comparison to Classroom Instruction (Fall 2006)

2008-04-22T14:46:07Z

Juffs: