https://learnlab.org/wiki/api.php?action=feedcontributions&user=Yunzhao&feedformat=atomLearnLab - User contributions [en]2024-03-29T10:33:21ZUser contributionsMediaWiki 1.31.12https://learnlab.org/wiki/index.php?title=Zhao_%26_MacWhinney_-_Learning_the_English_Article&diff=11977Zhao & MacWhinney - Learning the English Article2011-04-11T14:01:19Z<p>Yunzhao: Zhao & MacWhinney - English Article Usage moved to Zhao & MacWhinney - Learning the English Article</p>
<hr />
<div>==Summary Table==<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Yun (Helen) Zhao, Brian MacWhinney<br />
|-<br />
| '''Other Contributors''' || John Kowalski<br />
|-<br />
| '''Study Start Date''' || TBD<br />
|-<br />
| '''Study End Date''' || TBD<br />
|-<br />
| '''LearnLab Site''' || TBD<br />
|-<br />
| '''LearnLab Course''' || TBD<br />
|-<br />
| '''Number of Students''' || 161<br />
|-<br />
| '''Total Participant Hours''' || 213.95<br />
|-<br />
| '''Data available in DataShop''' || [https://pslcdatashop.web.cmu.edu/DatasetInfo?datasetId=447 Dataset: The Cognitive English Article Tutor - Study 1]<br><br />
* '''Pre/Post Test Score Data:''' TBD<br />
* '''Paper or Online Tests:''' TBD<br />
* '''Scanned Paper Tests:''' TBD<br />
* '''Blank Tests:''' TBD<br />
* '''Answer Keys: ''' TBD<br />
|}<br />
<br />
==Abstract==<br />
Documentation of this study is currently in progress.<br />
<br />
==Background and Significance==<br />
The current project focuses on the development of a cognitive tutoring system for the teaching of English articles – one of the most difficult grammatical forms for second language learners to learn and master. Articles are particularly difficult for learners whose first language (e.g., Chinese and Japanese) does not use articles. There are three factors that make this a difficult target structure: (1) there are dozens of difficult and conflicting rules determining article choice; (2) misuses of the articles usually do not cause miscommunication and therefore learners tend to ignore these errors; and (3) classroom instruction does not provide enough opportunities for learning many of the functions and cues that determine article choice. Cognitive tutoring systems can provide address each of these problems by giving simple illustrations of relevant cues, providing consistent feedback, and sampling across a wide range of genre types and usages. <br />
<br />
The research goal of the article tutor project is to promote robust learning and mastery of the English articles among Chinese EFL learners illuminated by principles from: (1) Experimental Psychology: Practices make perfect; Feedback promotes learning; (2) Developmental Psycholinguistics: Language is learned in context; Cue conflicts are crucial for learning; (3) Human-Computer Interaction: rule-based and exemplar-based instruction promotes learning in different ways; and (4) Second Language Acquisition: explicit types of instruction is in general more effective than implicit types of instruction; accurate metalinguistic knowledge representation is important. Synthesizing the above principles, the Cognitive Article Tutor designs exercise with nine genres of texts with rich article usages and provides explicit instruction in the form of explicit feedback.<br />
<br />
==Glossary==<br />
Explicit versus implicit instruction: <br />
<br />
There is a major distinction between explicit and implicit instruction in second language teaching and learning. This distinction is often operationalized in terms of explicit and implicit feedback given to students in the instructional settings. Following Dekeyser (1995), explicit instruction consists of explicit deduction (explicit rule presentation) or explicit induction (instructions to orient learner attention to forms or to induce metalinguistic hypotheses); implicit instruction indicated that no explicit rule statement took place in the treatment and no instructions attending to particular forms or formulating metalinguistic hypothesis were given to learners. Norris & Ortega (2000) did a meta-analysis study and examined the effectiveness of instruction methods in different instructional settings. They concluded that, in general, explicit types of instruction are more effective than implicit types of instruction.<br />
<br />
Similar to the general findings of L2 instructional studies, the available intelligent computer assisted language learning studies also suggested that explicit feedback is superior to implicit feedback especially when the learning task involves relatively complex structures whose grammatical rules are not salient in light of the examples. The most effective iCALL feedback is to “to respond to errors by giving a metalinguistic explanation in the form of a rule” (Hanson, p. 49) This general finding gives strength to the potential benefit of using cognitive tutor to teach the English articles, which is a complex and non-salient grammatical category.<br />
<br />
==Research Questions==<br />
Does the Cognitive Article Tutor that provides practice with corresponding explicit feedback increase L2 learners' performance of article usage in written production?<br />
<br />
== Study One==<br />
===Hypothesis===<br />
The Cognitive Article Tutor that provides practice with corresponding explicit feedback helps to increase L2 learners' performance of article usage in written production.<br />
<br />
===Independent Variables===<br />
The independent variable of the current study is the explicit feedback provided by the Cognitive Article Tutor.<br />
<br />
The explicit feedback of each grammatical rule is associated with one type of usage of English articles. Each explicit feedback is composed of three parts: (1) one grammatical rule name, (2) an explanation of the rule, and (3) several examples to further explain the rule. Whenever a learner make a mistake with one article choice, the tutor automatically provides explicit feedback composed of the above three levels of explanation.<br />
<br />
For example, one rule of the English articles is named "Non-count Abstract Noun". This is a rule associated with the zero article. The rule explanation describes as follows: "The article should be omitted when referring to a non-count abstract concept, emotion, or principle, even if this noun is modified by a preceding adjective". Following that, several examples are given to further explain the rule: (a) Prudence is the better part of valor, (b) Friction tends to resist gravity, (c) Statistical analysis could clear up the issue, and (d) I strive for clarity in my prose.<br />
<br />
===Dependent Variables===<br />
The dependence variable of the current study is learners' performance of article usage in written production. <br />
<br />
Norris & Ortega (2000) identified four general types of measurements in SLA studies testing the effect of explicit and/or implicit instruction: (a) metalinguistic judgments if the research participant was required to evaluate the appropriacy or grammaticality of L2 target structures as used in item prompts (e.g., grammaticality judgment tasks); (b) Selected response measures required participants to choose the correct response from a range of alternatives, typically either in answer to comprehension questions based on the use of the target L2 form(s) or in order to complete a sample segment of the target language with the appropriate target form(s) (e.g., multiple choice tests providing four options in verbal morphology); (c) constrained constructed response if they required the participant to produce the target form(s) under highly regulated circumstances, where the use of the appropriate form was essential for grammatical accuracy to occur. Constrained constructed response measures required learners to produce L2 segments ranging in length from a single word up to a full sentence, but all such measures were designed with the intent to test L2 ability to use the particular form within a highly controlled linguistic context (e.g., sentence combining with relative pronouns); (d) free constructed response measures were those measures that required participants to produce language with relatively few constraints and with meaningful communication as the goal for L2 production (e.g., oral interviews, written compositions). <br />
<br />
Norris & Ortega (2000) suggested that the type of outcome measures used in individual studies likely affects the magnitude of observed instructional effectiveness. Average effect sizes associated with metalinguistic judgments and free constructed response measures were substantially lower than those associated with selected-response or constrained constructed-response measures. Thus, study findings within the research domain may vary by as much as 0.91 standard deviation units depending on the type of outcome measure or measures employed. Therefore Norris & Ortega suggested researchers to triangulate outcome measures in order to overcome the possible bias of particular measurement that is more likely to produce larger effect size. <br />
<br />
<br />
To guarantee triangulation of outcome measures, the present study makes use of four outcome measures: (1) Untimed Grammaticality judgment test (~10min); (2) Untimed article choice test (~10min); (3) Timed free writing task (15min); (4) Untimed article rule explanation task (~5min). <br />
<br />
Untimed grammaticality judgment test (GJT) is a metalinguistic judgment test, which allows us to investigate the explicit knowledge representation of learners’ acquisition of articles. In the untimed GJT, participants are asked to judge whether a sentence is grammatical or not. Both grammatical and ungrammatical sentences examining English articles as well as other grammatical categories (past tense, subjunctive mood, relative clause, third person singular) are included in the GJT as control items. Untimed article choice test is paragraph level cloze test which requires participants to fill in all the articles in the given paragraphs. Timed free writing task is to ask participants to write as much as they can within 15 minutes. The participants are given picture prompts for the writing task. The untimed article rule explanation task is to give participants sentences with correct use of English articles and to ask participants to choose from a pool of four article rule explanations to explain what are the target rules in the given sentences. Except for the free writing task, the other three tasks are graded based on the correct responses that the participants supply. The free writing task is graded with all the noun phrases identified and judged by native speakers for accuracy of usage. Each piece of writing will produce one mean accuracy rate of article usage.<br />
<br />
===Results===<br />
We ran a pilot study among 60 non-English major Chinese learners of English in a Chinese university in Beijing in July 2010. The purpose of the pilot study was to test the effectiveness and accessibility of the instructional materials that are going to be used in the Cognitive Article Tutor. We performed the same pre-test, immediate post-test and delayed post-test with the 60 participants. We used the instruction materials to teach the English article rules to 40 learners. The rest 20 participants received English pronunciation training during the time when the 40 participants were receiving English article instruction. At the end of the article instruction, the 40 participants were asked to fill in a questionnaire about their feedback of the article instruction. Right now we are in the process of doing data analysis and synthesizing questionnaire feedback from the 40 learners who received article instruction so that we can improve the Cognitive Article tutor instructional materials.<br />
<br />
===Explanation===<br />
<br />
==Connections to Other Studies==<br />
==References==<br />
==Future Plans==</div>Yunzhaohttps://learnlab.org/wiki/index.php?title=Zhao_%26_MacWhinney_-_English_Article_Usage&diff=11978Zhao & MacWhinney - English Article Usage2011-04-11T14:01:19Z<p>Yunzhao: Zhao & MacWhinney - English Article Usage moved to Zhao & MacWhinney - Learning the English Article</p>
<hr />
<div>#REDIRECT [[Zhao & MacWhinney - Learning the English Article]]</div>Yunzhaohttps://learnlab.org/wiki/index.php?title=PSLC_People&diff=11976PSLC People2011-04-11T13:56:29Z<p>Yunzhao: /* Graduate Students */</p>
<hr />
<div>== '''The Executive Committee''' ==<br />
<br />
=== Directors ===<br />
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| [http://pact.cs.cmu.edu/koedinger.html '''Ken Koedinger'''] || Carnegie Mellon University || Human-Computer Interaction Institute<br />
|-<br />
| '''Charles Perfetti''' || University of Pittsburgh || Psychology, LRDC Director<br />
|}<br />
<br />
=== Managing Director ===<br />
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''Michael Bett''' || Carnegie Mellon University || Human-Computer Interaction Institute<br />
|}<br />
<br />
<br />
<br />
<br />
=== Members ===<br />
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| Aleven, Vincent || Carnegie Mellon University || Human-Computer Interaction<br />
|-<br />
| Eskenazi, Maxine || Carnegie Mellon University || Language Technologies Institute<br />
|-<br />
| Fiez, Julie || University of Pittsburgh || Psychology<br />
|-<br />
| Gordon, Geoff || Carnegie Mellon University || Machine Learning<br />
|-<br />
| Klahr, David || Carnegie Mellon University || Psychology<br />
|-<br />
| Lovett, Marsha || Carnegie Mellon University || Psychology<br />
|-<br />
| Nokes, Tim || University of Pittsburgh || LRDC<br />
|-<br />
| Resnick, Lauren || University of Pittsburgh || Learning Research and Development Center<br />
|-<br />
| Rose, Carolyn || Carnegie Mellon University || Human-Computer Interaction Institute/Language Technologies Institute<br />
|}<br />
<br />
== Advisory Board ==<br />
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| Aronson, Joshua || New York University || Applied Psychology<br />
|-<br />
| Atkinson, Robert || Arizona State University || Division of Psychology in Education<br />
|-<br />
| Azevedo, Roger || University of Memphis || Psychology<br />
|-<br />
| Biswas, Gautam || Vanderbilt University || Computer Science and Computer Engineering<br />
|-<br />
| Collins, Allan || Northwestern University || Education and Social Policy<br />
|-<br />
| Dede, Christopher || Harvard University || Technology in Education<br />
|-<br />
| Feuer, Michael || George Washington University || Graduate School of Education and Human Development<br />
|-<br />
| Goldman, Susan || University of Illinois || Psychology<br />
|-<br />
| Goldstone, Rob || Indiana University || Psychology<br />
|-<br />
| Griffiths, Tom || Berkeley || Psychology<br />
|-<br />
| Lesgold, Alan || University of Pittsburgh || School of Education<br />
|-<br />
| McNamara, Danielle || University of Memphis || Psychology<br />
|-<br />
| Li, Ping || Penn State University || Psychology<br />
|-<br />
| Minstrell, Jim || FACET Innovations, LLC Seattle, WA || <br />
|-<br />
| Schauble, Leona || Vanderbilt University || Teaching & Learning<br />
|-<br />
| Smith, Marshall (Mike) S.|| ||<br />
|}<br />
<br />
== Graduate Students ==<br />
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"<br />
<br />
| Adam Skory || Carnegie Mellon || <br />
|-<br />
| Benjamin Friedline || University of Pittsburgh || Linguistics<br />
|-<br />
| Colleen Davy || Carnegie Mellon || Psychology<br />
|-<br />
| Garbiel Parent || Carnegie Mellon || Language Technologies Institute<br />
|-<br />
| (Derek) Ho Leung Chan || University of Pittsburgh || Linguistics<br />
|-<br />
| Leida Tolentino || University of Pittsburgh || Psychology<br />
|-<br />
| Nora Presson || Carnegie Mellon || Psychology<br />
|-<br />
| Ruth Wylie || Carnegie Mellon || Human Computer Interaction Institute<br />
|-<br />
| Susan Dunlap || University of Pittsburgh || Psychology<br />
|-<br />
| Yun (Helen) Zhao || Carnegie Mellon || Second Language Acquisition<br />
|-<br />
| Benjamin Shih || Carnegie Mellon || <br />
|-<br />
| Collin Lynch || University of Pittsburgh || <br />
|-<br />
| Erik Zawadzki || Carnegie Mellon || <br />
|-<br />
| Nan Li || Carnegie Mellon || <br />
|-<br />
| Amy Ogan || Carnegie Mellon || <br />
|-<br />
| Dan Belenky || University of Pittsburgh || Psychology<br />
|-<br />
| Matthew Easterday || Carnegie Mellon || <br />
|-<br />
| Soniya Gadgil || University of Pittsburgh || Psychology<br />
|-<br />
| Yanhui Zhang || Carnegie Mellon || <br />
|-<br />
| Dejana Diziol || Freiburg || <br />
|-<br />
| Elizabeth Ayers || Carnegie Mellon || <br />
|-<br />
| Elsa Golden || Carnegie Mellon || <br />
|-<br />
| April Galyardt || Carnegie Mellon || Statistics<br />
|-<br />
| Jamie Jirout || Carnegie Mellon || Psychology<br />
|-<br />
| Martina Rau || Carnegie Mellon || Human Computer Interaction Institute<br />
|-<br />
| Tom Lauwers || Carnegie Mellon || <br />
|-<br />
| Tracy Sweet || Carnegie Mellon || Statistics<br />
|-<br />
| Kevin Del Rosa || Carnegie Mellon || <br />
|-<br />
| Turadg Aleahmad || Carnegie Mellon || Human Computer Interaction Institute<br />
|-<br />
| Gahgene Gweon || Carnegie Mellon || <br />
|-<br />
| Anagha Kulkarni (Joshi) || Carnegie Mellon || <br />
|-<br />
| Bryan Matlen || Carnegie Mellon || Psychology<br />
|-<br />
| Sung-Young Jung || University of Pittsburgh || <br />
|-<br />
| Gustavo Santos || Carnegie Mellon || <br />
|-<br />
| Hao-Chuan Wang || Carnegie Mellon || <br />
|-<br />
| Indrayana Rustandi || Carnegie Mellon || <br />
|-<br />
| Jessica Nelson || University of Pittsburgh || Psychology<br />
|-<br />
| Rohit Kumar || Carnegie Mellon || <br />
|-<br />
| Roxana Gheorghiu || University of Pittsburgh || <br />
|-<br />
| Tamar Degani || University of Pittsburgh || Psychology<br />
|-<br />
| Yan Mu || Carnegie Mellon || Psychology<br />
|-<br />
| Elijah Mayfield || Carnegie Mellon || <br />
|-<br />
| Erin Walker || Carnegie Mellon || <br />
|-<br />
| Iris Howley || Carnegie Mellon || Human Computer Interaction Institute<br />
|-<br />
| Tracy Clark || Univeristy of Pennslyvania || <br />
|-<br />
| Laurens Feestra || Netherlands || <br />
|-<br />
| Maaike Waalkens || Netherlands || <br />
|-<br />
| Mary Lou Vercellotti || University of Pittsburgh || Linguistics <br />
|-<br />
| Nozomi Tanaka || University of Pittsburgh || Linguistics <br />
|-<br />
| Eliane Stampfer || Carnegie Mellon || Human Computer Interaction Institute<br />
|-<br />
| Katherine Martin || University of Pittsburgh || Linguistics<br />
|}<br />
<br />
== Post Docs ==<br />
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"<br />
<br />
<br />
| Laura Halderman || University of Pittsburgh || <br />
|-<br />
| Seiji Isotani || Carnegie Mellon University || HCII<br />
|-<br />
| John Connelly || University of Pittsburgh || <br />
|-<br />
| Amy Crosson || University of Pittsburgh || LRDC<br />
|-<br />
| Min Chi || Carnegie Mellon || MLD<br />
|-<br />
| Ido Roll || University of British Columbia || <br />
|-<br />
| Stephanie Siler || Carnegie Mellon || Psychology<br />
|-<br />
| Zelha Tunc-Pekkan || Carnegie Mellon || HCII<br />
|-<br />
| Fan Cao || University of Pittsburgh || <br />
|-<br />
| Suzanne Adlof || University of Pittsburgh || <br />
|-<br />
<br />
| Candace Walkington || University of Texas || <br />
|-<br />
| Matthew Bernacki || University of Pittsburgh || <br />
|-<br />
| Gregory Dyke || University of Pittsburgh || <br />
|-<br />
| Sherice Clarke || University of Pittsburgh || <br />
|-<br />
| Oscar Saz || Carnegie Mellon University || LTI<br />
|-<br />
| Michael Yudelson || Carnegie Mellon University ||<br />
<br />
|}<br />
<br />
== Former Post Docs ==<br />
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"<br />
<br />
| Hua Ai || Georgia Institute of Technology || LTI<br />
|-<br />
| Alicia Chang || University of Delaware || Postdoctoral Researcher<br />
|-<br />
| Connie Guan Qun || University of Pittsburgh || <br />
|-<br />
| Chin-LungYang || University of Pittsburgh || <br />
|-<br />
| Scotty Craig || University of Memphis|| Research Assistant Professor, Institute for Intelligent Systems<br />
|-<br />
|}<br />
<br />
== Faculty ==<br />
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| Al Corbett || Carnegie Mellon || HCII<br />
|-<br />
| Alan Juffs || University of Pittsburgh || Linguistics<br />
|-<br />
| Brian Junker || Carnegie Mellon || Statisics<br />
|-<br />
| Brian MacWhinney || Carnegie Mellon || Psychology<br />
|-<br />
| Bruce McLaren || Carnegie Mellon || HCII<br />
|-<br />
| Carolyn Rosé || Carnegie Mellon || LTI/HCII<br />
|-<br />
| Charles Perfetti || University of Pittsburgh || LRDC<br />
|-<br />
| Christa Asterhan || Hebrew University || <br />
|-<br />
| David Klahr || Carnegie Mellon || Psychology<br />
|-<br />
| David Yaron || Carnegie Mellon || Chemistry<br />
|-<br />
| Geoff Gordon || Carnegie Mellon || Machine Learning<br />
|-<br />
| Jack Mostow || Carnegie Mellon || Robotics<br />
|-<br />
| Jim Greeno || University of Pittsburgh || Instruction and Learning<br />
|-<br />
| John Stamper || Carnegie Mellon || HCII<br />
|-<br />
| Ken Koedinger || Carnegie Mellon || HCII<br />
|-<br />
| Kirsten Butcher || University of Utah || Instructional Design & Educational Technology<br />
|-<br />
| Kurt VanLehn || Arizona State University || Computer Science and Engineering<br />
|-<br />
| Lauren Resnick || University of Pittsburgh || LRDC<br />
|-<br />
| Louis Gomez || University of Pittsburgh || School of Education<br />
|-<br />
| Marsha Lovett || Carnegie Mellon || Eberly Center<br />
|-<br />
| Mary Catherine O'Connor || Boston University || School of Education<br />
|-<br />
| Matthew Kam || Carnegie Mellon || HCII<br />
|-<br />
| Maxine Eskenazi || Carnegie Mellon || LTI<br />
|-<br />
| Nel de Jong || Vrije Universiteit Amsterdam || <br />
|-<br />
| Niels Pinkwart || Clausthal University of Technology || <br />
|-<br />
| Nikol Rummel || Ruhr-Universität Bochum || Psychology<br />
|-<br />
| Noboru Matsuda || Carnegie Mellon || HCII<br />
|-<br />
| Phil Pavlik || Carnegie Mellon || HCII<br />
|-<br />
| Richard Scheines || Carnegie Mellon || Philosphy<br />
|-<br />
| Ryan Baker || WPI || <br />
|-<br />
| Sandy Katz || University of Pittsburgh || LRDC<br />
|-<br />
| Sarah Michaels || Clark University || Education<br />
|-<br />
| Teruko Matamura || Carnegie Mellon || LTI<br />
|-<br />
| Tim Nokes || University of Pittsburgh || <br />
|-<br />
| Vincent Aleven || Carnegie Mellon || LTI<br />
|-<br />
| William Cohen || Carnegie Mellon || ML<br />
<br />
<br />
|}<br />
<br />
== Staff ==<br />
{| border=1 cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| Alida Skogsholm || Carnegie Mellon University || DataShop Manager<br />
|-<br />
| Bob Hausmann || Carnegie Learning || <br />
|-<br />
| Brett Leber || Carnegie Mellon University || DataShop/CTAT<br />
|-<br />
| Christy McGuire || Edalytics || <br />
|-<br />
| Cressida Magaro || Carnegie Mellon University || <br />
|-<br />
| Dorolyn Smith || University of Pittsburgh || <br />
|-<br />
| Duncan Spencer || Carnegie Mellon University || DataShop<br />
|-<br />
| Gail Kusbit || Carnegie Mellon University || Research Manager<br />
|-<br />
| Jo Bodnar || Carnegie Mellon University || <br />
|-<br />
| John Kowalski || Carnegie Mellon University || <br />
|-<br />
| Jonathan Sewall || Carnegie Mellon University || <br />
|-<br />
| Kevin Willows || Carnegie Mellon University || <br />
|-<br />
| Mark Haney || University of Pittsburgh || <br />
|-<br />
| Martin van Velsen || Carnegie Mellon University || <br />
|-<br />
| Michael Bett || Carnegie Mellon University || Managing Director<br />
|-<br />
| Mike Karabinos|| Carnegie Mellon University || <br />
|-<br />
| Ross Strader || Carnegie Mellon University || <br />
|-<br />
| Sandy Demi || Carnegie Mellon University || DataShop/CTAT<br />
|-<br />
| Scott Silliman || University of Pittsburgh || OLI<br />
|-<br />
| Shanwen Yu || Carnegie Mellon University || DataShop<br />
|-<br />
| Steve Ritter || Carnegie Learning || Founder<br />
|-<br />
| Thomas Harris || Edalytics || <br />
|-<br />
| Tristan Nixon || Carnegie Learning || <br />
|}</div>Yunzhaohttps://learnlab.org/wiki/index.php?title=Zhao_%26_MacWhinney_-_Learning_the_English_Article&diff=10973Zhao & MacWhinney - Learning the English Article2010-08-31T04:28:44Z<p>Yunzhao: /* Results */</p>
<hr />
<div>English Article Usage<br />
==Summary Table==<br />
<br />
==Abstract==<br />
Documentation of this study is currently in progress.<br />
<br />
==Background and Significance==<br />
The current project focuses on the development of a cognitive tutoring system for the teaching of English articles – one of the most difficult grammatical forms for second language learners to learn and master. Articles are particularly difficult for learners whose first language (e.g., Chinese and Japanese) does not use articles. There are three factors that make this a difficult target structure: (1) there are dozens of difficult and conflicting rules determining article choice; (2) misuses of the articles usually do not cause miscommunication and therefore learners tend to ignore these errors; and (3) classroom instruction does not provide enough opportunities for learning many of the functions and cues that determine article choice. Cognitive tutoring systems can provide address each of these problems by giving simple illustrations of relevant cues, providing consistent feedback, and sampling across a wide range of genre types and usages. <br />
<br />
The research goal of the article tutor project is to promote robust learning and mastery of the English articles among Chinese EFL learners illuminated by principles from: (1) Experimental Psychology: Practices make perfect; Feedback promotes learning; (2) Developmental Psycholinguistics: Language is learned in context; Cue conflicts are crucial for learning; (3) Human-Computer Interaction: rule-based and exemplar-based instruction promotes learning in different ways; and (4) Second Language Acquisition: explicit types of instruction is in general more effective than implicit types of instruction; accurate metalinguistic knowledge representation is important. Synthesizing the above principles, the Cognitive Article Tutor designs exercise with nine genres of texts with rich article usages and provides explicit instruction in the form of explicit feedback.<br />
<br />
==Glossary==<br />
Explicit versus implicit instruction: <br />
<br />
There is a major distinction between explicit and implicit instruction in second language teaching and learning. This distinction is often operationalized in terms of explicit and implicit feedback given to students in the instructional settings. Following Dekeyser (1995), explicit instruction consists of explicit deduction (explicit rule presentation) or explicit induction (instructions to orient learner attention to forms or to induce metalinguistic hypotheses); implicit instruction indicated that no explicit rule statement took place in the treatment and no instructions attending to particular forms or formulating metalinguistic hypothesis were given to learners. Norris & Ortega (2000) did a meta-analysis study and examined the effectiveness of instruction methods in different instructional settings. They concluded that, in general, explicit types of instruction are more effective than implicit types of instruction.<br />
<br />
Similar to the general findings of L2 instructional studies, the available intelligent computer assisted language learning studies also suggested that explicit feedback is superior to implicit feedback especially when the learning task involves relatively complex structures whose grammatical rules are not salient in light of the examples. The most effective iCALL feedback is to “to respond to errors by giving a metalinguistic explanation in the form of a rule” (Hanson, p. 49) This general finding gives strength to the potential benefit of using cognitive tutor to teach the English articles, which is a complex and non-salient grammatical category.<br />
<br />
==Research Questions==<br />
Does the Cognitive Article Tutor that provides practice with corresponding explicit feedback increase L2 learners' performance of article usage in written production?<br />
<br />
== Study One==<br />
===Hypothesis===<br />
The Cognitive Article Tutor that provides practice with corresponding explicit feedback helps to increase L2 learners' performance of article usage in written production.<br />
<br />
===Independent Variables===<br />
The independent variable of the current study is the explicit feedback provided by the Cognitive Article Tutor.<br />
<br />
The explicit feedback of each grammatical rule is associated with one type of usage of English articles. Each explicit feedback is composed of three parts: (1) one grammatical rule name, (2) an explanation of the rule, and (3) several examples to further explain the rule. Whenever a learner make a mistake with one article choice, the tutor automatically provides explicit feedback composed of the above three levels of explanation.<br />
<br />
For example, one rule of the English articles is named "Non-count Abstract Noun". This is a rule associated with the zero article. The rule explanation describes as follows: "The article should be omitted when referring to a non-count abstract concept, emotion, or principle, even if this noun is modified by a preceding adjective". Following that, several examples are given to further explain the rule: (a) Prudence is the better part of valor, (b) Friction tends to resist gravity, (c) Statistical analysis could clear up the issue, and (d) I strive for clarity in my prose.<br />
<br />
===Dependent Variables===<br />
The dependence variable of the current study is learners' performance of article usage in written production. <br />
<br />
Norris & Ortega (2000) identified four general types of measurements in SLA studies testing the effect of explicit and/or implicit instruction: (a) metalinguistic judgments if the research participant was required to evaluate the appropriacy or grammaticality of L2 target structures as used in item prompts (e.g., grammaticality judgment tasks); (b) Selected response measures required participants to choose the correct response from a range of alternatives, typically either in answer to comprehension questions based on the use of the target L2 form(s) or in order to complete a sample segment of the target language with the appropriate target form(s) (e.g., multiple choice tests providing four options in verbal morphology); (c) constrained constructed response if they required the participant to produce the target form(s) under highly regulated circumstances, where the use of the appropriate form was essential for grammatical accuracy to occur. Constrained constructed response measures required learners to produce L2 segments ranging in length from a single word up to a full sentence, but all such measures were designed with the intent to test L2 ability to use the particular form within a highly controlled linguistic context (e.g., sentence combining with relative pronouns); (d) free constructed response measures were those measures that required participants to produce language with relatively few constraints and with meaningful communication as the goal for L2 production (e.g., oral interviews, written compositions). <br />
<br />
Norris & Ortega (2000) suggested that the type of outcome measures used in individual studies likely affects the magnitude of observed instructional effectiveness. Average effect sizes associated with metalinguistic judgments and free constructed response measures were substantially lower than those associated with selected-response or constrained constructed-response measures. Thus, study findings within the research domain may vary by as much as 0.91 standard deviation units depending on the type of outcome measure or measures employed. Therefore Norris & Ortega suggested researchers to triangulate outcome measures in order to overcome the possible bias of particular measurement that is more likely to produce larger effect size. <br />
<br />
<br />
To guarantee triangulation of outcome measures, the present study makes use of four outcome measures: (1) Untimed Grammaticality judgment test (~10min); (2) Untimed article choice test (~10min); (3) Timed free writing task (15min); (4) Untimed article rule explanation task (~5min). <br />
<br />
Untimed grammaticality judgment test (GJT) is a metalinguistic judgment test, which allows us to investigate the explicit knowledge representation of learners’ acquisition of articles. In the untimed GJT, participants are asked to judge whether a sentence is grammatical or not. Both grammatical and ungrammatical sentences examining English articles as well as other grammatical categories (past tense, subjunctive mood, relative clause, third person singular) are included in the GJT as control items. Untimed article choice test is paragraph level cloze test which requires participants to fill in all the articles in the given paragraphs. Timed free writing task is to ask participants to write as much as they can within 15 minutes. The participants are given picture prompts for the writing task. The untimed article rule explanation task is to give participants sentences with correct use of English articles and to ask participants to choose from a pool of four article rule explanations to explain what are the target rules in the given sentences. Except for the free writing task, the other three tasks are graded based on the correct responses that the participants supply. The free writing task is graded with all the noun phrases identified and judged by native speakers for accuracy of usage. Each piece of writing will produce one mean accuracy rate of article usage.<br />
<br />
===Results===<br />
We ran a pilot study among 60 non-English major Chinese learners of English in a Chinese university in Beijing in July 2010. The purpose of the pilot study was to test the effectiveness and accessibility of the instructional materials that are going to be used in the Cognitive Article Tutor. We performed the same pre-test, immediate post-test and delayed post-test with the 60 participants. We used the instruction materials to teach the English article rules to 40 learners. The rest 20 participants received English pronunciation training during the time when the 40 participants were receiving English article instruction. At the end of the article instruction, the 40 participants were asked to fill in a questionnaire about their feedback of the article instruction. Right now we are in the process of doing data analysis and synthesizing questionnaire feedback from the 40 learners who received article instruction so that we can improve the Cognitive Article tutor instructional materials.<br />
<br />
===Explanation===<br />
<br />
==Connections to Other Studies==<br />
==References==<br />
==Future Plans==</div>Yunzhaohttps://learnlab.org/wiki/index.php?title=Zhao_%26_MacWhinney_-_Learning_the_English_Article&diff=10972Zhao & MacWhinney - Learning the English Article2010-08-31T04:15:19Z<p>Yunzhao: /* Dependent Variables */</p>
<hr />
<div>English Article Usage<br />
==Summary Table==<br />
<br />
==Abstract==<br />
Documentation of this study is currently in progress.<br />
<br />
==Background and Significance==<br />
The current project focuses on the development of a cognitive tutoring system for the teaching of English articles – one of the most difficult grammatical forms for second language learners to learn and master. Articles are particularly difficult for learners whose first language (e.g., Chinese and Japanese) does not use articles. There are three factors that make this a difficult target structure: (1) there are dozens of difficult and conflicting rules determining article choice; (2) misuses of the articles usually do not cause miscommunication and therefore learners tend to ignore these errors; and (3) classroom instruction does not provide enough opportunities for learning many of the functions and cues that determine article choice. Cognitive tutoring systems can provide address each of these problems by giving simple illustrations of relevant cues, providing consistent feedback, and sampling across a wide range of genre types and usages. <br />
<br />
The research goal of the article tutor project is to promote robust learning and mastery of the English articles among Chinese EFL learners illuminated by principles from: (1) Experimental Psychology: Practices make perfect; Feedback promotes learning; (2) Developmental Psycholinguistics: Language is learned in context; Cue conflicts are crucial for learning; (3) Human-Computer Interaction: rule-based and exemplar-based instruction promotes learning in different ways; and (4) Second Language Acquisition: explicit types of instruction is in general more effective than implicit types of instruction; accurate metalinguistic knowledge representation is important. Synthesizing the above principles, the Cognitive Article Tutor designs exercise with nine genres of texts with rich article usages and provides explicit instruction in the form of explicit feedback.<br />
<br />
==Glossary==<br />
Explicit versus implicit instruction: <br />
<br />
There is a major distinction between explicit and implicit instruction in second language teaching and learning. This distinction is often operationalized in terms of explicit and implicit feedback given to students in the instructional settings. Following Dekeyser (1995), explicit instruction consists of explicit deduction (explicit rule presentation) or explicit induction (instructions to orient learner attention to forms or to induce metalinguistic hypotheses); implicit instruction indicated that no explicit rule statement took place in the treatment and no instructions attending to particular forms or formulating metalinguistic hypothesis were given to learners. Norris & Ortega (2000) did a meta-analysis study and examined the effectiveness of instruction methods in different instructional settings. They concluded that, in general, explicit types of instruction are more effective than implicit types of instruction.<br />
<br />
Similar to the general findings of L2 instructional studies, the available intelligent computer assisted language learning studies also suggested that explicit feedback is superior to implicit feedback especially when the learning task involves relatively complex structures whose grammatical rules are not salient in light of the examples. The most effective iCALL feedback is to “to respond to errors by giving a metalinguistic explanation in the form of a rule” (Hanson, p. 49) This general finding gives strength to the potential benefit of using cognitive tutor to teach the English articles, which is a complex and non-salient grammatical category.<br />
<br />
==Research Questions==<br />
Does the Cognitive Article Tutor that provides practice with corresponding explicit feedback increase L2 learners' performance of article usage in written production?<br />
<br />
== Study One==<br />
===Hypothesis===<br />
The Cognitive Article Tutor that provides practice with corresponding explicit feedback helps to increase L2 learners' performance of article usage in written production.<br />
<br />
===Independent Variables===<br />
The independent variable of the current study is the explicit feedback provided by the Cognitive Article Tutor.<br />
<br />
The explicit feedback of each grammatical rule is associated with one type of usage of English articles. Each explicit feedback is composed of three parts: (1) one grammatical rule name, (2) an explanation of the rule, and (3) several examples to further explain the rule. Whenever a learner make a mistake with one article choice, the tutor automatically provides explicit feedback composed of the above three levels of explanation.<br />
<br />
For example, one rule of the English articles is named "Non-count Abstract Noun". This is a rule associated with the zero article. The rule explanation describes as follows: "The article should be omitted when referring to a non-count abstract concept, emotion, or principle, even if this noun is modified by a preceding adjective". Following that, several examples are given to further explain the rule: (a) Prudence is the better part of valor, (b) Friction tends to resist gravity, (c) Statistical analysis could clear up the issue, and (d) I strive for clarity in my prose.<br />
<br />
===Dependent Variables===<br />
The dependence variable of the current study is learners' performance of article usage in written production. <br />
<br />
Norris & Ortega (2000) identified four general types of measurements in SLA studies testing the effect of explicit and/or implicit instruction: (a) metalinguistic judgments if the research participant was required to evaluate the appropriacy or grammaticality of L2 target structures as used in item prompts (e.g., grammaticality judgment tasks); (b) Selected response measures required participants to choose the correct response from a range of alternatives, typically either in answer to comprehension questions based on the use of the target L2 form(s) or in order to complete a sample segment of the target language with the appropriate target form(s) (e.g., multiple choice tests providing four options in verbal morphology); (c) constrained constructed response if they required the participant to produce the target form(s) under highly regulated circumstances, where the use of the appropriate form was essential for grammatical accuracy to occur. Constrained constructed response measures required learners to produce L2 segments ranging in length from a single word up to a full sentence, but all such measures were designed with the intent to test L2 ability to use the particular form within a highly controlled linguistic context (e.g., sentence combining with relative pronouns); (d) free constructed response measures were those measures that required participants to produce language with relatively few constraints and with meaningful communication as the goal for L2 production (e.g., oral interviews, written compositions). <br />
<br />
Norris & Ortega (2000) suggested that the type of outcome measures used in individual studies likely affects the magnitude of observed instructional effectiveness. Average effect sizes associated with metalinguistic judgments and free constructed response measures were substantially lower than those associated with selected-response or constrained constructed-response measures. Thus, study findings within the research domain may vary by as much as 0.91 standard deviation units depending on the type of outcome measure or measures employed. Therefore Norris & Ortega suggested researchers to triangulate outcome measures in order to overcome the possible bias of particular measurement that is more likely to produce larger effect size. <br />
<br />
<br />
To guarantee triangulation of outcome measures, the present study makes use of four outcome measures: (1) Untimed Grammaticality judgment test (~10min); (2) Untimed article choice test (~10min); (3) Timed free writing task (15min); (4) Untimed article rule explanation task (~5min). <br />
<br />
Untimed grammaticality judgment test (GJT) is a metalinguistic judgment test, which allows us to investigate the explicit knowledge representation of learners’ acquisition of articles. In the untimed GJT, participants are asked to judge whether a sentence is grammatical or not. Both grammatical and ungrammatical sentences examining English articles as well as other grammatical categories (past tense, subjunctive mood, relative clause, third person singular) are included in the GJT as control items. Untimed article choice test is paragraph level cloze test which requires participants to fill in all the articles in the given paragraphs. Timed free writing task is to ask participants to write as much as they can within 15 minutes. The participants are given picture prompts for the writing task. The untimed article rule explanation task is to give participants sentences with correct use of English articles and to ask participants to choose from a pool of four article rule explanations to explain what are the target rules in the given sentences. Except for the free writing task, the other three tasks are graded based on the correct responses that the participants supply. The free writing task is graded with all the noun phrases identified and judged by native speakers for accuracy of usage. Each piece of writing will produce one mean accuracy rate of article usage.<br />
<br />
===Results===<br />
===Explanation===<br />
<br />
==Connections to Other Studies==<br />
==References==<br />
==Future Plans==</div>Yunzhaohttps://learnlab.org/wiki/index.php?title=Zhao_%26_MacWhinney_-_Learning_the_English_Article&diff=10971Zhao & MacWhinney - Learning the English Article2010-08-31T04:11:26Z<p>Yunzhao: /* Independent Variables */</p>
<hr />
<div>English Article Usage<br />
==Summary Table==<br />
<br />
==Abstract==<br />
Documentation of this study is currently in progress.<br />
<br />
==Background and Significance==<br />
The current project focuses on the development of a cognitive tutoring system for the teaching of English articles – one of the most difficult grammatical forms for second language learners to learn and master. Articles are particularly difficult for learners whose first language (e.g., Chinese and Japanese) does not use articles. There are three factors that make this a difficult target structure: (1) there are dozens of difficult and conflicting rules determining article choice; (2) misuses of the articles usually do not cause miscommunication and therefore learners tend to ignore these errors; and (3) classroom instruction does not provide enough opportunities for learning many of the functions and cues that determine article choice. Cognitive tutoring systems can provide address each of these problems by giving simple illustrations of relevant cues, providing consistent feedback, and sampling across a wide range of genre types and usages. <br />
<br />
The research goal of the article tutor project is to promote robust learning and mastery of the English articles among Chinese EFL learners illuminated by principles from: (1) Experimental Psychology: Practices make perfect; Feedback promotes learning; (2) Developmental Psycholinguistics: Language is learned in context; Cue conflicts are crucial for learning; (3) Human-Computer Interaction: rule-based and exemplar-based instruction promotes learning in different ways; and (4) Second Language Acquisition: explicit types of instruction is in general more effective than implicit types of instruction; accurate metalinguistic knowledge representation is important. Synthesizing the above principles, the Cognitive Article Tutor designs exercise with nine genres of texts with rich article usages and provides explicit instruction in the form of explicit feedback.<br />
<br />
==Glossary==<br />
Explicit versus implicit instruction: <br />
<br />
There is a major distinction between explicit and implicit instruction in second language teaching and learning. This distinction is often operationalized in terms of explicit and implicit feedback given to students in the instructional settings. Following Dekeyser (1995), explicit instruction consists of explicit deduction (explicit rule presentation) or explicit induction (instructions to orient learner attention to forms or to induce metalinguistic hypotheses); implicit instruction indicated that no explicit rule statement took place in the treatment and no instructions attending to particular forms or formulating metalinguistic hypothesis were given to learners. Norris & Ortega (2000) did a meta-analysis study and examined the effectiveness of instruction methods in different instructional settings. They concluded that, in general, explicit types of instruction are more effective than implicit types of instruction.<br />
<br />
Similar to the general findings of L2 instructional studies, the available intelligent computer assisted language learning studies also suggested that explicit feedback is superior to implicit feedback especially when the learning task involves relatively complex structures whose grammatical rules are not salient in light of the examples. The most effective iCALL feedback is to “to respond to errors by giving a metalinguistic explanation in the form of a rule” (Hanson, p. 49) This general finding gives strength to the potential benefit of using cognitive tutor to teach the English articles, which is a complex and non-salient grammatical category.<br />
<br />
==Research Questions==<br />
Does the Cognitive Article Tutor that provides practice with corresponding explicit feedback increase L2 learners' performance of article usage in written production?<br />
<br />
== Study One==<br />
===Hypothesis===<br />
The Cognitive Article Tutor that provides practice with corresponding explicit feedback helps to increase L2 learners' performance of article usage in written production.<br />
<br />
===Independent Variables===<br />
The independent variable of the current study is the explicit feedback provided by the Cognitive Article Tutor.<br />
<br />
The explicit feedback of each grammatical rule is associated with one type of usage of English articles. Each explicit feedback is composed of three parts: (1) one grammatical rule name, (2) an explanation of the rule, and (3) several examples to further explain the rule. Whenever a learner make a mistake with one article choice, the tutor automatically provides explicit feedback composed of the above three levels of explanation.<br />
<br />
For example, one rule of the English articles is named "Non-count Abstract Noun". This is a rule associated with the zero article. The rule explanation describes as follows: "The article should be omitted when referring to a non-count abstract concept, emotion, or principle, even if this noun is modified by a preceding adjective". Following that, several examples are given to further explain the rule: (a) Prudence is the better part of valor, (b) Friction tends to resist gravity, (c) Statistical analysis could clear up the issue, and (d) I strive for clarity in my prose.<br />
<br />
===Dependent Variables===<br />
===Results===<br />
===Explanation===<br />
<br />
==Connections to Other Studies==<br />
==References==<br />
==Future Plans==</div>Yunzhaohttps://learnlab.org/wiki/index.php?title=Zhao_%26_MacWhinney_-_Learning_the_English_Article&diff=10970Zhao & MacWhinney - Learning the English Article2010-08-31T04:03:12Z<p>Yunzhao: /* Independent Variables */</p>
<hr />
<div>English Article Usage<br />
==Summary Table==<br />
<br />
==Abstract==<br />
Documentation of this study is currently in progress.<br />
<br />
==Background and Significance==<br />
The current project focuses on the development of a cognitive tutoring system for the teaching of English articles – one of the most difficult grammatical forms for second language learners to learn and master. Articles are particularly difficult for learners whose first language (e.g., Chinese and Japanese) does not use articles. There are three factors that make this a difficult target structure: (1) there are dozens of difficult and conflicting rules determining article choice; (2) misuses of the articles usually do not cause miscommunication and therefore learners tend to ignore these errors; and (3) classroom instruction does not provide enough opportunities for learning many of the functions and cues that determine article choice. Cognitive tutoring systems can provide address each of these problems by giving simple illustrations of relevant cues, providing consistent feedback, and sampling across a wide range of genre types and usages. <br />
<br />
The research goal of the article tutor project is to promote robust learning and mastery of the English articles among Chinese EFL learners illuminated by principles from: (1) Experimental Psychology: Practices make perfect; Feedback promotes learning; (2) Developmental Psycholinguistics: Language is learned in context; Cue conflicts are crucial for learning; (3) Human-Computer Interaction: rule-based and exemplar-based instruction promotes learning in different ways; and (4) Second Language Acquisition: explicit types of instruction is in general more effective than implicit types of instruction; accurate metalinguistic knowledge representation is important. Synthesizing the above principles, the Cognitive Article Tutor designs exercise with nine genres of texts with rich article usages and provides explicit instruction in the form of explicit feedback.<br />
<br />
==Glossary==<br />
Explicit versus implicit instruction: <br />
<br />
There is a major distinction between explicit and implicit instruction in second language teaching and learning. This distinction is often operationalized in terms of explicit and implicit feedback given to students in the instructional settings. Following Dekeyser (1995), explicit instruction consists of explicit deduction (explicit rule presentation) or explicit induction (instructions to orient learner attention to forms or to induce metalinguistic hypotheses); implicit instruction indicated that no explicit rule statement took place in the treatment and no instructions attending to particular forms or formulating metalinguistic hypothesis were given to learners. Norris & Ortega (2000) did a meta-analysis study and examined the effectiveness of instruction methods in different instructional settings. They concluded that, in general, explicit types of instruction are more effective than implicit types of instruction.<br />
<br />
Similar to the general findings of L2 instructional studies, the available intelligent computer assisted language learning studies also suggested that explicit feedback is superior to implicit feedback especially when the learning task involves relatively complex structures whose grammatical rules are not salient in light of the examples. The most effective iCALL feedback is to “to respond to errors by giving a metalinguistic explanation in the form of a rule” (Hanson, p. 49) This general finding gives strength to the potential benefit of using cognitive tutor to teach the English articles, which is a complex and non-salient grammatical category.<br />
<br />
==Research Questions==<br />
Does the Cognitive Article Tutor that provides practice with corresponding explicit feedback increase L2 learners' performance of article usage in written production?<br />
<br />
== Study One==<br />
===Hypothesis===<br />
The Cognitive Article Tutor that provides practice with corresponding explicit feedback helps to increase L2 learners' performance of article usage in written production.<br />
<br />
===Independent Variables===<br />
Explicit feedback provided by the Cognitive Article Tutor<br />
<br />
===Dependent Variables===<br />
===Results===<br />
===Explanation===<br />
<br />
==Connections to Other Studies==<br />
==References==<br />
==Future Plans==</div>Yunzhaohttps://learnlab.org/wiki/index.php?title=Zhao_%26_MacWhinney_-_Learning_the_English_Article&diff=10969Zhao & MacWhinney - Learning the English Article2010-08-31T04:02:14Z<p>Yunzhao: /* Hypothesis */</p>
<hr />
<div>English Article Usage<br />
==Summary Table==<br />
<br />
==Abstract==<br />
Documentation of this study is currently in progress.<br />
<br />
==Background and Significance==<br />
The current project focuses on the development of a cognitive tutoring system for the teaching of English articles – one of the most difficult grammatical forms for second language learners to learn and master. Articles are particularly difficult for learners whose first language (e.g., Chinese and Japanese) does not use articles. There are three factors that make this a difficult target structure: (1) there are dozens of difficult and conflicting rules determining article choice; (2) misuses of the articles usually do not cause miscommunication and therefore learners tend to ignore these errors; and (3) classroom instruction does not provide enough opportunities for learning many of the functions and cues that determine article choice. Cognitive tutoring systems can provide address each of these problems by giving simple illustrations of relevant cues, providing consistent feedback, and sampling across a wide range of genre types and usages. <br />
<br />
The research goal of the article tutor project is to promote robust learning and mastery of the English articles among Chinese EFL learners illuminated by principles from: (1) Experimental Psychology: Practices make perfect; Feedback promotes learning; (2) Developmental Psycholinguistics: Language is learned in context; Cue conflicts are crucial for learning; (3) Human-Computer Interaction: rule-based and exemplar-based instruction promotes learning in different ways; and (4) Second Language Acquisition: explicit types of instruction is in general more effective than implicit types of instruction; accurate metalinguistic knowledge representation is important. Synthesizing the above principles, the Cognitive Article Tutor designs exercise with nine genres of texts with rich article usages and provides explicit instruction in the form of explicit feedback.<br />
<br />
==Glossary==<br />
Explicit versus implicit instruction: <br />
<br />
There is a major distinction between explicit and implicit instruction in second language teaching and learning. This distinction is often operationalized in terms of explicit and implicit feedback given to students in the instructional settings. Following Dekeyser (1995), explicit instruction consists of explicit deduction (explicit rule presentation) or explicit induction (instructions to orient learner attention to forms or to induce metalinguistic hypotheses); implicit instruction indicated that no explicit rule statement took place in the treatment and no instructions attending to particular forms or formulating metalinguistic hypothesis were given to learners. Norris & Ortega (2000) did a meta-analysis study and examined the effectiveness of instruction methods in different instructional settings. They concluded that, in general, explicit types of instruction are more effective than implicit types of instruction.<br />
<br />
Similar to the general findings of L2 instructional studies, the available intelligent computer assisted language learning studies also suggested that explicit feedback is superior to implicit feedback especially when the learning task involves relatively complex structures whose grammatical rules are not salient in light of the examples. The most effective iCALL feedback is to “to respond to errors by giving a metalinguistic explanation in the form of a rule” (Hanson, p. 49) This general finding gives strength to the potential benefit of using cognitive tutor to teach the English articles, which is a complex and non-salient grammatical category.<br />
<br />
==Research Questions==<br />
Does the Cognitive Article Tutor that provides practice with corresponding explicit feedback increase L2 learners' performance of article usage in written production?<br />
<br />
== Study One==<br />
===Hypothesis===<br />
The Cognitive Article Tutor that provides practice with corresponding explicit feedback helps to increase L2 learners' performance of article usage in written production.<br />
<br />
===Independent Variables===<br />
===Dependent Variables===<br />
===Results===<br />
===Explanation===<br />
<br />
==Connections to Other Studies==<br />
==References==<br />
==Future Plans==</div>Yunzhaohttps://learnlab.org/wiki/index.php?title=Zhao_%26_MacWhinney_-_Learning_the_English_Article&diff=10968Zhao & MacWhinney - Learning the English Article2010-08-31T04:01:25Z<p>Yunzhao: /* Research Questions */</p>
<hr />
<div>English Article Usage<br />
==Summary Table==<br />
<br />
==Abstract==<br />
Documentation of this study is currently in progress.<br />
<br />
==Background and Significance==<br />
The current project focuses on the development of a cognitive tutoring system for the teaching of English articles – one of the most difficult grammatical forms for second language learners to learn and master. Articles are particularly difficult for learners whose first language (e.g., Chinese and Japanese) does not use articles. There are three factors that make this a difficult target structure: (1) there are dozens of difficult and conflicting rules determining article choice; (2) misuses of the articles usually do not cause miscommunication and therefore learners tend to ignore these errors; and (3) classroom instruction does not provide enough opportunities for learning many of the functions and cues that determine article choice. Cognitive tutoring systems can provide address each of these problems by giving simple illustrations of relevant cues, providing consistent feedback, and sampling across a wide range of genre types and usages. <br />
<br />
The research goal of the article tutor project is to promote robust learning and mastery of the English articles among Chinese EFL learners illuminated by principles from: (1) Experimental Psychology: Practices make perfect; Feedback promotes learning; (2) Developmental Psycholinguistics: Language is learned in context; Cue conflicts are crucial for learning; (3) Human-Computer Interaction: rule-based and exemplar-based instruction promotes learning in different ways; and (4) Second Language Acquisition: explicit types of instruction is in general more effective than implicit types of instruction; accurate metalinguistic knowledge representation is important. Synthesizing the above principles, the Cognitive Article Tutor designs exercise with nine genres of texts with rich article usages and provides explicit instruction in the form of explicit feedback.<br />
<br />
==Glossary==<br />
Explicit versus implicit instruction: <br />
<br />
There is a major distinction between explicit and implicit instruction in second language teaching and learning. This distinction is often operationalized in terms of explicit and implicit feedback given to students in the instructional settings. Following Dekeyser (1995), explicit instruction consists of explicit deduction (explicit rule presentation) or explicit induction (instructions to orient learner attention to forms or to induce metalinguistic hypotheses); implicit instruction indicated that no explicit rule statement took place in the treatment and no instructions attending to particular forms or formulating metalinguistic hypothesis were given to learners. Norris & Ortega (2000) did a meta-analysis study and examined the effectiveness of instruction methods in different instructional settings. They concluded that, in general, explicit types of instruction are more effective than implicit types of instruction.<br />
<br />
Similar to the general findings of L2 instructional studies, the available intelligent computer assisted language learning studies also suggested that explicit feedback is superior to implicit feedback especially when the learning task involves relatively complex structures whose grammatical rules are not salient in light of the examples. The most effective iCALL feedback is to “to respond to errors by giving a metalinguistic explanation in the form of a rule” (Hanson, p. 49) This general finding gives strength to the potential benefit of using cognitive tutor to teach the English articles, which is a complex and non-salient grammatical category.<br />
<br />
==Research Questions==<br />
Does the Cognitive Article Tutor that provides practice with corresponding explicit feedback increase L2 learners' performance of article usage in written production?<br />
<br />
== Study One==<br />
===Hypothesis===<br />
===Independent Variables===<br />
===Dependent Variables===<br />
===Results===<br />
===Explanation===<br />
<br />
==Connections to Other Studies==<br />
==References==<br />
==Future Plans==</div>Yunzhaohttps://learnlab.org/wiki/index.php?title=Zhao_%26_MacWhinney_-_Learning_the_English_Article&diff=10967Zhao & MacWhinney - Learning the English Article2010-08-31T03:57:31Z<p>Yunzhao: /* Glossary */</p>
<hr />
<div>English Article Usage<br />
==Summary Table==<br />
<br />
==Abstract==<br />
Documentation of this study is currently in progress.<br />
<br />
==Background and Significance==<br />
The current project focuses on the development of a cognitive tutoring system for the teaching of English articles – one of the most difficult grammatical forms for second language learners to learn and master. Articles are particularly difficult for learners whose first language (e.g., Chinese and Japanese) does not use articles. There are three factors that make this a difficult target structure: (1) there are dozens of difficult and conflicting rules determining article choice; (2) misuses of the articles usually do not cause miscommunication and therefore learners tend to ignore these errors; and (3) classroom instruction does not provide enough opportunities for learning many of the functions and cues that determine article choice. Cognitive tutoring systems can provide address each of these problems by giving simple illustrations of relevant cues, providing consistent feedback, and sampling across a wide range of genre types and usages. <br />
<br />
The research goal of the article tutor project is to promote robust learning and mastery of the English articles among Chinese EFL learners illuminated by principles from: (1) Experimental Psychology: Practices make perfect; Feedback promotes learning; (2) Developmental Psycholinguistics: Language is learned in context; Cue conflicts are crucial for learning; (3) Human-Computer Interaction: rule-based and exemplar-based instruction promotes learning in different ways; and (4) Second Language Acquisition: explicit types of instruction is in general more effective than implicit types of instruction; accurate metalinguistic knowledge representation is important. Synthesizing the above principles, the Cognitive Article Tutor designs exercise with nine genres of texts with rich article usages and provides explicit instruction in the form of explicit feedback.<br />
<br />
==Glossary==<br />
Explicit versus implicit instruction: <br />
<br />
There is a major distinction between explicit and implicit instruction in second language teaching and learning. This distinction is often operationalized in terms of explicit and implicit feedback given to students in the instructional settings. Following Dekeyser (1995), explicit instruction consists of explicit deduction (explicit rule presentation) or explicit induction (instructions to orient learner attention to forms or to induce metalinguistic hypotheses); implicit instruction indicated that no explicit rule statement took place in the treatment and no instructions attending to particular forms or formulating metalinguistic hypothesis were given to learners. Norris & Ortega (2000) did a meta-analysis study and examined the effectiveness of instruction methods in different instructional settings. They concluded that, in general, explicit types of instruction are more effective than implicit types of instruction.<br />
<br />
Similar to the general findings of L2 instructional studies, the available intelligent computer assisted language learning studies also suggested that explicit feedback is superior to implicit feedback especially when the learning task involves relatively complex structures whose grammatical rules are not salient in light of the examples. The most effective iCALL feedback is to “to respond to errors by giving a metalinguistic explanation in the form of a rule” (Hanson, p. 49) This general finding gives strength to the potential benefit of using cognitive tutor to teach the English articles, which is a complex and non-salient grammatical category.<br />
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==Research Questions==<br />
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== Study One==<br />
===Hypothesis===<br />
===Independent Variables===<br />
===Dependent Variables===<br />
===Results===<br />
===Explanation===<br />
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==Connections to Other Studies==<br />
==References==<br />
==Future Plans==</div>Yunzhaohttps://learnlab.org/wiki/index.php?title=Zhao_%26_MacWhinney_-_Learning_the_English_Article&diff=10966Zhao & MacWhinney - Learning the English Article2010-08-31T03:52:06Z<p>Yunzhao: /* Background and Significance */</p>
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<div>English Article Usage<br />
==Summary Table==<br />
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==Abstract==<br />
Documentation of this study is currently in progress.<br />
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==Background and Significance==<br />
The current project focuses on the development of a cognitive tutoring system for the teaching of English articles – one of the most difficult grammatical forms for second language learners to learn and master. Articles are particularly difficult for learners whose first language (e.g., Chinese and Japanese) does not use articles. There are three factors that make this a difficult target structure: (1) there are dozens of difficult and conflicting rules determining article choice; (2) misuses of the articles usually do not cause miscommunication and therefore learners tend to ignore these errors; and (3) classroom instruction does not provide enough opportunities for learning many of the functions and cues that determine article choice. Cognitive tutoring systems can provide address each of these problems by giving simple illustrations of relevant cues, providing consistent feedback, and sampling across a wide range of genre types and usages. <br />
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The research goal of the article tutor project is to promote robust learning and mastery of the English articles among Chinese EFL learners illuminated by principles from: (1) Experimental Psychology: Practices make perfect; Feedback promotes learning; (2) Developmental Psycholinguistics: Language is learned in context; Cue conflicts are crucial for learning; (3) Human-Computer Interaction: rule-based and exemplar-based instruction promotes learning in different ways; and (4) Second Language Acquisition: explicit types of instruction is in general more effective than implicit types of instruction; accurate metalinguistic knowledge representation is important. Synthesizing the above principles, the Cognitive Article Tutor designs exercise with nine genres of texts with rich article usages and provides explicit instruction in the form of explicit feedback.<br />
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==Glossary==<br />
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==Research Questions==<br />
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== Study One==<br />
===Hypothesis===<br />
===Independent Variables===<br />
===Dependent Variables===<br />
===Results===<br />
===Explanation===<br />
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==Connections to Other Studies==<br />
==References==<br />
==Future Plans==</div>Yunzhao