https://learnlab.org/wiki/api.php?action=feedcontributions&user=Yaron&feedformat=atomLearnLab - User contributions [en]2024-03-28T20:59:20ZUser contributionsMediaWiki 1.31.12https://learnlab.org/wiki/index.php?title=Chemistry&diff=10528Chemistry2010-02-01T18:41:50Z<p>Yaron: </p>
<hr />
<div>= Chemistry LearnLab Course =<br />
<br />
<br />
The chemistry LearnLab consists of three main sets of course materials, each of which are used at a variety of classroom sites:<br />
<br />
# An online course in stoichiometry. This course is primarily used in high schools and as a review course for college introductory chemistry.<br />
# Online virtual laboratories and scenario-based learning activities, that are distributed through the [http://www.chemcollective.org/ ChemCollective] to more than 200 classrooms.<br />
# Online modules covering topics such as chemical equilibrium, acid-base chemistry and design of acid-base buffers. Many of these modules were built to support studies proposed by PSLC researchers. <br />
<br />
These materials include [http://www.mvphomevideo.com instructional videos], virtual laboratories, and pseudotutors. The activities span the topics covered in Carnegie Mellon's Modern Chemistry II course. Please see the [http://collective.chem.cmu.edu/pslc/chem106/ course materials page] for detailed information on this course, including all lecture notes, textbook reading, assignments, and assessments.<br />
<br />
== How to get involved ==<br />
<br />
If you are a learning scientist, there are a number of ways to participate in the chemistry learnlab.<br />
<br />
# You can build a study around existing material. For instance, a portion of the online stoichiometry course was modified in collaboration with the development team to create a study that compared personalized and impersonalized versions of the course videos. <br />
# The materials development effort is driven by the needs of both instructors and learning scientists. Please contact the development team if you have requests for materials development.<br />
# Analyze data deposited in the PSLC data shop by past learnlab studies. For instance, there is a large volume of data on students interacting with ChemCollective virtual laboratory. <br />
<br />
The domain of chemistry provides an interesting set of concepts and tasks for learning science studies and we look forward to working with you. Please contact the chair of the chemistry learnlab committee with ideas you have for studies, and we'll do our best to provide you with appropriate materials and find a classroom site for your ''in vivo'' study.<br />
<br />
If you are a chemistry instructor and would like to collaborate with PSLC researchers on learning studies, please contact David Yaron [yaron@cmu.edu]. You can become a site for an ongoing learnlab study, get in on the ground floor of a study that is currently under design, or propose a topic of interest and we will try to help you find appropriate collaborators to carry out your idea.<br />
<br />
== Details on Course Materials ==<br />
<br />
This section gives additional details on the materials listed at the top of this page.<br />
<br />
# Online course in stoichiometry. <br />
#*Designed as a bridge course for refreshing incoming students who are enrolled in introductory chemistry at Carnegie Mellon. It is now available through the [http://www.cmu.edu/oli/ Open Learning Initiative] and is getting increasing use in high school classrooms.<br />
#*Contains instructional videos, interactive flash pseudotutors, activities with fall back scaffolding, and Virtual Labs.<br />
#*Covers concepts ranging from dimensional analysis and the mole up through empirical formula determination and reaction stoichiometry. A [http://www.chemcollective.org/stoich/stoich_matrix.pdf complete list] of concepts and procedures covered in the course is also available. <br />
#*A full version of course is available for free at: [http://www.cmu.edu/oli http://www.cmu.edu/oli]<br />
# Online virtual laboratories and scenario-based learning activities, that are distributed through the [http://www.chemcollective.org/ ChemCollective].<br />
#*Over 100 virtual lab and scenario based learning activities for use in introductory college and high-school chemistry courses. <br />
#*Concepts covered include: molarity, stoichiometry, quantitative analysis, thermochemistry, chemical equilibrium, acids and bases, solubility. The activities span the topics covered in Carnegie Mellon's Modern Chemistry II course. Please see the [http://collective.chem.cmu.edu/pslc/chem106/ course materials page] for detailed information on this course, including all lecture notes, textbook reading, assignments, and assessments.<br />
#*Any virtual lab activity can produce a detailed trace of student interactions with the lab. <br />
#*Some of the materials combine virtual lab experiments with pseudotutors and other forms of support for problem solving. The development team will work with you to add such systems to any activity you would like to use in a study. <br />
#*The ChemCollective was selected for a "Digital Dozen" in Eisenhower National Clearinghouse in 2005, was featured in Gameology in 2006 and was awarded "Highly Recommended" from Schoolzone in 2007. Our conservative estimates 200 instructors use the activities in their classroom. 14 instructors have contributed activities to the collection, and 40 have contributed feedback. <br />
# Online course modules<br />
#*Current modules cover chemical equilibrium, acid-base chemistry and design of acid-base buffers.<br />
#*Designed by a team of chemists (Yaron, Karabinos), learning scientists (Davenport, Leinhardt, Greeno) and a learning technologist (Bunik).<br />
#*Instructional design is grounded in and guided by cognitive science. This has led to materials based on various aspects of PSLC learning theory, and designed in a way that allows these theories to be tested and refined. From a domain perspective, this has led to a new approach to the teaching of chemical equilibrium and acid-base chemistry which initial evidence suggests substantially enhances student learning. <br />
#*The current and planned modules cover the content of Carnegie Mellon's Modern Chemistry II. Please see the [http://collective.chem.cmu.edu/pslc/chem106/ course materials page] for detailed information on this course, including all lecture notes, textbook reading, assignments, and assessments. A list of knowledge components covered in the current modules is available on request to David Yaron.<br />
#*The modules are tested and studied using the PSLC datashop. Once refined, they will be rolled out to the [http://www.cmu.edu/oli/ Open Learning Initiative].<br />
<br />
= Current (ongoing) Studies =<br />
<br />
*[[Nokes - Game environments for Chemistry]]<br />
<br />
*[[Roll - Productive Failure in a Chemistry Virtual Lab]] <br />
<br />
*[[Penn - Discovering a Domain Model for Organic Chemistry]]<br />
<br />
*[[McLaren - The Assistance Dilemma And Discovery Learning]] <br />
<br />
*[[Mayer and McLaren - Social Intelligence And Computer Tutors]]</div>Yaronhttps://learnlab.org/wiki/index.php?title=Chemistry&diff=10527Chemistry2010-02-01T18:36:49Z<p>Yaron: </p>
<hr />
<div>= Chemistry LearnLab Course =<br />
<br />
<br />
The chemistry LearnLab consists of three main sets of course materials, each of which are used at a variety of classroom sites:<br />
<br />
# An online course in stoichiometry. This course is primarily used in high schools and as a review course for college introductory chemistry.<br />
# Online virtual laboratories and scenario-based learning activities, that are distributed through the [http://www.chemcollective.org/ ChemCollective] to more than 200 classrooms.<br />
# Online modules covering topics such as chemical equilibrium, acid-base chemistry and design of acid-base buffers. Many of these modules were built to support studies proposed by PSLC researchers. <br />
<br />
These materials include [http://www.mvphomevideo.com instructional videos], virtual laboratories, and pseudotutors. The activities span the topics covered in Carnegie Mellon's Modern Chemistry II course. Please see the [http://collective.chem.cmu.edu/pslc/chem106/ course materials page] for detailed information on this course, including all lecture notes, textbook reading, assignments, and assessments.<br />
<br />
== How to get involved ==<br />
<br />
If you are a learning scientist, there are a number of ways to participate in the chemistry learnlab.<br />
<br />
# You can build a study around existing material. For instance, a portion of the online stoichiometry course was modified in collaboration with the development team to create a study that compared personalized and impersonalized versions of the course videos. <br />
# The materials development effort is driven by the needs of both instructors and learning scientists. Please contact the development team if you have requests for materials development.<br />
# Analyze data deposited in the PSLC data shop by past learnlab studies. For instance, there is a large volume of data on students interacting with ChemCollective virtual laboratory. <br />
<br />
The domain of chemistry provides an interesting set of concepts and tasks for learning science studies and we look forward to working with you. Please contact the chair of the chemistry learnlab committee with ideas you have for studies, and we'll do our best to provide you with appropriate materials and find a classroom site for your ''in vivo'' study.<br />
<br />
If you are a chemistry instructor and would like to collaborate with PSLC researchers on learning studies, please contact David Yaron [yaron@cmu.edu]. You can become a site for an ongoing learnlab study, get in on the ground floor of a study that is currently under design, or propose a topic of interest and we will try to help you find appropriate collaborators to carry out your idea.<br />
<br />
== Details on Course Materials ==<br />
<br />
This section gives additional details on the materials listed at the top of this page.<br />
<br />
# Online course in stoichiometry. <br />
#*Designed as a bridge course for refreshing incoming students who are enrolled in introductory chemistry at Carnegie Mellon. It is now available through the [http://www.cmu.edu/oli/ Open Learning Initiative] and is getting increasing use in high school classrooms.<br />
#*Contains instructional videos, interactive flash pseudotutors, activities with fall back scaffolding, and Virtual Labs.<br />
#*Covers concepts ranging from dimensional analysis and the mole up through empirical formula determination and reaction stoichiometry. A [http://www.chemcollective.org/stoich/stoich_matrix.pdf complete list] of concepts and procedures covered in the course is also available. <br />
#*A full version of course is available for free at: [http://www.cmu.edu/oli http://www.cmu.edu/oli]<br />
# Online virtual laboratories and scenario-based learning activities, that are distributed through the [http://www.chemcollective.org/ ChemCollective].<br />
#*Over 100 virtual lab and scenario based learning activities for use in introductory college and high-school chemistry courses. <br />
#*Concepts covered include: molarity, stoichiometry, quantitative analysis, thermochemistry, chemical equilibrium, acids and bases, solubility. The activities span the topics covered in Carnegie Mellon's Modern Chemistry II course. Please see the [http://collective.chem.cmu.edu/pslc/chem106/ course materials page] for detailed information on this course, including all lecture notes, textbook reading, assignments, and assessments.<br />
#*Any virtual lab activity can produce a detailed trace of student interactions with the lab. <br />
#*Some of the materials combine virtual lab experiments with pseudotutors and other forms of support for problem solving. The development team will work with you to add such systems to any activity you would like to use in a study. <br />
#*The ChemCollective was selected for a "Digital Dozen" in Eisenhower National Clearinghouse in 2005, was featured in Gameology in 2006 and was awarded "Highly Recommended" from Schoolzone in 2007. Our conservative estimates 200 instructors use the activities in their classroom. 14 instructors have contributed activities to the collection, and 40 have contributed feedback. <br />
# Online course modules<br />
#*Current modules cover chemical equilibrium, acid-base chemistry and design of acid-base buffers.<br />
#*Designed by a team of chemists (Yaron, Karabinos), learning scientists (Davenport, Leinhardt, Greeno) and a learning technologist (Bunik).<br />
#*Instructional design is grounded in and guided by cognitive science. This has led to materials based on various aspects of PSLC learning theory, and designed in a way that allows these theories to be tested and refined. From a domain perspective, this has led to a new approach to the teaching of chemical equilibrium and acid-base chemistry which initial evidence suggests substantially enhances student learning. <br />
#*The current and planned modules cover the content of Carnegie Mellon's Modern Chemistry II. Please see the [http://collective.chem.cmu.edu/pslc/chem106/ course materials page] for detailed information on this course, including all lecture notes, textbook reading, assignments, and assessments. A list of knowledge components covered in the current modules is available on request to David Yaron.<br />
#*The modules are tested and studied using the PSLC datashop. Once refined, they will be rolled out to the [http://www.cmu.edu/oli/ Open Learning Initiative].<br />
<br />
= Current Studies =<br />
<br />
*[[Nokes - Game environments for Chemistry]]<br />
<br />
*[[Roll - Productive Failure in a Chemistry Virtual Lab]] <br />
<br />
*[[Penn - Discovering a Domain Model for Organic Chemistry]]</div>Yaronhttps://learnlab.org/wiki/index.php?title=Nokes_-_Game_environments_for_Chemistry&diff=10526Nokes - Game environments for Chemistry2010-02-01T18:31:33Z<p>Yaron: /* Study Two */</p>
<hr />
<div>==Summary Table==<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Tim Nokes, David Yaron<br />
|-<br />
| '''Other Contributers''' || Daniel Balenky, Michael Karabinos <br />
|-<br />
| '''Study Start Date''' || February, 2010<br />
|-<br />
| '''Study End Date''' || Ongoing<br />
|-<br />
| '''LearnLab Site''' || ChemCollective web site, and Carnegie Mellon Modern Chemistry (09-106)<br />
|-<br />
| '''LearnLab Course''' || Chemistry<br />
|-<br />
| '''Number of Students''' || 500 per month on web site, 150 in CMU class<br />
|-<br />
| '''Total Participant Hours''' || 500 per month on web site, 450 in CMU class<br />
|-<br />
| '''DataShop''' || Log files of student interactions with virtual lab and other instructional materials.<br />
|}<br />
<br />
==Abstract==<br />
This set of studies is built around two gaming environment in the chemistry learnlab. The first is online murder mystery activity that currently is carried out by about 500 students per month. The second is a chemistry game being built around the ChemCollective virtual lab.<br />
==Background & Significance==<br />
==Glossary==<br />
==Research questions==<br />
===Study One===<br />
Mixed Reception (http://www.chemcollective.org/mr/) is a online game activity in which students investigate a murder. Students use chemistry topics that are typically covered in the first few months of a high school chemistry course to solve the mystery. In addition, the mystery is set in a chemistry research group and is designed to expose students to the goals and processes of modern chemistry research.<br />
====Hypothesis====<br />
Engagement with a game set in an authentic chemistry context will alter students attitudes regarding the domain of chemistry and their perceptions of themselves as relates to science careers.<br />
====Independent Variables====<br />
This activity is freely available on the web. A questionaire is being added to the beginning of the activity to determine the instructional context (Is this activity part of a course? If so, what course?). Students are randomly partitioned into groups that differ only in the set of questions asked before and after engagement with the activity.<br />
====Dependent Variables====<br />
Students will be given a short questionaire at the start and the end of the activity. The questions are chosen from a pool that includes questions on self-efficacy, learning goals and career goals.<br />
====Results====<br />
The study is now designed and the Mixed Reception web site is being updated for data collection.<br />
====Explanation====<br />
<br />
===Study Two===<br />
The ChemCollective virtual lab has a curriculum base of about 100 activities. Many of these activities fall in the category or analytical chemistry, where students are asked to determine the contents of a solution (identifying the identity of the chemical species and/or their amounts). Such activities can be recast in a one-one-one game format. The game begins by having each student prepare a solution (the opponent's unknown) that they believe their opponent will have a hard time identifying. (The contents are constrained in a way that sets the difficulty level of the game, for instance, an easy level would be one strong acid, and a hard level would be a mixture of a weak acid and a weak base.) The students then take turn performing an experiment on their unknown solution, and can opt to use their turn to guess at the contents. <br />
====Hypothesis====<br />
====Independent Variables====<br />
An advantage of this game format is that each game can be cast in a non-game format that is highly parallel with regards to domain content: students can be given an unknown and asked to identify its contents, they can be given an unknown and asked to identify its contents with the fewest possible number of experiments, or they can play against an opponent as described above.<br />
====Dependent Variables====<br />
Data will be collected on interactions with the virtual lab, along with measures of student learning.<br />
====Results====<br />
====Explanation====<br />
This study is under design for deployment in late March 2010.<br />
<br />
==Further Information==<br />
===Connections to Other Studies===<br />
===Annotated Bibliography===<br />
===References===<br />
===Future Plans===</div>Yaronhttps://learnlab.org/wiki/index.php?title=Nokes_-_Game_environments_for_Chemistry&diff=10525Nokes - Game environments for Chemistry2010-02-01T18:31:03Z<p>Yaron: /* Study One */</p>
<hr />
<div>==Summary Table==<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Tim Nokes, David Yaron<br />
|-<br />
| '''Other Contributers''' || Daniel Balenky, Michael Karabinos <br />
|-<br />
| '''Study Start Date''' || February, 2010<br />
|-<br />
| '''Study End Date''' || Ongoing<br />
|-<br />
| '''LearnLab Site''' || ChemCollective web site, and Carnegie Mellon Modern Chemistry (09-106)<br />
|-<br />
| '''LearnLab Course''' || Chemistry<br />
|-<br />
| '''Number of Students''' || 500 per month on web site, 150 in CMU class<br />
|-<br />
| '''Total Participant Hours''' || 500 per month on web site, 450 in CMU class<br />
|-<br />
| '''DataShop''' || Log files of student interactions with virtual lab and other instructional materials.<br />
|}<br />
<br />
==Abstract==<br />
This set of studies is built around two gaming environment in the chemistry learnlab. The first is online murder mystery activity that currently is carried out by about 500 students per month. The second is a chemistry game being built around the ChemCollective virtual lab.<br />
==Background & Significance==<br />
==Glossary==<br />
==Research questions==<br />
===Study One===<br />
Mixed Reception (http://www.chemcollective.org/mr/) is a online game activity in which students investigate a murder. Students use chemistry topics that are typically covered in the first few months of a high school chemistry course to solve the mystery. In addition, the mystery is set in a chemistry research group and is designed to expose students to the goals and processes of modern chemistry research.<br />
====Hypothesis====<br />
Engagement with a game set in an authentic chemistry context will alter students attitudes regarding the domain of chemistry and their perceptions of themselves as relates to science careers.<br />
====Independent Variables====<br />
This activity is freely available on the web. A questionaire is being added to the beginning of the activity to determine the instructional context (Is this activity part of a course? If so, what course?). Students are randomly partitioned into groups that differ only in the set of questions asked before and after engagement with the activity.<br />
====Dependent Variables====<br />
Students will be given a short questionaire at the start and the end of the activity. The questions are chosen from a pool that includes questions on self-efficacy, learning goals and career goals.<br />
====Results====<br />
The study is now designed and the Mixed Reception web site is being updated for data collection.<br />
====Explanation====<br />
<br />
===Study Two===<br />
The ChemCollective virtual lab has a curriculum base of about 100 activities. Many of these activities fall in the category or analytical chemistry, where students are asked to determine the contents of a solution (identifying the identity of the chemical species and/or their amounts). Such activities can be recast in a one-one-one game format. The game begins by having each student prepare a solution (the opponent's unknown) that they believe their opponent will have a hard time identifying. (The contents are constrained in a way that sets the difficulty level of the game, for instance, an easy level would be one strong acid, and a hard level would be a mixture of a weak acid and a weak base.) The students then take turn performing an experiment on their unknown solution, and can opt to use their turn to guess at the contents. <br />
====Hypothesis====<br />
====Independent Variables====<br />
An advantage of this game format is that each game can be cast in a non-game format that is highly parallel with regards to domain content: students can be given an unknown and asked to identify its contents, they can be given an unknown and asked to identify its contents with the fewest possible number of experiments, or they can play against an opponent as described above.<br />
====Dependent Variables====<br />
Data will be collected on interactions with the virtual lab, along with measures of student learning.<br />
====Results====<br />
====Explanation====<br />
This study is under design for deployment in late March 2010.<br />
==Further Information==<br />
===Connections to Other Studies===<br />
===Annotated Bibliography===<br />
===References===<br />
===Future Plans===</div>Yaronhttps://learnlab.org/wiki/index.php?title=Nokes_-_Game_environments_for_Chemistry&diff=10524Nokes - Game environments for Chemistry2010-02-01T18:29:47Z<p>Yaron: /* Summary Table */</p>
<hr />
<div>==Summary Table==<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Tim Nokes, David Yaron<br />
|-<br />
| '''Other Contributers''' || Daniel Balenky, Michael Karabinos <br />
|-<br />
| '''Study Start Date''' || February, 2010<br />
|-<br />
| '''Study End Date''' || Ongoing<br />
|-<br />
| '''LearnLab Site''' || ChemCollective web site, and Carnegie Mellon Modern Chemistry (09-106)<br />
|-<br />
| '''LearnLab Course''' || Chemistry<br />
|-<br />
| '''Number of Students''' || 500 per month on web site, 150 in CMU class<br />
|-<br />
| '''Total Participant Hours''' || 500 per month on web site, 450 in CMU class<br />
|-<br />
| '''DataShop''' || Log files of student interactions with virtual lab and other instructional materials.<br />
|}<br />
<br />
==Abstract==<br />
This set of studies is built around two gaming environment in the chemistry learnlab. The first is online murder mystery activity that currently is carried out by about 500 students per month. The second is a chemistry game being built around the ChemCollective virtual lab.<br />
==Background & Significance==<br />
==Glossary==<br />
==Research questions==<br />
===Study One===<br />
Mixed Reception (http://www.chemcollective.org/mr/) is a online game activity in which students investigate a murder. Students use chemistry topics that are typically covered in the first few months of a high school chemistry course to solve the mystery. In addition, the mystery is set in a chemistry research group and is designed to expose students to the goals and processes of modern chemistry research.<br />
====Hypothesis====<br />
Engagement with a game set in an authentic chemistry context will alter students attitudes regarding the domain of chemistry and their perceptions of themselves as relates to science careers.<br />
====Independent Variables====<br />
This activity is freely available on the web. A questionaire is being added to the beginning of the activity to determine the instructional context (Is this activity part of a course? If so, what course?). Students are randomly partitioned into groups that differ only in the set of questions asked before and after engagement with the activity.<br />
====Dependent Variables====<br />
Students will be given a short questionaire at the start and the end of the activity. The questions are chosen from a pool that includes questions on self-efficacy, learning goals and career goals.<br />
====Results====<br />
The study is now designed and the Mixed Reception web site is being updated for data collection.<br />
====Explanation====<br />
===Study Two===<br />
The ChemCollective virtual lab has a curriculum base of about 100 activities. Many of these activities fall in the category or analytical chemistry, where students are asked to determine the contents of a solution (identifying the identity of the chemical species and/or their amounts). Such activities can be recast in a one-one-one game format. The game begins by having each student prepare a solution (the opponent's unknown) that they believe their opponent will have a hard time identifying. (The contents are constrained in a way that sets the difficulty level of the game, for instance, an easy level would be one strong acid, and a hard level would be a mixture of a weak acid and a weak base.) The students then take turn performing an experiment on their unknown solution, and can opt to use their turn to guess at the contents. <br />
====Hypothesis====<br />
====Independent Variables====<br />
An advantage of this game format is that each game can be cast in a non-game format that is highly parallel with regards to domain content: students can be given an unknown and asked to identify its contents, they can be given an unknown and asked to identify its contents with the fewest possible number of experiments, or they can play against an opponent as described above.<br />
====Dependent Variables====<br />
Data will be collected on interactions with the virtual lab, along with measures of student learning.<br />
====Results====<br />
====Explanation====<br />
This study is under design for deployment in late March 2010.<br />
==Further Information==<br />
===Connections to Other Studies===<br />
===Annotated Bibliography===<br />
===References===<br />
===Future Plans===</div>Yaronhttps://learnlab.org/wiki/index.php?title=Nokes_-_Game_environments_for_Chemistry&diff=10523Nokes - Game environments for Chemistry2010-02-01T18:29:24Z<p>Yaron: New page: ==Summary Table== {| border="1" cellspacing="0" cellpadding="5" style="text-align: left;" | '''PIs''' || Tim Nokes, David Yaron |- | '''Other Contributers''' || Daniel Balenky Michael Kara...</p>
<hr />
<div>==Summary Table==<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Tim Nokes, David Yaron<br />
|-<br />
| '''Other Contributers''' || Daniel Balenky Michael Karabinos (Instructional Designer, Carnegie Mellon)<br />
|-<br />
| '''Study Start Date''' || February, 2010<br />
|-<br />
| '''Study End Date''' || Ongoing<br />
|-<br />
| '''LearnLab Site''' || ChemCollective web site, and Carnegie Mellon Modern Chemistry (09-106)<br />
|-<br />
| '''LearnLab Course''' || Chemistry<br />
|-<br />
| '''Number of Students''' || 500 per month on web site, 150 in CMU class<br />
|-<br />
| '''Total Participant Hours''' || 500 per month on web site, 450 in CMU class<br />
|-<br />
| '''DataShop''' || Log files of student interactions with virtual lab and other instructional materials.<br />
|}<br />
==Abstract==<br />
This set of studies is built around two gaming environment in the chemistry learnlab. The first is online murder mystery activity that currently is carried out by about 500 students per month. The second is a chemistry game being built around the ChemCollective virtual lab.<br />
==Background & Significance==<br />
==Glossary==<br />
==Research questions==<br />
===Study One===<br />
Mixed Reception (http://www.chemcollective.org/mr/) is a online game activity in which students investigate a murder. Students use chemistry topics that are typically covered in the first few months of a high school chemistry course to solve the mystery. In addition, the mystery is set in a chemistry research group and is designed to expose students to the goals and processes of modern chemistry research.<br />
====Hypothesis====<br />
Engagement with a game set in an authentic chemistry context will alter students attitudes regarding the domain of chemistry and their perceptions of themselves as relates to science careers.<br />
====Independent Variables====<br />
This activity is freely available on the web. A questionaire is being added to the beginning of the activity to determine the instructional context (Is this activity part of a course? If so, what course?). Students are randomly partitioned into groups that differ only in the set of questions asked before and after engagement with the activity.<br />
====Dependent Variables====<br />
Students will be given a short questionaire at the start and the end of the activity. The questions are chosen from a pool that includes questions on self-efficacy, learning goals and career goals.<br />
====Results====<br />
The study is now designed and the Mixed Reception web site is being updated for data collection.<br />
====Explanation====<br />
===Study Two===<br />
The ChemCollective virtual lab has a curriculum base of about 100 activities. Many of these activities fall in the category or analytical chemistry, where students are asked to determine the contents of a solution (identifying the identity of the chemical species and/or their amounts). Such activities can be recast in a one-one-one game format. The game begins by having each student prepare a solution (the opponent's unknown) that they believe their opponent will have a hard time identifying. (The contents are constrained in a way that sets the difficulty level of the game, for instance, an easy level would be one strong acid, and a hard level would be a mixture of a weak acid and a weak base.) The students then take turn performing an experiment on their unknown solution, and can opt to use their turn to guess at the contents. <br />
====Hypothesis====<br />
====Independent Variables====<br />
An advantage of this game format is that each game can be cast in a non-game format that is highly parallel with regards to domain content: students can be given an unknown and asked to identify its contents, they can be given an unknown and asked to identify its contents with the fewest possible number of experiments, or they can play against an opponent as described above.<br />
====Dependent Variables====<br />
Data will be collected on interactions with the virtual lab, along with measures of student learning.<br />
====Results====<br />
====Explanation====<br />
This study is under design for deployment in late March 2010.<br />
==Further Information==<br />
===Connections to Other Studies===<br />
===Annotated Bibliography===<br />
===References===<br />
===Future Plans===</div>Yaronhttps://learnlab.org/wiki/index.php?title=Metacognition_and_Motivation&diff=10522Metacognition and Motivation2010-02-01T18:03:54Z<p>Yaron: /* Descendants */</p>
<hr />
<div>The Metacognition and Motivation thrust has two broad goals, 1) to develop a better understanding of how metacognitive processes and motivation interact with learner factors to influence robust student learning outcomes and 2) to test whether and how student learning environments can leverage improved metacognition and motivation to increase the robustness of student learning. Our research will focus on a small number of metacognitive abilities (e.g., help seeking, self-explanation, interpreting peer feedback, and interpreting textual descriptions of domain principles), and a broader range of affective and motivational variables including: challenge perception, boredom, frustration, performance goals, and off-task behavior.<br />
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The Metacognition and Motivation thrust builds on the Coordinative Learning (CL) cluster, while bringing a significant shift of focus. The M&M thrust continues some of the work in the Coordinative Learning cluster that focused on the metacognitive aspects of coordinating multiple sources of information, such as studies on analogical comparison and self-explanation of examples by Nokes, and studies on diagrammatic self-explanation by Aleven and Butcher. It will also build on work done in other thrusts, for example the work done by Hausmann and VanLehn on scaffolding self-explanations in peer collaborative settings, the work on help seeking by Roll, Aleven, et al, and the work on studying [[gaming the system]] and [[Off-Task Behavior]] by Baker et al. In addition, the M&M thrust aims to place greater emphasis on issues of motivation within learning sciences research than has been done so far within the PSLC.<br />
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We have recruited three senior consultants who are helping to increase both the quality of the Metacognition and Motivation research and its visibility within broader communities of metacognition and motivation researchers. They are: Dr. Barry J. Zimmerman, a pre-eminent scholar in metacognition and motivation (e.g., Schunk & Zimmerman, 2008), Dr. Josh Aronson, a distinguished expert in stereotypes, self-esteem, motivation, and attitudes, and Dr. Andrew Elliott, a well-known expert in achievement motivation and social motivation. <br />
<br />
We will pursue the following three broad research directions:<br />
''Create and validate automated detectors for affect, motivation, and meta-cognition''. We will start by enhancing the LearnLab infrastructure with technology for automatically monitoring metacognitive and affective variables, at a much finer grain-size, over longer durations, and for more students, than has been previously possible. <br />
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Specifically, we combine observational and questionnaire data with student log data (e.g. response times, patterns of activity), to develop machine-learned models that monitor, in real-time and moment-by-moment, affective, motivational, and metacognitive variables in interactive learning environments. In particular, we will develop detectors of such constructs as [[gaming the system]], [[Off-Task Behavior]], [[help-seeking]], boredom, frustration, engaged concentration, perception of challenge, self-efficacy, and performance goals. Once created, these detectors will only draw on information that is available to the learning environment in the normal course of its operation (student log data at the keystroke level, timing, and semantic levels), without requiring extra sensors, enabling these detectors to be used in authentic, unmodified learning settings. The combination of machine learning with observational and questionnaire data has already been successful at detecting a limited set of relevant constructs such as perception of challenge (de Vicente & Pain, 2002), [[gaming the system]] (Baker et al., 2008), and [[Off-Task Behavior]] (Baker, 2007). <br />
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These detectors will be implemented in learning software used in multiple LearnLabs, and will enable PSLC researchers across thrusts to study motivation and metacognition as mediating variables when evaluating interventions aimed at enhancing the robustness of student learning.<br />
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''Evaluate interventions aimed at supporting different metacognitive abilities''. The PSLC’s particular strength in this area is studying metacognition within the context of interactive learning environments. A key question is whether such environments can be as effective in fostering or supporting metacognitive skills as they have been in improving domain-specific learning. A number of recent in vivo experiments have revealed significantly improved domain learning among students given metacognitive tutoring support for self-explanation (Aleven & Koedinger, 2002), error-correction (Mathan & Koedinger, 2005), video-based prompting and peer collaborative scaffolding of self-explanation (Craig et al. 2007, 2008, submitted; Hausmann & Vanlehn, 2007a, 2007b), and remedial instruction for material missed through meta-cognitive errors (Baker et al, 2006). We will build on this earlier work, with interventions that attempt to support four metacognitive abilities: help seeking, self-explanation, interpreting peer feedback, and interpreting textual descriptions of domain principles. An important goal in the Metacognition and Motivation thrust is to develop interactive learning environments that can help students internalize this support, solidifying and generalizing their metacognitive skills so they will no longer need external support in future learning situations, and can approach a new domain with a general set of skills that can facilitate learning.<br />
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To evaluate the effectiveness of the interventions aimed at enhancing metacognition, we will look not only at the normal indicators of robust domain-level learning (i.e., transfer and retention), but also (and in particular) at whether future learning is accelerated. As appropriate, the detectors for metacognitive behaviors developed under goal 1 will be used to evaluate whether the targeted metacognitive behavior is enhanced both while the intervention is in place (as a manipulation check) as well as in future learning situations (to evaluate its role as a potential cause of accelerated future learning).<br />
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''Evaluate interventions aimed at inducing positive affect and motivation to persist''. As a complement to investigating how cognitive learning principles can improve and support metacognitive ability, we will also study the effect of interventions aimed at enhancing motivation, as a way of uncovering relationships between motivation, affect, and metacognition in interactive learning environments. We will focus on two types of interventions. First, inspired by the motivational impact of computer games, we will (a) identify features of games that could be adopted for use in interactive learning environments, and (b) evaluate the effect of adding these features. Our initial investigations will focus on trivial choice, “boss problems,” (challenge problems – e.g. Siegler & Jenkins, 1981, designed to look like end-of-level bosses in video games) student control over challenge level, and rewards. In addition, we will evaluate the effect of putting the student in a care-taking role where they need to tutor a synthetic student. (The synthetic student will be driven by the PSLC’s SimStudent learning agent technology.) Second, we will evaluate social interventions aimed at enhancing motivation: peer pressure and comparison with peers, including competition with simulated students.<br />
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== Descendants ==<br />
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<br />
To create a new project page, enclose your project name in a double set of brackets. Details for a project format may be [[ Project_Page_Template_and_Creation_Instructions | found here.]]<br />
<br />
<br />
*[[Nokes - Questionnaires]]<br />
*[[Baker - Building Generalizable Fine-grained Detectors]]<br />
*[[Roll - Inquiry]]<br />
*[[Pavlik - Dificulty and Strategy]]<br />
*[[Nokes - Dialectical Interaction and Robust Learning]]<br />
*[[Math Game Elements]]<br />
*[[Geometry Greatest Hits]]<br />
*[[Aleven - Causal Argumentation Game]]<br />
*[[Nokes - Game environments for Chemistry]]</div>Yaronhttps://learnlab.org/wiki/index.php?title=Penn_-_Discovering_a_Domain_Model_for_Organic_Chemistry&diff=10521Penn - Discovering a Domain Model for Organic Chemistry2010-02-01T18:00:30Z<p>Yaron: New page: ==Summary Table== {| border="1" cellspacing="0" cellpadding="5" style="text-align: left;" | '''PIs''' || John Penn (University of West Virginia), David Yaron, Geoff Gordon |- | '''Other Co...</p>
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<div>==Summary Table==<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || John Penn (University of West Virginia), David Yaron, Geoff Gordon<br />
|-<br />
| '''Other Contributers''' || Michael Karabinos<br />
|-<br />
| '''Study Start Date''' || January, 2010<br />
|-<br />
| '''Study End Date''' || January, 2011<br />
|-<br />
| '''LearnLab Site''' || University of West Virginia, Sophomore Organic Chemistry Course<br />
|-<br />
| '''LearnLab Course''' || Chemistry<br />
|-<br />
| '''Number of Students''' || 1500 (over 5 years)<br />
|-<br />
| '''Total Participant Hours''' || 15000<br />
|-<br />
| '''DataShop''' ||<br />
|}<br />
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==Abstract==<br />
This project is analyzing data collected over the past five years in a organic chemistry homework and test system (http://www.we-learn-horizon.com). This system is used for required homework activities throughout the course, and is also used to deliver the high-stakes exams that determine the course grade. The system is comprehensive in that it includes all topics covered in this year-long course.<br />
==Background & Significance==<br />
==Glossary==<br />
==Research questions==<br />
What information about a domain can be derived from a trace of student interactions with a set of questions that cover all topics in a course and are the primary mode of practice for students in such a course? Can this data be used to develop a cognitive model, including relations between topics and concepts?<br />
==Independent Variables==<br />
==Dependent Variables==<br />
==Hypothesis==<br />
==Results==<br />
==Explanation==<br />
==Further Information==<br />
John Penn is an external researcher (a professor at University of West Virginia) who has been developing a comprehensive question bank for organic chemistry for nearly a decade. Over this time, he has collected years of data on student interaction with the materials. He approached the center to find collaborations that could help him analyze this data. He visited the center, attending a PSLC lunch, a Chemistry course committee meeting, and a meeting with Yaron, Gordon and Karabinos to develop a plan for analyzing the data. The data is now being converted to the format needed for this analysis.<br />
===Connections to Other Studies===<br />
===Annotated Bibliography===<br />
===References===<br />
===Future Plans===</div>Yaronhttps://learnlab.org/wiki/index.php?title=Computational_Modeling_and_Data_Mining&diff=10520Computational Modeling and Data Mining2010-02-01T17:48:48Z<p>Yaron: /* Descendants */</p>
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<div>==Introduction==<br />
One of the greatest impacts of technology on 21st century education will be the scientific advances made possible by mining the vast explosion of learning data that is coming from educational technologies. The Computational Modeling and Data Mining (CMDM) Thrust is pursuing the scientific goal of using such data to advance precise, computational theories of how students learn academic content. We will accomplish this by drawing on and expanding the enabling technologies we have already built for collecting, storing, and managing large-scale educational data sets. For example, [http://www.learnlab.org/technologies/datashop/index.php DataShop] will grow to include larger and richer datasets coming not only from our LearnLab courses but also from thousands of schools using the Cognitive Tutor courses and from additional contexts where we can collect student dialogue data, measures of motivation and affect, and layered assessments of both student knowledge and metacognitive competencies. This growth in the amount, scope, and richness of learning data will make the [http://www.learnlab.org/technologies/datashop/index.php DataShop] an even more fertile cyber-infrastructure resource for learning science researchers to use. But to realize the full potential of that resource – to make new discoveries about the nature of student learning – researchers need new and powerful knowledge discovery tools – innovations that will occur within the CMDM Thrust.<br />
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The CMDM Thrust will pursue three related areas: 1) domain-specific models of student knowledge representation and acquisition, 2) domain-general models of [[Metacognition and Motivation|metacognitive, motivational]], and [[Social_and_Communicative_Factors_in_Learning|social processes]] as they impact student learning, and 3) predictive engineering models and methods that enable the design of large-impact instructional interventions.<br />
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== Developing Better Cognitive Models of ''Domain-Specific Content''==<br />
Understanding and engineering better human learning of complex academic topics is dependent upon accurate and usable models of the domains students are learning that result from [[cognitive task analysis]]. However, domain modeling has been a continual challenge, as student knowledge is not directly observable and its structure is often hidden by our “expert blind spots” ([[User:Koedinger|Koedinger]] & Nathan, 2004; Nathan & Koedinger, 2000). Key research questions are: a) Can the discovery of a domain’s knowledge structure be automated? b) Do [[knowledge component]] models provide a precise and predictive theory of [[transfer]] of learning? c) Can we integrate separate methods for modeling memory, learning, transfer, and guessing/slipping, to optimize models of student knowledge, and in turn optimize students' effective time on task?<br />
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One of the planned projects for Year 5 will build on our promising past results, obtained with the Cen, Koedinger, and Junker (2006) Learning Factor Analysis (LFA) algorithms. Specifically, we will, by broadening the generalizability of this domain-modeling approach, incorporating new knowledge-discovery methods, and increasing the level of automation of knowledge analysis so as to engage more researchers in applying this technique to even more content domains. To more fully automate the discovery of knowledge components, Pavlik will use Partially Ordered Knowledge Structures (POKS) (cf. Desmarais, et al., 1995) to build more complete and accurate representations of map the given domain and to capture the prerequisite relationships between hypothesized knowledge components and their predictions of performance. The models that this work produces will become the input to algorithms that can optimize for each student the amount of practice and ideal sequencing of instructional events for acquiring each knowledge component. These approaches will be applied to tutors across domains, including math, science, and language (particularly for English vocabulary and article learning domains). A related project will investigate the impact of combining LFA model refinement with improved moment-by-moment knowledge modeling, using a probabilistic model that uses student interaction data to estimate whether a student’s correct answer or error informs us about their knowledge or simply represents a guess or slip (Baker, Corbett & Aleven, 2008). In addition to clear applied benefits, these projects will advance a more precise science of reasoning and learning as it occurs in academic settings.<br />
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==Developing Models of ''Domain-General'' Learning and Motivational Processes==<br />
Our work toward developing high-fidelity models of student learning has involved capturing, quantifying, and modeling domain-general mechanisms that impact students’ learning and the robustness of that learning. In the first four years of the PSLC, our models have moved beyond addressing domain-specific cognition (e.g., the cognitive models behind the intelligent tutors for Physics, Algebra, and Geometry) to capture metacognitive aspects of learning (e.g., Aleven et al.’s, 2006, detailed model of help-seeking behavior), general mechanisms of learning (Matsuda et al., 2007) and motivational and affective constructs such as students’ off-task behavior (Baker, 2007), and whether a student is “gaming the system” (Baker et al., 2008; shown to be associated with boredom and confusion in Rodrigo et al, 2007). <br />
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A key Year 5 effort will extend the [http://www.cs.cmu.edu/~mazda/SimStudent SimStudent] project both as a theory-building tool and as an instruction-informing tool (Matsuda et al., 2008). We will use SimStudent to make predictions about the nature of students’ generalization errors and the effects of prior knowledge on students’ learning and transfer, testing these predictions using human-learning data in DataShop (Matsuda et al., 2009; see [[Application of SimStudent for Error Analysis]]). While psychological and neuroscientific models typically produce only reaction time predictions, these models will predict specific errors and forecast the pattern of reduction in those errors . Developing a system that integrates domain-general processes to produce human-like errors in inference, calculation, generalization, and the use of feedback/help/instructions would be both a major theoretical breakthrough, and an extremely useful tool for other researchers. <br />
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Looking forward to the renewal period, an important project will be to develop machine-learned models of student behaviors at a range of time scales, from momentary affective states like boredom and frustration (cf. Kapoor, Burleson, & Picard, 2007) to longer-term motivational and metacognitive constructs such as performance vs. learning orientation and self-regulated learning (Azevedo & Cromley, 2004; Elliott & Dweck, 1988; Pintrich, 2000; Winne & Hadwin, 1998). We will expand prior PSLC work by Baker and colleagues (Rodrigo et al, 2007, 2008; Baker et al, 2008) to explore causal connections between these models and existing models of motivation-related behaviors such as gaming the system and off-task behavior. We will pursue models of differences in cognitive, affective, social, and motivational factors as they relate to classroom culture, schools, and teachers. These proposed models would be, to our knowledge, the first systematic investigations of school-level effects factors affectingon fine-grained states of student learning.<br />
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==Developing Predictive ''Engineering Models'' to Inform Instructional Event Design==<br />
A fundamental theoretical problem for the sciences of learning and instruction is what we have called “the [[assistance dilemma|Assistance Dilemma]]”: optimizing the amount and timing of instruction so that it is neither too little nor too much, and neither too early nor too late (Koedinger & Aleven, 2007; Koedinger, 2008; Koedinger, Pavlik, McLaren, & Aleven, 2008). Two theoretical advances are necessary before we can resolve these broad questions. First, we need a clear delineation of the multiple possible dimensions of instructional assistance (e.g., worked examples, feedback, on-demand hints, self-explanation prompts, or optimally-spaced practice trials). We broadly define assistance to include not only direct verbal instruction, but also instructional scaffolds that prompt student thinking or action as well as implicit affordances or difficulties in the learning environment. Second, we need precise, predictive models of when increasing assistance (reducing difficulties) or decreasing assistance (increasing difficulties) is best for optimal robust learning. Existing theoretical work on this topic – like [[cognitive load]] theory (e.g., Sweller, 1994; van Merrienboer & Sweller, 2005), desirable difficulties (Bjork, 1994), and cognitive apprenticeship (Collins, Brown, & Newman, 1989) -- have not reached the stage of precise computational modeling that can be used to make a priori predictions about optimal levels of assistance. <br />
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We will use DataShop log data to make progress on the Assistance Dilemma by targeting dimensions of assistance one at a time and creating parameterized mathematical models that predict the optimal level of assistance to enhance robust learning (cf. Koedinger et al., 2008). Such a mathematical model has been achieved for the practice-interval dimension (changing the amount of time between practice trials), and progress is being made on the example-problem dimension (changing the ratio of examples to problems). These models generate the inverted-U shaped function curve characteristic of the Assistance Dilemma as a function of particular parameter values that describe the instructional context. These models are created and refined using student learning data from DataShop. We hypothesize that this form approach will work for other dimensions of assistance. These models will address the limitations of current theory indicated above by generating ''a priori'' predictions of what forms of assistance or difficulty will enhance learning. Further, these models will provide the basis for on-line algorithms that adapt to individual student differences and changes over time, optimizing the assistance provided to each student for each knowledge component at each time in their learning trajectory.<br />
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== [[CMDM Meetings]] ==<br />
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== Descendants ==<br />
<br />
To create a new project page, enclose your project name in a double set of brackets. Details for a project format may be [[ Project_Page_Template_and_Creation_Instructions | found here.]]<br />
<br />
<br />
*[[Gordon - Temporal learning for EDM]]<br />
*[[Koedinger - Discovery of Domain-Specific Cognitive Models]]<br />
*[[Koedinger - Toward a model of accelerated future learning]]<br />
*[[Baker - Building Generalizable Fine-grained Detectors]]<br />
*[[Chi - Induction of Adaptive Pedagogical Tutorial Tactics]]<br />
*[[Baker - Closing the Loop]]<br />
*[[Pavlik and Koedinger - Generalizing the Assistance Formula]]<br />
*[[Mayer_and_McLaren_-_Social_Intelligence_And_Computer_Tutors | McLaren and Mayer - Social Intelligence and Learning from "polite" tutors]]<br />
*[[Application of SimStudent for Error Analysis | Matsuda - Application of SimStudent for Error Analysis]]<br />
*[[Penn - Discovering a Domain Model for Organic Chemistry]]<br />
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== References ==<br />
* Azevedo, R., & Cromley, J. G. (2004). Does training on self-regulated learning facilitate students' learning with hypermedia? Journal of Educational Psychology, 96(3), 523-535.<br />
* Baker, R.S.J.d. (2007) Modeling and Understanding Students' Off-Task Behavior in Intelligent Tutoring Systems. Proceedings of ACM CHI 2007: Computer-Human Interaction, 1059-1068.<br />
* Baker, R.S.J.d., Corbett, A.T., Aleven, V. (2008) More Accurate Student Modeling Through Contextual Estimation of Slip and Guess Probabilities in Bayesian Knowledge Tracing. Proceedings of the 9th International Conference on Intelligent Tutoring Systems, 406-415<br />
* Baker, R.S.J.d., Corbett, A.T., Roll, I., Koedinger, K.R. (2008) Developing a Generalizable Detector of When Students Game the System. User Modeling and User-Adapted Interaction, 18, 3, 287-314.<br />
* Baker, R., Walonoski, J., Heffernan, N., Roll, I., Corbett, A., Koedinger, K. (2008) Why Students Engage in "Gaming the System" Behavior in Interactive Learning Environments. Journal of Interactive Learning Research, 19 (2), 185-224.<br />
* Bjork, R.A. (1994). Memory and metamemory considerations in the training of human beings. In J. Metcalfe and A. Shimamura (Eds.) Metacognition: Knowing about knowing. (pp.185-205). Cambridge, MA: MIT Press.<br />
* Collins, A., Brown, J. S., & Newman, S. E. (1989). Cognitive apprenticeship: Teaching the crafts of reading, writing, and mathematics. In L. B. Resnick. Knowing, Learning, and Instruction: Essays in Honor of Robert Glaser (pp. 453-494). Hillsdale, NJ: Erlbaum.<br />
* Desmarais, M., Maluf, A., Liu, J. (1995) User-expertise modeling with empirically derived probabilistic implication networks. User Modeling and User-Adapted Interaction, 5 (3-4), 283-315.<br />
* [[User:Koedinger|Koedinger]], K. R. & Aleven, V. (2007). Exploring the assistance dilemma in experiments with Cognitive Tutors. Educational Psychology Review, 19 (3): 239-264.<br />
* Koedinger, K. R., Pavlik Jr., P. I., McLaren, B. M., & Aleven, V. (2008). Is it better to give than to receive? The assistance dilemma as a fundamental unsolved problem in the cognitive science of learning and instruction. In B.C. Love, K. McRae, & V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. (pp.). Austin, TX: Cognitive Science Society.<br />
* Matsuda, N., Cohen, W. W., Sewall, J., Lacerda, G., & Koedinger, K. R. (2008). Why tutored problem solving may be better than example study: Theoretical implications from a simulated-student study. In B. P. Woolf, E. Aimeur, R. Nkambou & S. Lajoie (Eds.), Proceedings of the International Conference on Intelligent Tutoring Systems (pp. 111-121). Heidelberg, Berlin: Springer.<br />
* Matsuda, N., Cohen, W. W., Sewall, J., Lacerda, G., & Koedinger, K. R. (2007). Evaluating a simulated student using real students data for training and testing. In C. Conati, K. McCoy & G. Paliouras (Eds.), Proceedings of the international conference on User Modeling (LNAI 4511) (pp. 107-116). Berlin, Heidelberg: Springer.<br />
* McLaren, B.M., Lim, S., & Koedinger, K.R. (2008). When and How Often Should Worked Examples be Given to Students? New Results and a Summary of the Current State of Research. In B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 2176-2181). Austin, TX: Cognitive Science Society. <br />
* Nathan, M. J. & Koedinger, K.R. (2000). Teachers' and researchers' beliefs of early algebra development. Journal for Research in Mathematics Education, 31 (2), 168-190<br />
* Rodrigo, M.M.T., Baker, R.S.J.d., d'Mello, S., Gonzalez, M.C.T., Lagud, M.C.V., Lim, S.A.L., Macapanpan, A.F., Pascua, S.A.M.S., Santillano, J.Q., Sugay, J.O., Tep, S., Viehland, N.J.B. (2008) Comparing Learners' Affect While Using an Intelligent Tutoring Systems and a Simulation Problem Solving Game. Proceedings of the 9th International Conference on Intelligent Tutoring Systems, 40-49. <br />
* Rodrigo, M.M.T., Baker, R.S.J.d., Lagud, M.C.V., Lim, S.A.L., Macapanpan, A.F., Pascua, S.A.M.S., Santillano, J.Q., Sevilla, L.R.S., Sugay, J.O., Tep, S., Viehland, N.J.B. (2007) Affect and Usage Choices in Simulation Problem Solving Environments. Proceedings of Artificial Intelligence in Education 2007, 145-152.<br />
* Sweller, J. (1994). Cognitive load theory, learning difficulty and instructional design. Learning and Instruction, 4, 295–312.<br />
* [http://www.ou.nl/eCache/DEF/7/332.html Van Merriënboer, J.J.G.], & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17(1), 147-177.</div>Yaronhttps://learnlab.org/wiki/index.php?title=Roll_-_Productive_Failure_in_a_Chemistry_Virtual_Lab&diff=10519Roll - Productive Failure in a Chemistry Virtual Lab2010-02-01T17:44:51Z<p>Yaron: </p>
<hr />
<div>Productive Failure in a Chemistry Virtual Laboratory (Roll)<br />
==Summary Table==<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Ido Roll, David Yaron<br />
|-<br />
| '''Other Contributers''' || Michael Karabinos (Instructional Designer, Carnegie Mellon), Sophia Nussbaum (Course Instructor and Instructional Designer, University of British Columbia)<br />
|-<br />
| '''Study Start Date''' || March, 2010<br />
|-<br />
| '''Study End Date''' || March, 2011<br />
|-<br />
| '''LearnLab Site''' || University of British Columbia, Freshman Laboratory Course<br />
|-<br />
| '''LearnLab Course''' || Chemistry<br />
|-<br />
| '''Number of Students''' || 1100<br />
|-<br />
| '''Total Participant Hours''' || 3300<br />
|-<br />
| '''DataShop''' || Log files of student interactions with virtual lab and other instructional materials.<br />
|}<br />
<br />
==Abstract==<br />
This study is occuring in a set of online materials that students complete in preparation for a physical laboratory experience involving the design of a buffer solution. At the beginning of the online activities, students complete a set of activities in the ChemCollective virtual laboratory. In past years, these virtual lab activities have following a direct instruction approach, in which students add acid to a buffered versus unbuffered solution and compare the effects. This study adds an additional condition in which students are asked to create their own buffer solution, with only minimal guidance. This is a complex task which is the topic of the following instruction. Students are not expected to succeeed at the task in this initial exploratory phase, but the hypothesis is that engaging with the task at the beginning will better prepare them for the formal knowledge which is to follow.<br />
<br />
==Background & Significance==<br />
==Glossary==<br />
==Research questions==<br />
Will engagement with an initial exploratory phase of instruction promote student learning, even though the students are likely to fail at meeting the goals of the exploratory activity (designing a buffer solution with a specified pH and buffer capacacity)? <br />
==Independent Variables==<br />
==Dependent Variables==<br />
==Hypothesis==<br />
==Results==<br />
==Explanation==<br />
==Further Information==<br />
This study is currently under design and will be carried out at UBC following the break the university is taking for the winter olympics (data collection begins on March 1, 2010).<br />
===Connections to Other Studies===<br />
===Annotated Bibliography===<br />
===References===<br />
===Future Plans===</div>Yaronhttps://learnlab.org/wiki/index.php?title=Roll_-_Productive_Failure_in_a_Chemistry_Virtual_Lab&diff=10518Roll - Productive Failure in a Chemistry Virtual Lab2010-02-01T17:42:40Z<p>Yaron: New page: Productive Failure in a Virtual Laboratory (Roll) ==Summary Table== {| border="1" cellspacing="0" cellpadding="5" style="text-align: left;" | '''PIs''' || Ido Roll, David Yaron |- | '''Oth...</p>
<hr />
<div>Productive Failure in a Virtual Laboratory (Roll)<br />
==Summary Table==<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Ido Roll, David Yaron<br />
|-<br />
| '''Other Contributers''' || Michael Karabinos (Instructional Designer, Carnegie Mellon), Sophia Nussbaum (Course Instructor and Instructional Designer, University of British Columbia)<br />
|-<br />
| '''Study Start Date''' || March, 2010<br />
|-<br />
| '''Study End Date''' || March, 2011<br />
|-<br />
| '''LearnLab Site''' || University of British Columbia, Freshman Laboratory Course<br />
|-<br />
| '''LearnLab Course''' || Chemistry<br />
|-<br />
| '''Number of Students''' || 1100<br />
|-<br />
| '''Total Participant Hours''' || 3300<br />
|-<br />
| '''DataShop''' || Log files of student interactions with virtual lab and other instructional materials.<br />
|}<br />
<br />
==Abstract==<br />
This study is occuring in a set of online materials that students complete in preparation for a physical laboratory experience involving the design of a buffer solution. At the beginning of the online activities, students complete a set of activities in the ChemCollective virtual laboratory. In past years, these virtual lab activities have following a direct instruction approach, in which students add acid to a buffered versus unbuffered solution and compare the effects. This study adds an additional condition in which students are asked to create their own buffer solution, with only minimal guidance. This is a complex task which is the topic of the following instruction. Students are not expected to succeeed at the task in this initial exploratory phase, but the hypothesis is that engaging with the task at the beginning will better prepare them for the formal knowledge which is to follow.<br />
<br />
==Background & Significance==<br />
==Glossary==<br />
==Research questions==<br />
Will engagement with an initial exploratory phase of instruction promote student learning, even though the students are likely to fail at meeting the goals of the exploratory activity (designing a buffer solution with a specified pH and buffer capacacity)? <br />
==Independent Variables==<br />
==Dependent Variables==<br />
==Hypothesis==<br />
==Results==<br />
==Explanation==<br />
==Further Information==<br />
This study is currently under design and will be carried out at UBC following the break the university is taking for the winter olympics (data collection begins on March 1, 2010).<br />
===Connections to Other Studies===<br />
===Annotated Bibliography===<br />
===References===<br />
===Future Plans===</div>Yaronhttps://learnlab.org/wiki/index.php?title=Cognitive_Factors&diff=10517Cognitive Factors2010-02-01T17:41:25Z<p>Yaron: /* Descendents */</p>
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<div>The research in this thrust is aimed at understanding cognitive learning—changes in knowledge—that result from [[instructional events]]. It builds on work in the learning sciences field at large and on research carried out in the PSLC over its first four years within the [[Refinement and Fluency]] cluster and part of the [[Coordinative Learning]] cluster, thereby merging two themes that organized the first phase of the PSLC. Each of these clusters was concerned with identifying instructional events that produce robust learning. They differed mainly in that the relevant theme within the Coordinative Learning cluster had a specific focus on instructional events that included more than one input. (A second theme within the Coordinative Learning cluster was on instructional events that provoke learning events involving more than one reasoning method and this theme will be continued in the [[Metacognition and Motivation]] thrust). In the fifth year of the PSLC, we carry forward research from each of these clusters, while making a transition to an additional set of research questions. Although we frame this section in terms of the new Cognitive Factors thrust, the research carried out during the 5th year has been initiated in the current Refinement and Fluency and in part of the Coordinative Learning clusters. <br />
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Our work on cognitive factors encompasses a triangulated set of events around learning: learning events, instructional events, and assessment events. Anything from a lesson to an entire curriculum can be considered a sequence of events whose durations vary from seconds to semesters. The hypotheses of the Cognitive Factors Thrust concern how instructional procedures (e.g., decisions about the learner’s task, materials, practice, feedback) affect learning events and thus the outcomes of learning. Learning involves the acquisition of [[knowledge components]], an increase in the [[feature validity]] and the [[strength]] of these components, and the integration of these components through practice. Our basic hypotheses include the following:<br />
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* Explicitness: Instruction that draws the learner’s attention to valid features that support the relevant knowledge components leads to more robust learning than instruction that does not.<br />
* Assistance: The degree of assistance in the instruction affects learning in relation to student knowledge on specific knowledge components.<br />
* Practice: Practice schedules can be optimized using models of learning based on memory activation assumptions.<br />
* Integration: Knowledge components that are integrated during learning and practice lead to more robust learning and fluent performance across different tasks. <br />
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The research plan tests these hypotheses across knowledge domains, as exemplified by the following projects:<br />
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''Language background factors in L2 learning''. This work illustrates the synergies that develop in the PSLC’s LearnLab context, in this case between English as a second language (ESL) director Alan Juffs and other PSLC language researchers. In a prior cluster meeting, Juffs presented ESL classroom data that compared various L1 background students in their performance on transcribing their own speech, a standard piece of instruction in the ESL curriculum. The result that caught the interest of PSLC researchers (Dunlap, Guan, Perfetti) was the very poor spelling performance of Arabic-background students, relative to Spanish, Korean, and Chinese ESL students, despite comparable levels of spoken language performance. Furthermore, Juffs identified this discrepancy as a long-standing one in ESL instruction. Although one might hypothesize that a key factor is orthographic differences between L1 and L2, this seems unlikely here. Spanish to English is closer, but Chinese to English is farther in L1-L2 orthographic similarity. The first steps toward a new study have been taken with the help of a PSLC summer intern, who coded the errors made in spelling by all L1 background learners. The pattern of errors can be characterized as qualitatively similar, differing across languages quantitatively, suggesting a generalized English spelling problem. This analysis has led to the hypothesis that feature focusing—attention to full spelling patterns—is different across the L1 backgrounds, which we will test in a training experiment that focuses attention on spelling patterns.<br />
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''Second language vocabulary learning''. Another new project originating within the Refinement and Fluency cluster will study English vocabulary learning using REAP. Based on recent research by Balass on the trade-offs between explicit (dictionary-based) and implicit (inferences from text) instruction in learning new words by monolingual subjects (Bolger et al, 2008), the new work will apply this tradeoff idea to second language learners. The hypothesis is that allowing learners to view definitions is more effective after they have read a sentence containing the word to be learned. This hypothesis reflects ideas about assistance (giving a definition versus inferring it) and the assumption that learning word meanings from context depends on the overlapping memory traces established by specific encounters with the word (Bolger et al, 2008). REAP allows us to use authentic texts for studies with students of various L1 backgrounds learning English through reading texts in their areas of interest. In our experiments, we will vary the availability of definitions provided on-line as part of the text reading. <br />
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''Explicit instruction and practice schedules in algebra and second language learning''. Foreign language learning in classrooms has stimulated research on explicit vs implicit instruction, with conclusions favoring the value of explicit instruction (Norris and Ortega, 2000). A major conclusion from PSLC work is that instruction that draws attention to critical valid features—“feature focusing”—is important in acquiring knowledge components for complex tasks. This conclusion has evidence from studies of L2 learning of the English grammar by Levin, Friskoff, Pavlik, studies of radical learning by Dunlap et al and by Pavlik, and by studies by Zhang and MacWhinney and by Liu et al on learning spoken syllables through pin-yin (alphabetic spellings). Projects in French dictation (MacWhinney) and French grammar (Presson & MacWhinney), Chinese dictation (Zhang & MacWhinney), algebra (Pavlik) and arithmetical computation (Fiez) also reflect this theme. Much of this work has been combined with completely general hypotheses about practice, based on Pavlik and Anderson (2005)’s model that describes the trade-off between the benefit of spaced practice and the cost of longer retention intervals brought by spacing. The resulting optimized practice schedule has been tested in several PSLC studies of vocabulary learning in Chinese (Pavlik, MacWhinney, Koedinger; reported in Pavlik, 2006), cues to French gender (Presson, MacWhinney, & Pavlik). Important is the generality of the optimization model. It applies to all domain content and studies in both algebra and second language learning have nee carried out. The new work in second language and in algebra builds on the synergies that have emerged from collaborations between domain researchers (e.g. MacWhinney) and Pavlik around experiments and models for optimizing practice. For Chinese, MacWhinney, Zhang, and Pavlik have developed a tutor for Chinese dictation and vocabulary learning that is being used in 18 sites. Data from these sites will be used to test the results of practice schedules and the form of instructional events (e.g. cues to gender in French) with longer term measures of robust learning. Because each of the tutors logs results to DataShop, the student records are a rich source of data for further study, including researchers beyond the PSLC. <br />
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''Learning the logic of unconfounded experiments.'' We will extend our research on college level science topics (chemistry and physics) to middle school science, with a focus on the cross-domain topic of experimental design. The ability to design unconfounded experiments and make valid inferences from their outcomes is an essential skill in scientific reasoning. The key idea here is CVS: the Control of Variables Strategy. CVS is the fundamental idea underlying the design of unconfounded experiments from which valid, causal, inferences can be made. Its acquisition is an important step in the development of scientific reasoning skills , because it provides a strong constraint on search in the space of experiments (Klahr, 2000). The Tutor for Experimental Design (TED), developed by Klahr’s research team, builds on previous work studying the different paths of learning and transfer that result from teaching CVS using different instructional methods that span from direct instruction to discovery (Chen & Klahr, 1999) and show differences along the “physical-virtual” dimension (Triona & Klahr, 2007). We build on this by constructing of a semi-autonomous tutor, then developing a full computer based tutor in Pittsburgh middle school LearnLabs and carrying out in vivo experiments with TED. <br />
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''Integration of knowledge components.'' Isolated knowledge components are not sufficient to produce fluent use of knowledge. Integrating knowledge components is important both in authentic practice that follows acquisition of knowledge components but, we hypothesize, also in the initial acquisition of components. Some of our prior work in coordinative learning establishes some of the conditions that favor multiple inputs during learning (e.g., Davenport et al in stochiometry). And experiments on fluency support the value of repeated practice in single-topic speaking as way to support fluency (de Jong, Halderman and Perfetti). In new work we propose to build on progress we have made in the study of fluency in language (de Jong et al) and arithmetic (Fiez). For example, we will follow the discovery by de Jong and colleagues that when L2 speakers repeat a speech on a single topic, their fluency scores increase on a number of measures. We will test the hypothesis that this results from the advantage of retrieving the same conceptual and lexical knowledge and overall speech plan on successive attempts, allowing fluency to increase on procedural components supported by chunking of words to phrases. We are accumulating a large database in the English LearnLab that will support the testing of additional hypotheses. The idea that some relatively simple learning (e.g. 3-5 knowledge components) is supported by integration from the beginning is being tested by Liu, Guan & Perfetti in a study of learning to read Chinese characters. The hypothesis is that when students write unfamiliar characters within the same 60-second time period that they read the character and try to learn its meaning and pronunciation, they will show more robust learning measured by reading tasks. Underlying this hypothesis is the idea that the representation of a character (or other objects that follow structural principles) can be perceptual-motor as well as visual.<br />
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== Descendents ==<br />
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To create a new project page, enclose your project name in a double set of brackets. Details for a project format may be [[ Project_Page_Template_and_Creation_Instructions | found here.]]<br />
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*[[Klahr - TED]]<br />
*[[Perfetti - Read Write Integration]]<br />
*[[MacWhinney - Second Language Grammar]]<br />
*[[MacWhinney - Spanish Conjugation]]<br />
*[[Juffs - Feature Focus in Word Learning]]<br />
*[[Fostering fluency in second language learning | de Jong - Fluency]]<br />
*[[McLaren_-_The_Assistance_Dilemma_And_Discovery_Learning | McLaren - The Assistance Dilemma and Discovery Learning]]<br />
*[[Wylie - Intelligent Writing Tutor]]<br />
*[[REAP_main | Eskenazi - REAP]]<br />
*[[Roll - Productive Failure in a Chemistry Virtual Lab]]<br />
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=== References ===<br />
* Borek, A., McLaren, B.M., Karabinos, M., & Yaron, D. (2009). How Much Assistance is Helpful to Students in Discovery Learning? In U. Cress, V. Dimitrova, & M. Specht (Eds.), Proceedings of the Fourth European Conference on Technology Enhanced Learning, Learning in the Synergy of Multiple Disciplines (EC-TEL 2009), LNCS 5794, September/October 2009, Nice, France. (pp. 391-404). Springer-Verlag Berlin Heidelberg.</div>Yaron