Difference between revisions of "PSLC Year 5 Projects"

From LearnLab
Jump to: navigation, search
(Metacognition)
 
(12 intermediate revisions by the same user not shown)
Line 1: Line 1:
=== New Year 5 projects ===
+
For more full descriptions see [[PSLC Integrated Project Summary]].
==== Refinement & Fluency CLUSTER ==> Cognitive Factors THRUST [Chuck] ====  
+
=== Refinement & Fluency CLUSTER ==> Cognitive Factors THRUST [Chuck] ===  
 
* Macwhinney - Robustness-2nd Language Learning [[Learning_French_gender_cues_with_prototypes]],[[French_gender_cue_learning_through_optimized_adaptive_practice]], [[French_gender_prototypes]], [[French_gender_attention]]
 
* Macwhinney - Robustness-2nd Language Learning [[Learning_French_gender_cues_with_prototypes]],[[French_gender_cue_learning_through_optimized_adaptive_practice]], [[French_gender_prototypes]], [[French_gender_attention]]
  
Line 14: Line 14:
 
* '''**New**''' Liu, Perfetti, Wang, Wu, Guan - [[Integration_of_reading%2C_writing_and_typing_in_learning_Chinese_words| Integration of reading and writing in learning Chinese words and sentences]]
 
* '''**New**''' Liu, Perfetti, Wang, Wu, Guan - [[Integration_of_reading%2C_writing_and_typing_in_learning_Chinese_words| Integration of reading and writing in learning Chinese words and sentences]]
  
==== Coordinative Learning CLUSTER ==> CF or Metacognition & Motivation THRUST [Ken] ====
+
=== Coordinative Learning CLUSTER ==> CF or Metacognition & Motivation THRUST [Ken] ===
===== Metacognition =====
+
==== Metacognition ====
====== Example-Rule Coordination ======
+
===== Example-Rule Coordination =====
 
* Salden - Worked Examples in Geometry [[Does learning from worked-out examples improve tutored problem solving?]]
 
* Salden - Worked Examples in Geometry [[Does learning from worked-out examples improve tutored problem solving?]]
 
* McLaren - [[McLaren et al - Studying the Learning Effect of Personalization and Worked Examples in the Solving of Stoich Problems|Worked Examples in Chemistry]]
 
* McLaren - [[McLaren et al - Studying the Learning Effect of Personalization and Worked Examples in the Solving of Stoich Problems|Worked Examples in Chemistry]]
* Nokes - [[Bridging_Principles_and_Examples_through_Analogy_and_Explanation]] in Physics.  See also Interactive Communication.
+
* '''**Out of Date**''' Nokes - [[Bridging_Principles_and_Examples_through_Analogy_and_Explanation]] in Physics.  See also Interactive Communication.
 
* '''**New**''' Wylie, Mitamura, Koedinger - IWT [[Assistance_Dilemma_English_Articles]].  See also CF and CMDM thrusts.
 
* '''**New**''' Wylie, Mitamura, Koedinger - IWT [[Assistance_Dilemma_English_Articles]].  See also CF and CMDM thrusts.
  
Line 26: Line 26:
 
* '''**New**''' Aleven - [[Geometry_Greatest_Hits]]
 
* '''**New**''' Aleven - [[Geometry_Greatest_Hits]]
  
====== Visual-Verbal Coordination ======
+
===== Visual-Verbal Coordination =====
 
* Butcher- Visual-Verbal [[Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)]]
 
* Butcher- Visual-Verbal [[Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)]]
* Davenport - Visual Representations in Science [[Visual_Representations_in_Science_Learning]]
+
* '''**Out of Date-Was there Year 5 funding?**'''Davenport - Visual Representations in Science [[Visual_Representations_in_Science_Learning]]
* Chang [[Leverage_Learning_from_Chemistry_Visualizations_%28Ming_%26_Schoenfield%29]]
+
* '''**Incomplete--Add materials at least**''' Chang [[Leverage_Learning_from_Chemistry_Visualizations_%28Ming_%26_Schoenfield%29]]
 
* Reed Corbett Hoffman- [[Enhancing Learning through Computer Animation]]
 
* Reed Corbett Hoffman- [[Enhancing Learning through Computer Animation]]
 
* '''**New**''' Aleven - Multiple Interactive Representation [[Sequencing_learning_with_multiple_representations_of_rational_numbers_(Aleven%2C_Rummel%2C_%26_Rau)]]
 
* '''**New**''' Aleven - Multiple Interactive Representation [[Sequencing_learning_with_multiple_representations_of_rational_numbers_(Aleven%2C_Rummel%2C_%26_Rau)]]
  
===== Motivation =====
+
==== Motivation ====
 
* Baker - How Content and Interface Features Influence Student Choices Within the Learning Space  [[Baker_Choices_in_LE_Space]]
 
* Baker - How Content and Interface Features Influence Student Choices Within the Learning Space  [[Baker_Choices_in_LE_Space]]
 
* [[Mayer_and_McLaren_-_Social_Intelligence_And_Computer_Tutors]]
 
* [[Mayer_and_McLaren_-_Social_Intelligence_And_Computer_Tutors]]
 
* '''**New**''' Aleven- Improving student affect through adding game elements to mathematics LearnLabs [[Math_Game_Elements]]
 
* '''**New**''' Aleven- Improving student affect through adding game elements to mathematics LearnLabs [[Math_Game_Elements]]
  
==== Integrative Communication CLUSTER ==> Social Communicative THRUST [Chuck] ====  
+
=== Integrative Communication CLUSTER ==> Social Communicative THRUST [Chuck] ===  
 
* Nokes - Bridging Principles  [[Bridging_Principles_and_Examples_through_Analogy_and_Explanation]]  See also Coordinative Learning.
 
* Nokes - Bridging Principles  [[Bridging_Principles_and_Examples_through_Analogy_and_Explanation]]  See also Coordinative Learning.
 
* Van Lehn - Ill defined Physics  [[Ringenberg_Ill-Defined_Physics]]
 
* Van Lehn - Ill defined Physics  [[Ringenberg_Ill-Defined_Physics]]
Line 45: Line 45:
 
* '''**New**''' Nokes - Gadgil,Soniya Analogical Scaffolding in Collaborative Learning [[Analogical_Scaffolding_in_Collaborative_Learning]]
 
* '''**New**''' Nokes - Gadgil,Soniya Analogical Scaffolding in Collaborative Learning [[Analogical_Scaffolding_in_Collaborative_Learning]]
  
==== Computational Modeling and Data Mining THRUST [Ken]====  
+
=== Computational Modeling and Data Mining THRUST [Ken]===  
===== Knowledge Analysis: Developing Cognitive Models of Domain-Specific Content =====
+
==== Knowledge Analysis: Developing Cognitive Models of Domain-Specific Content ====
 
* '''**New**''' Nokes, Hausmann - [[Harnessing what you know]]: The role of analogy in robust learning  
 
* '''**New**''' Nokes, Hausmann - [[Harnessing what you know]]: The role of analogy in robust learning  
 
* Cen thesis
 
* Cen thesis
Line 52: Line 52:
 
* Cross referencing projects in other thrusts:
 
* Cross referencing projects in other thrusts:
 
**  Wylie [[Assistance_Dilemma_English_Articles|English Article Analysis]]
 
**  Wylie [[Assistance_Dilemma_English_Articles|English Article Analysis]]
===== Learning Analysis: Developing Models of Domain-General Learning and Motivational Processes =====
+
==== Learning Analysis: Developing Models of Domain-General Learning and Motivational Processes ====
 
* '''**New**''' Matsuda - SimStudent [[Application_of_SimStudent_for_Error_Analysis]]
 
* '''**New**''' Matsuda - SimStudent [[Application_of_SimStudent_for_Error_Analysis]]
 
* Cross referencing projects in other thrusts:
 
* Cross referencing projects in other thrusts:
 
** Mayer?  Baker?
 
** Mayer?  Baker?
  
===== Instructional Analysis: Developing Predictive Engineering Models to Inform Instructional Event Design =====
+
==== Instructional Analysis: Developing Predictive Engineering Models to Inform Instructional Event Design ====
 
* Cross referencing projects in other thrusts:
 
* Cross referencing projects in other thrusts:
 
** Mclaren- Assistance Dilemma, continuation of [[McLaren et al - Studying the Learning Effect of Personalization and Worked Examples in the Solving of Stoich Problems|Studying the Learning Effect of Personalization and Worked Examples in the Solving of Stoich Problems]]
 
** Mclaren- Assistance Dilemma, continuation of [[McLaren et al - Studying the Learning Effect of Personalization and Worked Examples in the Solving of Stoich Problems|Studying the Learning Effect of Personalization and Worked Examples in the Solving of Stoich Problems]]
 
** '''**New**''' Wylie, Mitamura, Koedinger - IWT [[Assistance_Dilemma_English_Articles]]  See also CL cluster and CF thrust.
 
** '''**New**''' Wylie, Mitamura, Koedinger - IWT [[Assistance_Dilemma_English_Articles]]  See also CL cluster and CF thrust.
 
=== Notes ===
 
New thrusts "absorb" work from past clusters.
 
 
=== Integrated Thrust Summaries ===
 
 
==== Metacognition & Motivation Thrust ====
 
The work in this thrust builds on prior work started before the renewal, particularly work in the Coordinative Learning Cluster.
 
===== Metacognition =====
 
Past work within the Coordinative Learning Cluster emphasized to broad themes: Example-Rule Coordination and Visual-Verbal Coordination. These themes involve instruction that provides students with multiple input sources and/or prompts for multiple lines of reasoning. A good self-regulated learned needs to have the metacognitive strategies to coordinate information coming from different sources and lines of reasoning.  We summarize Year 5 project results within these two themes as they address both whether providing multiple sources or reasoning prompts enhances student learning and whether metacognitive coordination processes can be supported or improved.
 
 
====== Example-Rule Coordination ======
 
Much of academic learning, particularly in Science, Math, Engineering, and Technology (SMET) domains but also in language learning, involves the acquisition of concepts and skills that must generalize across many situations if robust learning is to achieved.  Often instruction expresses such generalizations explicitly to students with verbal descriptions, which we call "rules" (see the top-left cell in Figure XX).  It may also communicate these generalizations by providing examples (bottom-left cell). Because "learning by doing" is recognized as critical to concept and skill acquisition, typical instruction also includes opportunities for students to practice application of the rules in "problems" (bottom-right cell).  All to rarely, students are asked to generate rules themselves from examples of worked out problem solutions -- prompting students to do so is called "self-explanation" (top-right cell). The optimal combination of these four kinds of instruction (or instructional events) has been the focus on many projects that cut across math, science, and language domains.  While typical instruction tends to focus on rules and practice opportunities (the main diagonal in Figure XX), these studies have now consistently demonstrated that a more balanced approach that includes at least as many examples and self-explanation opportunities leads to more robust learning.
 
 
* Salden - Worked Examples in Geometry [[Does learning from worked-out examples improve tutored problem solving?]]
 
 
 
* McLaren - [[McLaren et al - Studying the Learning Effect of Personalization and Worked Examples in the Solving of Stoich Problems|Worked Examples in Chemistry]]
 
* Nokes - [[Bridging_Principles_and_Examples_through_Analogy_and_Explanation]] in Physics.  See also Interactive Communication.
 
* '''**New**''' Wylie, Mitamura, Koedinger - IWT [[Assistance_Dilemma_English_Articles]].  See also CF and CMDM thrusts.
 
 
====== More Direct Support for MetaCognition ======
 
* Roll- Labgebra - [[Roll_IPL|Inventing rules as preparation for future learning]]. Highlights that will go into it:  1) Last year we completed a study with 7 classes at Steel Valley Middle School. We got positive results - cognitive and motivational benefits. There is also a cogsci paper, which will be the basis for the updated Wiki page. 2) Over the year since then we built a tutoring system for IPL.  3) 10 days ago I finished another study in Steel Valley Middle School evaluating the tutor.
 
* [[The Help Tutor Roll Aleven McLaren]]
 
 
====== Visual-Verbal Coordination ======
 
* Butcher- Visual-Verbal [[Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)]]
 
* Davenport - Visual Representations in Science [[Visual_Representations_in_Science_Learning]]
 
* Chang [[Leverage_Learning_from_Chemistry_Visualizations_%28Ming_%26_Schoenfield%29]]
 
* Reed Corbett Hoffman- [[Enhancing Learning through Computer Animation]]
 
* '''**New**''' Aleven - Multiple Interactive Representation [[Sequencing_learning_with_multiple_representations_of_rational_numbers_(Aleven%2C_Rummel%2C_%26_Rau)]]
 
 
===== Motivation =====
 
Consistent with the goals of the new Metacognition and Motivation Thrust, which will officially begin in Year 6, past PSLC projects have been begun investigating motivational issues.  We summarize results of projects
 
* Baker - How Content and Interface Features Influence Student Choices Within the Learning Space  [[Baker_Choices_in_LE_Space]]
 
* [[Mayer_and_McLaren_-_Social_Intelligence_And_Computer_Tutors]]
 
* '''**New**''' Aleven- Improving student affect through adding game elements to mathematics LearnLabs [[Math_Game_Elements]]
 
 
===== Bringing it Together:  Exploring Effects of Combining Principles =====
 
(Perhaps this should be saved for a cross-thrust section as there is CF, CMDM, and M&M involved.)
 
* '''**New**''' Aleven - [[Geometry_Greatest_Hits]]
 

Latest revision as of 13:39, 25 May 2009

For more full descriptions see PSLC Integrated Project Summary.

Refinement & Fluency CLUSTER ==> Cognitive Factors THRUST [Chuck]

Coordinative Learning CLUSTER ==> CF or Metacognition & Motivation THRUST [Ken]

Metacognition

Example-Rule Coordination
  • Roll- Labgebra - Inventing rules as preparation for future learning. Highlights that will go into it: 1) Last year we completed a study with 7 classes at Steel Valley Middle School. We got positive results - cognitive and motivational benefits. There is also a cogsci paper, which will be the basis for the updated Wiki page. 2) Over the year since then we built a tutoring system for IPL. 3) 10 days ago I finished another study in Steel Valley Middle School evaluating the tutor.
  • The Help Tutor Roll Aleven McLaren
  • **New** Aleven - Geometry_Greatest_Hits
Visual-Verbal Coordination

Motivation

Integrative Communication CLUSTER ==> Social Communicative THRUST [Chuck]

Computational Modeling and Data Mining THRUST [Ken]

Knowledge Analysis: Developing Cognitive Models of Domain-Specific Content

Learning Analysis: Developing Models of Domain-General Learning and Motivational Processes

Instructional Analysis: Developing Predictive Engineering Models to Inform Instructional Event Design