PSLC Year 5 Projects

New Year 5 projects

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

• Nel de Jong - Second Language Learning Fostering_fluency_in_second_language_learning, Fluency_Summer_Intern_Project_2008

• **Out of Date - Needs final update** McCormick - ESL self-correction of student-recorded speaking activities: Year 2 The_self-correction_of_speech_errors_(McCormick,_O’Neill_&_Siskin)

• **Out of Date** Julie Fiez - Fiez Project Plan A Novel Writing System
• **New** Wylie, Mitamura, Koedinger - IWT Assistance_Dilemma_English_Articles See also CL cluster and CMDM thrust.
• **New** Dunlap, Perfetti - Lexical Quality of English Second Language Learners Orthography
• **New** Balass, Nelson, Perfetti - Learning_ESL_Vocabulary_with_Context_and_Definitions:_Order_Effects_and_Self-Generation
• **New - Empty** Mizera - Formulaic sequences and the development of L2 oral fluency
• **New** Liu, Perfetti, Wang, Wu, Guan - Integration of reading and writing in learning Chinese words and sentences

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

Metacognition

Example-Rule Coordination

• Salden - Worked Examples in Geometry Does learning from worked-out examples improve tutored problem solving?
• McLaren - 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.

• 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

• Butcher- Visual-Verbal Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_&_Aleven)
• Davenport - Visual Representations in Science Visual_Representations_in_Science_Learning
• Chang Leverage_Learning_from_Chemistry_Visualizations_(Ming_&_Schoenfield)
• Reed Corbett Hoffman- Enhancing Learning through Computer Animation
• **New** Aleven - Multiple Interactive Representation Sequencing_learning_with_multiple_representations_of_rational_numbers_(Aleven,_Rummel,_&_Rau)

Motivation

• 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

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

• Nokes - Bridging Principles Bridging_Principles_and_Examples_through_Analogy_and_Explanation See also Coordinative Learning.
• Van Lehn - Ill defined Physics Ringenberg_Ill-Defined_Physics
• Walker - Collaborative Extensions Adaptive_Assistance_for_Peer_Tutoring_(Walker,_Rummer,_Koedinger)
• Katz - Automated Dialogue Extending_Reflective_Dialogue_Support_(Katz_&_Connelly)
• **New** Nokes - Gadgil,Soniya Analogical Scaffolding in Collaborative Learning Analogical_Scaffolding_in_Collaborative_Learning

Computational Modeling and Data Mining THRUST [Ken]

Knowledge Analysis: Developing Cognitive Models of Domain-Specific Content

• **New** Nokes, Hausmann - Harnessing what you know: The role of analogy in robust learning
• Cen thesis
• Pavlik
• Cross referencing projects in other thrusts:
  • Wylie English Article Analysis

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

• **New** Matsuda - SimStudent Application_of_SimStudent_for_Error_Analysis
• Cross referencing projects in other thrusts:
  • Mayer? Baker?

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

• Cross referencing projects in other thrusts:
  • Mclaren- Assistance Dilemma, continuation of 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.

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 - 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 - 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_&_Aleven)
• Davenport - Visual Representations in Science Visual_Representations_in_Science_Learning
• Chang Leverage_Learning_from_Chemistry_Visualizations_(Ming_&_Schoenfield)
• Reed Corbett Hoffman- Enhancing Learning through Computer Animation
• **New** Aleven - Multiple Interactive Representation Sequencing_learning_with_multiple_representations_of_rational_numbers_(Aleven,_Rummel,_&_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