Difference between revisions of "PSLC Year 5 Projects"

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The work in this thrust builds on prior work started before the renewal, particularly work in the Coordinative Learning Cluster.
 
The work in this thrust builds on prior work started before the renewal, particularly work in the Coordinative Learning Cluster.
 
===== Metacognition =====
 
===== Metacognition =====
−
Past work within the Coordinative Learning Cluster emphasized to board 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 the metacognitive coordination processes can be supported or improved.
+
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 ======
 
====== 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?]]
 
* Salden - Worked Examples in Geometry [[Does learning from worked-out examples improve tutored problem solving?]]
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 +
 
* 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.
 
* Nokes - [[Bridging_Principles_and_Examples_through_Analogy_and_Explanation]] in Physics.  See also Interactive Communication.

Revision as of 15:51, 22 May 2009

New Year 5 projects

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

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.


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
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

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.)

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