PSLC Year 5 Projects

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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.

PSLC studies in math, science, and language learning domains have been exploring the combination of worked-examples and self-explanation with computer-based tutoring during problem-solving practice. These studies bring together different research traditions 1) studies worked examples and cognitive load theory from Educational Psychology, particularly in Europe, 2) self-explanation studies primarily from cognitive science and psychology, and 3) intelligent tutoring system primarily from Computer Science.

As discussed in Schwonke, Renkel, Krieg, Wittwer, Aleven, & Salden (2009), past studies of worked example effects had compared against a control condition involving unsupported problem solving. This award-winning project has demonstrated the benefit of adding worked examples even in the context of a stronger control condition, namely, problem solving with instructional support of an intelligent tutor. Students spend take 20% less time in the example condition and learn as much or more on a variety of robust learning measures. The project has further demonstrated that a computer tutor that automatically adapts the transition from worked examples to problem solving leads to even further gains in robust student learning (Salden, Aleven, Renkl, & Schwonke, 2009).

Reflecting the benefits of a center in general and of the PSLC infrastructure in particular, this line of research has involved 5 laboratory studies and 3 in vivo studies run in labs and classrooms in Freiburg, Germany and Pittsburgh. These studies were all run in the context of the Geometry Cognitive Tutor, which automates delivery of complex instruction, insures reliable implementation of experimental differences, and provides rich process data (every 10 seconds) over hours of instruction. These studies involved more than 900 students and an average of 4 hours of learning time per student.

While the in vivo studies demonstrate that these substantial effects are robust to the high variability in real classroom studies, the associated lab studies allow more in depth investigation of learning process and learning theory. In particular, resent results from one of the lab studies reported in Schwonke et al. (2009) enhance theoretical understanding of complex human learning processes, particularly how and how deeply students choose to reason about instructional examples.

Table XX. Categories and examples of students' self-explanations

  • Principle-based explanation The learner verbalizes and elaborates on a mathematical principle. Mentioning a principle without some elaboration would not be coded as a principle-based explanation “Oh, that is major-minor arc, that means I’ve to subtract the minor arc from 360°”
  • Visual mapping The learner tries to relate content organized in different external representations and/or different visual tools (verbally as well as supported by gestures) “Where is angle ETF. Ah, has to be this one” (learner is pointing at corresponding spots in the graphic)
  • Goal–operator combination The learner verbalizes a (sub-)goal together with operators that help to accomplish this (sub-)goal “You can calculate this arc of a circle by subtracting 33° from 360°”

The example condition provided both more principle-based self-explanations and more visual mapping explanations whereas the problem condition provided many more goal–operator combination explanations. Principle-based and visual mapping self-explanations are consistent deeper processing that places more attention on the geometry rules and the non-trivial mapping of the rules to specific situations. These explanations suggest greater attention to the if-part or retrieval features of relevant knowledge components. Goal-operator explanations attend more to the arithmetic, that which must be done in problem-solving (the then-part). The arithmetic processing may be strengthening prerequisite knowledge, but is not directly relevant to the target geometric content. The greater number of principle-based and visual mapping self-explanation in the example group is consistent with the theory that example study frees cognitive resources to allow learners to engage in deeper processing whereas problem-solving distracts from such processing and encourages greater focus on problem completion.

Investigations of correlations between self-explanation behavior partially support this explanation. Visual mapping explanations were significantly correlated with conceptual transfer, but principle-based explanations were not. Both principle-based and goal-operator explanations were significantly correlated with procedural transfer. It appears that greater use of examples enhances deeper processing (at least of the mapping type) and that in turn leads to greater conceptual understanding and transfer. Recall, that this achievement benefit was observed on top of substantial efficiency benefit in which example students needed 20% less instructional time.

  • Schwonke, R., Renkel, A., Krieg, C, Wittwer, J., Aleven, V., Salden, R. J. C. M. (2009). The Worked-example Effect: Not an Artefact of Lousy Control Conditions. Computers in Human Behavior, 25, 258-266.
  • Salden, R. J. C. M., Aleven, V. A. W. M. M., Renkl, A., & Schwonke, R. (2009). Worked examples and tutored problem solving: Redundant or synergistic forms of support? Topics in Cognitive Science, 1, 203-213.


The Center-mode focus on the issues of worked examples and self-explanation has allowed for cross-domain investigations of how these principles may further enhance student learning beyond already effective intelligent tutoring systems. These studies have been performed in Chemistry, Algebra, Physics, and English as a Second Language. The Chemistry studies replicated the result from Geometry that replacing half of the problems in a tutoring system with worked examples leads to more efficient learning. Across three studies, one at the college level and two [CHECK] at the high school level, McLaren et al (200X) [GET] found students learned just as much but in about 20% less time. If scaled to a semester long course, students using an example-based approach would have more than 3 free weeks!

[WHAT'S THE NEW PART OF BRUCE'S WIKI?]

Unlike the Geometry studies, these studies did not have a conceptual transfer test, but a study in progress does so.

Other PSLC studies have explored the potential benefits of worked examples and self-explanation a

Related notion of analogical comparison -- another method for students to process examples more deeply ...

[WHAT'S THE NEW PART OF TIM'S WIKI?]

A study in English is exploring whether the benefits of worked examples and self-explanation extend from math and science domains to language learning. This study is summarized in the Cognitive Factors section below. Interestingly, while there is some evidence that self-explanation helps in early learning, it does not appear to have as strong a benefit. This provides an important theoretical puzzle: Under what conditions is prompted self-explanation a productive strategy? Ongoing theoretical and empirical work is investigating this question.

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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