Scaffolding Problem Solving with Annotated Worked-Out Examples to Promote Deep Learning

About:

This study compares the relative utility of an intelligent tutoring system that uses procedure-based hints to a version that uses worked-out examples for learning college level physics. In order to test which strategy produced better gains in competence, two versions of Andes were used: one offered participants graded hints and the other offered annotated, worked-out examples in response to their help requests. We found that proviiding examples was at least as effective as the hint sequences and was more efficient in terms of the number of problems it took to obtain the same level of mastery.

People:

• Michael A. Ringenberg, University of Pittsburgh, Graduate Student, Intelligent Systems Program
• Kurt VanLehn, University of Pittsburgh, Professor, Intelligent Systems Program and Computer Science

Publications:

• Ringenberg, Michael & Kurt VanLehn; "Scaffolding Problem Solving with Annotated Worked-Out Examples to Promote Deep Learning." In Intelligent Tutoring Systems: Eighth International Conference (ITS 2006), Jhongli, Taiwan. Springer-Verlag Lecture Notes in Computer Science.Winner of Best Paper First Authored by a Student Award.

Sponsors & Affiliations:

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• Grant #0354420

Resources:

• Worked-out Example 1
• Worked-out Example 2
• Worked-out Example 3
• Worked-out Example 4
• Worked-out Example 5
• Questions used in study post-test