Work

Scaffolding Problem Solving with Embedded Examples to Promote Deep Learning

Year: 2007

Type: article

Abstract: This study compared the relative utility of an intelligent tutoring system that uses procedure-based hints to a version that uses worked-out examples. The system, Andes, taught 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 providing examples was at least as effective as the hintsequences and was more efficient in terms of the number of problems it took to obtain the same level of mastery.

Source: D-Scholarship@Pitt (University of Pittsburgh)

Author: Michael Ringenberg

Language: English

Topic: Intelligent Tutoring Systems and Adaptive Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: green

Funder: National Science Foundation

Award: SLC Center: Pittsburgh Science of Learning Center: Studying Robust Learning with Learning Experiments in Real Classrooms

OpenAlex ID: W1576126524