Work

Training learners to self-explain: Designing instructions and examples to improve problem solving

Year: 2016

Type: article

Abstract: In this experiment, we integrated two learning methods – subgoal learning and constructive learning – to explore their interactions and effects on solving computer programming problems. We taught learners to solve problems using worked example and practice problem pairs with one of three kinds of instructional design that either did not highlight the subgoals, described the subgoals, or prompted participants to describe the subgoals for themselves. In addition, we varied the distance of transfer between the worked example and practice problem pairs. We found that instructions that highlighted subgoals improved performance on later problem solving tasks. The groups that performed best were those that received subgoal descriptions with farther transfer between examples and practice problems and those that described subgoals for themselves with nearer transfer.

Source: DigitalCommons@UNO

Authors: Lauren E. Margulieux, Briana B. Morrison, Mark Guzdial, Richard Catrambone

Institution: Georgia Institute of Technology

Language: English

Topic: Teaching and Learning Programming

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: green

Funder: National Science Foundation

Award: Type 1: Using Instructional Design Techniques to Create Distance CS Education to Support In-Service Teachers