Year: 2009
Type: article
Abstract: The current research investigates a combination of two instructional approaches, tutored problem solving and worked examples. Tutored problem solving with automated tutors has proven to be an effective instructional method. Worked-out examples have been shown to be an effective complement to untutored problem solving, but it is largely unknown whether they are an effective complement to tutored problem solving. Further, while computer-based learning environments offer the possibility of adaptively transitioning from examples to problems while tailoring to an individual learner, the effectiveness of such machine-adapted example fading is largely unstudied. To address these research questions, one lab and one classroom experiment were conducted. Both studies compared a standard Cognitive Tutor with two example-enhanced Cognitive Tutors, in which the fading of worked-out examples occurred either in a fixed way or adaptively. Results indicate that the adaptive fading of worked-out examples leads to higher transfer performance on delayed posttests than the other two methods.
Source: Topics in Cognitive Science
Institutions: University of Freiburg, Carnegie Mellon University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: bronze
Funder: National Science Foundation
OpenAlex ID: W2054487443