Abstract:
Learning systems can utilize many practice exercises, ranging from simple multiple-choice questions to complex problem-solving activities. In this article, we propose a c...Show MoreMetadata
Abstract:
Learning systems can utilize many practice exercises, ranging from simple multiple-choice questions to complex problem-solving activities. In this article, we propose a classification framework for such exercises. The framework classifies exercises in three main aspects: 1) the primary type of interaction; 2) the presentation mode; and 3) the integration in the learning system. For each of these aspects, we provide a systematic mapping of available choices and pointers to relevant research. For developers of learning systems, the framework facilitates the design and implementation of exercises. For researchers, the framework provides support for the design, description, and discussion of experiments dealing with student modeling techniques and algorithms for adaptive learning. One of the aims of the framework is to facilitate replicability and portability of research results in adaptive learning.
Published in: IEEE Transactions on Learning Technologies ( Volume: 13, Issue: 4, 01 Oct.-Dec. 2020)

Masaryk University, Brno, Czech Republic
Radek Pelánek received the Ph.D. degree in computer science from Masaryk University, Brno, Czechia, for his work on formal verification.
Currently, he is the Leader of the Adaptive Learning Group with Masaryk University, and is interested in both theoretical research in user modeling and practical development of adaptive learning systems. Since 2010, his research interests include educational data mining and learning analy...Show More
Radek Pelánek received the Ph.D. degree in computer science from Masaryk University, Brno, Czechia, for his work on formal verification.
Currently, he is the Leader of the Adaptive Learning Group with Masaryk University, and is interested in both theoretical research in user modeling and practical development of adaptive learning systems. Since 2010, his research interests include educational data mining and learning analy...View more

Masaryk University, Brno, Czech Republic
Radek Pelánek received the Ph.D. degree in computer science from Masaryk University, Brno, Czechia, for his work on formal verification.
Currently, he is the Leader of the Adaptive Learning Group with Masaryk University, and is interested in both theoretical research in user modeling and practical development of adaptive learning systems. Since 2010, his research interests include educational data mining and learning analytics.
Radek Pelánek received the Ph.D. degree in computer science from Masaryk University, Brno, Czechia, for his work on formal verification.
Currently, he is the Leader of the Adaptive Learning Group with Masaryk University, and is interested in both theoretical research in user modeling and practical development of adaptive learning systems. Since 2010, his research interests include educational data mining and learning analytics.View more