A Classification Framework for Practice Exercises in Adaptive Learning Systems

PELÁNEK, Radek. A Classification Framework for Practice Exercises in Adaptive Learning Systems. IEEE Transactions on Learning Technologies. 2020, vol. 13, No 4, p. 734-747. ISSN 1939-1382. Available from: https://dx.doi.org/10.1109/TLT.2020.3027050.

Basic information

• Original name: A Classification Framework for Practice Exercises in Adaptive Learning Systems
• Authors: PELÁNEK, Radek (203 Czech Republic, guarantor, belonging to the institution).
• Edition: IEEE Transactions on Learning Technologies, 2020, 1939-1382.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: United States of America
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• Impact factor: Impact factor: 3.720
• RIV identification code: RIV/00216224:14330/20:00118359
• Organization unit: Faculty of Informatics
• Doi: http://dx.doi.org/10.1109/TLT.2020.3027050
• UT WoS: 000600838500008
• Keywords in English: adaptive learning; classification; framework; student modeling
• Tags: International impact, Reviewed

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.