Publication details
A Classification Framework for Practice Exercises in Adaptive Learning Systems
| Authors | |
|---|---|
| Year of publication | 2020 |
| Type | Article in Periodical |
| Magazine / Source | IEEE Transactions on Learning Technologies |
| MU Faculty or unit | |
| Citation | |
| Web | https://ieeexplore.ieee.org/document/9210602 |
| Doi | http://dx.doi.org/10.1109/TLT.2020.3027050 |
| Keywords | adaptive learning; classification; framework; student modeling |
| Description | 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. |