Identifying learning styles in learning management systems by using indications from students' behaviour

Sabine Graf, Kinshuk, Tzu Chien Liu

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

144 Citations (Scopus)

Abstract

Making students aware of their learning styles and presenting them with learning material that incorporates their individual learning styles has potential to make learning easier for students and increase their learning progress. This paper proposes an automatic approach for identifying learning styles with respect to the Felder-Silverman learning style model by inferring their learning styles from their behaviour during they are learning in an online course. The approach was developed for learning management systems, which are commonly used in elearning. In order to evaluate the proposed approach, a study with 127 students was performed, comparing the results of the automatic approach with those of a learning style questionnaire. The evaluation yielded good results and demonstrated that the proposed approach is suitable for identifying learning styles. By using the proposed approach, students' learning styles can be identified automatically and be used for supporting students by considering their individual learning styles.

Original languageEnglish
Title of host publicationProceedings - The 8th IEEE International Conference on Advanced Learning Technologies, ICALT 2008
Pages482-486
Number of pages5
DOIs
Publication statusPublished - 2008
Event8th IEEE International Conference on Advanced Learning Technologies, ICALT 2008 - Santander, Spain
Duration: 1 Jul. 20085 Jul. 2008

Publication series

NameProceedings - The 8th IEEE International Conference on Advanced Learning Technologies, ICALT 2008

Conference

Conference8th IEEE International Conference on Advanced Learning Technologies, ICALT 2008
Country/TerritorySpain
CitySantander
Period1/07/085/07/08

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