Vilnius Gediminas Technical University (LITHUANIA)
About this paper:
Appears in: INTED2019 Proceedings
Publication year: 2019
Pages: 3758-3765
ISBN: 978-84-09-08619-1
ISSN: 2340-1079
doi: 10.21125/inted.2019.0959
Conference name: 13th International Technology, Education and Development Conference
Dates: 11-13 March, 2019
Location: Valencia, Spain
The paper aims to analyse application of dedicated psychological questionnaires and educational data mining (EDM) to identify students’ learning styles and thus to create conditions to personalise learning.

Dedicated psychological questionnaires could help us to establish individual probabilistic suitability indexes for each analysed student and each learning activity in e.g. virtual Learning Environment (VLE) to identify which learning activities are the most suitable for particular student. Students’ learning styles-based probabilistic suitability index shows the level of suitability of given learning content, activity or environment to particular student. The higher is probabilistic suitability index the better learning activity fits particular student’s needs. Using appropriate EDM methods and techniques, we could analyse what particular learning activities (and appropriate VLE tools) were practically used by these students earlier, and to what extent. After that, the data on practical use of VLE-based learning activities or tools should be compared with students’ probabilistic suitability indexes. In the case of any noticeable discrepancies, students’ profiles and accompanied probabilistic suitability indexes should be identified more precisely, and students’ personal leaning paths in VLE should be corrected according to new identified data. Thus, using EDM, we could noticeably enhance students’ learning quality and effectiveness.

In the paper, first of all, related research review is provided. Second, methodology to personalise learning using both dedicated psychological questionnaires and educational data mining methods and techniques to identify students’ learning styles is presented. Third, some real-life examples of applying both methods using Felder-Silverman Learning Styles Model are presented. The paper is concluded by the statement that the best way to exactly identify students’ learning styles is consistent application of both dedicated psychological questionnaires and educational data mining.
Learning personalisation, learning styles, dedicated psychological questionnaires, educational data mining, Felder-Silverman Learning Styles Model.