1 Vilnius Gediminas Technical University; Vilnius University Institute of Mathematics and Informatics (LITHUANIA)
2 Vilnius Gediminas Technical University (LITHUANIA)
3 Vilnius University Institute of Mathematics and Informatics (LITHUANIA)
About this paper:
Appears in: EDULEARN17 Proceedings
Publication year: 2017
Pages: 10180-10188
ISBN: 978-84-697-3777-4
ISSN: 2340-1117
doi: 10.21125/edulearn.2017.0928
Conference name: 9th International Conference on Education and New Learning Technologies
Dates: 3-5 July, 2017
Location: Barcelona, Spain
The paper aims to analyse application of learning analytics / educational data mining (LA / EDM) to support learning personalisation and optimisation in virtual learning environment Moodle. LA / EDM are known as the measurement, collection, analysis, and reporting of data about learners and their contexts to understand and optimise learning and environments in which it occurs. In the paper, appropriate literature review is performed on LA / EDM methods and techniques that could be applied to personalise students’ learning in Moodle. After that, the authors’ original methodology to personalise learning is presented. First of all, existing Moodle-based learning activities and tools are analysed to be further interlinked with appropriate students’ learning styles. For this purpose, Felder-Silverman learning styles model (FSLSM) is applied in the research. Students’ learning styles according to FSLSM are interlinked with the most suitable Moodle-based learning activities and tools using expert evaluation method. After that, a group of students is analysed in terms of identifying their individual learner profiles according to Soloman-Felder index of learning styles questionnaire. After identifying individual learner profiles, probabilistic suitability indexes are calculated for each analysed student and each Moodle-based learning activity to identify which learning activities or tools are the most suitable for particular student. From theoretical point of view, the higher is probabilistic suitability index the better learning activity or tool fits particular student’s needs. On the other hand, students practically used some learning activities or tools in real learning practice in Moodle before identifying the aforementioned probabilistic suitability indexes. Here we could hypothesise that students preferred to practically use particular Moodle-based learning activities or tools that fit their learning needs mostly. Thus, using appropriate LA / EMD methods and techniques, it would be helpful to analyse what particular learning activities or tools were practically used by these students in Moodle, and to what extent. After that, the data on practical use of Moodle-based learning activities or tools should be compared with students’ probabilistic suitability indexes. In the case of any noticeable discrepancies, students’ profiles and accompanied suitability indexes should be identified more precisely, and students’ personal learning paths in Moodle should be corrected according to new identified data. In this way, after several iterations, we could noticeably enhance students’ learning quality and effectiveness.
Learning analytics, educational data mining, learning personalisation, virtual learning environments, students’ learning styles.