Group student profiling in massive open online courses using educational data mining

Abdelghani Hmich, Abdelmajid Badri and Aicha Sahel

International Journal of Data Science, 2022, vol. 7, issue 1, 78-94

Abstract: Student profiling is one of the great accomplishments in the field of educational data mining (EDM). Student profile can mainly offer the most exact description of students in order to be able to offer students the most appropriate personalisation and recommendation. In this paper, we use a demarche for discovering group Massive Open Online Course (MOOC) student profile in relation to their quiz performance. This demarche is based on the combination of hierarchical density-based spatial clustering of applications with noise (HDBSCAN) and apriori algorithm, first the groups of students with similar learning characteristic are extracted by using the clustering algorithm HDBSCAN, then the extracted groups were analysed by using apriori algorithm in order to characterise the distinct profiles of group of students using data collected from a MOOC in Moodle platform. The results show that there are three groups of students organised by quiz performance and learning behaviours. First group is characterised by the low quiz performance, the second group is characterised by the good quiz performance and the third group is characterised by the excellent quiz performance.

Keywords: student profiling; EDM; educational data mining; E-learning; MOOCs; Massive Open Online Courses; clustering; HDBSCAN; association rules mining. (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=124358 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdsci:v:7:y:2022:i:1:p:78-94

Access Statistics for this article

More articles in International Journal of Data Science from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().