eprintid: 30316 rev_number: 14 eprint_status: archive userid: 19147 dir: disk0/00/03/03/16 datestamp: 2019-02-28 14:14:40 lastmod: 2021-04-02 15:59:34 status_changed: 2019-02-28 14:14:40 type: conference_item metadata_visibility: show creators_name: Bovo, Angela creators_name: Sanchez, Stéphane creators_name: Heguy, Olivier creators_name: Duthen, Yves creators_idrefppn: 183135180 creators_idrefppn: 078096898 creators_idrefppn: 061330302 title: Clustering Moodle data as a tool for profiling students subjects: subjects_INFO abstract: This paper describes the first step of a research project with the aim of predicting students' performance during an online curriculum on a LMS and keeping them from falling behind. Our research project aims to use data mining, machine learning and artificial intelligence methods for monitoring students in e-learning trainings. This project takes the shape of a partnership between computer science / artificial intelligence researchers and an IT firm specialized in e-learning software. We wish to create a system that will gather and process all data related to a particular e-learning course. To make monitoring easier, we will provide reliable statistics, behaviour groups and predicted results as a basis for an intelligent virtual tutor using the mentioned methods. This system will be described in this article. In this step of the project, we are clustering students by mining Moodle log data. A first objective is to define relevant clustering features. We will describe and evaluate our proposal. A second objective is to determine if our students show different learning behaviours. We will experiment whether there is an overall ideal number of clusters and whether the clusters show mostly qualitative or quantitative differences. Experiments in clustering were carried out using real data obtained from various courses dispensed by a partner institute using a Moodle platform. We have compared several classic clustering algorithms on several group of students using our defined features and analysed the meaning of the clusters they produced. date: 2013 publisher: IEEE faculty: info divisions: IRIT keywords: LMS - Moodle data clustering - Moodle log data mining - Artificial intelligence methods - E-learning course - E-learning software - E-learning trainings - Intelligent virtual tutor - Learning behaviours - Machine learning - Online curriculum - Student mon language: en has_fulltext: TRUE view_date_year: 2013 full_text_status: public pres_type: paper pagerange: 121-126 event_title: 2nd International Conference on e-Learning and e-Technologies in Education - ICEEE 2013 event_type: conference harvester_local_overwrite: eprintid harvester_local_overwrite: userid harvester_local_overwrite: date harvester_local_overwrite: official_url harvester_local_overwrite: issn harvester_local_overwrite: dir harvester_local_overwrite: keywords harvester_local_overwrite: pagerange harvester_local_overwrite: publisher harvester_local_overwrite: volume harvester_local_overwrite: creators_name harvester_local_overwrite: faculty harvester_local_overwrite: site harvester_local_overwrite: abstract harvester_local_overwrite: title harvester_local_overwrite: publication harvester_local_overwrite: type harvester_local_overwrite: number harvester_local_overwrite: note harvester_local_overwrite: ispublished harvester_local_overwrite: id_number harvester_local_overwrite: event_title harvester_local_overwrite: pres_type harvester_local_overwrite: event_location harvester_local_overwrite: series harvester_local_overwrite: isbn harvester_local_overwrite: book_title harvester_local_overwrite: editors_name harvester_local_overwrite: department harvester_local_overwrite: thesis_type harvester_local_overwrite: pages harvester_local_overwrite: place_of_pub harvester_local_overwrite: divisions harvester_local_overwrite: subjects harvester_local_overwrite: language harvester_local_overwrite: event_type harvester_local_overwrite: creators_idrefppn site: ut1 citation: Bovo, Angela , Sanchez, Stéphane, Heguy, Olivier and Duthen, Yves (2013) Clustering Moodle data as a tool for profiling students. In: 2nd International Conference on e-Learning and e-Technologies in Education - ICEEE 2013. document_url: https://publications.ut-capitole.fr/id/eprint/30316/1/assistant_20480809_3081734252_0.pdf