Towards an Ontological Learner’s Modeling During and After the COVID-19 Pandemic

Abstract

The health crisis and the unprecedented upheaval in the education systems which it caused are far from being over, consequently, the adaptation of the learning experience is most needed, and it should take into consideration the criteria of this specific crisis and its impact on the physical and mental health of the learners. In this article, we aimed to present an ontologybased learner model that will bring together the pedagogical and psychological characteristics, but also the health risks generated by the epidemic on the learners, following a Knowledge-Engineering Methodology. We developed an ontology that combines the IMS-LIP standard features and the learner characteristic. It is ready for different uses in different systems and situations during and after the COVID-19 pandemic, and it will give a global representation of the learner in order to allow him to get the best-adapted courses.

A design of a multi-agent recommendation system using ontologies and rule-based reasoning: pandemic context

“…The learner model must represent the characteristics and information provided explicitly by the learner himself (questionnaires, multiple-choice questions) or implicitly by automatically retrieving information from the environment, for example, by obtaining the location and type of the device used, or by inference from the analysis of the interaction between the user and the environment [19]. In this system, the ontological learner model developed is based on our previous work [20] with the integration of other learner's characteristics, as shown in Figure 1. The model we used is compliant with the learner information package (IMS LIP) and it includes five dimensions: − Context: it groups the characteristics likely to affect the learner's situation (the device used, the location, the frequency of access to the system and the type of internet connection).…”

“…For depression, the learner will answer a short questionnaire that takes 5 to 7 minutes with 16 items, this is the quick inventory of depressive symptomatology (QIDS) a self-report rating scale that reveals the severity of symptoms and symptomatic change. The severity of depression ranges from 0 to 27 as follows (0-5) none, (6-10) mild, (11)(12)(13)(14)(15) moderate, (16)(17)(18)(19)(20) severe, (21-27) very severe [33]. To detect anxiety disorder, the learner must pass the generalized anxiety disorder scale (GAD-7), a 13-questions survey.…”

A Social Profile Ontology to Enhance Learner Experience in MOOCs

“…Several research works propose the building of the learner profile in different areas. An ontology-based learner profile [14] is developed using Knowledge Engineering Methodology. The ontology combines the pedagogical and psychological characteristics of the learner as well as the health risks generated by the COVID 19 epidemic.…”