A semantic approach to enhance moodle with personalization

Abstract

The purpose of this paper is to present a framework for an adaptive mechanism implemented in Moodle in order to improve learning outcomes and students' satisfaction with the learning process. The proposed mechanism adapts the learning content within the course according to students' characteristics expressed by their learning style. In our study, student's learning style is dynamically determined by monitoring students’ actions and activities during the learning process, and detecting patterns of behavior that correspond to specific learning style. Semantic web technologies are in the background of the entire adaptive system. In order to examine the effectiveness of the proposed model and students' feedback, an evaluation study was conducted on two groups of students. Students from the control group had access to standard Moodle course, while students from experimental group had access to personalized learning content. The results indicated that students' performance was improved by using the proposed framework, while the student's feedback from regarding its usefulness was positive.

What is needed to build a personalized recommender system for K-12 students’ E-Learning? Recommendations for future systems and a conceptual framework

“…Students submit input on their learning style via a form or questionnaire in the manual technique, while the system updates the students’ learning style based on their learning behavior in the automatic method. Researchers have employed a human method to determine the preferred learning style of the initial students and used an automatic method to update and confirm the original learning styles (Bhaskaran et al, 2019 ; Perišić et al, 2018 ). Others relied solely on the automated way to obtain the information (Fakooa et al, 2019 ).…”

A Modular and Semantic Approach to Personalised Adaptive Learning: WASPEC 2.0

“…Personalisation via extending LMSs includes designs such as MAL (Moodle Adaptive Learning), which allows learners to be customised based on their learning styles [25]. MAL was developed as an ontology to define the connections between learners, learning resources, and learner actions on the platform.…”

An Ontology to Formalize a Creative Problem Solving Activity

“…Most reviewed approaches attempt to give a general model the learner (i.e., in a diversity of learning tasks). From that perspective, learner modeling is often directed towards classification of learners into several learner profiles with regard to a set of characteristics, and by doing so to select the more adequate resources for a given learner (e.g., according to learning styles in [22]). This is also the case for knowledge representation beyond ontology approaches, such as those related to the xAPI and game-based learning profiles [23].…”