Stimulate Engagement and Motivation in MOOCs Using an Ontologies Based Multi-Agents System

Abstract

Today, Massive Open Online Courses (MOOCs) have the potential to enable free online education on an enormous scale. However, a concern often raised about MOOCs is the consistently high dropout rate of MOOC learners. Although many thousands of learners enroll on these courses, a very small proportion actually complete the course.This work is at the heart of this issue. It is interested in contributing on multi-agents systems and ontologies to describe the learning preferences and adapt educational resources to learner profile in MOOCs platforms. The primary aim of this work is to exploit the potential of multi-agents systems and ontologies to improve learners' engagement and motivation in MOOCs platforms and therefore reduce the drop-out rates.As part of the contribution of this work, the paper proposes a model of Multi-Agent System (MAS), based on ontologies for adapting the learning resources proposed to a learner in a MOOCs platform according to his learning preferences. To model an adequate online course, the determination of learner's preferences is done through the analysis of learner behavior relying on his indicator MBTI (Myers Briggs Type Indicator). The proposed model integrates the main functionalities of an intelligent tutoring system: profiling, updating of the profile, selection, adaptation and presentation of adequate resources. The architecture of the proposed system is composed on two main agents, four ontologies and a set of modules implemented. by integrating learner model and content adaptation concepts in order to promote engagement and motivation of such learners. Finally, the paper proceeds to describe the proposed architecture to implement the framework and concludes with future works.Stimulate Engagement and Motivation in MOOCs Using an Ontologies Based Multi-Agents System participant (learner) in four levels. These levels are described as Sub-Ontologies (SO): OLearner = {SOIdentity, SOPreferences , SOCapacity, SOHistory} (Fig. 2).

Dynamic Identification of Learning Styles in MOOC Environment Using Ontology Based Browser Extension

“…al. [15] have designed an approach with ontology-based multi-agent systems to describe a learners' requirements and make sure the required resources will be incorporated in the MOOC. The main aim of this approach was to manage drop-out students and improve learner's engagement and interest in the MOOC courses.…”

Dynamic Selection Approach to Overcome the Demotivation of Learners in a Community Learning System

“…These same techniques can be adopted in the context of heterogeneous grouping by creating first homogeneous groups, and then taking a learner from each group to form a heterogeneous group. Generally, the heterogeneous and mixed groupings are considered as optimization problems and therefore optimization methods, such as genetic algorithms, are used to find the best possible combinations [15] [16].…”

“…According to Wenger et al, only 25% of members represent the hard core of a CoP, 30% are considered as active members who participate less regularly, while 45% are considered as peripheral members who typically learn by observing the interactions between the core and the active members [25]. In our previous works, we have identified the main demotivating factors that can lead to a lack of participation of members to the community interactions [15][26] [27]. We summarize them in the following points:…”

Animated Pedagogical Agents to Assist Learners and to keep them motivated on Online Learning Environments (LMS or MOOC)

“…They suppose also that Pedagogical Agents are adaptable and versatile and can Address Learners'Sociocultural Needs. Similar research suggests integrating agents into MOOCs to adapt learning resources to the learner based on his preferences and learning style [17]. Research made in [32] proposes a Recommendation System for MOOCs based on the concept of generating predictions according to other learners' experiences.…”

“…This is a recurring problem that has resulted in a lot of recent research. El Mhouti [17], presents a synthesis of the literature on the different reasons. He cites the main reasons as follows: no intention to complete, starting late, lack of time, course difficulty, lack of support, lack of digital or learning skills, bad experiences and expectations, peer reviewing, no adaptation is provided.…”