Towards a hybrid recommendation approach using a community detection and evaluation algorithm

In social learning platforms, community detection algorithms are used to identify groups of learners with similar interests, behavior, and levels. While, recommendation algorithms personalize the learning experience based on learners' profile information, including interests and past behavior. Combining these algorithms can improve the recommendation quality by identifying learners with similar needs and interests for more accurate and relevant suggestions. Community detection enhances recommendations by identifying groups of learners with similar needs and interests. Leveraging their similarities, recommendation algorithms generate more accurate suggestions. In this article, we propose a novel approach that combines community detection and recommendation algorithms into a single framework to provide learners with personalized recommendations and opportunities for collaborative learning. Our proposed approach consists of three steps: first, applying the maximal clique-based algorithm to detect learning communities with common characteristics and interests; second, evaluating learners within their communities using static and dynamic evaluation; and third, generating personalized recommendations within each detected cluster using a recommendation system based on correlation and co-occurrence. To evaluate the effectiveness of our proposed approach, we conducted experiments on a real-world dataset. Our results show that our approach outperforms existing methods in terms of modularity, precision, and accuracy

SIMSAD : simulation d'un modele elabore selon la methode de specification de logiciels SADT

SIGLECNRS T Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Towards a New Generation of Intelligent Tutoring Systems

In this paper, a new approach of intelligent tutoring systems based on adaptive workflows and serious games is proposed. The objective is to use workflows for learning and evaluation process in the activity-based learning context. We aim to implement a system that allow the coexistence of an intelligent tutor and a human tutor who could control and follow-up the execution of the learning processes and intervene in blocking situations. Serious games will be the pillar of the evalu-ation process. The purpose is to provide new summative evaluation methods that increase learner’s motivation and encourage them to learn

Automatic Composition of Instructional Units in Virtual Learning Environments

In this paper, a new approach for automatic composition of instructional units based on a new variant of Harmony Search Algorithm is proposed. The purpose is to solve curriculum sequencing issue by designing and arranging learning content in a suitable sequence. By suitable sequence we mean a learning sequence that fits learner level and presents the content in a way that conveys its structure to learner. Results show that the proposed approach is promising. For instance, individualized courseware plan are generated “on the spot” carefully considering both students characteristics and subject-matter coherence

Cognitive Learning Style Detection in e-Learning Environments using Artificial Neural Network

COVID-19 pandemic has impacted all aspects of our lives including learning. With the particular growth of e-learning, teaching approaches are being implemented at a distance on online platforms due to this pandemic. In this context, to make student involved throughout the online course, it is recommended to create an efficient platform similar to the traditional learning mode.  In this study, we aims to improve learning style detection process by exploring additional such as cognitive traits. In fact, we have proposed novel approach based on Artificial neural network that classify students according to their level of cognitive learning styles in real-time. The proposed automated approach will certainly provide tutors with exhaustive information that helps them in achieving an improved and innovative online learning method. The results obtained are quite interesting and demonstrate the relevance of our solution

Effects of Social Constructivist Mobile Learning Environments on Knowledge Acquisition: A Meta-Analysis

This meta-analysis has two aims: a) to address the main effects of social constructivist mobile learning environments on learners’ knowledge acquisition and their academic achievements b) to address potential factors regarding design principles and instructional methods for successful social constructivist mobile environments in a blended learning context. We selected 24 articles that meet the inclusion criteria: empirical studies implementing mobile learning in a blended environment using social constructivism approach. The selected studies are not identical in terms of instructional strategies, tools and devices, period and student’s expertise level. These factors lead to variations in the magnitude of the effect sizes. The review reveals that there is a positive effect of mobile learning on the knowledge acquisition, learners’ achievements, attitudes and motivation despite the high cognitive load. This is shown through the combined effect size. A last remarkable finding related to retention is that students in such environments fulfill their academic tests, but remember less the acquired knowledge after a retention period