Intelligent Agents for Dynamic Optimization of Learner Performances in an Online System

Abstract

Aim/Purpose: To identify and rectify the learning difficulties of online learners. Background: The major cause of learners’ failure and non-acquisition of knowledge relates to their weaknesses in certain areas necessary for optimal learning. We focus on e-learning because, within this environment, the learner is mostly affected by these vulnerabilities due to the lack of direct contact with the teacher, who would be able to point out the learner’s difficulties and help to rectify them. Methodology: The research sample was 49 learners enrolled in an online course. We focused on three cognitive factors: language, memory, and reasoning. We propose an approach to optimize learners’ performances based on two intelligent agents that model the role of a teacher: the “detector agent” and the “rectifier agent”. Contribution: The intelligent agents beneficially contribute to e-learning enrichment and the development of cognitive skills and solidification of knowledge acquisition. This is achieved by strengthening the memory, the assimilation of lessons by improving language skills, and the reinforcement of problem solving by developing reasoning and analysis capacity. Findings: The results show that the proposed approach efficiently detects the weaknesses of learners and resolves them intelligently. Future Research: The approach toward e-learning performance can be improved by focusing on other factors and intelligent agents that can improve the yield for learners and more effectively optimize system operation for their perceived needs.

Interaction in Online System is A Favor Key for Learners’ Success

“…In this context, a simulation approach of removing ambiguity context of a difficult topic to understand through the virtual interaction is implemented. This approach aims at helping learners understand a subject by a reciprocal exchange between a set of virtual learners, including the target learner .In order to achieve this objective, we have adopted the intelligent agents as a key solution to our modelling regarding the benefits they offer [25][26]. We have associated each learner to autonomous agents group which act and react automatically and intervene on the scheduled time in the system.…”

Study Smart Not Hard

“…However, the concentration of the learner is variable and is not under the direct control of learners (Poissant, Falardeau & Poellhuber, 1993) These goals are achieved by integrating intelligent agents that act in an automated and dynamic way in the distance learning system. The first agent modeled in our approach is "Detector Agent" (DA) (Kamsa et al, 2017). This agent plays several roles to support the learner in learning process.…”

Using The Intelligent Agents for Planning The Learning Time in A Distance Learning System

“…In this context, the first goal of our approach is to help the online learner to schedule automatically and dynamically his learning time by offering him a personal schedule adapted to his rhythm and his availability. This work is done by intelligent agents whose role is to build and manage the timetable specific to each learner [4], [5].…”