An Ontology-based Adaptive Personalized E-learning System, Assisted by Software Agents on Cloud Storage

TL;DR

This paper presents an ontology-based, adaptive e-learning system utilizing software agents and cloud storage to personalize learning experiences based on learner behavior and styles.

Contribution

It introduces an integrated ontology-driven framework with software agents for real-time personalization in e-learning, leveraging cloud storage for scalability.

Findings

Effective adaptation to learner styles demonstrated

Software agents successfully monitor and modify learning paths

System shows improved engagement through personalization

Abstract

E-learning and online education have made great strides in the recent past. It has moved from a knowledge transfer model to a highly intellect, swift and interactive proposition capable of advanced decision-making abilities. Two challenges have been observed during the exploration of recent developments in e-learning. Firstly, to incorporate e-learning systems effectively in the evolving semantic web environment and secondly, to realize adaptive personalization according to the learner's changing behavior. An ontology-driven system has proposed to implement the Felder-Silverman learning style model in addition to the learning contents, to validate its integration with the semantic web environment. Software agents are employed to monitor the learner's actual learning style and modify them accordingly. The learner's learning style and their modifications are made within the proposed e-learning system. Cloud storage is used as the primary back-end in order to maintain the ontology, databases and other required server resources. To verify the system, comparisons are made between the information presented and adaptive learning styles of the learner along with actions of agents according to learners' behavior. Finally, various conclusions are drawn by exploring the learner's behavior in an adaptive environment for the proposed e-learning system.