Semantic Web Technologies for a Better E-Learning Feedback

Abstract

Nowadays, the web serves as a major high-tech environment for individualized learning which is suggested by teachers and is based on interactive learning objects. To effectively complete the learning process, students require detailed feedback regarding how to enhance their knowledge, skills, and motivation. This paper introduces an automated student assessment system for e-Learning environments that leverages semantic web technologies to provide comprehensive feedback by directly evaluating hands-on computer skills. The research aims to create this automated assessment framework through two key components: (i) ontological descriptions of various computer tasks, and (ii) system interfaces that integrate with computer operating systems to monitor and interpret user actions. The developed system employs a context-aware ontology that semantically maps user interactions to specific learning activities, enabling real-time assessment of practical computer skills. This ontological framework transforms raw input data into meaningful educational metrics, allowing for immediate feedback generation. The architecture incorporates specialized monitoring mechanisms that track user interactions while maintaining system stability and user privacy. Implementation results demonstrate the system's capability to overcome limitations of conventional e-Learning evaluation methods, particularly through its provision of instant, actionable feedback. In a prototype evaluation involving a document-formatting task with 25 students, the system achieved an overall assessment accuracy of 94.2%. The prototype successfully assesses complex, multi-step tasks across various software applications, providing students with specific guidance for improvement. Preliminary testing confirms the system's effectiveness in creating more engaging and personalized learning experiences while reducing the assessment burden on educators by over 95% for the evaluated tasks. This approach represents a substantial advancement in digital learning assessment, offering a scalable solution for practical skill evaluation in web-based educational environments.