Mining User Access Patterns Efficiently for Adaptive e-Learning Environment
Renuka Mahajan · International Journal of e-Education e-Business e-Management and e-Learning · 2012
Web is an excellent tool for imparting distance education. Several sophisticated e-learning environments have been developed and are used around the world and will revolutionize the research and education field in years to come. e-learning is powerful, because it allows individuals to learn 'anywhere, anytime' and gives instant access to specific knowledge. However, different behavior, attitudes and aptitudes of individuals affect their learning, and these learning environments need to be adapted according to these individual differences, in order to maximize learning outcomes. This is known as adaptive web based e-learning. The same technology is being developed for e-commerce sites also, to track and understand customer's buying behavior. Predicting their needs will help in improving usability of e- commerce site, but, hardly any efforts are done in education and research field to understand learners' behavior in e- learning systems and modify the e-learning site accordingly. Academicians contributing to web content in e-learning environments, have absolutely no medium on how to evaluate a learner's activities and differentiate between their on-line behaviors, to make e-learning more effective. Similarly, for a learner using e-learning site, the full potential of the site is not exploited due to lack of recommendations/hints from the site that should adapt its course content to learner's learning pace, interest or previous behavior. Adaptive e-learning environment is self-improving and helps • Educators- in evaluating learning process, thereby making the web application more effective for learners. • e-learners- web application could automatically guide the learner's activities and intelligently recommend resources, content and suggest areas to improve their performance based on online assignment results. For this, the web log data of completed activities and sequence of events, user profile data along with their evaluation result data can be mined, to deliver tailored e- content, as required by the learner.