Automatic Detection Techniques to Detect E-Learners on E-Learning System: A Comparative Analysis

J. U. F. RAJPER, SAMINA RAJPER, S. JALBANI, B. BALOCH

Abstract


E-learning is an Information and Communication technology (ICT) weaved distance education. Users of e-learning systems have now paved into the era of web 3.0 after web 1.0 and web 2.0. But, still the e-learners’ user modeling is a research demanding dimension. From personalized e-learning systems to Recommender e-learning systems, user modeling is required. For user modeling, e-learners’ detection on web based learning systems are required. The objective of this study is to review the research studies in this most demanding research dimension of e-learning. This survey of research studies will be conducted to review past research studies from 2000-2016. The survey will be helpful to compare and classify the ADLS (Automatic learning styles’ detecting techniques) and detect most robust techniques. Because it is revealed that to identify a robust technique for starting research study by researchers, scholars is very tedious and time taken job. This research study will contribute to scholars, academicians and e-learning community.

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