Abstract
Most e-Learning web application known as Learning Management Systems are associated with collaboration in a web page. It allows a user to interact directly with multiple application in any web platform together with other users. However, the action of the users has not been thoroughly analyzed. Due to the medium of teaching, implementation is through online. It is nec-essary to analyse each student behaviour characteristics of blended learning implementation so that lecturer can adjust how online activities are per-formed. In this paper, we propose a conceptual model in profiling student behaviour in e-Learning based on metadata approach and Community of In-quiry Model. We adopt a metadata approach in collecting student experience in e-Learning and Community of Inquiry Model to mapping the online stu-dent experiences. This conceptual model provides the basis for evaluating student behaviour characteristics in online learning with the goal of im-proved student engagement and online activity design.
Highlights
A learning process involves the process of acquiring and modifying knowledge, skills, strategies, beliefs, attitudes, and behaviours [1]–[3]
It is known as a Learning Management System (LMS) [11]
Clustering and prediction method applied to analyse the e-Learning characteristic based on defined student profile model
Summary
A learning process involves the process of acquiring and modifying knowledge, skills, strategies, beliefs, attitudes, and behaviours [1]–[3]. Blended learning (BL) offers an attractive education program by combining teaching and learning (T&L) activities through the use of information technology [13]. In this mechanism, a student learns at least in part through online delivery of content and instruction with some element of student control over time, place, path or pace [14], [15]. The student profiling in e-Learning comes from the concept of web user profiling which is involved in building semantic-based user profile (consist of contact information, educational history, demographic, and preference/interest, etc.) from the unstructured web [16] It is fundamental issues for understanding user behaviour on the online platform. Clustering and prediction method applied to analyse the e-Learning characteristic based on defined student profile model
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