Data mining in course management systems: Moodle case study and tutorial

The potential of computer-mediated internships for higher education

“…Although, literature (see Kim et al, 2012) argues that HEPs may reduce the impact of this issue by assigning dedicated faculty advisors to each intern, the information asymmetry problem that underpins this limitation remains intact. CMIs are able to assist in the reduction of the underlying information asymmetry problem because their technological underpinning aids employer organizations in the collection of an individual intern's activities and progress, as well as the provision of such information to HEPs and faculty advisors (Romero et al, 2007). Such information should be readily available within CMIs because an intern's day-to-day work activities are performed in a computer mediated online environment where activity and performance tracking is easily achieved (Romero et al, 2007;Shanks et al, 2003).…”

“…CMIs are able to assist in the reduction of the underlying information asymmetry problem because their technological underpinning aids employer organizations in the collection of an individual intern's activities and progress, as well as the provision of such information to HEPs and faculty advisors (Romero et al, 2007). Such information should be readily available within CMIs because an intern's day-to-day work activities are performed in a computer mediated online environment where activity and performance tracking is easily achieved (Romero et al, 2007;Shanks et al, 2003). HEPs and employer organizations may even be able to share intern specific performance information through continuous real-time tracking, similar to the progress and performance tracking that is utilized in most online learning management systems (Desmarais and Baker, 2012).…”

Granular Mining of Student’s Learning Behavior in Learning Management System Using Rough Set Technique

“…Several e-learning researches have been conducted in order to enhance Moodle's performance [1,5,6]. Graf and Kinshuk [5] has extended Moodle's capability by implementing adaptation of the learning material based on the student's learning style.…”

“…Kerdprasop et al [1] enhanced LMS functionality to individualize the learning content with induction ability. Romero et al [6] had developed data mining tool to help instructors preprocess or apply mining techniques, such as statistics, visualization, classification, clustering and association rule mining from Moodle data. E-learning systems developed using Moodle accumulate an enormous amount of information which is very valuable for analyzing students' behavior and could create a gold mine of educational data [7].…”

“…The irrelevant rules are further filtered by measuring different rules support, rules length and rules accuracy. It is scrutinized that the rules with the length in between [4,8], and the rules support in the range of [6,43] succumb the highest accuracy with 96.62%. …”