EDM System

Introduction

The design and implementation of e-learning systems have grown exponentially in the last years, spurred by the fact that neither students nor teachers are bound to a specific location and that this form of ubiquitous is virtually independent of any specific hardware platforms. These systems accumulate a vast amount of information which is very valuable in analyzing students’ behavior through Datamining algorithms, and to assist authors in detecting possible errors, shortcomings, and improvements.

EDM & Immediate recursive feedback

Many of the typical pedagogies provide little immediate feedback and recursive feedback to students regarding educational content and require teachers (tutors) to spend hours grading routine assignments. These summary assessments do not help students to improve. This type of learning is not very proactive about showing students how to improve comprehension and increase the performance and fail to take advantage of digital resources that can significantly improve the learning process. Both these issues can be solved with Computer added assessments and using NLP based methods. It will further increase the quantum of analyzable data.

Source of Data for EDM

Data collected from the learning process inside in a Learning Management System (LMS), Course Management System (CMS), or Virtual Learning Environment (VLE) so-called “big data” make it possible for EDM, to mine learning information for insights regarding student performance and tutoring.

Effects of EDM on e-Learning

Abd Elaal has identified the following areas of e-learning where using data mining tools, useful information can easily be obtained:

· Analysis and visualization of data

· Providing feedback for supporting instructors

· Recommendations for students

· Predicting student performance

· Student modeling

· Detecting undesirable student behaviors

· Grouping students

· Social network analysis

· Developing concept maps

· Constructing courseware

· Planning and scheduling

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References:

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