Early Warning System - Faculty of Science and Technology
The emergence of e-learning has introduced significant improvements in the way courses are taught and delivered, making this new form of education widely accepted. E-learning systems provide multiple ways of learning (self-paced, collaborative, synchronous & asynchronous, tutorial-based, etc.) and incorporate numerous interactive online activities. Therefore, articulating and identifying user behavior in the e-learning systems can provide new insights into, and improve understanding of how students view and undertake the web-based learning tools and activities. Data accumulated in the system, such as login frequency, participation indexes and completion rates could greatly aid in identifying satisfactory and unsatisfactory performance.
There are two phases of the Early Warning System (EWS)
Phase 1 attempts to improve the quality of online education by introducing innovative mechanisms to motivate and monitor learners progressively according to an acceptable performance threshold. This threshold may be set by the Course Coordinator and may vary from activity to activity and from course to course.
Phase 2 involves the deployment of Machine Learning Techniques (MLTs) to intelligently examine the interaction and activity reports in the Learning Management System (LMS) to diagnose each studentís academic performance. The MLT, precisely Artificial Neural Network (ANN), will be trained on past offering data of the course to derive a predictive map that permits rating studentís ability to pass a course at various points during the semester.
The Early Warning System (EWS) aims to be an intelligent monitoring and feedback system to identify at-risk students during the semester such that corrective actions could be taken before it becomes too late. With many courses venturing into online mode, a self-enthused approach is believed to be more proactive as students can self-monitor their progress and take remedial actions by participating in various online activities.
The systemís functionality:
The module offers the following features for students:
For more details see the Student User Manual
EWS Student Workshop
Date: Friday, 1st August and Monday, 4th August
Venue: U8 Lecture Theater
EWS In Refresher and Induction Program
Date: 21st to 25th July
Venue: Teaching Lab 1, Japan-Pacific ICT Centre
For any queries or information, please contact: