Building a Smart E-Portfolio Platform for Optimal E-Learning Objects Acquisition

Abstract EN

In modern education, an e-portfolio platform helps students in acquiring e-learning objects in a learning activity.

Quality is an important consideration in evaluating the desirable e-learning object.

Finding a means of determining a high quality e-learning object from a large number of candidate e-learning objects is an important requirement.

To assist student learning in a modern e-portfolio platform, this work proposed an optimal selection approach determining a reasonable e-learning object from various candidate e-learning objects.

An optimal selection approach which uses advanced information techniques is proposed.

Each e-learning object undergoes a formalization process.

An Information Retrieval (IR) technique extracts and analyses key concepts from the student’s previous learning contexts.

A context-based utility model computes the expected utility values of various e-learning objects based on the extracted key concepts.

The expected utility values of e-learning objects are used in a multicriteria decision analysis to determine the optimal selection order of the candidate e-learning objects.

The main contribution of this work is the demonstration of an effective e-learning object selection method which is easy to implement within an e-portfolio platform and which makes it smarter.