On Developing a Framework for Knowledge-based Learning Indicator System in the Context of Learning Analytics

Ahmed Ewais

Research output: Chapter in Book/Report/Conference proceedingConference paper


This paper addresses the issue of designing a learning-based analytics indicator system using domain-specific modeling languages. The proposed system incorporates a performance management component for monitoring and enhancing the learning process for computer systems engineering students. Using the proposed system, the instructors and learning policymakers will define indicators in a qualitative and/or quantitative manner, and the system will automatically compute the values of these indicators recommending a set of actions to assist the stakeholders of the learning-teaching process. Accordingly, they will be able to make appropriate decisions to amend and update the learning resources and processes. Additionally, the system will classify and cluster the learners according to their learning levels and interests using different data mining techniques. Another important component is the acquisition of learning sources from heterogeneous data and information sources available on the Web. In this context, unlike traditional approaches that rely on a single data source for constructing the learning sources, we will exploit multiple Web-based data learning sources.

Original languageEnglish
Title of host publicationIntelligent Decision Technologies 2019
Subtitle of host publicationProceedings of the 11th KES International Conference on Intelligent Decision Technologies (KES-IDT 2019)
Number of pages11
Publication statusPublished - 2019
Event11th International KES Conference -
Duration: 17 Jun 201919 Jun 2019


Conference11th International KES Conference
Internet address


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