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Prerequisite Relations among Knowledge Units: A Case Study of Computer Science Domain

Fatema Nafa1,*, Amal Babour2, Austin Melton3
1 Department of Computer Science, Salem State University, Salem, MA 01970, USA
2 Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
3 Department of Computer Science, Kent State University, Kent, OH 44240, USA
* Corresponding Author: Fatema Nafa. Email:
(This article belongs to this Special Issue: Soft Computing Techniques in Materials Science and Engineering)

Computer Modeling in Engineering & Sciences 2022, 133(3), 639-652. https://doi.org/10.32604/cmes.2022.020084

Received 03 November 2021; Accepted 14 March 2022; Issue published 03 August 2022

Abstract

The importance of prerequisites for education has recently become a promising research direction. This work proposes a statistical model for measuring dependencies in learning resources between knowledge units. Instructors are expected to present knowledge units in a semantically well-organized manner to facilitate students’ understanding of the material. The proposed model reveals how inner concepts of a knowledge unit are dependent on each other and on concepts not in the knowledge unit. To help understand the complexity of the inner concepts themselves, WordNet is included as an external knowledge base in this model. The goal is to develop a model that will enable instructors to evaluate whether or not a learning regime has hidden relationships which might hinder students’ ability to understand the material. The evaluation, employing three textbooks, shows that the proposed model succeeds in discovering hidden relationships among knowledge units in learning resources and in exposing the knowledge gaps in some knowledge units.

Keywords

Knowledge graph; text mining; knowledge unit; graph mining

Cite This Article

Nafa, F., Babour, A., Melton, A. (2022). Prerequisite Relations among Knowledge Units: A Case Study of Computer Science Domain. CMES-Computer Modeling in Engineering & Sciences, 133(3), 639–652.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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