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Predicting Students’ Final Degree Classification Using an Extended Profile

Al-Sudani, Sahar, Ramaswamy, Palaniappan (2019) Predicting Students’ Final Degree Classification Using an Extended Profile. Education and Information Technologies, . ISSN 1360-2357. E-ISSN 1573-7608. (doi:10.1007/s10639-019-09873-8)

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http://dx.doi.org/10.1007/s10639-019-09873-8

Abstract

The students’ progression and attainment gap are considered as key performance indicators of many universities worldwide. Therefore, universities invest significantly in resources to reduce the attainment gap between good and poor performing students. In this regard, various mathematical models have been utilised to predict students’ performances in the hope of informing the support team to intervene at an early stage of the at risk student’s at the university. In this work, we used a combination of institutional, academic, demographic, psychological and economic factors to predict students’ performances using a multi-layered neural network (NN) to classify students’ degrees into either a good or basic degree class. To our knowledge, the usage of such an extended profile is novel. A feed-forward network with 100 nodes in the hidden layer trained using Levenberg-Marquardt learning algorithm was able to achieve the best performance with an average classification accuracy of 83.7%, sensitivity of 77.37%, specificity of 85.16%, Positive Predictive Value of 94.04%, and Negative Predictive Value of 50.93%. The NN model was also compared against other classi?ers specifically k-Nearest Neighbour, Decision Tree and Support Vector Machine on the same dataset using the same features. The results indicate that the NN outperforms all other classi?ers in terms of overall classification accuracy and shows promise for the method to be used in Student Success ventures in the universities in an automatic manner.

Item Type: Article
DOI/Identification number: 10.1007/s10639-019-09873-8
Uncontrolled keywords: Attainment gap, Degree classification, Neural network, Student’s performance, Student success
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 20 Jan 2019 13:24 UTC
Last Modified: 30 May 2019 08:46 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/71736 (The current URI for this page, for reference purposes)
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