JACIII Vol.28 No.3 pp. 668-678
doi: 10.20965/jaciii.2024.p0668

Research Paper:

Student Progression and Dropout Rates Using Convolutional Neural Network: A Case Study of the Arab Open University

Mohamed Sayed ORCID Icon

Faculty of Computer Studies, Arab Open University
P.O.Box 830, Ardiya 92400, Kuwait

September 9, 2023
February 15, 2024
May 20, 2024
artificial intelligence, convolutional neural network, deep learning, dropout prediction, student learning and management systems

Pre-trained convolutional neural network (CNN) structures are considered as one of the emerging education management tools that can help improve the quality of education by allowing decision makers to manipulate important indicators. These indicators, which are categorized as student and institution specific factors, may influence student progress, retention or dropout rates. In this paper, we develop a deep learning model of predicting students’ satisfactions and their expected outcomes and associated early failures. The model can also predict dropout rates and identify the main baseline risk factors that influence such rates. The academic data of 12,000 students enrolled from 2018 in the Arab Open University student information system are used as CNNs training dataset to ensure that all institution levels are represented. Then, the trained network provides a probabilistic model that indicates, for each student, the probability of dropout. Based on the prediction model, the study presents an early warning system framework to generate alerts and recommendations to allow early and effective institutional intervention. Experiments are achieved by using the proposed dataset and the performance of our approach is considerably better compared to the competitive models in terms of training/validation accuracy and mean square errors.

Convolutional neural network for student dropout model

Convolutional neural network for student dropout model

Cite this article as:
M. Sayed, “Student Progression and Dropout Rates Using Convolutional Neural Network: A Case Study of the Arab Open University,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.3, pp. 668-678, 2024.
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