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Advanced Learning Analytics in Academic Education: Academic Performance Forecasting Based on an Artificial Neural Network

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  • Ayman G. Fayoumi

    (Faculty of Computing and Information Technology, King Abdulaziz University, Saudi Arabia)

  • Amjad Fuad Hajjar

    (Faculty of Engineering, King Abdulaziz University, Saudi Arabia)

Abstract

The integration of innovative data mining and decision-making techniques in the context of higher education is a bold initiative towards enhanced performance. Predictive and descriptive analytics add interesting insights for significant aspects the education. The purpose of this article is to summarize a novel approach for the adoption of artificial intelligence (AI) techniques towards forecasting of academic performance. The added value of applying AI techniques for advanced decision making in education is the realization that the scientific approach to standard problems in academia, like the enhancement of academic performance is feasible. For the purpose of this research the authors promote a research in Saudi Arabia. The vision of the Knowledge Society in the Kingdom of Saudi Arabia is a critical milestone towards digital transformation. The human capital and the integration of industry and academia has to be based on holistic approaches to skills and competencies management. One of the main objectives of an academic decision maker is to ensure that academic resources are adequately planned and that students are properly advised. To achieve such an objective, an extensive analysis of large volumes of data may be required. This research develops a decision support system (DSS) that is based on an artificial neural network (ANN) model that can be deployed for effective academic planning and advising. The system is based on evaluating academic metrics against academic performance for students. The model integrates inputs from relevant academic data sources into an autonomous ANN. A simulation of real data on an ANN is conducted to validate the system's accuracy. Moreover, an ANN is compared with different mathematical approaches. The system enables the quality assurance of planning, advising, and the monitoring of academic decisions. The overall contribution of this work is a novel approach to the deployment of Artificial Intelligent for advanced decision making in higher education. In future work this model is integrated with big data and analytics research for advanced visualizations

Suggested Citation

  • Ayman G. Fayoumi & Amjad Fuad Hajjar, 2020. "Advanced Learning Analytics in Academic Education: Academic Performance Forecasting Based on an Artificial Neural Network," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 16(3), pages 70-87, July.
    • Handle: RePEc:igg:jswis0:v:16:y:2020:i:3:p:70-87
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    Cited by:

    1. Xiaona Xia, 2022. "Application Technology on Collaborative Training of Interactive Learning Activities and Tendency Preference Diversion," SAGE Open, , vol. 12(2), pages 21582440221, April.
    2. Sabhi Rajae & Abdelbaki Jamal Eddine & Taouab Omar & Eddaoudi Faissal & Abdelbaki Noureddine, 2024. "Managing emotions and algorithms: the delicate equilibrium between artificial intelligence and behavioral finance [Gérer les émotions et les algorithmes : l'équilibre délicat entre l'intelligence a," Post-Print hal-04644322, HAL.

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