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Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network

Author

Listed:
  • Bashir Khan Yousafzai

    (Department of Computer Science, Abdul Wali Khan University, Mardan 23200, Pakistan)

  • Sher Afzal Khan

    (Department of Computer Science, Abdul Wali Khan University, Mardan 23200, Pakistan)

  • Taj Rahman

    (Department of Computer Science, Qurtuba University of Science and Information Technology, Peshawar 25000, Pakistan)

  • Inayat Khan

    (Department of Computer Science, University of Buner, Buner 19290, Pakistan)

  • Inam Ullah

    (College of Internet of Things (IoT) Engineering, Changzhou Campus, Hohai University (HHU), Nanjing 213022, China)

  • Ateeq Ur Rehman

    (Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan)

  • Mohammed Baz

    (Department of Computer Engineering, College of Computer and Information Technology, Taif University, Taif 21994, Saudi Arabia)

  • Habib Hamam

    (Faculty of Engineering, Moncton University, Moncton, NB E1A3E9, Canada)

  • Omar Cheikhrouhou

    (CES Laboratory, National School of Engineers of Sfax, University of Sfax, Sfax 3038, Tunisia)

Abstract

Educational data generated through various platforms such as e-learning, e-admission systems, and automated result management systems can be effectively processed through educational data mining techniques in order to gather highly useful insights into students’ performance. The prediction of student performance from historical academic data is a highly desirable application of educational data mining. In this regard, there is an urgent need to develop an automated technique for student performance prediction. Existing studies on student performance prediction primarily focus on utilizing the conventional feature representation schemes, where extracted features are fed to a classifier. In recent years, deep learning has enabled researchers to automatically extract high-level features from raw data. Such advanced feature representation schemes enable superior performance in challenging tasks. In this work, we examine the deep neural network model, namely, the attention-based Bidirectional Long Short-Term Memory (BiLSTM) network to efficiently predict student performance (grades) from historical data. In this article, we have used the most advanced BiLSTM combined with an attention mechanism model by analyzing existing research problems, which are based on advanced feature classification and prediction. This work is really vital for academicians, universities, and government departments to early predict the performance. The superior sequence learning capabilities of BiLSTM combined with attention mechanism yield superior performance compared to the existing state-of-the-art. The proposed method has achieved a prediction accuracy of 90.16%.

Suggested Citation

  • Bashir Khan Yousafzai & Sher Afzal Khan & Taj Rahman & Inayat Khan & Inam Ullah & Ateeq Ur Rehman & Mohammed Baz & Habib Hamam & Omar Cheikhrouhou, 2021. "Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network," Sustainability, MDPI, vol. 13(17), pages 1-21, August.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:17:p:9775-:d:626114
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    Citations

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    Cited by:

    1. Ming Li & Xiangru Wang & Yi Wang & Yuting Chen & Yixuan Chen, 2022. "Study-GNN: A Novel Pipeline for Student Performance Prediction Based on Multi-Topology Graph Neural Networks," Sustainability, MDPI, vol. 14(13), pages 1-20, June.
    2. Chih-Chang Yu & Yufeng (Leon) Wu, 2021. "Early Warning System for Online STEM Learning—A Slimmer Approach Using Recurrent Neural Networks," Sustainability, MDPI, vol. 13(22), pages 1-17, November.
    3. Ahsan Bin Tufail & Inam Ullah & Ateeq Ur Rehman & Rehan Ali Khan & Muhammad Abbas Khan & Yong-Kui Ma & Nadar Hussain Khokhar & Muhammad Tariq Sadiq & Rahim Khan & Muhammad Shafiq & Elsayed Tag Eldin &, 2022. "On Disharmony in Batch Normalization and Dropout Methods for Early Categorization of Alzheimer’s Disease," Sustainability, MDPI, vol. 14(22), pages 1-22, November.

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