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A novel methodology using RNN + LSTM + ML for predicting student’s academic performance

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Abstract

In the profession of education, predicting students' academic success is an essential responsibility. This study introduces a novel methodology for predicting students' pass or fail outcome in certain courses. The system utilises academic, demographic, emotional, and VLE sequence information of students. Traditional prediction methods often struggle to capture the temporal dynamics inherent in student data, such as learning trajectories, study habits, and evolving performance patterns. In response, this research leverages Recurrent Neural Network (RNNs) and Long Short Term Memory (LSTM) network (LSTMs), which are specifically designed to model sequences and long-term dependencies from OULAD and self-generated Emotional dataset. By incorporating these architectures, the proposed methodology excels in capturing the intricate relationships between various factors over time. Further, various ML models such as Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB) and Decision Tree (DT) are integrated with RNN + LSTM to enhance the predictive power of model. The proposed system with RNN + LSTM + RF techniques gained approximately 97% accuracy that is comparatively higher than RNN + LSTM + SVM, RNN + LSTM + NB and RNN + LSTM + DT i.e., 90.67%, 86.45% & 84.42% respectively.

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References

  • Alhakbani, H. A., & Alnassar, F. M. (2022, March). Open Learning Analytics: A Systematic Review of Benchmark Studies Using Open University Learning Analytics Dataset (OULAD). In Proceedings of the 2022 7th International Conference on Machine Learning Technologies (ICMLT '22). Association for Computing Machinery, New York, NY, USA, (pp. 81–86). https://doi.org/10.1145/3529399.3529413

  • Al-Shehri, H., Al-Qarni, A., Al-Saati, L., Batoaq, A., Badukhen, H., Alrashed, S., & Olatunji, S. O. (2017). Student performance prediction using support vector machine and k-nearest neighbor. In 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), Windsor, ON, Canada (pp. 1–4). https://doi.org/10.1109/CCECE.2017.7946847

  • Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students’ performance using educational data mining. Computers & Education, 113, 177–194. https://doi.org/10.1016/j.compedu.2017.05.007

    Article  Google Scholar 

  • Asif, R., Merceron, A., & Pathan, M. K. (2015). Investigating performance of students: a longitudinal study. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (LAK '15). Association for Computing Machinery, New York, NY (pp. 108–112). https://doi.org/10.1145/2723576.2723579

  • Awaludin, Muryan, & Muryan. (2022). Optimization of Naïve Bayes Algorithm Parameters for Student Graduation Prediction at Universitas Dirgantara Marsekal Suryadarma. Journal of Information System, informatic and Computing, 6(1), 91–106.

    Google Scholar 

  • Babulal S. D. K., & Agrawal B. (2019) Student Performance Classification using Adaptive DNN with SVM Approach. International Journal of All Research Education and Scientific Methods (IJARESM), 7(5), 85–93. Retrieved from http://www.ijaresm.com/student-performance-classification-using-adaptive-dnn-with-svm-approach

  • Bendangnuksung, P. P. (2018). Students’ performance prediction using deep neural network. International Journal of Applied Engineering Research, 13(2), 1171–1176.

    Google Scholar 

  • Bharara, S., Sabitha, S., & Bansal, A. (2018). Application of learning analytics using clustering data Mining for Students’ disposition analysis. Education and Information Technologies, 23, 957–984.

    Article  Google Scholar 

  • Binh, H. T., & Duy, B. T. (2017). Predicting students' performance based on learning style by using artificial neural networks. In 2017 9th International Conference on Knowledge and Systems Engineering (KSE), Hue, Vietnam (pp. 48–53). https://doi.org/10.1109/KSE.2017.8119433

  • Burman, I., & Som, S. (2019). Predicting students academic performance using support vector machine. In 2019 Amity International Conference on Artificial Intelligence (AICAI), Dubai, United Arab Emirates (pp. 756–759). https://doi.org/10.1109/AICAI.2019.8701260

  • Choong, A. C. H., & Lee, N. K. (2017). Evaluation of convolutionary neural networks modeling of DNA sequences using ordinal versus one-hot encoding method. In 2017 International Conference on Computer and Drone Applications (IConDA), Kuching, Malaysia (pp. 60–65). https://doi.org/10.1109/ICONDA.2017.8270400

  • Dalipi, F., Imran, A. S., & Kastrati, Z. (2018). MOOC dropout prediction using machine learning techniques: Review and research challenges. In 2018 IEEE global engineering education conference (EDUCON) (pp. 1007–1014). IEEE.

  • Divyabharathi, Y., & Someswari, P. (2018). A framework for student academic performance using naive Bayes prediction technique. Journal of Advancement in Engineering and Technology, 6(3), 1–4.

    Google Scholar 

  • El Aissaoui, O., El Madani, Y. E. A., Oughdir, L., Dakkak, A., & El Allioui, Y. (2019). A Multiple Linear Regression-Based Approach to Predict Student Performance. In International Conference on Advanced Intelligent Systems for Sustainable Development (pp. 9–23). Springer, Cham. https://doi.org/10.1007/978-3-030-36653-7_2

  • Francis, B. K., & Babu, S. S. (2019) Predicting Academic Performance of Students Using a Hybrid Data Mining Approach. Journal of Medical Systems, 43(6), 162. Published 2019 Apr 30. https://doi.org/10.1007/s10916-019-1295-4.

  • Gerritsen, L., & Conijn, R. (2017). Predicting student performance with Neural Networks. Tilburg University.

    Google Scholar 

  • Ghiasi, M. M., Zendehboudi, S., & Mohsenipour, A. A. (2020). Decision tree-based diagnosis of coronary artery disease: CART model. Computer Methods and Programs in Biomedicine, 192, 105400.

    Article  Google Scholar 

  • Golding, P. & Donaldson, O. (2006). Predicting Academic Performance. In Proceedings Frontiers in Education 36th Annual Conference, San Diego, CA, USA, pp. 21–26. https://doi.org/10.1109/FIE.2006.322661

  • Goold, A., & Rimmer, R. (2000). Factors Affecting Performance in First-year Computing. ACM SIGCSE Bulletin, 32(2), 39–43.

    Article  Google Scholar 

  • Hao, J., Gan, J., & Zhu, L. (2022). MOOC performance prediction and personal performance improvement via Bayesian network. Education and Information Technologies, 27(5), 7303–7326.

    Article  Google Scholar 

  • Hasan, R. (2018) “Student Academic Performance Prediction by using Decision Tree Algorithm.” 2018 4th International Conference on Computer and Information Sciences (ICCOINS), Kuala Lumpur, Malaysia (pp. 1–5). https://doi.org/10.1109/ICCOINS.2018.8510600

  • He, Y., Chen, R., Li, X., Hao, C., Liu, S., Zhang, G., & Jiang, B. (2020). Online at-risk student identification using RNN-GRU joint neural networks. Information, 11(10), 474.

    Article  Google Scholar 

  • Heissel, J. A., Levy, D. J., & Adam, E. K. (2017). Stress, sleep, and performance on standardized tests: Understudied pathways to the achievement gap. AERA Open, 3(3), 2332858417713488.

    Article  Google Scholar 

  • Hlioui, F., Aloui, N., & Gargouri, F. (2020). Withdrawal prediction framework in virtual learning environment. International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 11(3), 47–64.

    Google Scholar 

  • Hu, Q., & Rangwala, H. (2018). Course-specific markovian models for grade prediction. In Advances in Knowledge Discovery and Data Mining: 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3–6, 2018, Proceedings, Part II 22 (pp. 29–41). Springer International Publishing.

    Google Scholar 

  • Hu, Y. J., Ku, T. H., Jan, R. H., Wang, K., Tseng, Y. C., & Yang, S. F. (2012). Decision tree-based learning to predict patient controlled analgesia consumption and readjustment. BMC Medical Informatics and Decision Making, 12(1), 1–15.

    Article  Google Scholar 

  • Hung, J. L., Shelton, B. E., Yang, J., & Du, X. (2019). Improving predictive modeling for at-risk student identification: A multistage approach. IEEE Transactions on Learning Technologies, 12(2), 148–157.

    Article  Google Scholar 

  • Kim, B. H., Vizitei, E., & Ganapathi, V. (2018). GritNet: Student performance prediction with deep learning. arXiv preprint arXiv:1804.07405. https://doi.org/10.48550/arXiv.1804.07405

  • Kukkar, A., Mohana, R., Nayyar, A., Kim, J., Kang, B. G., & Chilamkurti, N. (2019). A novel deep-learning-based bug severity classification technique using convolutional neural networks and random forest with boosting. Sensors, 19(13), 2964.

    Article  Google Scholar 

  • Kukkar, A., Mohana, R., Kumar, Y., Nayyar, A., Bilal, M., & Kwak, K. S. (2020). Duplicate Bug Report Detection and Classification System Based on Deep Learning Technique. IEEE Access, 8, 200749–200763.

    Article  Google Scholar 

  • Kukkar, A., Mohana, R., Sharma, A., & Nayyar, A. (2023). Prediction of student academic performance based on their emotional wellbeing and interaction on various e-learning platforms. Education and Information Technologies, 1–30. https://doi.org/10.1007/s10639-022-11573-9

  • Kumar, M., & Singh, A. J. (2017). Evaluation of Data Mining Techniques for Predicting Student’s Performance. International Journal of Modern Education and Computer Science, 9(8), 25.

    Article  Google Scholar 

  • Li, W., Gao, M., Li, H., Xiong, Q., Wen, J., & Wu, Z. (2016). Dropout prediction in MOOCs using behavior features and multi-view semi-supervised learning. In 2016 international joint conference on neural networks (IJCNN), Vancouver, BC, Canada, (pp. 3130–3137). https://doi.org/10.1109/IJCNN.2016.7727598

  • Okubo, F., Yamashita, T., Shimada, A., & Ogata, H. (2017). A neural network approach for students' performance prediction. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference (LAK '17). Association for Computing Machinery, New York, NY, USA (pp. 598–599). https://doi.org/10.1145/3027385.3029479

  • Onan, A. (2020). Mining opinions from instructor evaluation reviews: A deep learning approach. Computer Applications in Engineering Education, 28(1), 117–138.

    Article  Google Scholar 

  • Oskouei, R. J., & Askari, M. (2014). Predicting academic performance with applying data mining techniques (Generalizing the results of two different case studies). Computer Engineering and Applications Journal, 3(2), 79.

    Article  Google Scholar 

  • Pallathadka, H., Wenda, A., Ramirez-Asís, E., Asís-López, M., Flores-Albornoz, J., & Phasinam, K. (2023). Classification and prediction of student performance data using various machine learning algorithms. Materials Today: Proceedings, 80, 3782–3785.

    Google Scholar 

  • Pandey, M., & Taruna, S. (2016). Towards the integration of multiple classifier pertaining to the Student’s performance prediction. Perspectives in Science, 8, 364–366.

  • Patil, V., Suryawanshi, S., Saner, M., Patil, V., & Sarode, B. (2017). Student performance prediction using prediction data mining techniques. International Journal of Scientific Development and Research, 2(6), 163–167.

    Google Scholar 

  • Qiu, F., Zhang, G., Sheng, X., Jiang, L., Zhu, L., Xiang, Q., Jiang, B., & Chen, P. K. (2022). Predicting students’ performance in e-learning using learning process and behaviour data. Scientific Reports, 12(1), 453.

    Article  Google Scholar 

  • Raihana, Z., & Farah Nabilah, A. M. (2018). Prediction of students based on quality of life and academic performance by using support vector machine. Journal of Academia UiTMNegeri Sembilan, 6(1), 45–52.

    Google Scholar 

  • Rienties, B., Boroowa, A., Cross, S., Kubiak, C., Mayles, K., & Murphy, S. (2016). Analytics4Action Evaluation Framework: A Review of Evidence-Based Learning Analytics Interventions at the Open University UK. Journal of Interactive Media in Education, 2016(1), EJ1089327. https://doi.org/10.5334/jime.394

  • Rosedi, N. F., & Mat Jaafar, N. N. (2018). A study on the influence of Human Resource Management (HRM) practices towards employee performance at Tesco Bandar Sungai Petani, Kedah. https://ir.uitm.edu.my/id/eprint/35150/

  • Roy, S., & Garg, A. (2017). Analyzing performance of students by using data mining techniques a literature survey. In 2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON), Mathura, India (pp. 130–133). https://doi.org/10.1109/UPCON.2017.8251035

  • Schochet, P. Z., & Chiang, H. S. (2010). Error Rates in Measuring Teacher and School Performance Based on Student Test Score Gains. NCEE 2010–4004. National Center for Education Evaluation and Regional Assistance.

    Google Scholar 

  • Schumacher, C., & Ifenthaler, D. (2018). Features students really expect from learning analytics. Computers in Human Behavior, 78, 397–407. [CrossRef].

    Article  Google Scholar 

  • Seeja, R. D., & Suresh, A. (2019). Deep learning based skin lesion segmentation and classification of melanoma using support vector machine (SVM). Asian Pacific Journal of Cancer Prevention: APJCP, 20(5), 1555.

    Article  Google Scholar 

  • Sekeroglu, B., Dimililer, K., &Tuncal, K. (2019). Student performance prediction and classification using machine learning algorithms. In Proceedings of the 2019 8th International Conference on Educational and Information Technology (pp. 7–11). Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3318396.3318419

  • Sherstinsky, A. (2020). Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica d: Nonlinear Phenomena, 404, 132306.

    Article  MathSciNet  Google Scholar 

  • Shridharan, M., Willingham, A., Spencer, J., Yang, T. Y., & Brinton, C. (2018). Predictive learning analytics for video-watching behavior in MOOCs. In 2018 52nd Annual Conference on Information Sciences and Systems (CISS), Princeton, NJ, USA, (pp. 1–6). https://doi.org/10.1109/CISS.2018.8362323

  • Sievertsen, H. H., Gino, F., & Piovesan, M. (2016). Cognitive fatigue influences students’ performance on standardized tests. Proceedings of the National Academy of Sciences, 113(10), 2621–2624.

    Article  Google Scholar 

  • Su, Y., Liu, Q., Liu, Q., Huang, Z., Yin, Y., Chen, E., & Hu, G. (2018). Exercise-enhanced sequential modeling for student performance prediction. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32, No. 1). https://doi.org/10.1609/aaai.v32i1.11864

  • Tan, M., & Shao, P. (2015). Prediction of student dropout in e-Learning program through the use of machine learning method. International Journal of Emerging Technologies in Learning, 10(1), 11. https://doi.org/10.3991/ijet.v10i1.4189

    Article  MathSciNet  Google Scholar 

  • Teoh, T. T., & Rong, Z. (2022). Regression. In Artificial Intelligence with Python (pp. 163-181). Singapore: Springer.

    Book  Google Scholar 

  • Tomasevic, N., Gvozdenovic, N., & Vranes, S. (2020). An overview and comparison of supervised data mining techniques for student exam performance prediction. Computers & Education, 143, 103676.

    Article  Google Scholar 

  • Too, J., Abdullah, A. R., & Saad, N. M. (2019). Classification of hand movements based on discrete wavelet transform and enhanced feature extraction. International Journal of Advanced Computer Science and Applications, 10(6), 83–89.

    Article  Google Scholar 

  • Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98–110.

    Article  Google Scholar 

  • Visin, F., Kastner, K., Cho, K., Matteucci, M., Courville, A. & Bengio, Y. (2015). Renet: A recurrent neural network based alternative to convolutional networks. arXiv (Cornell University). https://arxiv.org/pdf/1505.00393.pdf

  • Wang, C., Ren, K., Lou, W., & Li, J. (2010). Toward publicly auditable secure cloud data storage services. IEEE Network, 24(4), 19–24.

    Article  Google Scholar 

  • Wang, X., Yang, D., Wen, M., Koedinger, K., & Rosé, C. P. (2015). Investigating How Student’s Cognitive Behavior in MOOC Discussion Forums Affect Learning Gains. International Educational Data Mining Society.

  • Wong, J. H., & Gales, M. (2016). Sequence student-teacher training of deep neural networks. ISCA. https://doi.org/10.17863/CAM.779

  • Yaacob, W. F. W., Nasir, S. A. M., Yaacob, W. F. W., & Sobri, N. M. (2019). Supervised data mining approach for predicting student performance. Indones. J. Electr. Eng. Comput. Sci, 16(3), 1584–1592.

    Google Scholar 

  • Yang, F., & Li, F. W. (2018). Study on student performance estimation, student progress analysis, and student potential prediction based on data mining. Computers & Education, 123, 97–108.

    Article  Google Scholar 

  • Yang, L., Wu, H., Jin, X., Zheng, P., Hu, S., Xu, X., Yu, W., & Yan, J. (2020). Study of cardiovascular disease prediction model based on random forest in eastern China. Scientific Reports, 10(1), 1–8.

    Google Scholar 

  • Yehuala, M. A. (2015). Application of Data Mining Techniques For Student Success And Failure Prediction (The Case Of Debre_Markos University). International Journal of Scientific & Technology Research, 4(4), 91–94.

    Google Scholar 

  • Yi, C., & Kang-Yi, C. (2018). Predictive analytics approach to improve and sustain college students’ non-cognitive skills and their educational outcome. Sustainability, 10, 4012. [CrossRef].

    Article  Google Scholar 

  • Yousafzai, B. K., Khan, S. A., Rahman, T., Khan, I., Ullah, I., Ur Rehman, A., ... &Cheikhrouhou, O. (2021). Student-performulator: student academic performance using hybrid deep neural network. Sustainability, 13(17), 9775.

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Authors and Affiliations

  1. Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

    Ashima Kukkar

  2. ASET, Amity University of Punjab, Chandigarh, India

    Rajni Mohana

  3. Jaypee University of Information Technology, Solan, Himachal Pradesh, India

    Aman Sharma

  4. Graduate School, Faculty of Information Technology, Duy Tan University, Da Nang, 550000, Vietnam

    Anand Nayyar

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Kukkar, A., Mohana, R., Sharma, A. et al. A novel methodology using RNN + LSTM + ML for predicting student’s academic performance. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-023-12394-0

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