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Regression analysis of student academic performance using deep learning

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Abstract

Educational data mining helps the educational institutions to perform effectively and efficiently by exploiting the data related to all its stakeholders. It can help the at-risk students, develop recommendation systems and alert the students at different levels. It is beneficial to the students, educators and authorities as a whole. Deep learning has gained momentum in various domains especially image processing with a large dataset. We devise a regression model for analyzing the academic performance of the students using deep learning. We have applied regression using deep learning and linear regression on the dataset. For such models with smaller datasets, to tackle the issue of overfitting is critical. Hence, the parameters can be tuned to deal with such issues. The deep learning model records a mean absolute score (mae) of 1.61 and loss 4.7 with the value of k = 3. While the linear regression model yields a loss of 6.7 and mae score of 1.97. The deep learning model outperforms the linear regression model. The model may be successfully extended to other programmes to mine and predict the performance of the learners.

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References

  • Abhinav, K., Subramanian, V., Dubey, A., Bhat, P., & Venkat, A. D. (2018). LeCoRe: A framework for modeling Learner’s preference. In EDM.

  • Abu Tair, M. M., & El-Halees, A. M. (2012). Mining educational data to improve students’ performance: A case study. Mining Educational Data to Improve Students’ Performance: A Case Study, 2(2).

  • Adekitan, A. I., & Salau, O. (2019). The impact of engineering students’ performance in the first three years on their graduation result using educational data mining. Heliyon, 5(2), e01250.

    Article  Google Scholar 

  • Ahmed, A. B. E. D., & Elaraby, I. S. (2014). Data mining: A prediction for student’s performance using classification method. World Journal of Computer Application and Technology, 2(2), 43–47.

    Google Scholar 

  • Algarni, A. (2016). Data mining in education. International Journal of Advanced Computer Science and Applications, 7(6), 456–461.

    Article  Google Scholar 

  • Al-Radaideh, Q. A., Al-Shawakfa, E. M., & Al-Najjar, M. I. (2006, December). Mining student data using decision trees. In International Arab Conference on Information Technology (ACIT’2006), Yarmouk University, Jordan.

  • Baradwaj, B. K., & Pal, S. (2012). Mining educational data to analyze students’ performance. arXiv preprint arXiv:1201.3417.

  • Ben-Zadok, G., Hershkovitz, A., Mintz, E., & Nachmias, R. (2009). Examining online learning processes based on log files analysis: A case study. In 5th International Conference on Multimedia and ICT in Education (m-ICTE’09).

  • Bhise, R. B., Thorat, S. S., & Supekar, A. K. (2013). Importance of data mining in higher education system. IOSR Journal Of Humanities And Social Science (IOSR-JHSS), 6(6), 18–21.

    Article  Google Scholar 

  • Campbell, C. M., & Cabrera, A. F. (2014). Making the mark: Are grades and deep learning related? Research in Higher Education, 55(5), 494–507.

    Article  Google Scholar 

  • Carmona, C., Castillo, G., & Millán, E. (2007, September). Discovering student preferences in e-learning. In Proceedings of the international workshop on applying data mining in e-learning (pp. 33–42).

  • Deng, L. (2014). A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Transactions on Signal and Information Processing, 3.

  • Gadhavi, M., & Patel, C. (2017). Student final grade prediction based on linear regression. Indian Journal of Computer Science and EngineeringIndian Journal of Computer Science and Engineering, 8(3), 274–279.

    Google Scholar 

  • Goyal, M., & Vohra, R. (2012). Applications of data mining in higher education. International Journal of Computer Science Issues (IJCSI), 9(2), 113.

    Google Scholar 

  • Guo, B., Zhang, R., Xu, G., Shi, C., & Yang, L. (2015, July). Predicting students performance in educational data mining. In 2015 International Symposium on Educational Technology (ISET) (pp. 125–128). IEEE.

  • Hernández-Blanco, A., Herrera-Flores, B., Tomás, D., & Navarro-Colorado, B. (2019). A systematic review of deep learning approaches to educational data mining. Complexity, 2019, 1–22.

    Article  Google Scholar 

  • Hijaz, S. T., & Naqvi, S. R. (2006). Factors affecting students’ performance: A case of private colleges in Bangladesh. Journal of Sociology, 3(1), 44–45.

    Google Scholar 

  • Hussain, S., Muhsion, Z. F., Salal, Y. K., Theodoru, P., KurtoÄŸlu, F., & Hazarika, G. C. (2019). Prediction model on student performance based on internal assessment using deep learning. International Journal of Emerging Technologies in Learning (iJET), 14(08), 4–22.

    Article  Google Scholar 

  • Jiawei, H., & Kamber, M. (2011) Data mining: Concepts and techniques, (the Morgan Kaufmann series in data management systems), vol. 2.

  • Kaur, H., & Bathla, E. G. (2018). Student performance prediction using educational data mining techniques. International Journal on Future Revolution in Computer Science & Communication Engineering, 4(12), 93–97.

    Google Scholar 

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

  • Laxman, S., & Sastry, P. S. (2006). A survey of temporal data mining. Sadhana, 31(2), 173–198.

    Article  MathSciNet  Google Scholar 

  • Mannila, H. (1996, June). Data mining: Machine learning, statistics, and databases. In Proceedings of 8th International Conference on Scientific and Statistical Data Base Management (pp. 2–9). IEEE.

  • Mardikyan, S., & Badur, B. (2011). Analyzing teaching performance of instructors using data mining techniques. Informatics in Education, 10(2), 245–257.

    Article  Google Scholar 

  • Mihăescu, M. C. (2011, September). Classification of learners using linear regression. In 2011 Federated Conference on Computer Science and Information Systems (FedCSIS) (pp. 717–721). IEEE.

  • Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to linear regression analysis (Vol. 821). Hoboken: John Wiley & Sons.

    MATH  Google Scholar 

  • Nichat, A. A., & Raut, D. A. B. (2017). Predicting and analysis of student performance using decision tree technique. International Journal, 5, 7319–7328.

    Google Scholar 

  • Oyerinde, O. D., & Chia, P. A. (2017). Predicting students’ academic performances–a learning analytics approach using multiple linear regression.

  • Padhy, N., Mishra, D., & Panigrahi, R. (2012). The survey of data mining applications and feature scope. arXiv preprint arXiv:1211.5723.

  • Pandey, U. K., & Pal, S. (2011). Data mining: A prediction of performer or underperformer using classification. arXiv preprint arXiv:1104.4163.

  • Piad, K. C., Dumlao, M., Ballera, M. A., & Ambat, S. C. (2016, July). Predicting IT employability using data mining techniques. In 2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC) (pp. 26–30). IEEE.

  • Priya, K. S., & Kumar, A. S. (2013). Improving the student’s performance using educational data mining. International Journal of Advanced Networking and Applications, 4(4), 1806.

    Google Scholar 

  • Ramesh, V. A. M. A. N. A. N., Parkavi, P., & Ramar, K. (2013). Predicting student performance: A statistical and data mining approach. International Journal of Computer Applications, 63(8), 35–39.

    Article  Google Scholar 

  • Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 601–618.

    Article  Google Scholar 

  • Salas-Rueda, R. A. (2016). The impact of usable system for regression analysis in higher education. International Journal of Educational Technology in Higher Education, 13(1), 14.

    Article  Google Scholar 

  • Shahiri, A. M., & Husain, W. (2015). A review on predicting student's performance using data mining techniques. Procedia Computer Science, 72, 414–422.

    Article  Google Scholar 

  • Shih, Y. C., Huang, P. R., Hsu, Y. C., & Chen, S. Y. (2012). A complete understanding of disorientation problems in web-based learning. Turkish Online Journal of Educational Technology-TOJET, 11(3), 1–13.

    Google Scholar 

  • Srimani, P. K., & Patil, M. M. (2014). Regression model for Edu-data in technical education system: A linear approach. In ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India, vol II (pp. 785–793). Springer, Cham.

  • Sultana, J., Rani, M. U., & Farquad, M. A. H. (2009). Student’s performance prediction using deep learning and data mining methods. International Journal of Recent Technology and Engineering, 8(1S4), 1–4.

    Google Scholar 

  • Suthar, V., & Tarmizi, R. (2010). Effects of students’ beliefs on mathematics and achievement of university students: Regression analysis approach. Journal of social sciences, 6(2), 146–152.

    Article  Google Scholar 

  • Thomas, E. H., & Galambos, N. (2004). What satisfies students? Mining student-opinion data with regression and decision tree analysis. Research in Higher Education, 45(3), 251–269.

    Article  Google Scholar 

  • Vora, D. R., & Iyer, K. (2018). EDM–survey of performance factors and algorithms applied. International Journal of Engineering & Technology, 7(2.6), 93–97.

    Article  Google Scholar 

  • Wang, L., Sy, A., Liu, L., & Piech, C. (2017). Learning to represent student knowledge on programming exercises using deep learning. International Educational Data Mining Society.

  • Xing, W., & Du, D. (2019). Dropout prediction in MOOCs: Using deep learning for personalized intervention. Journal of Educational Computing Research, 57(3), 547–570.

    Article  Google Scholar 

  • Yadav, S. K., Bharadwaj, B., & Pal, S. (2012a). Data mining applications: A comparative study for predicting student’s performance. arXiv preprint arXiv:1202.4815.

  • Yadav, S. K., Bharadwaj, B., & Pal, S. (2012b). Mining education data to predict student’s retention: A comparative study. arXiv preprint arXiv:1203.2987.

  • Zhou, Q., Quan, W., Zhong, Y., Xiao, W., Mou, C., & Wang, Y. (2018). Predicting high-risk students using internet access logs. Knowledge and Information Systems, 55(2), 393–413.

    Article  Google Scholar 

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Authors and Affiliations

  1. Dibrugarh University, Dibrugarh, Assam, India

    Sadiq Hussain

  2. University of Plovdiv “PaisiiHilendarski”, Plovdiv, Bulgaria

    Silvia Gaftandzhieva & Rositsa Doneva

  3. Statistics Discipline, Khulna University, Khulna, Bangladesh

    Md. Maniruzzaman

  4. Science College, Baghdad University, Baghdad, Iraq

    Zahraa Fadhil Muhsin

Authors
  1. Sadiq Hussain
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  2. Silvia Gaftandzhieva
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  3. Md. Maniruzzaman
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  4. Rositsa Doneva
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  5. Zahraa Fadhil Muhsin
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Correspondence to Sadiq Hussain.

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Hussain, S., Gaftandzhieva, S., Maniruzzaman, M. et al. Regression analysis of student academic performance using deep learning. Educ Inf Technol 26, 783–798 (2021). https://doi.org/10.1007/s10639-020-10241-0

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