Improving Students' Performance Prediction using LSTM and Neural Network

Subject Areas : Majlesi Journal of Telecommunication Devices

Hussam Abduljabar Salim Ahmed ¹ , Razieh Asgarnezhad ²

1 - Department of Computer Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
2 - Department of Computer Engineering, Aghigh Institute of Higher Education Shahinshahr, 8314678755, Isfahan, Iran

Received: 2023-01-27 Accepted : 2023-04-03 Published : 2023-09-01

Keywords: deep learning, LSTM, Educational Data Mining, Artificial Neural Network,

Abstract :

Educational data mining utilizes information from academic fields to develop renewed techniques and spot unusual patterns to gauge students' academic achievement. Evaluating student learning is a complicated issue. Data mining in this field enables to predict students' performance to recommend performance in universities. Therefore, the current authors have recently seen the rapid growth of data mining and knowledge extraction as tools used by academic institutions to optimize student learning processes. Here, a method based on a certain kind of artificial neural network called Long Short Term Memory recurrent neural network for prediction will operate. The proposed approach tries to use the educational characteristics of different people to predict the best educational process future educational. It career for students and thereby take steps to improve the effectiveness of the educational system. For comparison, one of the newest algorithms presented in this field was implemented using the proposed technique. The evaluations' findings were performed in the form of two scenarios with different data sizes and different amounts of test and training data. For the evaluation, the dataset taken from an online educational system was used. The evaluation results are presented in the form of four well-known criteria precision, recall, accuracy, and F1, which demonstrate the superiority of the proposed method.

References:

• [1] Baker, R.S., Corbett, A.T., Koedinger, K.R. “Detecting Student Misuse of Intelligent Tutoring Systems,” Proceedings of the 7th International Conference on Intelligent Tutoring Systems, pp.531-540, 2004.


• [2] Romero, C., & Ventura, S.“Data mining in Education,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), pp.12–27, 2013.


• [3] Araque, F., Roldan, C., & Salguero, A. “Factors influencing university dropout rates,” Journal of Computer & Education, 53, pp.563–574, 2009.


• [4] Abu-Naser, S. S., Zaqout, I. S., Abu Ghosh, M., Atallah, R. R., & Alajrami, E. (2015). Predicting student performance using artificial neural network: In the faculty of engineering and information technology.


• [5] Zacharis, Nick Z. "Predicting student academic performance in blended learning using Artificial Neural Networks." International Journal of Artificial Intelligence and Applications 7.5 (2016): 17-29.


• [6] Sivagowry S, Dr.Durairaj M, “PSO - An Intellectual Technique for Feature Reduction on Heart Malady Anticipation Data”, International Journal of Advanced Research in computer science and software engineering, vol 4(9), pp 610-621, 2014.


• [7] Zaidah ibrahim,” predicting students’ academic performance: comparing artificial neural network, decision tree and linear regression”, 21st annual sas malaysia forum, 5th september 2007.


• [8] Mueen, A., Zafar, B., & Manzoor, U. (2016). Modeling and predicting students' academic performance using data mining techniques. International Journal of Modern Education and Computer Science, 8(11), 36.


• [9] Hochreiter, Sepp, and Jürgen Schmidhuber. "Long short-term memory." Neural computation 9.8 (1997): 1735-1780.


• Zaidah ibrahim,” predicting students’ academic performance: comparing artificial neural network, decision tree and linear regression”, 21st annual sas malaysia forum, 5th september 2007.


• Romero, S. Ventura and E. García, “Data mining in course management systems: Moodle case study and tutorial”, Computers & Education, vol. 51, no. 1, (2008), pp. 368-384.


• M. Arsad, N. Buniyamin and J. L. A. Manan, “A neural network students' performance prediction model (NNSPPM)”, In Smart Instrumentation, Measurement and Applications (ICSIMA), 2013 IEEE International Conference on. IEEE, (2013), pp. 1-5.


• T. N. Hien and P. Haddawy, “A decision support system for evaluating international student applications”, In Frontiers In Education Conference-Global Engineering: Knowledge Without Borders, Opportunities Without Passports, 2007. FIE'07. 37th Annual.IEEE, (2007), pp. F2A-1.


• Abu Naser, “Predicting Learners Performance Using Artificial Neural Networks in Linear Programming Intelligent Tutoring Systems”, IJAIA, vol. 3, no. 2, (2012).


• Kanakana1, and A. Olanrewaju, “Predicting student performance in Engineering Education using an artificial neural network at Tshwane university of technology”, ISEM 2011 Proceedings, (2011) September 21-23, Stellenbosch, South Africa.


• Kyndt, M. Musso, E. Cascallar and F. Dochy, “Predicting academic performance in higher education: Role of cognitive, learning and motivation”, Earli Conference 2011, 14th edition, Exeter, UK, (2011).


• Mukta and A. Usha, “A study of academic performance of business school graduates using neural network and statistical techniques”, Expert Systems with Applications, Elsevier Ltd., vol. 36, no. 4, (2009).


• Stamos and V. Andreas, “An Artificial Neural Network for Predicting Student Graduation Outcomes”, Proceedings of the World Congress on Engineering and Computer Science (2008) “WCECS 2008”, San Francisco, USA.


• Amrieh, E. A., Hamtini, T., & Aljarah, I. (2016). Mining educational data to predict student’s academic performance using ensemble methods. International Journal of Database Theory and Application, 9(8), 119-136.


• https://www.kaggle.com/aljarah/xAPI-Edu-Data.


• Sepp Hochreiter; Jürgen Schmidhuber (1997)."Long short-term memory".Neural Computation. 9 (8): 1735–1780.


• Li, Xiangang; Wu, Xihong (2014-10-15). "Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition". arXiv:1410.4281 [cs.CL].