Open Access
Issue
ITM Web Conf.
Volume 43, 2022
The International Conference on Artificial Intelligence and Engineering 2022 (ICAIE’2022)
Article Number 01013
Number of page(s) 9
DOI https://doi.org/10.1051/itmconf/20224301013
Published online 14 March 2022
  1. www.pisa.oecd.org(2019) [Google Scholar]
  2. Hellas, Arto, et al. “Predicting academic performance: a systematic literature review.” Proceedings companion of the 23rd annual ACM conference on innovation and technology in computer science education. 2018. [Google Scholar]
  3. Ajibade, Samuel & Bahiah, Nor & Shamsuddin, Siti Mariyam.(2019). A Novel Hybrid Approach of AdaboostM2 Algorithm and Differential Evolution for Prediction of Student Performance. International Journal of Scientific & Technology Research.8.65-70. [Google Scholar]
  4. Z. Ibrahim, D. Rusli, Predicting students’ academic performance: comparing artificial neural network, decision tree and linear regression, in: 21st Annual SAS Malaysia Forum, 5th September, 2007. [Google Scholar]
  5. www.men.gov.ma(2019) [Google Scholar]
  6. National Efficiency Assessment Program data PNEA 2016. [Google Scholar]
  7. National Efficiency Assessment Program data PNEA 2016 [Google Scholar]
  8. Shah, Saher.(2012). Predicting factors that affect students’ academic performance by using data mining techniques. [Google Scholar]
  9. Yasmeen Altujjar, Wejdan Altamimi, Isra Al-Turaiki, Muna Al-Razgan, Predicting Critical Courses Affecting Students Performance: A Case Study, Procedia Computer Science, Volume 82, 2016. [Google Scholar]
  10. Nurafifah, Mohammad Suhaimi, et al. “Review on predicting students’ graduation time using machine learning algorithms.” International Journal of Modern Education and Computer Science 11.7 (2019). [Google Scholar]
  11. Kasih, Julianti & Ayub, Mewati & Susanto, Sani. (2013). Predicting students’ final passing results using the Apriori Algorithm. World Transactions on Engineering and Technology Education. [Google Scholar]
  12. Baradwaj, Brijesh Kumar, and Saurabh Pal. “Mining educational data to analyze students’ performance.” arXiv preprint arXiv:1201.3417 (2012). [Google Scholar]
  13. Stretch, Pedro; Cruz, Luís; Soares, Carlos; Mendes-Moreira, João; Abreu, Rui, “A Comparative Study of Classification and Regression Algorithms for Modelling Students’ Academic Performance,” International Educational Data Mining Society, p. 4, 2015. [Google Scholar]
  14. ElGamal, A. F. (2013). An educational data mining model for predicting student performance in programming course. International Journal of Computer Applications, 70(17), 22-28. [CrossRef] [Google Scholar]
  15. Huang, S., & Fang, N. (2013). Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models. Computers & Education, 61, 133-145. [CrossRef] [Google Scholar]
  16. Romero, C., López, M. I., Luna, J. M., & Ventura, S.(2013). Predicting students' final performancefrom participation in on-line discussion forums. Computers & Education, 68, 458-472. [CrossRef] [Google Scholar]
  17. Arnold, K. E., & Pistilli, M. D. (2012, April). Course signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 267-270). [CrossRef] [Google Scholar]
  18. Minaei-Bidgoli, B., Kashy, D. A., Kortemeyer, G., & Punch, W. F. (2003, November). Predicting student performance: an application of data mining methods with an educational web-based system. In 33rd Annual Frontiers in Education, 2003. FIE2003. (Vol. 1, pp. T2A-13). IEEE. [Google Scholar]
  19. Falakmasir, M. H., & Habibi, J. (2010, June). Using educational data mining methods to study the impact of virtual classroom in e-learning. In Educational Data Mining 2010. [Google Scholar]
  20. González, J. M. B., & DesJardins, S. L. (2002). Artificial neural networks: A new approach to predicting application behavior. Research in Higher Education, 43(2), 235-258. [CrossRef] [Google Scholar]
  21. Bauer, J. A., Thomas, T. S., Cauraugh, J. H., Kaminski, T. W., & Hass, C. J. (2001). Impact forces and neck muscle activity in heading by collegiate female soccer players. Journal of sports sciences, 19(3), 171-179. [CrossRef] [Google Scholar]
  22. Calvo-Flores, M. Delgado, et al. “Predicting students’ marks from Moodle logs using neural network models.” Current Developments in Technology-Assisted Education 1.2 (2006):586-590. [Google Scholar]
  23. Prakash, T. Nikil, and A. Aloysius. “Data preprocessing in sentiment analysis using Twitter data.” International Educational Applied Research Journal (IEARJ) Volume 3. [Google Scholar]
  24. Yu, Lean, Shouyang Wang, and Kin Keung Lai. “An integrated data preparation scheme for neural network data analysis.” IEEE Transactions on Knowledge and Data Engineering 18.2(2005):217-230. [Google Scholar]
  25. Badr, G., Algobail, A., Almutairi, H., & Almutery, M. (2016). Predicting students’ performance in university courses: a case study and tool in KSU mathematics department. Procedia Computer Science, 82, 80-89. [CrossRef] [Google Scholar]
  26. Nathans, Laura L., Frederick L. Oswald, and Kim Nimon. “Interpreting multiple linear regression: A guidebook of variable importance.” Practical Assessment, Research, and Evaluation 17.1(2012): 9. [Google Scholar]
  27. https://pycaret.org/ (2019). [Google Scholar]
  28. [Srivastava, et al. in their 2014 paper Dropout: A Simple Way to Prevent Neural Networks from Overfitting], is a technique where randomly selected neurons was ignored during training. [Google Scholar]
  29. Gray, C. McGuinness, P. Owende, An application of classification models to predict learner progression in tertiary education, in: Advance Computing Conference (IACC), 2014 IEEE International, IEEE, 2014, pp. 549–554. [CrossRef] [Google Scholar]
  30. O’connor, Evelyn A., and Marian C. Fish. “Differences in the Classroom Systems of Expert and Novice Teachers.” (1998). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.