Е-Публикации
Технически университет - София

Abstract: Artificial Intelligence (AI) is increasingly used in online education platforms to provide valuable insights into students’ performance and success. However, the complexity of AI models makes it challenging for educators to interpret the specific factors that influence whether a student is going to pass or fail. Utilizing the Open University Learning Analytics Dataset (OULAD), this study employs various machine learning and deep learning techniques for predicting students’ success, along with SHapley Additive exPlanations (SHAP) as an Explainable Artificial Intelligence (XAI) technique, to understand the key factors behind success or failure. Unlike traditional statistical methods that explore variable relationships, this AI-driven approach uses advanced deep learning techniques to identify patterns and insights, allowing for a better understanding of the factors influencing student success. Additionally, this study focuses on identifying students at risk of failure using XAI techniques, specifically SHAP, to interpret model outputs by breaking down how specific factors contribute to a student’s success. This method enables targeted interventions to support their success. Results reveal that student engagement and registration timelines are critical factors affecting performance. The customized models achieve up to 94% accuracy for the designed tasks, outperforming traditional approaches. This study contributes to the use of AI in education and offers practical insights not only for educators but also for administrators and policymakers to enhance the quality and effectiveness of online learning.

References

1. Tseng S.F. Tsao Y.W. Yu L.C. Chan C.L. Lai K.R. Who will pass? Analyzing learner behaviors in MOOCs Res. Pract. Technol. Enhanc. Learn. 2016 11 8 10.1186/s41039-016-0033-5 30613241
2. Gardner J. Brooks C. Student success prediction in MOOCs User Model. User-Adapt. Interact. 2018 28 127 203 10.1007/s11257-018-9203-z
3. Khosravi H. Shum S.B. Chen G. Conati C. Tsai Y.S. Kay J. Knight S. Martinez-Maldonado R. Sadiq S. Gašević D. Explainable artificial intelligence Artif. Intell. Comput. Educ. 2022 3 100074 10.1016/j.caeai.2022.100074
4. Shabaninejad S. Khosravi H. Abdi S. Indulska M. Sadiq S. Incorporating explainable learning analytics to assist educators with identifying students in need of attention Proceedings of the Ninth ACM Conference on Learning@Scale Roosevelt Island, NY, USA 1–3 June 2022 384 388
5. De Laet T. Mothilal R.K. Broos T. Pinxten M. Predicting First-year engineering student success: From traditional statistics to machine learning Proceedings of the 46th SEFI Annual Conference 2018 Copenhagen, Denmark 17–21 September 2018 Volume 46 322 329
6. Tinto V. Dropout from Higher Education: A Theoretical Synthesis of Recent Research Rev. Educ. Res. 1975 45 89 125 10.3102/00346543045001089
7. Dagar Y.A. Constructivism: A Paradigm for Teaching and Learning Arts Soc. Sci. J. 2016 7 1 4 10.4172/2151-6200.1000200
8. Hassija V. Chamola V. Mahapatra A. Singal A. Goel D. Huang K. Scardapane S. Spinelli I. Mahmud M. Hussain A. Interpreting black-box models: A review on explainable artificial intelligence Cogn. Comput. 2024 16 45 74 10.1007/s12559-023-10179-8
9. Atakishiyev S. Salameh M. Yao H. Goebel R. Explainable artificial intelligence for autonomous driving: A comprehensive overview and field guide for future research directions IEEE Access 2024 12 101603 101625 10.1109/ACCESS.2024.3431437
10. Bekler M. Yilmaz M. Ilgın H.E. Assessing Feature Importance in Eye-Tracking Data within Virtual Reality Using Explainable Artificial Intelligence Techniques Appl. Sci. 2024 14 6042 10.3390/app14146042
11. Romano D. Novielli P. Diacono D. Cilli R. Pantaleo E. Amoroso N. Tangaro S. Insights from Explainable Artificial Intelligence of Pollution and Socioeconomic Influences for Respiratory Cancer Mortality in Italy J. Pers. Med. 2024 14 430 10.3390/jpm14040430
12. Yagin F.H. Colak C. Algarni A. Gormez Y. Guldogan E. Ardigò L.P. Hybrid Explainable Artificial Intelligence Models for Targeted Metabolomics Analysis of Diabetic Retinopathy Diagnostics 2024 14 1364 10.3390/diagnostics14131364
13. Carvalho D.D. Goethel M.F. Silva A.J. Vilas-Boas J.P. Pyne D.B. Fernandes R.J. Swimming Performance Interpreted through Explainable Artificial Intelligence (XAI)—Practical Tests and Training Variables Modelling Appl. Sci. 2024 14 5218 10.3390/app14125218
14. Hussain S. Khan M.Q. Student-performulator: Predicting students’ academic performance at secondary and Intermediate level using machine learning Ann. Data Sci. 2023 10 637 655 10.1007/s40745-021-00341-0 38624826
15. Pallathadka H. Wenda A. Ramirez-Asís E. Asís-López M. Flores-Albornoz J. Phasinam K. Classification and prediction of student performance data using various machine learning algorithms Mater. Today Proc. 2023 80 3782 3785 10.1016/j.matpr.2021.07.382
16. Chen Y. Zhai L. A comparative study on student performance prediction using machine learning Educ. Inf. Technol. 2023 28 12039 12057 10.1007/s10639-023-11672-1
17. Hooda M. Rana C. Dahiya O. Shet J.P. Singh B.K. Integrating LA and EDM for improving students Success in higher Education using FCN algorithm Math. Probl. Eng. 2022 2022 7690103 10.1155/2022/7690103
18. Shapley L.S. 17. A Value for n-Person Games Contributions to the Theory of Games (AM-28) Kuhn H.W. Tucker A.W. Princeton University Press Princeton, NJ, USA 1953 Volume II 307 318 10.1515/9781400881970-018
19. Schunk D.H. Social cognitive theory APA Educational Psychology Handbook, Vol 1: Theories, Constructs, and Critical Issues American Psychological Association Washington, DC, USA 2012 Volume 1 101 123
20. Adams N.E. Bloom’s Taxonomy of Cognitive Learning Objectives J. Med. Libr. Assoc. JMLA 2015 103 152 153 10.3163/1536-5050.103.3.010 26213509
21. Kuzilek J. Hlosta M. Zdrahal Z. Open University Learning Analytics dataset Sci. Data 2017 4 170171 10.1038/sdata.2017.171
22. Butucha K.G. Emerging Trends in Student Engagement in the 21St Century Contemporary World Baraton Interdiscip. Res. J. 2016 6 39 43
23. Tartavulea C.V. Albu C.N. Albu N. Dieaconescu R.I. Petre S. Online Teaching Practices and the Effectiveness of the Educa-tional Process in the Wake of the COVID-19 Pandemic Amfiteatru Econ. 2020 22 920
24. Breiman L. Random forests Mach. Learn. 2001 45 5 32 10.1023/A:1010933404324
25. Friedman J.H. Greedy function approximation: A gradient boosting machine Ann. Stat. 2001 29 1189 1232 10.1214/aos/1013203451
26. Keller J.M. Gray M.R. Givens J.A. A fuzzy k-nearest neighbor algorithm IEEE Trans. Syst. Man Cybern. 1985 SMC-15 580 585 10.1109/TSMC.1985.6313426
27. Shanmuganathan S. Artificial Neural Network Modelling: An Introduction Springer International Publishing Berlin/Heidelberg, Germany 2016 1 14
28. O’Shea K. An introduction to convolutional neural networks arXiv 2015 1511.08458
29. Hochreiter S. Long Short-term Memory Neural Comput MIT-Press Cambridge, UK 1997
30. Chen T. Guestrin C. Xgboost: A scalable tree boosting system Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining San Francisco, CA, USA 13–17 August 2016 785 794
31. Cohen S. The evolution of machine learning: Past, present, and future Artificial Intelligence and Deep Learning in Pathology Elsevier Berlin/Heidelberg, Germany 2021 1 12
32. Lundberg S. A unified approach to interpreting model predictions arXiv 2017 1705.07874
33. Lim K.K. Lee C.S. Investigating Learner’s Online Learning Behavioural Changes during the COVID-19 Pandemic Proc. Assoc. Inf. Sci. Technol. 2021 58 777 779 10.1002/pra2.559
34. Jereb E. Jerebic J. Urh M. Studying Habits in Higher Education Before and After the Outbreak of the COVID-19 Pandemic Athens J. Educ. 2023 10 67 84 10.30958/aje.10-1-4

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