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EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 7, Issue 11 (November 2020), Pages: 10-24


 Original Research Paper

 Title: Employing artificial intelligence techniques for student performance evaluation and teaching strategy enrichment: An innovative approach

 Author(s): Lalbihari Barik *, Omar Barukab, Adel Ali Ahmed


 Faculty of Computing and Information Technology in Rabigh, King Abdulaziz University, Rabigh 21911, Jeddah, Saudi Arabia

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 * Corresponding Author. 

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An intelligent tutoring system is an excellent Artificial Intelligence (AI) alternative for the haunting problems of the teaching and evaluation system in university education. It evinces a paradigm shift in the current system by employing AI techniques to evaluate students’ performance and enrich the myriad teaching strategies. Unlike in regular classes where a teacher has to control 30 to 50 students, a teacher has to monitor hundreds of students, which is quite difficult and mentally exhausting. In such circumstances, mentors or teachers alone are not enough for monitoring the students and offering each student’s optimum attention and care. A new and original approach is needed to facilitate reliable and flexible methods of university student monitoring systems. The system should be able to evaluate the performance of many students, predict the final grade, and formulate intelligent decisions in real-time. Several computer-based models of AI are progressively performing an important role in teaching and performance evaluation of students. This paper proposes a new strategy to illustrate the advantages of applying AI techniques to predict the final grade of students. The validation process was carried out with the real-time 1000 students’ dataset of 12 core and 18 elective courses in Bachelor of Computer Science during the academic year 2018-2019. In this paper, hybrid SVM with a Fuzzy Expert System is proposed to show the techniques proficiency for teaching and students’ final grade prediction and the possibility of future work. 

 © 2020 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (

 Keywords: Intelligent tutoring systems, Student performance, Teaching strategies, AI techniques

 Article History: Received 9 February 2020, Received in revised form 12 May 2020, Accepted 27 June 2020


This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under grant No. G-446-830-34. The authors, therefore, acknowledge with DSR technical and financial support.

 Compliance with ethical standards

 Conflict of interest: The authors declare that they have no conflict of interest.


 Barik L, Barukab O, and Ahmed AA (2020). Employing artificial intelligence techniques for student performance evaluation and teaching strategy enrichment: An innovative approach. International Journal of Advanced and Applied Sciences, 7(11): 10-24

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 References (23)

  1. Ata R and Koçyigit Y (2010). An adaptive neuro-fuzzy inference system approach for prediction of tip speed ratio in wind turbines. Expert Systems with Applications, 37(7): 5454-5460.   [Google Scholar]
  2. Bae C, Yeh WC, Chung YY, and Liu SL (2010). Feature selection with intelligent dynamic swarm and rough set. Expert Systems with Applications, 37(10): 7026-7032.   [Google Scholar]
  3. Banakar A and Azeem MF (2008). Artificial wavelet neural network and its application in neuro-fuzzy models. Applied Soft Computing, 8(4): 1463-1485.   [Google Scholar]
  4. Bydžovská H (2015). Are collaborative filtering methods suitable for student performance prediction? In: Pereira F, Machado P, Costa E, and Cardoso A (Eds.), Portuguese conference on artificial intelligence: 425-430. Springer, Cham, Switzerland.   [Google Scholar]
  5. Bydžovská H (2016). A comparative analysis of techniques for predicting student performance. In the 9th International Educational Data Mining Society, Raleigh, USA: 306-311.   [Google Scholar]
  6. Bydžovská H and Popelínský L (2014). The influence of social data on student success prediction. In the 18th International Database Engineering and Applications Symposium, Association for Computing Machinery, Porto, Portugal: 374-375.   [Google Scholar]
  7. Cheng MY, Tsai HC, and Sudjono E (2010). Evolutionary fuzzy hybrid neural network for project cash flow control. Engineering Applications of Artificial Intelligence, 23(4): 604-613.   [Google Scholar]
  8. De Nooy W, Mrvar A, and Batagelj V (2018). Exploratory social network analysis with Pajek: Revised and expanded edition for updated software. Vol. 46, Cambridge University Press, Cambridge, UK.   [Google Scholar]
  9. Dilek S, Çakır H, and Aydın M (2015). Applications of artificial intelligence techniques to combating cyber crimes: A review. International Journal of Artificial Intelligence and Applications, 6(1): 21-39.   [Google Scholar]
  10. Dimitriou L, Tsekeris T, and Stathopoulos A (2008). Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow. Transportation Research Part C: Emerging Technologies, 16(5): 554-573.   [Google Scholar]
  11. Dong Y, Xiang B, Wang T, Liu H, and Qu L (2010). Rough set-based SAR analysis: An inductive method. Expert Systems with Applications, 37(7): 5032-5039.   [Google Scholar]
  12. Esfahanipour A and Aghamiri W (2010). Adapted neuro-fuzzy inference system on indirect approach TSK fuzzy rule base for stock market analysis. Expert Systems with Applications, 37(7): 4742-4748.   [Google Scholar]
  13. Fan YN, Tseng TLB, Chern CC, and Huang CC (2009). Rule induction based on an incremental rough set. Expert Systems with Applications, 36(9): 11439-11450.   [Google Scholar]
  14. Harackiewicz JM, Barron KE, Tauer JM, and Elliot AJ (2002). Predicting success in college: A longitudinal study of achievement goals and ability measures as predictors of interest and performance from freshman year through graduation. Journal of Educational Psychology, 94(3): 562-575.   [Google Scholar]
  15. KAU (2018). Our history. King Abdul-Aziz University. Jeddah, Saudi Arabia. Available online at:
  16. Koprinska I, Stretton J, and Yacef K (2015). Students at risk: Detection and remediation. In the 8th International Conference on Educational Data Mining, Madrid, Spain: 512-515.   [Google Scholar]
  17. Manouselis N, Drachsler H, Vuorikari R, Hummel H, and Koper R (2011). Recommender systems in technology enhanced learning. In: Ricci F, Rokach L, Shapira B, and Kantor P (Eds.), Recommender systems handbook: 387-415. Springer, Boston, USA.   [Google Scholar]
  18. Matuszyk P and Spiliopoulou M (2014). Hoeffding-CF: Neighbourhood-based recommendations on reliably similar users. In the International Conference on User Modeling, Adaptation, and Personalization, Springer, Aalborg, Denmark: 146-157.   [Google Scholar]
  19. Murtagh F and Contreras P (2012). Algorithms for hierarchical clustering: An overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(1): 86-97.   [Google Scholar]
  20. Nghe NT, Janecek P, and Haddawy P (2007). A comparative analysis of techniques for predicting academic performance. In the 37th Annual Frontiers in Education Conference-Global Engineering: Knowledge without Borders, Opportunities without Passports, IEEE, Milwaukee, USA: T2G-7.   [Google Scholar]
  21. Nižnan J, Pelánek R, and Rihák J (2015). Student models for prior knowledge estimation. In the 8th International Educational Data Mining Society, Madrid, Spain: 109-116.   [Google Scholar]
  22. Romero C, López MI, Luna JM, and Ventura S (2013). Predicting students' final performance from participation in on-line discussion forums. Computers and Education, 68: 458-472.   [Google Scholar]
  23. Strecht P, Cruz L, Soares C, and Mendes-Moreira J (2015). A comparative study of classification and regression algorithms for modelling students' academic performance. In the 8th International Conference on Educational Data Mining, Madrid, Spain: 392-395.   [Google Scholar]