Development of an Intelligent Tutoring System Using Bayesian Networks and Fuzzy Logic for a Higher Student Academic Performance
Source abstract
Abstract
In this experimental study, an intelligent tutoring system called the fuzzy Bayesian intelligent tutoring system (FB-ITS), is developed by using artificial intelligence methods based on fuzzy logic and the Bayesian network technique to adaptively support students in learning environments. The effectiveness of the FB-ITS was evaluated by comparing it with two other versions of an Intelligent Tutoring System (ITS), fuzzy ITS and Bayesian ITS, separately. Moreover, it was evaluated by comparing it with an existing traditional e-learning system. In order to evaluate whether the academic performance of the students in different learning groups differs or not, analysis of covariance (ANCOVA) was used based on the students’ pre-test and post-test scores. The study was conducted with 120 undergraduate university students. Results showed that students who studied using FB-ITS had significantly higher academic performance on average compared to other students who studied with the other systems. Regarding the time taken to perform the post-test, the results indicated that students who used the FB-ITS needed less time on average compared to students who used the traditional e-learning system. From the results, it could be concluded that the new system contributed in terms of the speed of performing the final exam and high academic success.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Intelligent Tutoring Systems and Adaptive Learning · Online Learning and Analytics · Educational Technology and Assessment
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Engineering, Atilim University, Ankara 06830, Turkey
Department of Software Engineering, Atilim University, Ankara 06830, Turkey
References (45)
Show source-provided references
1. Fuzzy Logic for Adaptive Instruction in an E-learning Environment for Computer Programming10.1109/TFUZZ.2014.2310242 ↗
2. Yang, G., Kinshuk, K., and Graf, S. (2010, January 1–3). A practical student model for a locationaware and context-sensitive Personalized Adaptive Learning System. Proceedings of the IEEE Technology for Education Conference, Bombay, India.
3. Pecori, R. (2018). A Virtual Learning Architecture Enhanced by Fog Computing and Big Data Streams. Futur. Internet, 10.10.3390/fi10010004 ↗
4. Yan, J., and Agada, R. (2018, January 12–14). Intelligent Agent Embodied Online Tutoring System. Proceedings of the 2018 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA.10.1109/CSCI46756.2018.00123 ↗
5. Augmenting Quality of Experience in Distance Learning Using Fog Computing10.1109/MIC.2019.2936754 ↗
6. QuizMap: Open social student modeling and adaptive navigation support with TreeMaps
7. Yang, C. (2009). An Intelligent Tutoring System Based on Personalized Learning, University of Regina.
8. Luis, R.M.M.F., Llamas-Nistal, M., and Iglesias, M.J.F. (2017, January 21–24). Iglesias, Enhancing learners’ experience in e-learning based scenarios using Intelligent tutoring systems and learning analytics: First results from a perception survey. Proceedings of the 2017 12th Iberian Conference on Information Systems and Technologies (CISTI), Lisbon, Portugal.10.23919/CISTI.2017.7975976 ↗
9. Bypassing the Intractable Problem of Student Modelling
10. Almohammadi, K. (2016). Type-2 Fuzzy Logic based Systems for Adaptive Learning and Teaching within Intelligent E- Learning Environments. [Ph.D. Thesis, University of Essex].
11. Eryılmaz, M., Adabashi, A.M., and Yazıcı, A. (2019). Artificial Intelligence Methods in E-Learning. Handbook of Research on Faculty Development for Digital Teaching and Learning, IGI-global.10.4018/978-1-5225-8476-6.ch015 ↗
12. How, M.-L., and Hung, D. (2019). Educational Stakeholders’ Independent Evaluation of an Artificial Intelligence-Enabled Adaptive Learning System Using Bayesian Network Predictive Simulations. Educ. Sci., 9.10.3390/educsci9020110 ↗
13. Toward an Adaptive Learning System Framework: Using Bayesian Network to Manage Learner Model10.3991/ijet.v7i4.2290 ↗
14. Users-Centric Adaptive Learning System Based on Interval Type-2 Fuzzy Logic for Massively Crowded E-Learning Platforms10.1515/jaiscr-2016-0008 ↗
15. Using Bayesian Networks to Manage Uncertainty in Student Modeling10.1023/A:1021258506583 ↗
16. Mouenis, A.T., Souhaib, A., and Mohamed, K. (2015). Learner Modeling Based on Bayesian Networks. E-Learn. Instr. Des. Organ. Strategy Manag.10.5772/61021 ↗
17. Zadeh_FuzzySetTheory_1965.pdf10.1016/S0019-9958(65)90241-X ↗
18. Almohammadi, K., and Hagras, H. (2013, January 7–10). An adaptive fuzzy logic based system for improved knowledge delivery within intelligent e-learning platforms. Proceedings of the the IEEE International Conference on Fuzzy Systems, Hyderabad, India.10.1109/FUZZ-IEEE.2013.6622350 ↗
19. Ishak, I. (2015, January 2). Application of fuzzy logic to student performance in calculation subjects. Proceedings of the 4th National Symposium & Exhibition on Business & Accounting 2015, Muadzam Shah, Malaysia.
20. Fuzzy Modeling for Performance-based Assessment
21. Peter, L., and Regina, S. (2013). Intelligent Tutoring Systems: Measuring Student Effort During Assessment, University of Regina Faculty of Graduate Studies and Research.
22. Trausan-Matu, S., Boyer, K.E., Crosby, M., and Panourgia, K. (2014). Implementation of an Intelligent Tutoring System for Online Homework Support in an Efficacy Trial. Intelligent Tutoring Systems, Springer. ITS 2014; Lecture Notes in Computer Science.10.1007/978-3-319-07221-0 ↗
23. A Survey of Artificial Intelligence Techniques Employed for Adaptive Educational Systems within E-Learning Platforms10.1515/jaiscr-2017-0004 ↗
24. Bernard, J., Chang, T.-W., Popescu, E., and Graf, S. (2015, January 22–26). Using Artificial Neural Networks to Identify Learning Styles. Proceedings of the 17th International Conference, Artificial Intelligence in Education (AIED), Madrid, Spain.10.1007/978-3-319-19773-9_57 ↗
25. Decade review (1999–2009): Progress of application of artificial intelligence tools in student diagnosis10.1504/IJSHC.2009.031006 ↗
26. Adaptive E-learning using Genetic Algorithms
27. Personalized Course Evolutionary Based on Genetic Algorithm10.14257/ijmue.2014.9.11.25 ↗
28. Designing intelligent tutoring systems: Bayesian approach
29. Propose of fuzzy logic-based students’ learning assessment
30. Moreno, F.J.T., and Moreno, M.J.G. (2005). Using Bayesian Networks in the Global Adaptive E-Learning Process, EUNIS.
31. Authoring of probabilistic sequencing in adaptive hypermedia with Bayesian networks
32. A Fuzzy Logic-based Personalized Learning System for Supporting Adaptive English Learning
33. Rule-Based Fuzzy Logic for Automatic Learning Process in an E-learning Environment
34. Asopa, P., Asopa, S., Joshi, N., and Mathur, I. (2016, January 21–24). Evaluating student performance using fuzzy inference system in fuzzy ITS. Proceedings of the 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India.10.1109/ICACCI.2016.7732318 ↗
35. Andes: A Coached Problem Solving Environment for Physics10.1007/3-540-45108-0_17 ↗
36. Myneni, L.S., and Narayanan, N.H. (2012, January 16–18). ViPS-An Intelligent Tutoring System for Exploring and Learning Physics through Simple Machines. Proceedings of the 4th International Conference on Computer Supported Education Volume 2: CSEDU, Porto, Portugal.
37. Millán, E., Jiménez, G., Belmonte, M.-V., and Perez-De-La-Cruz, J.-L. (2015, January 22–26). Learning Bayesian Networks for Student Modeling. Proceedings of the 17th International Conference, Artificial Intelligence in Education (AIED), Madrid, Spain.10.1007/978-3-319-19773-9_100 ↗
38. Analysing Student Programs in the PHP Intelligent Tutoring System10.1007/s40593-014-0014-z ↗
39. SITS: A solution-based intelligent tutoring system for students’ acquisition of problem-solving skills in computer programming10.1080/14703297.2016.1189346 ↗
40. Comparing Student Model Accuracy with Bayesian Network and Fuzzy Logic in Predicting Student Knowledge Level10.14257/ijmue.2014.9.4.12 ↗
41. Intelligent tutoring systems: An overview10.1007/BF00168958 ↗
42. Kadie, C.M., Hovel, D., and Horvitz, E. (2001). Msbnx: A Component-Centric Toolkit for Modeling and Inference with Bayesian Networks, Microsoft Research. Available online: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-2001-67.pdf.
43. Kahraman, H.T., Colak, I., and Sagiroglu, S. (2007, January 13–15). A Web Based Adaptive Educational System. Proceedings of the 6th International Conference on Machine Learning and Applications, ICMLA, Cincinnati, OH, USA.10.1109/ICMLA.2007.37 ↗
44. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences, Lawrence Earlbaum Associates. [2nd ed.].
45. Dynamic learner profiling and automatic learner classification for adaptive e-learning environment