Development of fuzzy logic based student performance prediction system
DOI:
https://doi.org/10.35335/cit.Vol15.2024.714.pp284-290Keywords:
Data Analysis, Decision Making, Fuzzy Logic, Higher Education, Prediction, Student PerformanceAbstract
Improving students' academic performance is a key goal in the context of higher education. However, the process of identifying students who require additional support is often complicated and complex. Traditional approaches in analyzing student performance data tend to be limited in handling data uncertainty and complexity. Therefore, the development of fuzzy logic-based decision-making systems is becoming increasingly important. This research aims to develop a fuzzy logic-based decision-making system to predict student performance accurately and efficiently. This approach utilizes fuzzy logic concepts to handle uncertainty and complexity in data, and allows the integration of various input factors, such as exam results, class participation, and other variables, in the decision-making process. The research methods include collecting historical student performance data, modeling fuzzy variables for inputs and outputs, developing fuzzy inference rules, and implementing and testing the system using split test data. Numerical example results show that the system is able to provide predictions of student performance by considering relevant input variables. In addition, the system also offers the potential to improve the efficiency of educational interventions by identifying at-risk students faster and more precisely. As such, the development of this fuzzy logic-based decision-making system is expected to make a significant contribution to efforts to improve the quality and equity of higher education by ensuring that every student gets the support they need to reach their full academic potential.
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References
U. Teichler, “Higher education and the world of work: Conceptual frameworks, comparative perspectives, empirical findings,” in Higher Education and the World of Work, Brill, 2019.
M. Chankseliani and T. McCowan, “Higher education and the sustainable development goals,” High. Educ., vol. 81, no. 1, pp. 1–8, 2021.
C. A. Bonfield, M. Salter, A. Longmuir, M. Benson, and C. Adachi, “Transformation or evolution?: Education 4.0, teaching and learning in the digital age,” High. Educ. Pedagog., vol. 5, no. 1, pp. 223–246, 2020.
H. de Wit, “Internationalization in higher education, a critical review,” SFU Educ. Rev., vol. 12, no. 3, pp. 9–17, 2019.
J. Zajda, Globalisation and education reforms: Creating effective learning environments, vol. 25. Springer, 2021.
H. Waheed, S.-U. Hassan, N. R. Aljohani, J. Hardman, S. Alelyani, and R. Nawaz, “Predicting academic performance of students from VLE big data using deep learning models,” Comput. Human Behav., vol. 104, p. 106189, 2020.
E. Alyahyan and D. Dü?tegör, “Predicting academic success in higher education: literature review and best practices,” Int. J. Educ. Technol. High. Educ., vol. 17, pp. 1–21, 2020.
M. Tsiakmaki, G. Kostopoulos, S. Kotsiantis, and O. Ragos, “Fuzzy-based active learning for predicting student academic performance using autoML: a step-wise approach,” J. Comput. High. Educ., vol. 33, no. 3, pp. 635–667, 2021.
A. Charitopoulos, M. Rangoussi, and D. Koulouriotis, “On the use of soft computing methods in educational data mining and learning analytics research: A review of years 2010–2018,” Int. J. Artif. Intell. Educ., vol. 30, pp. 371–430, 2020.
Y. I. Daradkeh and I. Tvoroshenko, “Technologies for making reliable decisions on a variety of effective factors using fuzzy logic,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 5, 2020.
O. Iatrellis, E. Stamatiadis, N. Samaras, T. Panagiotakopoulos, and P. Fitsilis, “An intelligent expert system for academic advising utilizing fuzzy logic and semantic web technologies for smart cities education,” J. Comput. Educ., vol. 10, no. 2, pp. 293–323, 2023.
V. Radu, F. Radu, A. I. Tabirca, S. I. Saplacan, and R. Lile, “Bibliometric Analysis of Fuzzy Logic Research in International Scientific Databases.,” Int. J. Comput. Commun. Control, vol. 16, no. 1, 2021.
R. C. Deo, Z. M. Yaseen, N. Al-Ansari, T. Nguyen-Huy, T. A. M. Langlands, and L. Galligan, “Modern artificial intelligence model development for undergraduate student performance prediction: An investigation on engineering mathematics courses,” IEEE Access, vol. 8, pp. 136697–136724, 2020.
A. Cervero, A. Castro-Lopez, L. Álvarez-Blanco, M. Esteban, and A. Bernardo, “Evaluation of educational quality performance on virtual campuses using fuzzy inference systems,” PLoS One, vol. 15, no. 5, p. e0232802, 2020.
E. Pourjavad and R. V Mayorga, “A comparative study and measuring performance of manufacturing systems with Mamdani fuzzy inference system,” J. Intell. Manuf., vol. 30, pp. 1085–1097, 2019.
K. Elbaz, S.-L. Shen, A. Zhou, D.-J. Yuan, and Y.-S. Xu, “Optimization of EPB shield performance with adaptive neuro-fuzzy inference system and genetic algorithm,” Appl. Sci., vol. 9, no. 4, p. 780, 2019.
S. Pargaonkar, “A Comprehensive Review of Performance Testing Methodologies and Best Practices: Software Quality Engineering,” Int. J. Sci. Res., vol. 12, no. 8, pp. 2008–2014, 2023.
D. Kaur, S. Uslu, K. J. Rittichier, and A. Durresi, “Trustworthy artificial intelligence: a review,” ACM Comput. Surv., vol. 55, no. 2, pp. 1–38, 2022.
F. Magrabi et al., “Artificial intelligence in clinical decision support: challenges for evaluating AI and practical implications: a position paper from the IMIA Technology Assessment & Quality Development in Health Informatics Working Group and the EFMI Working Group for As,” Yearb. Med. Inform., vol. 28, no. 1, p. 128, 2019.
M. Ery?lmaz and A. Adabashi, “Development of an intelligent tutoring system using bayesian networks and fuzzy logic for a higher student academic performance,” Appl. Sci., vol. 10, no. 19, p. 6638, 2020.
J. A. Rojas, H. E. Espitia, and L. A. Bejarano, “Design and optimization of a fuzzy logic system for academic performance prediction,” Symmetry (Basel)., vol. 13, no. 1, p. 133, 2021.
M. Dhokare, S. Teje, S. Jambukar, and V. Wangikar, “Evaluation of academic performance of students using fuzzy logic,” in 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), 2021, pp. 1–5.
A. Khan and S. K. Ghosh, “Student performance analysis and prediction in classroom learning: A review of educational data mining studies,” Educ. Inf. Technol., vol. 26, pp. 205–240, 2021.
A. Rivas, A. Gonzalez-Briones, G. Hernandez, J. Prieto, and P. Chamoso, “Artificial neural network analysis of the academic performance of students in virtual learning environments,” Neurocomputing, vol. 423, pp. 713–720, 2021.
A. Alam and A. Mohanty, “Predicting Students’ Performance Employing Educational Data Mining Techniques, Machine Learning, and Learning Analytics,” in International Conference on Communication, Networks and Computing, 2022, pp. 166–177.
R. P. Vásquez, A. A. Aguilar-Lasserre, M. V. López-Segura, L. C. Rivero, A. A. Rodríguez-Duran, and M. A. Rojas-Luna, “Expert system based on a fuzzy logic model for the analysis of the sustainable livestock production dynamic system,” Comput. Electron. Agric., vol. 161, pp. 104–120, 2019.
M. R. C. Qazani, H. Asadi, M. Rostami, S. Mohamed, C. P. Lim, and S. Nahavandi, “Adaptive motion cueing algorithm based on fuzzy logic using online dexterity and direction monitoring,” IEEE Syst. J., vol. 16, no. 2, pp. 1945–1953, 2021.
V. Ojha, A. Abraham, and V. Snášel, “Heuristic design of fuzzy inference systems: A review of three decades of research,” Eng. Appl. Artif. Intell., vol. 85, pp. 845–864, 2019.
K. Raza, “Fuzzy logic based approaches for gene regulatory network inference,” Artif. Intell. Med., vol. 97, pp. 189–203, 2019.
P. Nagaraj and P. Deepalakshmi, “An intelligent fuzzy inference rule?based expert recommendation system for predictive diabetes diagnosis,” Int. J. Imaging Syst. Technol., vol. 32, no. 4, pp. 1373–1396, 2022.
F. D. Paiva, R. T. N. Cardoso, G. P. Hanaoka, and W. M. Duarte, “Decision-making for financial trading: A fusion approach of machine learning and portfolio selection,” Expert Syst. Appl., vol. 115, pp. 635–655, 2019.
K. Khosravi et al., “A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods,” J. Hydrol., vol. 573, pp. 311–323, 2019.
A. S. Aydiner, E. Tatoglu, E. Bayraktar, and S. Zaim, “Information system capabilities and firm performance: Opening the black box through decision-making performance and business-process performance,” Int. J. Inf. Manage., vol. 47, pp. 168–182, 2019.
H. A. Mengash, “Using data mining techniques to predict student performance to support decision making in university admission systems,” Ieee Access, vol. 8, pp. 55462–55470, 2020.
V. Campos-Guzmán, M. S. García-Cáscales, N. Espinosa, and A. Urbina, “Life Cycle Analysis with Multi-Criteria Decision Making: A review of approaches for the sustainability evaluation of renewable energy technologies,” Renew. Sustain. Energy Rev., vol. 104, pp. 343–366, 2019.
T. Izumi, V. Sukhwani, A. Surjan, and R. Shaw, “Managing and responding to pandemics in higher educational institutions: initial learning from COVID-19,” Int. J. Disaster Resil. Built Environ., vol. 12, no. 1, pp. 51–66, 2021.
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