Fuzzification of Items of Media and Educational Materials and Tools

Musavian, S. S.; Taghizade, A.; Ahmadi, F. Z.; Norouzi, S.

doi:10.22061/jecei.2024.11019.757

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	Fuzzification of Items of Media and Educational Materials and Tools
Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 7، دوره 13، شماره 1، فروردین 2025، صفحه 81-92 اصل مقاله (1.06 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2024.11019.757
نویسندگان
S. S. Musavian^* ¹؛ A. Taghizade¹؛ F. Z. Ahmadi²؛ S. Norouzi¹
¹Department of Educational Sciences, Farhangian University, Tehran, Iran.
²Health and Physical Education. Dept., Organization for Educational Research and Planning, Tehran, Iran.
تاریخ دریافت: 04 تیر 1403، تاریخ بازنگری: 22 مرداد 1403، تاریخ پذیرش: 03 شهریور 1403
چکیده
Background and Objectives: The purpose of this study is to propose a solution for using large fuzzy sets in assessment tasks with a significant number of items, focusing on the assessment of media and educational tools. Ensuring fairness is crucial in evaluation tasks, especially when different evaluators assign different ratings to the same process or their ratings may even vary in different situations. Also, previous non-fuzzy assessment methods show that the mean value of assessors scores is not a good representation when the variance of scores is significant. Fuzzy evaluation methods can solve this problem by addressing the uncertainty in evaluation tasks. Although some studies have been conducted on fuzzy assessment, but their main focus is fuzzy calculations and no solution has been proposed for the problem arising when fuzzy rule set is considerably huge. Methods: Fuzzy rules are the main key for fuzzy inference. This part of a fuzzy system often is generated by experts. In this study,15 experts were asked to create the set of fuzzy rules. Fuzzy rules relate inputs to outputs by descriptive linguistic expressions. Making these expressions is so more convenient than if we determine an exact relationship between inputs and outputs. The number of fussy rules has an exponential relationship with the number of inputs. Therefore, for a task with more than say 6 inputs, we should deal with a huge set of fuzzy rules. This paper presents a solution that enables the use of large fuzzy sets in fuzzy systems using a multi-stage hierarchical approach. Results: Justice is always the most important issue in an assessment process. Due to its nature, a fuzzy calculation-based assessment provides an assessment in a just manner. Since many assessment tasks are often involved more than 10 items to be assessed, generating a fuzzy rule set is impossible. Results show the final score is very sensitive to slight differences in score of an item given by assessors. Besides that, assessors often are not able to consider all items simultaneously to assign a coefficient for the effect of each item on final score. This will be seriously a problem when the final score depends on many input items. In this study, we proposed a fuzzy analysis method to ensure equitable evaluation of educational media and instructional tools within the teaching process. Results of none-fuzzy scoring system show that final score has intense variations when assessment is down in different times and by different assessors. It is because of the manner that importance coefficients are calculated for each item of assessment. In fuzzy assessment no importance coefficient is used for each item. Conclusion: In this study, a novel method was proposed to determine the score of an activity, a task, or a tool that is designed for learning purposes based on Fuzzy sets and their respective calculations. Because of the nature of fuzzy systems, approximate descriptive expressions are used to relate input items to final score instead of an exact function that is impossible to be estimated. Fuzzy method is a robust system that ensure us a fair assessment.
کلیدواژه‌ها
AI assessment؛ Fuzzification Assessment Items؛ Fuzzy Assessment؛ Educational Media Assessment؛ Big Fuzzy Rules Set

مراجع
[1] T. Khomeiny, T. R. Kusuma, A. N. Handayani, A. P. Wibawa, A. H. S. Irianti, “Grading system recommendations for students using fuzzy mamdani logic,” in Proc. 2020 4th international conference on vocational education and training (icovet): 1-6, 2020. [2] T. Glushkova, V. Ivanova, B. Zlatanov, “Beyond traditional assessment: A fuzzy logic-infused hybrid approach to equitable proficiency evaluation via online practice tests,” Mathematics, 12(3): 371, 2024. [3] OECD, “Evaluation and assessment frameworks for improving school outcomes: Common policy challenges summary,” 2010. [4] R. D. Penfield, “Fairness in test scoring,” in Fairness in Educational Assessment and Measurement, 1st Ed., pp: 55-76, 2016. [5] T. Penfield, M. Baker, R. Scoble, M. Wykes, “Assessment, evaluations, and definitions of research impact: A review,” Res. Eval., 23(1): 21-32, 2013. [6] H. L. Andrade, “A critical review of research on student self-assessment,” Front. Educ., 4, 2019. [7] A. Markov, Z. Seleznyova, V. Lapshin, “Credit scoring methods: Latest trends and points to consider,” J. Finance Data Sci., 8: 180-201, 2022. [8] E. H. Mamdani, “Application of fuzzy logic to approximate reasoning using linguistic synthesis,” IEEE Trans. Comput., 26(12): 1182-1191, 1977. [9] A. Yunan, M. Ali, “Study and implementation of the fuzzy Mamdani and Sugeno methods in decision making on selection of outstanding students at the South Aceh polytechnic,” Inotera, 5(2): 152-164, 2020. [10] C. M. Reigeluth, P. C. Honebein, “Will instructional methods and media ever live in unconfounded harmony? Generating useful media research via the instructional theory framework,” Educ. Technol. Res. Dev., 1-21, 2023. [11] Z. Zhou, Y. Li, “Teaching quality evaluation of higher education based on intuitionistic fuzzy information,” in Proc. 2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE): 1-7, 2023. [12] S. S. Musavian, “Assessment of laboratory courses using fuzzy reasoning,” Journal of Educational Sciences & Psychology, 3(2): 19, 2013. [13] O. Sheveleva, V. Dobrynin, M. Mastroianni, Y. Goncharova, “A fuzzy model for assessing acquired competencies,” in Proc. E3S Web of Conferences, 419: 02024, 2023. [14] A. V. Ryabko, O. V. Zaika, R. P. Kukharchuk, T. A. Vakaliuk, V. V. Osadchyi, “Methods for predicting the assessment of the quality of educational programs and educational activities using a neuro-fuzzy approach,” in CTE Workshop Proc., 9: 154-169, 2022. [15] M. N. Raheema, A. M. Al-Khazzar, J. S. Hussain, “Prediction of students’ achievements in e-learning courses based on adaptive neuro-fuzzy inference system,” Int. J. Fuzzy Log. Intell. Syst., 22(2): 213-222, 2022. [16] M. N. Raheema, A. M. Al-Khazzar, J. S. Hussain, “Prediction of students' achievements in e-learning courses based on adaptive neuro-fuzzy inference system,” Int. J. Fuzzy Log. Intell. Syst., 22(2), 213-222, 2022. [17] J. A. Rojas, H. E. Espitia, L. A. Bejarano, “Design and optimization of a fuzzy logic system for academic performance prediction,” Symmetry, 13(1): 133, 2021. [18] Y. Liu, X. Zhang, “Evaluating the undergraduate course based on a fuzzy AHP-FIS model,” Int. J. Mod. Educ. Comput. Sci., 12(6), 55-66, 2020. [19] M. Rahmanian, “A new fuzzy approach for teacher’s performance evaluation,” Int. Res. J. Mod. Eng. Technol. Sci., 3(01): 252-258, 2021. [20] X. Sun, C. Cai, P. Su, N. Bao, N. Liu, “A university teachers’ teaching performance evaluation method based on type-II fuzzy sets,” Mathematics, 9(17): 2126, 2021. [21] S. S. Musavian, “Assessment of laboratory courses using fuzzy reasoning,” J. Educ. Sci., 12(6), 2013. [22] A. Cervero, A. Castro-Lopez, L. Álvarez-Blanco, M. Esteban, A. Bernardo, “Evaluation of educational quality performance on virtual campuses using fuzzy inference systems,” PLoS One, 15(5): e0232802, 2020. [23] T. Z. H. T. Petra, M. J. A. Aziz, “Analysing student performance in higher education using fuzzy logic evaluation,” Int. J. Sci. Technol. Res., 10(1): 322-327, 2021. [24] M. Alaa et al., “Assessment and ranking framework for the English skills of pre-service teachers based on fuzzy Delphi and TOPSIS methods,” IEEE Access, 7: 126201-126223, 2019. [25] N. A. Namlı, O. Şenkal, “Using the fuzzy logic in assessing the programming performance of students,” Int. J. Assess. Tools Educ., 5(4): 701-712, 2018. [26] M. A. S. Yudono et al., “Fuzzy decision support system for ABC university student admission selection,” in Proc. Int. Conf. Econ., Manag. Account. (ICEMAC 2021): 230-237, 2022. [27] T. A. Thakreh, O. K. Chaudhari, N. Dhawade, “A fuzzy logic multi-criteria approach for evaluation of teachers’ performance,” Adv. Fuzzy Math., 12(1): 129-145, 2017. [28] M. G. Voskoglou, “Fuzzy logic as a tool for assessing students’ knowledge and skills,” Educ. Sci., 3(2): 208-221, 2013. [29] J. A. Montero, R. M. Alsina, J. A. Morán, M. Cid, “Fuzzy logic system for students’ evaluation,” in Proc. Lecture Notes in Computer Science: 1246-1253, 2005. [30] L. A. Zadeh, “Fuzzy sets,” Inf. Control, 8(3): 338-353, 1965. [31] I. Adeyanju, O. O. Bello, M. A. Adegboye, “Machine learning methods for sign language recognition: A critical review and analysis,” Intell. Syst. Appl., 12: 200056, 2021.
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Fuzzification of Items of Media and Educational Materials and Tools