Application of fuzzy system in assessment of practical courses: Developing an educational multimedia

Jafarkhani, F.

doi:10.22061/jte.2018.2397.1622

Document Type : Original Research Paper

Author

F. Jafarkhani

Department of Educational Technology, Faculty of Education Sciences, Allameh Tabataba’i University. Tehran. Iran

https://doi.org/10.22061/jte.2018.2397.1622

Abstract

Background and Objectives:To motivate learners to learn, their needs for security, belonging, and respect must be met, and they must be encouraged to make cognitive and developmental choices. In many ways, the only effective way to achieve this goal is to set up an education in which all learners feel somewhat successful. This sense of accomplishment leads to a realistic level of expectation. The feeling of success encourages the urgent need for progress and prepares success for future progress. Any method of ranking that makes learners feel defeated is irrational and destructive and damages their motivation for academic achievement. Thus, there is a need for some kind of evaluation that can help maintain motivation to be aware of the learner's strengths and weaknesses without imposing personal opinion. One of the applications of fuzzy logic is in evaluating educational functions. The expansion of fuzzy collections has extended its applications to other sciences and technologies, as it is widely used today in the fields of control, image processing, medicine, social and economic studies, and intelligence in various fields. The need for systems of measurement and evaluation that could take into account other aspects of growth as well as cognitive aspect, has been one of the challenges in education.
Method and Materials: This paper, using fuzzy logic measures, evaluated the performance of the multimedia production course unit for a group of 30 female students randomly selected among Allameh Tabataba’i university students who added the course unit in their sixth semester (50 people). The scoring was implemented using MATLAB software. The results were correlated with the common scoring method by Pearson correlation (0.78), which indicated strong structural validity with the fuzzy method.
Findings: The results showed that qualitative variables affecting multimedia production (subject, message design, instructional design and production skill) can be evaluated using fuzzy method possibilities. Therefore, due to the need to enhance operational and practical skills of the students in real life, it is possible to use the fuzzy system capability in instruction and evaluation so that other growth aspects of the learners such as emotional aspect, creativity or production skills will not be ignored.
Conclusion: It is suggested that this method be used in the evaluation of other practical courses such as designing learning environments, web-based teaching and other skills required by student-teachers. Especially in environments where the emphasis is on the learner and the students should be aware of the strengths and weaknesses of their learning process. It also provides a platform to define evaluation rules with the consent of learners according to the analysis of their characteristics and needs. In this way, by maintaining motivation, one can hope to continue learning.

Keywords

20.1001.1.20080441.1397.12.3.7.5

Main Subjects

Electronic learning- virtual

References

[1] Bazargan A. Instructional assessment. Tehran: Samt Publication; 2008. Persian.

[2] Hooman HA. Educational and psychological assessment. Tehran: Parsa Publication; 2006. Persian.

[3] Amir Teymouri MH. Instructional design messages. Tehran: Samt Publication; 2012. Persian.

[4] Angeli C, Tsaggari A. Examining the effects of learning in dyads with computer – based multimedia on third-grade students’ performance in history. Computers & Education. 2016; 92: 171-180.

[5] Mayer ER. The promise of multimedia learning: Using the same instructional design methods across different media. Journal of Learning & Instruction. 2016; 13: 125-139.

[6] Cheung CK. Confidence building, empowerment, and engagement: An argument for practicing media literacy in special education settings in Hong Kong, In C.K Cheung (Eds.), Media literacy Education in China (pp. 65-72). Singapore: Springer; 2016.

[7] Wang L. Developing & evaluating an interactive multimedia instructional tool: Learning outcomes. Journal of Educational Multimedia & Hypermedia. 2008; 17(1): 43-53.

[8] Fardanesh, H. [Translation of Educational systems design]. Romizofski AG (Author). Tehran: Samt Publication; 2012. Persian.

[9] Noohi S, Hosseini SM, Rokhsarizadeh H, Saboori A, Alishiri GHH. Progress motivation among Baqiyatallah university of medical sciences students and its relationship with academic achievement. Iranian Journal of Military Medicine. 2012; 14(3), 200-204. Persian.

[10] Zadeh LA. Fuzzy sets. Information and Control. 1965; 8: 338-353.

[11] Ayat S, Najafabadi E. Integration of IT and fuzzy as a new evaluation method: The case of mathematics. Technology and IT and Educational Sciences. 2010;1(2): 146-129. Persian.

[12] Brown GBJ, endlebury M. Assessing student learning in higher education (1^st ed). London: Routledge; 1997.

[13] Nasr A, Sharifian F, Abedi L. Components of academic achievement and its application by professors. Andishehaye Novin Tarbiati. 2009; 5(1): 9-34. Persian.

[14] Azar A, Khefelahi A, Danaeefard H, Alipoordarvish Z. Designing a fair model for human resource management. Pajooheshhaye Modiriat Manabe Ensani. 2010; 2(2): 28-1. Persian.

[15] Haghanni M, Ramezanpoor P. A new method in estimating manpower. Proceedings of 17^th International Conference Electronic (pp.1-8); 2002. Persian.

[16] Haghanni M. Application of fuzzy logic in academic performance. Education. 1997; 50: 53-68. Persian.

[17] Akkurt S, Tayfur G, Can S. Fuzzy logic model for the prediction of cement compressive strength. Cement and Concrete Research. 2004; 34(8): 1429-1433.

[18] Barman M, Choudhury JP, Biswas S. Detection of degree of sickness of affected eye using fuzzy logic and histogram analysis. Intelligent Computing and Applications, 343 of the Series Advances in, Intelligent Systems and Computing. 2015; 177-187.

[19] Abbaspour G, Yousef SR. Predicting soil fragmentation during tillage operation using fuzzy logic approach. Journal of Terramechanics. 2015;1(57): 61-69.

[20] Ranjbar R, Vakilzadeh R, Soleimanali M. Fuzzy approach in Islam system. Fegh va Hoghoghe Eslami. 2011; 2(3): 119-144. Persian.

[21] Zitouni D, Guinhouya BC. Fuzzy logic for characterizing the moderate intensity of physical activity in children. Journal of Science and Medicine in Sport. 2016; 19(2): 142-148.

[22] Palmer I, Wood E, Nagy S, Garcia G, Bashir M, Campbell R. Digital forensics education: a multidisciplinary curriculum model. Proceedings of 1st International Conference on Digital Forensics and Cyber Crime (pp. 3-15). Cham: Springer; 2015.

[23] Zhou HD. An Adaptive and Intelligent Tutoring System with Fuzzy Reasoning Capabilities. Journal of Convergence Information Technology. 2015; 101: 1-6.

[24] Karshki H, Pakmehr H, Mohammadzadeh A. (2014). Fuzzy system and curriculum. Proc. of 1st National Conference on Curriculum Change in Education (pp.189-191). Birjand: University of Birjand; 2015. Persian.

[25] Moreira TD, Machado MA. A fuzzy logic application in virtual education. Independent Journal of Management & Production. 2015; 1(2): 362-77.

[26] Saroukhani B, Sadeghipour SH. The application of fuzzy system in evaluating social sciences concepts. Social Sciences of Iran. 2015; 8(3): 47-64. Persian.

[27] Marin T, Săvoiu G. Addressing the dimensions of Education and integrated curriculum via generalized fuzzy logic. Euromentor Journal-Studies about Education. 2015; 1: 63-72.

[28] Gokmen G, Akinci T, Tektas M, Onat N, Kocyigit G, Tektas N. Evaluation of student performance inlaboratory applications using fuzzy logic. Social and Behavioral Sciences. 2010; 2(2): 902-909.

[29] Ozdemir, O., & Tekin, A. Evaluation of the presentation skills of the pre-service teachers via fuzzy logic. Computers in Human Behavior. 2016; 61: 288-299.

[30] Bai, S. M., & Chen, S. M. Evaluating students’ learning achievement using fuzzy membership functions and fuzzy rules. Expert Systems with Applications. 2008; 34(1): 399-410.