Fuzzy Conjoint Need Assessment Method for Model Development Justification
DOI:
https://doi.org/10.11113/mjfas.v20n1.3211Keywords:
Fuzzy conjoint method, Triangular fuzzy number, Need assessment, Model development, Mathematics problem-solving abilityAbstract
This paper proposes a Fuzzy Conjoint Need Assessment Method (FCNAM) procedure for model development justification. The failure to select the analysis method and neglect the data when completing the needs assessment, before developing a model, leads to incorrect implementation of the justification. There is a misunderstanding in the requirements analysis, and certain elements that should be considered in the model's design and development may be overlooked. Therefore, as an alternative method in the model development justification procedure, the fuzzy conjoint analysis method together with the triangular fuzzy number is used as a data analysis medium in the need assessment survey. As an implementation and evaluation of the FCNAM procedure, a need assessment survey involving 53 mathematics teachers was conducted to justify the need for the development of a student's mathematics problem-solving ability (SMPSA) measurement model. This procedure is proven to comply with the guideline need assessment, which is to successfully confirm gaps related to mathematics problem-solving ability, describe the perspective of teachers as users and practitioners of the proposed model and synthesize the level of development needs of the model involved. This paper contributes knowledge about the application of the triangular fuzzy number-based conjoint method in perception surveys and introduces a more effective model development justification procedure.
References
Cuiccio, C., & Husby-Slater, M. (2018). Needs assessment guidebook. Supporting the development of district and school needs assessments. Washington, D.C.: American Institutes for Research. Retrieved from https://statesupportnetwork.ed.gov/system/files/needsassessmentguidebook-508_003.pdf.
Holloway, K., Arcus, K., & Orsborn, G. (2018). Training needs analysis - The essential first step for continuing professional development design. Nurse education in practice, 28, 7-12. https://doi.org/10.1016/j.nepr.2017.09.001.
Sarkar, S. (2013). Competency based training need assessment – Approach in Indian companies. Organizacija, 46(6). Retrieved from http://organizacija.fov.uni-mb.si/index.php/organizacija/article/view/531.
Sarala, N. & Kavitha, R. (2015). Model of mathematics teaching: A fuzzy set approach. IOSR Journal of Mathematics, 11(1-1), 19-22. Doi: 10.9790/5728-11111922.
Albayrak, E. and Özcan Erkan Akgün, O. E. (2022). A program development model for information technologies curriculum in secondary schools. Participatory Educational Research (PER), 9(5), 161-182. http://dx.doi.org/10.17275/per.22.109.9.5.
Roberson, L., Kulik, C. T., & Pepper, M. B. (2003). Using needs assessment to resolve controversies in diversity training design. Group & Organization Management, 28(1), 148-174. https://doi.org/10.1177/1059601102250028.
Gupta, K. (2011). A practical guide to needs assessment. John Wiley & Sons.
Kwok, R. C. W., Ma, J., Vogel, D., & Zhou, D. (2001). Collaborative assessment in education: An application of a fuzzy GSS. Information & Management, 39(3), 243-253.
Jeong, J.S. & Gonzalez-Gomez, D. (2020). Assessment of sustainability science education criteria in online-learning through fuzzy-operational and multi-decision analysis and professional survey. Heliyon, 6(2020), 1-11. https://doi.org/10.1016/j.heliyon.2020.e04706.
Sato-Ilic, M. & Ilic, P. (2013). Fuzzy dissimilarity based multidimensional scaling and its application to collaborative learning data. Procedia Computer Science, 20(2013), 490-495.
Turksen, I. B., and Willson, I. A. (1994). Fuzzy set preference model for consumer choice. Fuzzy Sets and Systems, 68, 253-353. https://doi.org/10.1016/0165-0114(94)90182-1.
Volarić, T., Brajković, E. & Sjekavica, T. (2014). Integration of FAHP and TOPSIS methods for the selection of appropriate multimedia application for learning and teaching. International Journal of Mathematical Models and Methods in Applied Sciences, 8(2014), 224-232.
Gopal, K., Salim, N.R. & Ayub, A. F. M. (2020). Malaysian undergraduates' perceptions of learning statistics: study on attitudes towards statistics using fuzzy conjoint analysis. ASM Science Journal, 13(2020), 1-7. https://doi.org/10.32802/asmscj.2020.sm26(2.15).
Osman, R., Ramli, N., Badarudin, Z., Ujang, S., Ayub, H. and Asri, S. N. F. (2019). Fuzzy number conjoint method to analyse students’ perceptions on the learning of calculus. Journal of Physics: Conference Series, 1366(2019), 012117. Doi:10.1088/1742-6596/1366/1/012117.
Halim, A. B. A. and Idris, A. (2022). The application of triangular fuzzy number-based conjoint analysis method in measuring students’ satisfaction toward UTM bus services. Proc. Sci. Math., 9, 33-43.
Kasim, Z. and Sukri, N. L. M. (2022). Measuring student’s perception on mathematics learning using fuzzy conjoint analysis. Journal of Computing Research and Innovation, 7(1), 85-95. Doi: 10.24191/jcrinn.v7i1.270.
Mukhtar, N. I. and Sulaiman, N. H. (2021). Triangular fuzzy number-based conjoint analysis method and its application in analyzing factors influencing postgraduates program selection. Malaysian Journal of Mathematical Sciences, 15(2), 283-291.
Sri Andayani, Sri Hartati, Wardoyo, R. & Mardapi, D. (2017). Decision-making model for student assessment by unifying numerical and linguistic data. International Journal of Electrical and Computer Engineering (IJECE), 7(1), 363-373. Doi: 10.11591/ijece.v7i1.pp363-373
Bakar, M. A. A. and Ab Ghani, A. T. (2022). Capturing the contribution of fuzzy and multi-criteria decision-making analytics: A review of the computational intelligence approach to classroom assessment sustainability. International Journal of Industrial Engineering & Production Research, 33(4), 1-15. Doi: 10.22068/ijiepr.33.4.13
Zimmermann, H. J. (2001) Fuzzy set theory and its applications. Springer Science and Business Media, Dordrecht. https://doi.org/10.1007/978-94-010-0646-0.
Kandasamy, W. V., & Smarandache, F. (2003). Fuzzy cognitive maps and neutrosophic cognitive maps. Infinite Study.
Hsieh, C. H. and Chen, S. H. (1999). Similarity of generalized fuzzy numbers with graded mean integration representation. Proceedings of the 8th International Fuzzy Systems Association World Congress, Taipei, 551-555.
Anderson, J. R. (1993). Problem solving and learning. American Psychologist, 48(1), 35-44. https://doi.org/10.1037/0003-066X.48.1.35.
Abdullah, A. H., Fadil, S. S., Rahman, S. N. S. A., Tahir, L. M. & Hamzah, M. H. (2019). Emerging patterns and problems of higher-order thinking skills (HOTS) mathematical problem-solving in the Form-three assessment (PT3). South African Journal of Education, 39(2), 1-18. https://doi.org/10.15700/saje.v39n2a1552.
Schoenfeld, A. H. (2016). Learning to think mathematically: Problem solving, metacognition, and sense making in mathematics. Journal of Education, 196(2), 1-38.
García, T., Boom, J., Kroesbergen, E. H., Núñez, J. C. & Rodríguez, C. (2019). Planning, execution, and revision in mathematics problem solving: Does the order of the phases matter? Studies in Educational Evaluation, 61(2019), 83-93. https://doi.org/10.1016/j.stueduc.2019.03.001.
Losenno, K. M., Muis, K. R., Munzar, B., Denton, C. A. & Perry, N. E. (2020). The dynamic roles of cognitive reappraisal and self-regulated learning during mathematics problem solving: A mixed methods investigation. Contemporary Educational Psychology, 61(2020), 101869. https://doi.org/10.1016/j.cedpsych.2020.101869.
Mulyono & Hadiyanti, R. (2018). Analysis of mathematical problem-solving ability based on metacognition on problem-based learning. IOP Conf. Series: Journal of Physics: Conf. Series, 983(2018), 012157. Doi :10.1088/1742-6596/983/1/012157.
Osman, S., Che Yang, C. N. A., Abu, M. S., Ismail, N., Jambari, H. & Kumar, J. A. (2018). Enhancing students’ mathematical problem-solving skills through bar model visualization technique. Int Elect J Math Ed., 13(3), 273-279.
Parrot, M. A. S. & Leong, K. E. (2018). Impact of using graphing calculator in problem solving. International Electronic Journal of Mathematics Education, 13(3), 139-148. https://doi.org/10.12973/iejme/2704.
Schindler, M. & Bakker, A. (2020). Affective field during collaborative problem posing and problem solving: a case study. Educational Studies in Mathematics, 105(2020), 303-324.
Phonapichat P., Wongwanich, S. & Sujiva, S. (2014). An analysis of elementary school students’ difficulties in mathematical problem solving. Procedia-Social and Behavioral Sciences, 116(2014), 3169-3174. Doi: 10.1016/j.sbspro.2014.01.728.
Mohd, N., Mahmood, T. F. P. T. & Ismail, M. N. (2011). Factors that influence students in Mathematics achievement. International Journal of Academic Research, 3(3). 49-54.
McRae, K. (2016). Cognitive emotion regulation: a review of theory and scientific findings. Current Opinion in Behavioral Sciences, 10, 119-124.
Alpar, G. & Hoeve, M. V. (2019). Towards growth-mindset mathematics teaching in the Netherlands in C.M. Stracke (ed.), LINQ, EPiC Series in Education Science, 2, 1-17.
Otoo, D., Iddrisu, W. A., Kessie, J. A. & Larbi, E. (2018). Structural model of students’ interest and self-motivation to learning mathematics. Education Research International, 2018, 1-10.
Ching, B. H. H. (2017). Mathematics anxiety and working memory: Longitudinal associations with mathematical performance in Chinese children. Contemporary Educational Psychology, 51(2017), 99-113.
Hohnen, B. & Murphy, T. (2016). The optimum context for learning; drawing on neuroscience to inform best practice in the classroom. Educational & Child Psychology, 33(1), 75-90.
Alvi, E., Mursaleen, H., & Batool, Z. (2016). Beliefs, processes and difficulties associated with mathematical problem solving of grade 9 students. Pakistan Journal of Educational Research and Evaluation, 1(1), 85-110.
Leo, I. D. & Muis, K. R. (2020). Confused, now what? A cognitive-emotional strategy training (CEST) intervention for elementary students during mathematics problem solving. Contemporary Educational Psychology, 62(2020), 101879
Molenberghs, P., Trautwein, F. M., Bockler, A., Singer, T. & Kanske, P. (2016). Neural correlates of metacognitive ability and of feeling confident: a large-scale fMRI study. Social Cognitive and Affective Neuroscience, 2016, 1942-1951. Doi: 10.1093/scan/nsw093.
Stojanović, J., Petkovic, D., Alarifi, I. M., Cao, Y., Denic, N., Ilic, J., . . . Milickovic, M. (2021). Application of distance learning in mathematics through adaptive neuro-fuzzy learning method. Computers & Electrical Engineering, 93, 107270.
Isabelo, V. & Silao, Jr. (2018). Factors affecting the mathematics problem solving skills of Filipino pupils. International Journal of Scientific and Research Publications, 8(2), 487-497.
Do, Q. H. & Chen, J. F. (2013). A comparative study of hierarchical ANFIS and ANN in predicting student academic performance. WSEAS Transactions on Information Science and Applications, 12(10), 396-405.
Hwang, G. J., Sung, H. Y., Chang, S. C. & Huang, X. C. (2020). A fuzzy expert system-based adaptive learning approach to improving students’ learning performances by considering affective and cognitive factors. Computers and Education: Artificial Intelligence, 1(2020), 100003.
Wei, S. H., & Chen, S. M. (2006). A new similarity measure between generalized fuzzy numbers. In SCIS & ISIS SCIS & ISIS 2006 (pp. 315-320). Japan Society for Fuzzy Theory and Intelligent Informatics.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Mohamad Ariffin Abu Bakar, Ahmad Termimi Ab Ghani, Mohd Lazim Abdullah
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
