Investigation of educational mathematics mobile applications (EMMAs) with multi-criteria decision-making methods: A TOPSIS algorithm implementation

Abstract

The number and variety of educational mathematics mobile applications (EMMAs) make it difficult to select mobile applications for mathematics learning and teaching. Therefore, in this study, multi-criteria decision-making (MCDM) techniques, which are effectively used in a wide variety of disciplines, were applied to choose among alternative applications according to specified criteria. In this context, it was aimed to determine which of the 13 considered EMMAs that work on Android-based tools and were proposed by experts according to certain features were most effective with the help of the TOPSIS algorithm, one of the popular MCDM methods. The results obtained from an evaluation using 10 criteria (4 evaluator-independent, 6 evaluator-dependent) were analysed with MATLAB. As a result, the Desmos: Graphing Calculator application was found to rank first among the 13 EMMAs in order of importance. Considering the results obtained, it can be said that the use of MCDM techniques in such decision problems can facilitate the work of decision-makers.

Keywords

• Educational Mobile Mathematics Applications
• Multi-Criteria Decision-Making
• TOPSIS

References

1. Baker, J., Ashill, N., Amer, N., & Diab, E. (2018). The internet dilemma: An exploratory study of luxury firms’ usage of internet-based technologies. Journal of Retailing and Consumer Services, 41, 37-47.
2. Başaran, S., & El Homsi, F. (2022). Mobile Mathematics Learning Application Selection using Fuzzy TOPSIS. International Journal of Advanced Computer Science and Applications, 13(2).
3. Başaran, S., & Haruna, Y. (2017). Integrating FAHP and TOPSIS to evaluate mobile learning applications for mathematics. Procedia Computer Science, 120, 91-98.
4. Borba, M. C., Askar, P., Engelbrecht, J., Gadanidis, G., Llinares, S., & Aguilar, M. S. (2016). Blended learning, e-learning and mobile learning in mathematics education. ZDM, 48(5), 589-610.
5. Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education, 59(3), 1054-1064.
6. Daher, W., & Baya'a, N. (2012). Characteristics of middle school students learning actions in outdoor mathematical activities with the cellular phone. Teaching Mathematics and its Applications: An International Journal of the IMA, 31(3), 133-152.
7. Dubé, A. K., Kacmaz, G., Wen, R., Alam, S. S., & Xu, C. (2020). Identifying quality educational apps: Lessons from ‘top’ mathematics apps in the Apple App store. Education and Information Technologies, 25(6), 5389-5404.
8. Elfeky, A. I. M., & Masadeh, T. S. Y. (2016). The Effect of Mobile Learning on Students' Achievement and Conversational Skills. International Journal of higher education, 5(3), 20-31.

9. Ezhilarasan, N., & Vijayalakshmi, C. (2020). Optimization of Fuzzy programming with TOPSIS Algorithm. Procedia Computer Science, 172, 473-479.
10. Franklin, T., & Peng, L. W. (2008). Mobile math: Math educators and students engage in mobile learning. Journal of Computing in Higher Education, 20(2), 69-80.
11. Güler, M., Bütüner, S. Ö., Danişman, Ş., & Gürsoy, K. (2021). A meta-analysis of the impact of mobile learning on mathematics achievement. Education and Information Technologies, 1-21.
12. Hamidi, H., & Chavoshi, A. (2018). Analysis of the essential factors for the adoption of mobile learning in higher education: A case study of students of the University of Technology. Telematics and Informatics, 35(4), 1053-1070.
13. Harrison, T. R., & Lee, H. S. (2018). iPads in the mathematics classroom: Developing criteria for selecting appropriate learning apps. International Journal of Education in Mathematics, Science and Technology, 6(2), 155-172.
14. Haydon, T., Hawkins, R., Denune, H., Kimener, L., McCoy, D., & Basham, J. (2012). A comparison of iPads and worksheets on math skills of high school students with emotional disturbance. Behavioral Disorders, 37(4), 232-243.
15. Hung, C. M., Huang, I., & Hwang, G. J. (2014). Effects of digital game-based learning on students’ self-efficacy, motivation, anxiety, and achievements in learning mathematics. Journal of Computers in Education, 1(2), 151-166.
16. Hwang, G. J., & Chang, H. F. (2011). A formative assessment-based mobile learning approach to improving the learning attitudes and achievements of students. Computers & Education, 56(4), 1023-1031.
17. Hwang, C.L. & Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. Verlag Berlin Heidelberg New York: Springer.
18. Hwang, G. J., & Wu, P. H. (2014). Applications, impacts and trends of mobile technology-enhanced learning: a review of 2008–2012 publications in selected SSCI journals. International Journal of Mobile Learning and Organisation, 8(2), 83-95.
19. Ibrahim, N. K., Hammed, H., Zaidan, A. A., Zaidan, B. B., Albahri, O. S., Alsalem, M. A., Mohammed, R. T., Jasim, A. N., Shareef, A. H., Jalood, N. S., Baqer, M. J., Nidhal, S., Almahdi, E. M., & Alaa, M. (2019). Multi-criteria evaluation and benchmarking for young learners’ English language mobile applications in terms of LSRW skills. IEEE Access, 7, 146620-146651.
20. Jeno, L. M., Vandvik, V., Eliassen, S., & Grytnes, J. A. (2019). Testing the novelty effect of an m-learning tool on internalization and achievement: A Self-Determination Theory approach. Computers & Education, 128, 398-413.
21. Kay, R., & Kwak, J. Y. (2018). Comparing types of mathematics apps used in primary school classrooms: an exploratory analysis. Journal of Computers in Education, 5(3), 349-371.
22. Kluge, A., & Dolonen, J. (2015). Using mobile games in the classroom. Mobile Learning and Mathematics, 106-121.
23. Kyriakides, A. O., Meletiou-Mavrotheris, M., & Prodromou, T. (2016). Mobile technologies in the service of students’ learning of mathematics: the example of game application ALEX in the context of a primary school in Cyprus. Mathematics Education Research Journal, 28(1), 53-78
24. Lai, C. L., & Hwang, G. J. (2014). Effects of mobile learning time on students' conception of collaboration, communication, complex problem–solving, meta–cognitive awareness and creativity. International Journal of Mobile Learning and Organisation, 8(3-4), 276-291.
25. Laricchia, F. (2022, July 18). Market share of mobile operating systems worldwide 2012-2022. Retrieved July 25, 2022, from https://www.statista.com/statistics/272698/global-market-share-held-by-mobile-operating-systems-since-2009/
26. Larkin, K. (2015). An app! An app! My kingdom for an app: An 18-month quest to determine whether apps support mathematical knowledge building. In Digital games and mathematics learning (pp. 251–276). Dordrecht, Netherlands: Springer.
27. Linkov, I., Satterstrom, F.K., Kiker, G., Seager, T.P, Bridges, T., Gardner, K.H., Rogers, S.H., Belluck, D.A. ve Meyer., A. (2006). Multicriteria Decision Analysis: A Comprehensive Decision Approach for Management of Contaminated Sediments. Risk Analysis. 26: 61-78.
28. Mahdavi, I., Amiri, N.M., Heidarzade, A. ve Nourifar, R. (2008). Designing a model of fuzzy TOPSIS in multiple criteria decision making. Applied Mathematics and Computation. 1-11.
29. Martin, F., & Ertzberger, J. (2013). Here and now mobile learning: An experimental study on the use of mobile technology. Computers & Education, 68, 76-85.
30. Mendoza, G.A. & Prabhub, R. (2000). Multiple criteria decision making approaches to assessing forest sustainability using criteria and indicators: a case study. Forest Ecology and Management. 131: 107-126.
31. Namukasa, I. K., Gadanidis, G., Sarina, V., Scucuglia, S., & Aryee, K. (2016). Selection of apps for teaching difficult mathematics topics: An instrument to evaluate touch-screen tablet and smartphone mathematics apps. In International perspectives on teaching and learning mathematics with virtual manipulatives (pp. 275-300). Springer, Cham.
32. Pohekar, S.D. & Ramachandran, M. (2004). Application of multi-criteria decision making to sustainable energy planning-A review. Renewable and Sustainable Energy Reviews. 8: 365–381.
33. Riconscente, M. M. (2013). Results from a controlled study of the iPad fractions game Motion Math. Games and Culture, 8(4), 186-214.
34. Saedi, N., Taghizade, A., & Hatami, J. (2018). The Effect of Mobile Learning Applications on Students' High-level Cognitive Skills. Interdisciplinary Journal of Virtual Learning in Medical Sciences, 9(4).
35. Shank, D. B., & Cotten, S. R. (2014). Does technology empower urban youth? The relationship of technology use to self-efficacy. Computers & Education, 70, 184-193.
36. Uslu, B., Gür, Ş., Eren, T., & Özcan, E. (2020). Mobil uygulama seçiminde etkili olan kriterlerin belirlenmesi ve örnek uygulama. İstanbul İktisat Dergisi, 70(1), 113-139.
37. Volaric, T., Brajkovic, 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, 224-232.
38. Zeleny, M. (1974). A Consept of Compromise Solutions and The Method of The Displaced İdeal. Comput Operat Res. 1: 479-496.
39. Zeleny, M. (2005). Human Systems Management: Integrating Knowledge, Management and Systems. World Scientific.
40. Zhao, C., Muthu, B., & Shakeel, P. M. (2021). Multi-Objective Heuristic Decision Making and Benchmarking for Mobile Applications in English Language Learning. Transactions on Asian and Low-Resource Language Information Processing, 20(5), 1-16