Mobile Prototyping As A Gammified Learning Tool
DOI:
https://doi.org/10.56294/saludcyt20251754Keywords:
learning, digital, teaching, gamification, performanceAbstract
Introduction: Teaching modalities from the pandemic migrate to the digital environment, being an indispensable tool for learning. Objective: The present study aims to develop the prototyping of a mobile application focused on improving learning in the area of mathematics, using the Design Sprint methodology to determine the characteristics of usability and acceptance by students in the fifth year of the Santo Tomás Apostol de Riobamba Educational Unit (UESTAR). Methodology: The research approach is mixed qualitative-quantitative, from the interviews conducted with the teachers of the institution allowed to identify the difficulties of the teaching-learning process related to basic arithmetic operations, the surveys applied to students who used the prototype could be evaluated according to the criteria the level of acceptance and usability of the interface. Result: In the prototyping process incurs 5 stages according to the Design Sprint methodology understanding, sketch, decision, prototype and validation that contributes to the design of a tangible product of high fidelity ready for evaluation and determine if it meets the parameters of the UX / UI design. Conclusion: It was concluded that the prototype meets the acceptance of students and teachers based on intuitive navigation, consistency and simplicity, recommending that it be applied as a didactic resource for learning in the area of mathematics.
References
1. Caprara, L., & Caprara, C. (2022). Effects of virtual learning environments: A scoping review of literature. Education and information technologies, 27(3), 3683-3722. https://link.springer.com/article/10.1007%2Fs10639-021-10768-w
2. Seufert, C., Oberdörfer, S., Roth, A., Grafe, S., Lugrin, J. L., & Latoschik, M. E. (2022). Classroom management competency enhancement for student teachers using a fully immersive virtual classroom. Computers & Education, 179, 104410. https://doi.org/10.1016/j.compedu.2021.104410
3. Willermark, S., & Islind, A. S. (2022). Seven educational affordances of virtual classrooms. Computers and Education Open, 3, 100078. https://doi.org/10.1016/j.caeo.2022.100078
4. Hu, X., Zhang, L., Liu, J., Fan, J., You, Y., & Wu, Y. (2023, June). GPTR: gestalt-perception transformer for diagram object detection. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 1, pp. 899-907). https://doi.org/10.1609/aaai.v37i1.25169
5. Zhang, Y., Soydaner, D., Behrad, F., Koßmann, L., & Wagemans, J. (2024). Investigating the Gestalt Principle of Closure in Deep Convolutional Neural Networks. arXiv preprint arXiv:2411.00627. http://dx.doi.org/10.14428/esann/2024.ES2024-111
6. Lee, J., & Wang, L. (2021). A method for designing and analyzing automotive software architecture: A case study for an autonomous electric vehicle. In 2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) (pp. 20-26). IEEE. http://dx.doi.org/10.1109/ICCEAI52939.2021.00004
7. Schlemmer, A., & Padovani, S. (2021). Mapeamento sobre a experiência prévia com design centrado no usuário (DCU) de desenvolvedores de sistemas e-Gov. Ergodesign & HCI, 9(2), 67-86. http://dx.doi.org/10.22570/ergodesignhci.v9i2.1585
8. Bala, M. Y., & Damla, K. (2021). A review of human-computer interaction design approaches towards information systems development. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 12(1), 229-250. http://dx.doi.org/10.18662/brain/12.1/180
9. Schmidt, A., Elagroudy, P., Draxler, F., Kreuter, F., & Welsch, R. (2024). Simulating the human in HCD with ChatGPT: Redesigning interaction design with AI. Interactions, 31(1), 24-31. http://dx.doi.org/10.1145/3637436
10. Tai, P., Ding, P., Wang, F., Gong, A., Li, T., Zhao, L., ... & Fu, Y. (2024). Brain-computer interface paradigms and neural coding. Frontiers in neuroscience, 17, 1345961. http://dx.doi.org/10.3389/fnins.2023.1345961
11. Salem, I. M. E. S. (2024). Implications of virtual identity work on social practice for university faculty assistants via social media. Egyptian Journal of Public Opinion Research, 23(3), 141-193. https://doi.org/10.21608/joa.2024.367298
12. Kryjevskaia, M., Heron, P. R., & Heckler, A. F. (2021). Intuitive or rational? Students and experts need to be both. Physics today, 74(8), 28-34. http://dx.doi.org/10.1063/PT.3.4813
13. Oktafina, A., Jannah, F. A., Rizky, M. F., Ferly, M. V., Tangtobing, Y. D., & Natasia, S. R. (2021). Evaluasi usability website menggunakan metode heuristic evaluation studi kasus:(website dinas pekerjaan umum kota xyz). Antivirus: Jurnal Ilmiah Teknik Informatika, 15(2), 134-146. https://doi.org/10.35335/mandiri.v11i1.146
14. Alanzi, T. (2021). A review of mobile applications available in the app and google play stores used during the COVID-19 outbreak. Journal of multidisciplinary healthcare, 45-57. https://doi.org/10.2147/jmdh.s285014
15. Stocchi, L., Pourazad, N., Michaelidou, N., Tanusondjaja, A., & Harrigan, P. (2022). Marketing research on Mobile apps: past, present and future. Journal of the Academy of Marketing Science, 1-31. https://doi.org/10.1007/s11747-021-00815-w
16. Molenaar, I. (2022). Towards hybrid human‐AI learning technologies. European Journal of Education, 57(4), 632-645. http://dx.doi.org/10.1111/ejed.12527
17. Alismaiel, O. A., Cifuentes-Faura, J., & Al-Rahmi, W. M. (2022). Online learning, mobile learning, and social media technologies: An empirical study on constructivism theory during the COVID-19 pandemic. Sustainability, 14(18), 11134. https://doi.org/10.3390/su141811134
18. Özer Sanal, S., & Erdem, M. (2023). Examination of Special Education with Constructivism: A Theoretical and Review Study. European Educational Researcher, 6(1), 1-20. http://dx.doi.org/10.31757/euer.611
19. Criollo-C, S., Guerrero-Arias, A., Jaramillo-Alcázar, Á., & Luján-Mora, S. (2021). Mobile learning technologies for education: Benefits and pending issues. Applied Sciences, 11(9), 4111. http://dx.doi.org/10.3390/app11094111
20. Goundar, M. S., & Kumar, B. A. (2022). The use of mobile learning applications in higher education institutes. Education and Information Technologies, 27(1), 1213-1236. https://link.springer.com/article/10.1007/s10639-021-10611-2
21. Chuang, S. (2021). The applications of constructivist learning theory and social learning theory on adult continuous development. Performance Improvement, 60(3), 6-14. http://dx.doi.org/10.1002/pfi.21963
22. Güler, M., Bütüner, S. Ö., Danişman, Ş., & Gürsoy, K. (2022). A meta-analysis of the impact of mobile learning on mathematics achievement. Education and Information Technologies, 27(2), 1725-1745. https://link.springer.com/article/10.1007/s10639-021-10640-x
23. Ntouvaleti, M., & Katsanos, C. (2022). Validity of the open card sorting method for producing website information structures. In CHI Conference on human factors in computing systems extended abstracts (pp. 1-7). https://doi.org/10.1145/3491101.3519734
24. Pampoukidou, S., & Katsanos, C. (2021). Test-retest reliability of the open card sorting method. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-7). https://doi.org/10.1145/3411763.3451750
25. Papadakis, S., Kalogiannakis, M., & Zaranis, N. (2021). Teaching mathematics with mobile devices and the Realistic Mathematical Education (RME) approach in kindergarten. Advances in Mobile Learning Educational Research, 1(1), 5-18. http://dx.doi.org/10.25082/AMLER.2021.01.002
26. Dahlan, T., Darhim, D., & Juandi, D. (2022). How Digital Applications as Mathematics Learning Media in The Automation Era. Journal of Positive Psychology and Wellbeing, 6(2), 199-211. https://journalppw.com/index.php/jppw/article/view/2871
27. Fadda, D., Pellegrini, M., Vivanet, G., & Zandonella Callegher, C. (2022). Effects of digital games on student motivation in mathematics: A meta‐analysis in K‐12. Journal of Computer Assisted Learning, 38(1), 304-325. http://dx.doi.org/10.1111/jcal.12618
28. Poçan, S., Altay, B., & Yaşaroğlu, C. (2023). The effects of mobile technology on learning performance and motivation in mathematics education. Education and Information Technologies, 28(1), 683-712. http://dx.doi.org/10.1007/s10639-022-11166-6
29. Ebadi, S., & Raygan, A. (2023). Investigating the facilitating conditions, perceived ease of use and usefulness of mobile-assisted language learning. Smart Learning Environments, 10(1), 30. http://dx.doi.org/10.1186/s40561-023-00250-0
30. Al-Fahim, N. H., Ateeq, A. A., Abro, Z., Milhem, M., Alzoraiki, M., Alkadash, T. M., & Nagi, M. (2024). Factors influencing the mobile banking usage: mediating role of perceived usefulness. In Digital technology and changing roles in managerial and financial accounting: theoretical knowledge and practical application (Vol. 36, pp. 115-128). Emerald Publishing Limited. http://dx.doi.org/10.1108/S1479-351220240000036011
31. Curum, B., & Khedo, K. K. (2021). Cognitive load management in mobile learning systems: principles and theories. Journal of Computers in Education, 8(1), 109-136. http://dx.doi.org/10.1007/s40692-020-00173-6
32. Lu, A., Deng, R., Huang, Y., Song, T., Shen, Y., Fan, Z., & Zhang, J. (2022). The roles of mobile app perceived usefulness and perceived ease of use in app-based Chinese and English learning flow and satisfaction. Education and Information Technologies, 27(7), 10349-10370. http://dx.doi.org/10.1007/s10639-022-11036-1.
Published
Issue
Section
License
Copyright (c) 2025 Heidy Elizabeth Vergara-Zurita , Ana Lucía Rivera-Abarca, Héctor Oswaldo Aguilar-Cajas, Jazmín Isabel García-Guerra, Miguel Angel Duque-Vaca, Jessica Andrea Barreto-Bonilla (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.