Innovating mobile learning in statistics: Profiling university students' needs and expectations
DOI:
https://doi.org/10.52041/iase25.107Abstract
This study explores university students’ profiles and expectations for the use of a mobile application to support statistics learning. Using a mixed-methods approach, 440 undergraduates participated in a survey combining quantitative Likert-scale responses and over 200 qualitative open-ended answers. A factor analysis revealed four key dimensions shaping students’ perceptions toward mobile-assisted learning, while cluster analysis identified three distinctive user profiles with varying levels of engagement. Topic modeling of qualitative responses revealed critical preferences and concerns, such as the need for real-data applications, interactive tools, gamification features, and intuitive design. Concerns included excessive advertising, poor interface usability, and lack of personalized feedback. These findings offer a framework for developing adaptable, student-centered mobile applications in statistics education, aligned with learner diversity and pedagogical goals.References
Bargagliotti, A., Franklin, C., Arnold, P., Gould, R., Johnson, S., Perez, L., & Spangler, D. (2020). Pre- K-12 Guidelines for Assessment and Instruction in Statistics Education II (GAISE II). American Statistical Association.
Ben-Zvi, D., Gravemeijer, K., & Ainley, J. (2017). Design of statistics learning environments. In D. Ben-Zvi, K. Makar, & J. Garfield (Eds.), International handbook of research in statistics education (pp. 473–502). Springer.
Blei, D., Ng, A., & Jordan, M. (2003). Latent Dirichlet Allocation. Journal of machine Learning research, 3, 993–1022.
Carver, R., Everson, M., Gabrosek, J., Horton, N., Lock, R., Mocko, M., Rossmann, A., Roswell, G. H., Vellemann, P., Witmer, J. & Wood, B. (2016). Guidelines for assessment and instruction in statistics education, College report. American Statistical Association.
Chance, B., delMas, R., & Garfield, J. (2004). Reasoning about sampling distributions. In D. Ben-Zvi & J. Garfield (Eds.), The challenge of developing statistical literacy, reasoning and thinking (pp. 295–323). Dordrecht: Springer.
González, J. A., López, M., Cobo, E., & Cortés, J. (2018). Assessing shiny apps through student feedback: Recommendations from a qualitative study. Computer Applications in Engineering Education, 26(5), 1813–1824. https://doi.org/10.1002/cae.21932
Hepworth, K., Ivey, C., Canon, C., & Holmes, H. A. (2019). Embedding online, design-focused data visualization instruction in an upper-division undergraduate atmospheric science course. Journal of Geoscience Education, 68(2), 168–183. https://doi.org/10.1080/10899995.2019.1656022
Herrera, J. (2023). Herramientas y aplicaciones para la enseñanza de la estadística en educación superior: Una revisión sistemática [Unpublished master’s thesis]. Pontificia Universidad Católica de Valparaíso.
Kolpashnikova, K., & Bartolic, S. (2019). Digital divide in quantitative methods: The effects of computer-assisted instruction and students’ attitudes on knowledge acquisition. Journal of Computer Assisted Learning, 35(2), 208–217. https://doi.org/10.1111/jcal.12322
Li, Q. (2021). Computer technology in the course of probability and mathematical statistics. In Application of intelligent systems in multi-modal information analytics: 2021 international conference on multi-modal information analytics (Vol. 2, pp. 531–538). Springer.
Zetterqvist, L. (2017). Applied problems and use of technology in an aligned way in basic courses in probability and statistics for engineering students–a way to enhance understanding and increase motivation. Teaching Mathematics and its Applications: An International Journal of the IMA, 36(2), 108–122. https://doi.org/10.1093/teamat/hrx004
Zieffler, A., Garfield, J., Alt, S., Dupuis, D., Holleque, K., & Chang, B. (2008). What does research suggest about the teaching and learning of introductory statistics at the college level? A review of the literature. Journal of Statistics Education, 16(2), 122. https://doi.org/10.1080/10691898.2008.11889566
