Logit Model Study on MOOCs’ User Ratings
Case Study on Udemy
Resumen
The research was motivated by the growing significance of online courses and the need to understand their success factors through the lens of user experience. Addressing a gap in existing knowledge, the study aims to provide insights into the determinants of course success, specifically focusing on user ratings, and to offer recommendations for enhancing online learning experiences. By analyzing Udemy course data, the study seeks to uncover the factors influencing the success of a course, as perceived by students. The study utilizes logistic regression models to analyze data from Udemy courses, focusing on variables that contribute to the success of a course from the user experience standpoint. The conceptual framework incorporates elements from literature to explain unusual distributions in continuous variable values. The research reveals crucial insights into the determinants of online course success, emphasizing the impact of user ratings on perceived success. The model sheds light on the nuanced dynamics influencing the user experience. The article concludes with practical recommendations for stakeholders, offering a roadmap for future research endeavors in the field of online education.
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Referencias bibliográficas
Abelson, R. P. (1985). A variance explanation paradox: When a little is a lot. Psychological Bulletin, 97(1), 129–133. https://doi.org/10.1037/0033-2909.97.1.129
Awadh, W. A., Sulaiman, R. B., and Mahmoud, M. A. (2025). Aspect based sentiment analysis in MOOCs: a systematic literature review introducing the MASC MEF framework. Journal of King Saud University – Computer and Information Sciences, 37(2). https://doi.org/10.1007/s44443-025-00018-1
Baqach, A., and Battou, A. (2024). A new sentiment analysis model to classify students’ reviews on MOOCs. Education and Information Technologies, 29, 16813–16840. https://doi.org/10.1007/s10639-024-12526-0
Carver, R. P. (1993). The Case against Statistical Significance Testing, Revisited. The Journal of Experimental Education, 61(4), 287–292. http://www.jstor.org/stable/20152382
Consul, P. C. (1990). On some properties and applications of quasi—binomial distribution. Communications in Statistics - Theory and Methods, 19(2), 477–504. https://doi.org/10.1080/03610929008830214
Dey, D., Haque, M. S., Islam, M. M., Aishi, U. I., Shammy, S. S., Mayen, M. S., Noor, S. T. A., and Uddin, M. J. (2025). The proper application of logistic regression model in complex survey data: a systematic review. BMC Medical Research Methodology, 25, 15. https://doi.org/10.1186/s12874-024-02454-5
Götz, F. M., Gosling, S. D., and Rentfrow, P. J. (2024). Effect sizes and what to make of them. Nature Human Behaviour, 8, 798-800. https://doi.org/10.1038/s41562-024-01858-z
Greene, J. A., Oswald, C. A., and Pomerantz, J. (2015). Predictors of retention and achievement in a massive open online course. American Educational Research Journal, 52(5), 925–955. https://doi.org/10.3102/0002831215584621
Hartig, F. (2024). DHARMa: Residual Diagnostics for Hierarchical Regression Models. R package version 0.4.7. https://github.com/florianhartig/DHARMa
Hossain, b. d. (2022, October 10). Udemy Courses: 209K courses detailed information and comments. https://www.kaggle.com/datasets/hossaingh/udemy-courses
Hu, N., Pavlou, P. A., and Zhang, J. (2017). On self-selection biases in online product reviews. In MIS Quarterly: Management Information Systems (Vol. 41, Issue 2, pp. 449–471). University of Minnesota. https://doi.org/10.25300/MISQ/2017/41.2.06
Hua, Y., Stead, T. S., George, A., and Ganti, L. (2025). Clinical risk prediction with logistic regression: Best practices, validation techniques, and applications in medical research. Academic Medicine and Surgery. https://doi.org/10.62186/001c.131964
La Rocca, L., Lupparelli, M., and Roverato, A. (2024). On the comparison of regression coefficients across multiple logistic models with binary predictors. Metrika. https://doi.org/10.1007/s00184-024-00976-y
Li, L., Swiecki, Z., Gašević, D., and Chen, G. (2022). Popularity Prediction in MOOCs: A Case Study on Udemy. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13355 LNCS. https://doi.org/10.1007/978-3-031-11644-5_56
Liu, Z., Tang, Q., Ouyang, F., Long, T., and Liu, S. (2024). Profiling students’ learning engagement in MOOC discussions to identify learning achievement: An automated configurational approach. Computers and Education, 219, Article 105109. https://doi.org/10.1016/j.compedu.2024.105109
Liyanagunawardena, T. R., Adams, A. A., and Williams, S. A. (2013). MOOCs: A systematic study of the published literature 2008-2012. International Review of Research in Open and Distance Learning, 14(3), 202–227. https://doi.org/10.19173/irrodl.v14i3.1455
Maldonado-Mahauad, J., Pérez-Sanagustín, M., Kizilcec, R. F., Morales, N., and Munoz-Gama, J. (2018). Mining theory-based patterns from big data: Identifying self-regulated learning strategies in massive open online courses. Computers in Human Behavior, 80, 179–196. https://doi.org/10.1016/j.chb.2017.11.011
Mozahem, N. A. (2021). The online marketplace for business education: An exploratory study. International Journal of Management Education, 19(3). https://doi.org/10.1016/j.ijme.2021.100544
Nurhudatiana, A., and Caesarion, A. S. (2020). Exploring User Experience of Massive Open Online Courses (MOOCs): A Case Study of Millennial Learners in Jakarta, Indonesia. ACM International Conference Proceeding Series, 44–49. https://doi.org/10.1145/3383923.3383968
Onan, A. (2020). Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach. Computer Applications in Engineering Education, 28(3), 536–549. https://doi.org/10.1002/cae.22253
Perugini, A., Gambarota, F., Toffalini, E., Lakens, D., Pastore, M., Finos, L., Psicostat Core Team, and Altoè, G. (2025). The benefits of reporting critical effect size values. Advances in Methods and Practices in Psychological Science, 8(2), 1–10. https://doi.org/10.1177/25152459251335298
Ram, K., and Wickham, H. (2023). wesanderson: A Wes Anderson Palette Generator.
Roseman, I. A., and Read, S. J. (2007). Psychologist at Play: Robert P. Abelson's Life and Contributions to Psychological Science. Perspectives on Psychological Science, 2(1), 20–25. https://doi.org/10.1111/j.1745-6916.2007.00031.x
Salamah, U. G., and Helmi, R. A. A. (2018). MOOC platforms: A review and comparison. International Journal of Engineering and Technology (UAE), 7(4). https://doi.org/10.14419/ijet.v7i4.11.20690
Sebbaq, H., and El Faddouli, N. (2024). Towards Quality Assurance in MOOCs: A Comprehensive Review and Micro-Level Framework. The International Review of Research in Open and Distributed Learning, 25(1), 1–23. https://doi.org/10.19173/irrodl.v25i1.7544
Sjoberg, D. D., Whiting, K., Curry, M., Lavery, J. A., and Larmarange, J. (2021). Reproducible Summary Tables with the gtsummary Package. R Journal, 13(1). https://doi.org/10.32614/rj-2021-053
Team, R. C. (2021). R: A Language and Environment for Statistical Computing. In R Foundation for Statistical Computing. https://www.R-project.org/
Veletsianos, G., and Shepherdson, P. (2016). A systematic analysis and synthesis of the empirical MOOC literature published in 2013–2015. The International Review of Research in Open and Distributed Learning, 17(2), 198–221. https://doi.org/10.19173/irrodl.v17i2.2448
Veall, M. R., and Zimmermann, K. F. (1994). Goodness of Fit Measures in the Tobit Model. In Oxford Bulletin of Economics and Statistics (Vol. 56, Issue 4).https://doi.org/10.1111/j.1468-0084.1994.tb00022.x
Veall, M. R., and Zimmermann, K. F. (1996). Pseudo-R2 measures for some common limited dependent variable models. Journal of Economic Surveys, 10(3). https://doi.org/10.1111/j.1467-6419.1996.tb00013.x
Wei, T., and Simko, V. (2021). R package ‘corrplot’: Visualization of a Correlation Matrix. https://github.com/taiyun/corrplot
Wiberg, R. A. W., Gaggiotti, O. E., Morrissey, M. B., and Ritchie, M. G. (2017). Identifying consistent allele frequency differences in studies of stratified populations. Methods in Ecology and Evolution, 8(12). https://doi.org/10.1111/2041-210X.12810
Yin, D., Mitra, S., and Zhang, H. (2016). When do consumers value positive vs. negative reviews? An empirical investigation of confirmation bias in online word of mouth. Information Systems Research, 27(1). https://doi.org/10.1287/isre.2015.0617
Yindeemak, A., Limpinan, P., Pasmala, R., Nammanee, M., and Jantakoon, T. (2025). Trends in Massive Open Online Courses (MOOCs) Research Over the Past Ten Years (2015–2024): A Bibliometric Analysis. Journal of Education and Learning, 14(5), 209–228. https://doi.org/10.5539/jel.v14n5p209
Zawacki-Richter, O., Bozkurt, A., Alturki, U., and Aldraiweesh, A. (2018). What research says about MOOCs – An explorative content analysis. The International Review of Research in Open and Distributed Learning, 19(1), 242–259. https://doi.org/10.19173/irrodl.v19i1.3356
Zhang, J., Sziegat, H., Perris, K., and Zhou, C. (2019). More than access: MOOCs and changes in Chinese higher education. Learning, Media and Technology, 44(2), 108–123. https://doi.org/10.1080/17439884.2019.1602541
Zou, X., Wang, Y., and Xie, H. (2023). Educational data mining in MOOCs: Current trends and future directions. IEEE Transactions on Learning Technologies, 16(1), 112–125.
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