The Mediating Role of Learning Analytics
Insights into Student Approaches to Learning and Academic Achievement in Latin America
DOI:
https://doi.org/10.18608/jla.2024.8149Keywords:
student approaches to learning , mediation analysis, learning analytics , Latin America, research paperAbstract
Researchers in learning analytics have created indicators with learners’ trace data as a proxy for studying learner behaviour in a college course. Student Approaches to Learning (SAL) is one of the theories used to explain these behaviours, distinguishing between deep, surface, and organized study. In Latin America, researchers have demonstrated that organized approaches to learning could be more effective in higher education, leading to better performance and course approval. However, further analysis of student behavioural data is needed to understand this relationship and inform interventions targeting study habits and academic performance. In this study, we analyzed the relationship between student approaches to learning and their final grade in six college courses, using behavioural trace data as a mediator variable. Specifically, we conducted a quantitative study in two Latin American institutions where data of different granularity was collected from their Learning Management Systems. We observed that most learning analytics indicators do not mediate the effect between approaches to learning and course performance. However, there was evidence for fine-grained indicators acting as total mediators. Implications are discussed at methodological and pedagogical levels, aiming to inform the advancement of learning analytics in the region and its use for supporting student learning.
References
Biggs, J. (1993). What do inventories of students’ learning processes really measure? A theoretical review and clarification. British Journal of Educational Psychology, 63(1), 3–19. https://doi.org/10.1111/j.2044-8279.1993.tb01038.x
Biggs, J., Kember, D., & Leung, D. Y. P. (2001). The revised two-factor study process questionnaire: R-SPQ-2F. British Journal of Educational Psychology, 71(1), 133–149. https://doi.org/10.1348/000709901158433
Celis, S., López, D., & Silva, J. (2019, March). Analyzing the influence of online behaviors and learning approaches on academic performance in first year engineering. Proceedings of the 2nd Latin American Workshop on Learning Analytics (LALA 2019), 18–19 March 2019, Valdivia, Chile (pp. 110–121). CEUR Workshop Proceedings. https://ceur-ws.org/Vol-2425/paper21.pdf
Chan, A. K. M., Botelho, M. G., & Lam, O. L. T. (2021). The relation of online learning analytics, approaches to learning and academic achievement in a clinical skills course. European Journal of Dental Education, 25(3), 442–450. https://doi.org/10.1111/eje.12619
Conijn, R., Snijders, C., Kleingeld, A., & Matzat, U. (2017). Predicting student performance from LMS data: A comparison of 17 blended courses using Moodle LMS. IEEE Transactions on Learning Technologies, 10(1), 17–29. https://doi.org/10.1109/TLT.2016.2616312
Fiedler, K., Schott, M., & Meiser, T. (2011). What mediation analysis can (not) do. Journal of Experimental Social Psychology, 47(6), 1231–1236. https://doi.org/10.1016/j.jesp.2011.05.007
Freiberg Hoffmann, A., & Fernández Liporace, M. M. (2016). Enfoques de aprendizaje en estudiantes universitarios argentinos según el R-SPQ-2F: Análisis de sus propiedades psicométricas [Learning approaches in Argentine university students according to the R-SPQ-2F: Analysis of its psychometric properties]. Revista Colombiana de Psicología, 25(2), 307–329. https://doi.org/10.15446/rcp.v25n2.51874
Freiberg Hoffmann, A., & Romero Medina, A. (2019). Validación del approaches and Study Skills Inventory for Students (ASSIST) en Universitarios de Buenos Aires, Argentina [Validation of approaches and Study Skills Inventory for Students (ASSIST) in undergraduates from Buenos Aires, Argentina]. Acción Psicológica, 16(2), 1–16. https://dx.doi.org/10.5944/ap.16.2.23042
Fryer, L. K., & Vermunt, J. D. (2018). Regulating approaches to learning: Testing learning strategy convergences across a year at university. British Journal of Educational Psychology, 88(1), 21–41. https://doi.org/10.1111/bjep.12169
Guzmán-Valenzuela, C., Rojas-Murphy Tagle, A., & Gómez-González, C. (2020). Polifonía epistémica de la investigación sobre las experiencias estudiantiles: El caso latinoamericano [Epistemic polyphony of research on student experiences: The Latin-American case]. Education Policy Analysis Archives/Archivos Analíticos de Políticas Educativas, 28(96). https://doi.org/10.14507/epaa.28.4919
Guzmán-Valenzuela, C., Gómez-González, C., Rojas-Murphy Tagle, A., & Lorca-Vyhmeister, A. (2021). Learning analytics in higher education: A preponderance of analytics but very little learning? International Journal of Educational Technology in Higher Education, 18, 23. https://doi.org/10.1186/s41239-021-00258-x
Han, F., Pardo, A., & Ellis, R. A. (2020). Students’ self-report and observed learning orientations in blended university course design: How are they related to each other and to academic performance? Journal of Computer Assisted Learning, 36(6), 969–980. https://doi.org/10.1111/jcal.12453
Han, F., & Ellis, R. A. (2021). Assessing the quality of university student experiences in blended course designs: An ecological perspective. Higher Education Research & Development, 40(5), 964–980. https://doi.org/10.1080/07294360.2020.1800597
Hilliger, I., Ortiz-Rojas, M., Pesántez-Cabrera, P., Scheihing, E., Tsai, Y.-S., Muñoz-Merino, P. J., Broos, T., Whitelock-Wainright, A., & Pérez-Sanagustín, M. (2020a). Identifying needs for learning analytics adoption in Latin American universities: A mixed-methods approach. Internet and Higher Education, 45, 100726. https://doi.org/10.1016/j.iheduc.2020.100726
Hilliger, I., Ortiz-Rojas, M., Pesántez-Cabrera, P., Scheihing, E., Tsai, Y.-S., Muñoz-Merino, P. J., Broos, T., Whitelock-Wainright, A., Gašević, D., & Pérez-Sanagustín, M. (2020b). Towards learning analytics adoption: A mixed methods study of data‐related practices and policies in Latin American universities. British Journal of Educational Technology, 51(4), 915–937. https://doi.org/10.1111/bjet.12933
Hilliger, I., Astudillo, G., & Baier, J. (2023). Lacking time: A case study of student and faculty perceptions of academic workload in the COVID-19 pandemic. Journal of Engineering Education, 112(3), 796–815. https://doi.org/10.1002/jee.20525
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: With applications in R. Springer.
Jovanović, J., Gašević, D., Dawson, S., Pardo, A., & Mirriahi, N. (2017). Learning analytics to unveil learning strategies in a flipped classroom. The Internet and Higher Education, 33, 74–85. https://doi.org/10.1016/j.iheduc.2017.02.001
Jovanović, J., Saqr, M., Joksimović, S., & Gašević, D. (2021). Students matter the most in learning analytics: The effects of internal and instructional conditions in predicting academic success. Computers & Education, 172, 104251. https://doi.org/10.1016/j.compedu.2021.104251
Khalil, M., Prinsloo, P., & Slade, S. (2023). The use and application of learning theory in learning analytics: A scoping review. Journal of Computing in Higher Education, 35, 573–594. https://doi.org/10.1007/s12528-022-09340-3
Leitner, P., Khalil, M., & Ebner, M. (2017). Learning analytics in higher education: A literature review. In A. Peña-Ayala (Ed.), Learning analytics: Fundaments, applications, and trends: A view of the current state of the art to enhance e-learning (pp. 1–23). Springer, Cham. https://dx.doi.org/10.1007/978-3-319-52977-6_1
López, M. J., Catalán, X., Ibáñez, A., González, C., López, D., Silva, J., & Celis, S. (2020). Enfoques de aprendizaje: Uso de plataformas digitales y desempeño académico en la educación superior [Approaches to learning: Use of digital platforms and academic performance in higher education]. Unpublished internal company document. https://nmedsup.cl/wp-content/uploads/2021/06/PolicyBrief7-2.pdf
Lopez González, D. (2022). Cuestionario VOCES: Vida, oportunidades y experiencias en la educación superior [VOICES questionnaire: Life, opportunities and experiences in higher education]. https://nmedsup.cl/wp-content/uploads/2022/07/INFORME_Final_VOCES.pdf
López-Pernas, S., & Saqr, M. (2021). Bringing synchrony and clarity to complex multi-channel data: A learning analytics study in programming education. IEEE Access, 9, 166531–166541. https://doi.org/10.1109/access.2021.3134844
Matcha, W., Gašević, D., Ahmad Uzir, N., Jovanović, J., Pardo, A., Lim, L., Maldonado-Mahauad, J., Gentili, S., Pérez-Sanagustín, M., & Tsai, Y.-S. (2020). Analytics of learning strategies: Role of course design and delivery modality. Journal of Learning Analytics, 7(2), 45–71. https://doi.org/10.18608/jla.2020.72.3
Molenaar, I., Horvers, A., & Baker, R. S. (2021). What can moment-by-moment learning curves tell about students’ self-regulated learning? Learning and Instruction, 72, 101206. https://doi.org/10.1016/j.learninstruc.2019.05.003
Pérez-Sanagustín, M., Pérez-Álvarez, R., Maldonado-Mahauad, J., Villalobos, E., & Sanza, C. (2022). Designing a Moodle plugin for promoting learners’ self-regulated learning in blended learning. In I. Hilliger, P. J. Muñoz-Merino, T. De Laet, A. Ortega-Arranz, & T. Farrell (Eds.), Educating for a new future: Making sense of technology-enhanced learning adoption (pp. 324–339). Lecture Notes in Computer Science, vol. 13450 (EC-TEL 2022). Springer, Cham. https://doi.org/10.1007/978-3-031-16290-9_24
Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138(2), 353–387. https://doi.org/10.1037/a0026838
Siemens, G., & Gašević, D. (2012). Guest editorial: Learning and knowledge analytics. Journal of Educational Technology & Society, 15(3), 1–2. https://drive.google.com/file/d/1SJQZSFOrix9_WZTvBtzvUL70bsLa_eqQ/view
Smith, A. P. (2019). Student workload, well-being and academic attainment. In L. Longo & M. C. Leva (Eds.), Human Mental Workload: Models and Applications: Proceedings of the Third International Symposium (H-WORKLOAD 2019), 14–15 March 2019, Rome, Italy (pp. 35–47). Springer, Cham. https://doi.org/10.1007/978-3-030-32423-0_3
Souto-Iglesias, A., & Baeza Romero, M. T. (2018). A probabilistic approach to student workload: Empirical distributions and ECTS. Higher Education, 76(6), 1007–1025. https://www.jstor.org/stable/45116841
Parpala, A., & Lindblom-Ylänne, S. (2012). Using a research instrument for developing quality at the university. Quality in Higher Education, 18(3), 313–328. https://doi.org/10.1080/13538322.2012.733493
Parpala, A., Lindblom-Ylänne, S., Komulainen, T., & Hirsto, L. (2010). Students’ approaches to learning and their experiences of the teaching-learning environment in different disciplines. British Journal of Educational Psychology, 80(2), 269–282. https://doi.org/10.1348/000709909X476946
Postareff, L., Mattsson, M., Lindblom-Ylänne, S., & Hailikari, T. (2017). The complex relationship between emotions, approaches to learning, study success and study progress during the transition to university. Higher Education, 73(3), 441–457. https://doi.org/10.1007/s10734-016-0096-7
Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36(4), 717–731. https://doi.org/10.3758/BF03206553
Santelices, M. V., & Celis, S. (2022). Introduction to the special issue: Student experience in Latin American higher education. Education Policy Analysis Archives, 30(59). https://doi.org/10.14507/epaa.30.7549
Tabuenca, B., Greller, W., & Verpoorten, D. (2022). Mind the gap: Smoothing the transition to higher education fostering time management skills. Universal Access in the Information Society, 21(2), 367–379. https://doi.org/10.1007/s10209-021-00833-z
Tuononen, T., Parpala, A., & Lindblom-Ylänne, S. (2020). Complex interrelations between academic competences and students’ approaches to learning: Mixed-methods study. Journal of Further and Higher Education, 44(8), 1080–1097. https://doi.org/10.1080/0309877X.2019.1648776
Vallat, R. (2018). Pingouin: Statistics in Python. Journal of Open Source Software, 3(31), 1026, https://doi.org/10.21105/joss.01026
Wang, Q., & Mousavi, A. (2023). Which log variables significantly predict academic achievement? A systematic review and meta-analysis. British Journal of Educational Technology, 54(1), 142–191. https://doi.org/10.1111/bjet.13282
Xerri, M. J., Radford, K., & Shacklock, K. (2018). Student engagement in academic activities: A social support perspective. Higher Education, 75(4), 589–605. https://doi.org/10.1007/s10734-017-0162-9
Xie, Q., Xhang, L.-F., & King, R. B. (2022). Why do students change their learning approaches? A mixed-methods study. Educational Psychology, 42(9), 1089–1108. https://doi.org/10.1080/01443410.2022.2049708
Yin, H., González, C., & Huang, S. (2018). Undergraduate students’ approaches to studying and perceptions of learning context: A comparison between China and Chile. Higher Education Research & Development, 37(7), 1530–1544. https://doi.org/10.1080/07294360.2018.1494142
Zhao, T., Liu, H., Roeder, K., Lafferty, J., & Wasserman, L. (2012). The huge package for high-dimensional undirected graph estimation in R. Journal of Machine Learning Research, 13(37), 1059–1062. https://www.jmlr.org/papers/volume13/zhao12a/zhao12a.pdf
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