Perspectives and Opportunities for Learning Analytics Integration: A Qualitative Study in Mexican Universities

Tomás Bautista Godínez; Gerardo  Castañeda Garza; Ricardo  Pérez Mora; Héctor G. Ceballos; Verónica  Luna de la Luz; José Gerardo  Moreno-Salinas; Irma Rocío Zavala-Sierra; Roberto Santos-Solórzano; Carlos Iván Moreno Arellano; Melchor  Sánchez-Mendiola

doi:10.18608/jla.2024.8125

Authors

Tomás Bautista Godínez National Autonomous University of Mexico https://orcid.org/0000-0002-3786-9100
Gerardo Castañeda Garza Tecnológico de Monterrey https://orcid.org/0000-0003-3830-5232
Ricardo Pérez Mora Universidad de Guadalajara https://orcid.org/0000-0002-1853-3580
Héctor G. Ceballos Tecnológico de Monterrey https://orcid.org/0000-0002-2460-3442
Verónica Luna de la Luz National Autonomous University of Mexico https://orcid.org/0000-0001-5875-1681
José Gerardo Moreno-Salinas National Autonomous University of Mexico https://orcid.org/0000-0001-6539-0749
Irma Rocío Zavala-Sierra National Autonomous University of Mexico https://orcid.org/0009-0006-2609-6436
Roberto Santos-Solórzano National Autonomous University of Mexico https://orcid.org/0000-0002-2469-0048
Carlos Iván Moreno Arellano Universidad de Guadalajara https://orcid.org/0000-0001-9960-7459
Melchor Sánchez-Mendiola National Autonomous University of Mexico https://orcid.org/0000-0002-9664-3208

DOI:

https://doi.org/10.18608/jla.2024.8125

Keywords:

learning analytics, Mexico, higher education, qualitative research, Latin America, research paper

Abstract

The adoption of learning analytics (LA) in higher education institutions (HEIs) in Mexico is still at an early stage despite increasing global interest and advances in the field. The use of educational data remains a challenging puzzle for many universities, which strive to provide students, teachers, and institutional administrators with information and insights to better understand their performance. The objective of this study was to identify the perspectives of teachers, students, and administrators about the use of educational data to explore opportunities for the adoption and integration of LA in three different Mexican universities. A qualitative approach was used, adopting instruments and guidelines previously developed in the framework of Learning Analytics for Latin America (LALA) project, adapting them to the Mexican context. Methods included 1) structured interviews with high-level institutional administrators and 2) focus groups with students, teachers, and other educational administrators. Results showed that perceptions are oriented toward improving school performance through data-based feedback, with ethical responsibility. Emergent categories were physical and mental health, development of healthy relationships and well-being, feedback style, and governance in a bureaucratic setting. The specific modern construct of LA still needs to be internalized and disseminated to Mexican universities’ educational stakeholders to increase the likelihood of successful adoption.

References

Acosta-Gonzaga, E., & Ramirez-Arellano, A. (2020). Estudio comparativo de técnicas de analítica del aprendizaje para predecir el rendimiento académico de los estudiantes de educación superior [Comparative study of learning analytics techniques to predict student academic performance in higher education]. CienciaUAT, 15(1), 63–74. https://doi.org/10.29059/cienciauat.v15i1.1392

Alvarado-Uribe, J., Mejía-Almada, P., Masetto Herrera, A. L., Molontay, R., Hilliger, I., Hegde, V., Montemayor Gallegos, J. E., Ramírez Díaz, R. A., & Ceballos, H. G. (2022). Student dataset from Tecnologico de Monterrey in Mexico to predict dropout in higher education. Data, 7(9), 119. https://doi.org/10.3390/data7090119

ANUIES. (2023). Información estadística de educación superior [Higher education statistical information]. http://www.anuies.mx/informacion-y-servicios/informacion-estadistica-de-educacion-superior

Canales Sánchez, D., Bautista Godínez, T., Moreno Salinas, J. G., García-Minjares, M., & Sánchez-Mendiola, M. (2022). Academic trajectories analysis with a life-course approach: A case study in medical students. Cogent Education, 9(1), 2018118. https://doi.org/10.1080/2331186X.2021.2018118

Cechinel, C., Ochoa, X., Lemos dos Santos, H., Carvalho Nunes, J. B., Rodés, V., & Marques Queiroga, E. (2020). Mapping learning analytics initiatives in Latin America. British Journal of Educational Technology, 51(4), 892–914. https://doi.org/10.1111/bjet.12941

Cobo, C., & Aguerrebere, C. (2018). Building capacity for learning analytics in Latin America. In C. P. Lim, & V. L. Tinio (Eds.), Learning analytics for the Global South (pp. 58–67). Foundation for Information Technology Education and Development. https://dl4d.org/wp-content/uploads/2018/03/Learning-Analytics_Response-from-Latin-America-1.pdf

Espinoza-Guanuche, D. G., Campoverde-Molina, M., & Maldonado-Mahauad, J. (2020). Análisis bibliométrico sobre learning analytics en Latinoamérica [Bibliometric analysis of learning analytics in Latin America]. Dominio de las Ciencias, 6(4), 780–826. https://dialnet.unirioja.es/servlet/articulo?codigo=8638134

Vuorikari, R., & Castaño Muñoz, J. (2016). Research evidence on the use of learning analytics: Implications for education policy. European Commission, Joint Research Centre. https://www.doi.org/10.2791/955210

Ferreyra, M. M., Avitabile, C., & Botero Álvarez, J., Haimovich Paz, F., & Urzúa, S. (2017). At a crossroads: Higher education in Latin America and the Caribbean. World Bank Publications. https://doi.org/10.1596/978-1-4648-1014-5

Foucault, M. (2010). La hermenéutica del Sujeto. In M. Foucault, Obras esenciales (pp. 925-936). Paidós Básica.

Garcia, S., Marques, E. C. M., Mello, R. F., Gašević, D., & Falcão, T. P. (2021). Aligning expectations about the adoption of learning analytics in a Brazilian higher education institution. In I. Roll, D. McNamara, S. Sosnovsky, R. Luckin, & V. Dimitrova (Eds.), Proceedings of the 22nd International Conference on Artificial Intelligence in Education (AIED 2021), 14–18 June 2021, Utrecht, The Netherlands (pp. 173–177). Springer. https://doi.org/10.1007/978-3-030-78270-2_31

Hilliger, I., Pérez-Sanagustín, M., Ortíz, M., Pesántez, P., Scheihing, E., Tsai, Y.-S., Muñoz-Merino, P. J., & Broos, T. (2019). Assessing institutional needs for learning analytics adoption in Latin American higher education. Companion Proceedings of the 9th International Conference on Learning Analytics and Knowledge (LAK ’19), 4–8 March 2019, Tempe, AZ, USA (pp. 901–911). Society for Learning Analytics Research (SoLAR). https://www.solaresearch.org/wp-content/uploads/2019/08/LAK19_Companion_Proceedings.pdf

Hilliger, I., Ortiz-Rojas, M., Pesántez-Cabrera, P., Scheihing, E., Tsai, Y.-S., Muñoz-Merino, P. J., Broos, T., Whitelock-Wainwright, A., & Pérez-Sanagustín, M. (2020a). Identifying needs for learning analytics adoption in Latin American universities: A mixed-methods approach. The 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‐Wainwright, 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

Ifenthaler, D., & Gibson, D. (Eds.). (2020). Adoption of data analytics in higher education learning and teaching. Springer Cham. https://doi.org/10.1007/978-3-030-47392-1

Kitto, K., Hicks, B., & Buckingham Shum, S. (2023). Using causal models to bridge the divide between big data and educational theory. British Journal of Educational Technology, 54(5), 1095–1124. https://doi.org/10.1111/bjet.13321

Larrabee Sønderlund, A., Hughes, E., & Smith, J. (2019). The efficacy of learning analytics interventions in higher education: A systematic review. British Journal of Educational Technology, 50(5), 2594–2618. https://doi.org/10.1111/bjet.12720

Lee, L.-K., Cheung, S. K. S., & Kwok, L.-F. (2020). Learning analytics: Current trends and innovative practices. Journal of Computers in Education, 7(1), 1–6. https://doi.org/10.1007/s40692-020-00155-8

Lim, L.-A., Dawson, S., Gašević, D., Joksimović, S., Pardo, A., Fudge, A., & Gentili, S. (2021). Students’ perceptions of, and emotional responses to, personalised learning analytics-based feedback: An exploratory study of four courses. Assessment & Evaluation in Higher Education, 46(3), 339–359. https://doi.org/10.1080/02602938.2020.1782831

Maldonado-Mahauad, J., Hilliger, I., Pérez-Sanagustín, M., Millecamp, M., Verbert, K., & Ochoa, X. (2018). The LALA project: Building capacity to use learning analytics to improve higher education in Latin America. Companion Proceedings of the 8th International Conference on Learning Analytics and Knowledge: Towards User-Centred Learning Analytics (LAK ’18), 5–9 March 2018, Sydney, NSW, Australia. Society for Learning Analytics Research (SoLAR). https://drive.google.com/file/d/1wN-swZRDiWjf9W4kY25YjA4uyxlWHDcy/view

Monteverde-Suárez, D., González-Flores, P., Santos-Solórzano, R., García-Minjares, M., Zavala-Sierra, I., & Sánchez-Mendiola, M. (2021). Predicting medicine students’ achievement and analyzing related attributes with ANN and Naïve Bayes. Companion Proceedings of the 11th International Conference on Learning Analytics and Knowledge (LAK ’21), 12–16 April 2021, Online (pp. 100–102). Society for Learning Analytics Research (SoLAR). https://www.solaresearch.org/wp-content/uploads/2021/04/LAK21_CompanionProceedings.pdf

Newton, P. M., Najabat-Lattif, H. F., Santiago, G., & Salvi, A. (2021). The learning styles neuromyth is still thriving in medical education. Frontiers in Human Neuroscience, 15, 708540. https://doi.org/10.3389/fnhum.2021.708540

OECD. (2019). Higher education in Mexico: Labour market relevance and outcomes. OECD Publishing. https://doi.org/10.1787/9789264309432-en

Pardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45(3), 438–450. https://doi.org/10.1111/bjet.12152

Rincon-Flores, E. G., Lopez-Camacho, E., Mena, J., & Olmos, O. (2022). Teaching through learning analytics: Predicting student learning profiles in a physics course at a higher education institution. International Journal of Interactive Multimedia and Artificial Intelligence, 7(7), 82–89. https://doi.org/10.9781/ijimai.2022.01.005

Salas-Pilco, S. Z., & Yang, Y. (2020). Learning analytics initiatives in Latin America: Implications for educational researchers, practitioners and decision makers. British Journal of Educational Technology, 51(4), 875–891. https://doi.org/10.1111/bjet.12952

Saga, V. L., & Zmud, R. W. (1993). The nature and determinants of IT acceptance, routinization, and infusion. Proceedings of the IFIP TC8 Working Conference on Diffusion, Transfer and Implementation of Information Technology, 11–13 October 1993, New York, NY, United States (pp. 67–86). Elsevier Science Inc. https://dl.acm.org/doi/10.5555/646302.686655

Sánchez-Mendiola, M. (2022). Higher education in Mexico. In K. Pelletier, M. McCormack, J. Reeves, J. Robert, & N. Arbino (Eds.), 2022 EDUCAUSE horizon report, teaching and learning edition (pp. 45–46). EDUCAUSE. https://library.educause.edu/-/media/files/library/2022/4/2022hrteachinglearning.pdf?la=en&hash=6F6B51DFF485A06DF6BDA8F88A0894EF9938D50B

Sánchez-Mendiola, M., Manzano-Patiño, A. P., García-Minjares, M., Casanova, E. B., Herrera Penilla, C. J., Goytia-Rodríguez, K., & Martínez-González, A. (2023). Large-scale diagnostic assessment in first-year university students: Pre- and transpandemic comparison. Educational Assessment, Evaluation and Accountability, 35, 503–523. https://doi.org/10.1007/s11092-023-09410-9

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

Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529. https://doi.org/10.1177/0002764213479366

Steiner, C. M., Kickmeier‐Rust, M. D., & Albert, D. (2015). Let’s talk ethics: Privacy and data protection framework for a learning analytics toolbox. Ethics and Privacy in Learning Analytics (#EP4LA), 16 March 2015, Poughkeepsie, NY, United States. https://graz.elsevierpure.com/en/publications/lets-talk-ethics-privacy-and-data-protection-framework-for-a-lear

Talamás-Carvajal, J. A., & Ceballos, H. G. (2023). A stacking ensemble machine learning method for early identification of students at risk of dropout. Education and Information Technologies, 28, 12169–12189. https://doi.org/10.1007/s10639-023-11682-z

Tong, A., Sainsbury, P., & Craig, J. (2007). Consolidated criteria for reporting qualitative research (COREQ): A 32-item checklist for interviews and focus groups. International Journal for Quality in Health Care, 19(6), 349–357. https://doi.org/10.1093/intqhc/mzm042

Tsai, Y.-S., & Gašević, D. (2017). Learning analytics in higher education — Challenges and policies: A review of eight learning analytics policies. Proceedings of the 7th International Conference on Learning Analytics and Knowledge (LAK ’17), 13–17 March 2017, Vancouver, BC, Canada (pp. 233–242). ACM Press. https://doi.org/10.1145/3027385.3027400

Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98–110. https://doi.org/10.1016/j.chb.2018.07.027

Yan, V. X., & Fralick, C. M. (2022). Consequences of endorsing the individual learning styles myth: Helpful, harmful, or harmless? In D. H. Robinson, V. X. Yan, & J. A. Kim (Eds.), Learning styles, classroom instruction, and student achievement. Springer Cham. https://doi.org/10.1007/978-3-030-90792-1_6

Perspectives and Opportunities for Learning Analytics Integration

A Qualitative Study in Mexican Universities

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