Learning analytics in higher education: a review of impact scientific literature





Learning analytics, university, higher education, bibliometrics, Scopus, scientific production


Learning analytics can be defined as a series of techniques for collecting, analyzing and granting processable data generated by students. Their objective is to develop appropriate strategies to improve the learning processes, the performance of the students or that of the institution itself. These types of techniques are especially useful to establish patterns of action that guide and guide the educational process in higher education. Under these premises, the present research aims to analyze the scientific production with the greatest impact on the use of learning analytics in higher education. For this purpose, a quantitative methodology has been followed, based on ten variables: year of publication, periodicals, authors, institutions, countries, type of document, publication format, publication area, language and most cited articles. The results project a research trend that is fully on the rise, especially due to the greater scientific production that has occurred in recent years (2015-2018), highlighting countries such as Australia, the United States and the United Kingdom. The publications, in their great majority, come from conferences and are published in English. The knowledge areas of Computational Sciences and Social Sciences stand out.



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Antelm, A. M., Gil. A., Cacheiro, M.L. y Pérez, E. (2018). Causas del fracaso escolar: un análisis desde la perspectiva del profesorado y el alumnado. Enseñanza & Teaching, 36(1), 129-149.

Alonso-García, S., Rodríguez-García, A. M., & Cáceres-Reche, M. (2018). Análisis de la Acción Tutorial y su Incidencia en el Desarrollo Integral del Alumnado. El Caso de la Universidad de Castilla La Mancha, España. Formación universitaria, 11(3), 63-72.

Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In Learning analytics (pp. 61-75). Springer, New York, NY.

Cabedo, A., Galán, M.E. & Gómez, N. (2018). UniTICarte: Redes Colaborativas para el desarrollo de proyectos creativos en la formación de Maestros en la Universidad. Dedica. Revista de Educação e Humanidades, 12, 119-129.

Chiappe, A., & Rodríguez, L. P. (2017). Learning Analytics in 21st century education: a review. Ensaio: Avaliação e Políticas Públicas em Educação, 25(97), 971-991.

Coffrin, C., Corrin, L., de Barba, P., & Kennedy, G. (2014, March). Visualizing patterns of student engagement and performance in MOOCs. In Proceedings of the fourth international conference on learning analytics and knowledge (pp. 83-92). ACM.

Dietz-Uhler, B., & Hurn, J. E. (2013). Using learning analytics to predict (and improve) student success: A faculty perspective. Journal of Interactive Online Learning, 12(1), 17-26.

Ferguson, R., & Shum, S. B. (2012, April). Social learning analytics: five approaches. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 23-33). ACM.

Gasevic, D., Kovanovic, V., Joksimovic, S., & Siemens, G. (2014). Where is research on massive open online courses headed? A data analysis of the MOOC Research Initiative. The International Review of Research in Open and Distributed Learning, 15(5), 134-176.

Gedrimiene, E., Silvola, A., Pursiainen, J., Rusanen, J., & Muukkonen, H. (2019). Learning Analytics in Education: Literature Review and Case Examples From Vocational Education. Scandinavian Journal of Educational Research, 1-15.

Johnson, L., Becker, S. A., Cummins, M., Estrada, V., Freeman, A., & Hall, C. (2016). NMC horizon report: 2016 higher education edition (pp. 1-50). The New Media Consortium.

Hernández Sampieri, R., Fernández Collado, C. & Baptista Lucio, P. (2016). Metodología de la investigación (6ª Edición). México: MC Graw Hill Education.

Hinojo-Lucena, F., Mingorance-Estrada, Á., Trujillo-Torres, J., Aznar-Díaz, I., & Cáceres Reche, M. (2018). Incidence of the Flipped Classroom in the Physical Education Students’ Academic Performance in University Contexts. Sustainability, 10(5), 1334.

Huda, M., Maseleno, A., Atmotiyoso, P., Siregar, M., Ahmad, R., Jasmi, K., & Muhamad, N. (2018). Big data emerging technology: insights into innovative environment for online learning resources. International Journal of Emerging Technologies in Learning (iJET), 13(1), 23-36.

Klein, C., Lester, J., Rangwala, H., & Johri, A. (2019). Technological barriers and incentives to learning analytics adoption in higher education: insights from users. Journal of Computing in Higher Education, 1-22.

López-Meneses, E., Vázquez-Cano, E., & Román, P. (2015). Análisis e implicaciones del impacto del movimiento MOOC en la comunidad científica: JCR y Scopus (2010-13). Comunicar: Revista científica iberoamericana de comunicación y educación, 22(44), 73-80. DOI http://dx.doi.org/10.3916/C44-2015-08

Macfadyen, L. P., & Dawson, S. (2012). Numbers are not enough. Why e-learning analytics failed to inform an institutional strategic plan. Journal of Educational Technology & Society, 15(3),149-163.

Mengual-Andrés, S., Vázquez-Cano, E., & López-Meneses, E. (2017). La productividad científica sobre MOOC: aproximación bibliométrica 2012-2016 a través de SCOPUS. RIED. Revista Iberoamericana de Educación a Distancia, 20(1), 39-58.

Picciano, A. G. (2012). The evolution of big data and learning analytics in American higher education. Journal of Asynchronous Learning Networks, 16(3), 9-20.

Siemens, G. (2012, April). Learning analytics: envisioning a research discipline and a domain of practice. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 4-8). ACM.

Long, P., & Siemens, G. (2014). Penetrating the fog: analytics in learning and education. Italian Journal of Educational Technology, 22(3), 132-137.

Mingorance, A. C., Trujillo, J. M., Cáceres, P., & Torres, C. (2017). Mejora del rendimiento académico a través de la metodología de aula invertida centrada en el aprendizaje activo del estudiante universitario de ciencias de la educación. Journal of sport and health research, 9(1), 129-136.

Montgomery, A. P., Mousavi, A., Carbonaro, M., Hayward, D. V., & Dunn, W. (2019). Using learning analytics to explore self‐regulated learning in flipped blended learning music teacher education. British Journal of Educational Technology, 50(1), 114-127.

Moreira, A., Bates, T., & Mota, J. (2019). What future (s) for distance education universities? Towards an open network-based approach/ ¿Qué futuro para las universidades de educación a distancia? Hacia un enfoque abierto basado en la red. RIED. Revista Iberoamericana de Educación a Distancia, 22(1), 107-126.

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Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510-1529.

Ulstad, S. O., Halvari, H., & Deci, E. L. (2018). The Role of Students’ and Teachers’ Ratings of Autonomous Motivation in a Self-Determination Theory Model Predicting Participation in Physical Education. Scandinavian Journal of Educational Research, 1-16.

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How to Cite

Cáceres Reche, P., Rodríguez-García, A.-M., Gómez García, G., & Rodríguez Jiménez, C. (2020). Learning analytics in higher education: a review of impact scientific literature. IJERI: International Journal of Educational Research and Innovation, (13), 32–46. https://doi.org/10.46661/ijeri.4584