Learning analytics and data-driven education: A growing field
DOI:
https://doi.org/10.5944/ried.23.2.27105Keywords:
learning analytics, educational technology, data-based education, data science, educational science, educational research.Abstract
The growing presence of digital mediation systems in most educational spaces —whether face-to-face or not, formalized or open, and at basic or lifelong learning levels— has accelerated the advance of learning analytics and the use of data in education as a common practice. Using digital educational tools facilitates the interaction between students, teachers and learning resources in the digital world, and generates a remarkable volume of data that can be analyzed by applying a variety of methodologies. Thus, research focused on information generated by student activity in digital spaces has risen exponentially. Based on this evidence, this special issue shows a set of studies in the field of data-driven educational research and the field of digital learning, which enriches knowledge about learning processes and management of teaching in digitally mediated spaces.
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Baker, R. S. (2019). Challenges for the future of educational data mining: The Baker learning analytics prizes. JEDM, Journal of Educational Data Mining, 11(1), 1–17. https://doi.org/10.5281/zenodo.3554745
Barocas, S., & Boyd, D. (2017). Engaging the ethics of data science in practice. Communications of the ACM, 60(11), 23–25. https://doi.org/10.1145/3144172
Buckingham Shum, S., Ferguson, R., & Martinez-Maldonado, R. (2019). Human-Centred Learning Analytics. Journal of Learning Analytics, 6(2), 1–9. https://doi.org/10.18608/jla.2019.62.1
Dollinger, M., & Lodge, J. M. (2018, March). Co-creation strategies for learning analytics. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge (pp. 97–101). https://doi.org/10.1145/3170358.3170372
Gursoy, M. E., Inan, A., Nergiz, M. E., & Saygin, Y. (2016). Privacy-preserving learning analytics: challenges and techniques. IEEE Transactions on Learning technologies, 10(1), 68–81. https://doi.org/10.1109/TLT.2016.2607747
Holstein, K., & Doroudi, S. (2019). Fairness and equity in learning analytics systems (FairLAK). In Companion Proceedings of the Ninth International Learning Analytics & Knowledge Conference (LAK 2019).
Hartong, S., & Förschler, A. (2019). Opening the black box of data-based school monitoring: Data infrastructures, flows and practices in state education agencies. Big Data & Society. https://doi.org/10.1177/2053951719853311
Leitner, P., Ebner, M., & Ebner, M. (2019). Learning Analytics Challenges to Overcome in Higher Education Institutions. In Utilizing Learning Analytics to Support Study Success (pp. 91–104). Springer, Cham.
Mandinach, E. B., & Gummer, E. S. (2016). Data literacy for educators: Making it count in teacher preparation and practice. Teachers College Press.
Shibani, A., Knight, S., & Buckingham Shum, S. (2020). Educator perspectives on learning analytics in classroom practice. The Internet and Higher Education, 46(2020). https://doi.org/10.1016/j.iheduc.2020.100730
Tsai, Y. S., & Gasevic, D. (2017, March). Learning analytics in higher education---challenges and policies: a review of eight learning analytics policies. In Proceedings of the seventh international learning analytics & knowledge conference (pp. 233–242).
Wagenmakers, E. J., Wetzels, R., Borsboom, D., van der Maas, H. L., & Kievit, R. A. (2012). An agenda for purely confirmatory research. Perspectives on Psychological Science, 7(6), 632–638. https://doi.org/10.1177%2F1745691612463078
Wilson, A., Watson, C., Thompson, T. L., Drew, V., & Doyle, S. (2017). Learning analytics: Challenges and limitations. Teaching in Higher Education, 22(8), 991–1007. https://doi.org/10.1080/13562517.2017.1332026
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