9. Big Data, Educational Data Mining, and Learning Analytics

 

Educational data mining and learning analytics use big data generated from Learning Management Systems and organizational computer systems to track learner connections and interactions. These analytics can inform adult and higher education decision making and planning related to levels of learner attrition, course failures, and repeat classes. This episode explores how big data, educational data mining, and learning analytics are influencing how instructors assess and evaluate students’ learning behaviors and develop interventions in the interest of optimizing both the learning efforts and environments of learners.

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AHE FutureTech Episode 9 Transcript

 

References

• Aguilar, S. J. (2018). Learning analytics: At the nexus of big data, digital innovation, and social justice in education. TechTrends, 62(1), 37-45.
• Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing Teaching and Learning through Educational Data Mining and Learning Analytics: An Issue Brief. Office of Educational Technology, US Department of Education.
• Conn, R. (2020). Seven Key Differences Between Data Analytics and Data Mining. https://www.codemotion.com/magazine/dev-hub/big-data-analyst/data-analytics-data-mining/#. Accessed on 2-10-2021.
• Gedrimiene, E., Silvola, A., Pursiainen, J., Rusanen, J., & Muukkonen, H. (2020). Learning analytics in education: Literature review and case examples from vocational education. Scandinavian Journal of Educational Research, 64(7), 1105-1119.
• Herodotou, C., Rienties, B., Boroowa, A., Zdrahal, Z., & Hlosta, M. (2019). A large-scale implementation of predictive learning analytics in higher education: the teachers’ role and perspective. Educational Technology Research and Development, 67(5), 1273-1306.
• Lee, L. K., & Cheung, S. K. (2020). Learning analytics: Current trends and innovative practices. Journal of Computers in Education, 7(1), 1-6.
• 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.
• Picciano, A. G. (2014). Big data and learning analytics in blended learning environments: Benefits and concerns. IJIMAI, 2(7), 35-43.
• Secades, V. A., & Arranz, O. (2016). Big data & eLearning: A binomial to the future of the knowledge society. IJIMAI, 3(6), 29-33.
•  Yupangco, J., (2020). The Essential Guide to Learning Analytics in the Age of Big Data. https://www.lambdasolutions.net/blog/essential-guide-to-learning-analytics-in-the-age-of-big-data-lf2