Challenges of Learning Analytics Execution in the Educational System

Author:

Singh Manoj Kumar1

Affiliation:

1. Mekelle University, Ethiopia

Abstract

Education for the twenty-first century continues to promote discoveries in the field through learning analytics. The problem is that the rapid embrace of learning analytics diverts educators' attention from clearly identifying requirements and implications of using learning analytics in higher education. Learning analytics is a promising emerging field, yet higher education stakeholders need to become further familiar with issues related to the use of learning analytics in higher education. This chapter addresses the above problem and design of learning analytics implementations: the practical shaping of the human tactics involved in taking on and using analytic equipment, records, and reviews as part of an educational enterprise. This is an overwhelming but equally essential set of design choices from the ones made within the advent of the learning analytics structures themselves. Finally, this chapter's implications for learning analytics teachers and students and areas requiring further studies are highlighted.

Publisher

IGI Global

Reference21 articles.

1. Aguilar, S. (2015). Exploring and measuring students’ sense-making practices around representations of their academic information. Doctoral Consortium Poster presented at the 5 thInternational Conference on Learning Analytics and Knowledge (LAK ’15), Poughkeepsie, NY.

2. Arnold, K. E., Lonn, S., & Pistilli, M. D. (2014). An exercise in institutional reflection: The learning analytics readiness instrument (LARI). In Proceedings of the 4th International Conference on Learning Analytics and Knowledge (pp. 163–165). New York: ACM.

3. Beheshitha, S. S., Hatala, M., Gašević, D., & Joksimović, S. (2016). The role of achievement goal orientations when studying effect of learning analytics visualizations. In Proceedings of the 6th International Conference on Learning Analytics & Knowledge (pp. 54–63). New York: ACM.

4. Rethinking models of feedback for learning: the challenge of design

5. Elias, T. (2011). Learning analytics: Definitions, processes and potential (Report). Retrieved from http://learninganalytics.net/LearningAnalyticsDefinitionsProcessesPotential.pdf

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