Learning Analytics: Richer Perspectives Across Stakeholders

Shane Dawson
Dragan Gašević
Jelena Jovanović
Arnon Hershkovitz

Abstract


This issue of the Journal of Learning Analytics features seven research papers, complemented by a practitioner research paper (Dvorak & Jia). Papers by McCoy and Shih, and Knight, Brozina, and Novoselich discuss the important topic of educators working with educational data, alongside (in the latter paper) student perspectives on learning analytics. Douglas, Bermel, Alam, and Madhavan; and Waddington, Nam, Lonn, and Teasley offer empirical insight on developing a richer perspective on learning material interaction and engagement in online learning contexts (MOOCs, and LMS’ respectively). Dvorak and Jia bring a practitioner perspective to the issue in their discussion of approaches to analyzing online work habits via timeliness, regularity, and intensity. Sutherland and White, and Vieira, Goldstein, Purzer, and Magana offer focus on specific subject-based learning activities (algebra learning, and student experimentation strategies in engineering design, respectively). Finally, Howley and Rosé discuss the complex interactions of theory and method in computational modeling of group learning processes. The issue also features a special section on learning analytics tutorials, edited by Gašević and Pechenizkiy. The editorial concludes with a report of the recent ‘hot spots section’ consultation from the editorial team of the journal.


Keywords


learning analytics; tutorials; MOOCs; early warning systems; learning management systems; practitioners; predictive analytics

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References


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McPherson, J., Tong, H. L., Fatt, J. S., & Liu, D. Y. T. (2016). Student perspectives on data provision and use: starting to unpack disciplinary differences. In Proceedings of the 6th International ACM Conference on Learning Analytics and Knowledge. Edinburgh, UK: ACM Press. https://doi.org/10.1145/2883851.2883945

Schwartz, D. L., & Arena, D. (2013). Measuring what matters most: Choice-based assessments for the digital age. Boston Massachusetts: MIT Press.

Wise, A. F., Vytasek, J. M., Hausknecht, S., & Zhao, Y. (2016). Developing Learning Analytics Design Knowledge in the “Middle Space”: The Student Tuning Model and Align Design Framework for Learning Analytics Use. Online Learning, 20(2). Retrieved from http://olj.onlinelearningconsortium.org/index.php/olj/article/view/783




DOI: http://dx.doi.org/10.18608/jla.2016.33.1

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