A blog post attempting to capture what was said in the presentation is now available.
This draft short paper describes in a bit more detail the IRAC framework that is mentioned in the presentation and formed the structure for the workshop.
The following collection of Powerpoint slides provided the skeleton for our first workshop around Learning Analytics. The four questions arise from the IRAC framework and are to do with Information Representation, Affordances and Change. In reality there are 6 questions
- What’s your context?
- What’s the task?
- What Information do you need, how do you access it and how do you analyse it?
- How will you Represent your findings?
- What Affordances for actions can you provide?
- How are you going to Change all of the above?
Introduction and context and task
Covers the introduction to the workshop and the question of context and task. In these slides “task” is covered by a “cook’s tour” of learning analytics applications as a concrete way to provide ideas to what is possible with learning analytics.
The aim here is to give an introduction to the idea of big data and how it looks at information and subsequently the tools and approaches you have to being able to analyse it. Also examines questions around getting access to information such as legal, ethical and the more technical.
Looks at why representation is important and some of the options that are available and some of those that don’t work so well (e.g. start of a critique of dashboards).
Examines the reasons behind the argument that learning analytics tools and the organisations employing them need to seriously look at and aim to improve the affordances that exist for action arising from the insights gained from the representation.
The last step is to look back and see how, who and what can be changed about this whole process. The assumption being that there exist a large number of reasons why everything about a learning analytics intervention needs to open to on-going change.
Most of the sources used in the workshop are included below.
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Baker, R., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3–17.
Biggs, J. (2001). The Reflective Institution: Assuring and Enhancing the Quality of Teaching and Learning. Higher Education, 41(3), 221–238.
Campbell, J., DeBlois, P., & Oblinger, D. (2007). Academic analytics: A new tool for a new era. EDCAUSE Review, 42(4), 40–42.
Carroll, J. M., Kellog, W. A., & Rosson, M. B. (1991). The Task-Artifact Cycle. In John Millar Carroll (Ed.), Designing Interaction: Psychology at the Human-Computer Interface (pp. 74–102). Cambridge, UK: Cambridge University Press.
Clow, D. (2012). The learning analytics cycle. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge – LAK Õ12, 134–138. doi:10.1145/2330601.2330636
Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, (August), 1–13. doi:10.1080/13562517.2013.827653
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Kay, D., Korn, N., & Oppenheim, C. (2012). Legal, Risk and Ethical Aspects of Analytics in Higher Education. CETIS Analytics Series (Vol. 1, pp. 1–30). Bolton.
Lockyer, L., Heathcote, E., & Dawson, S. (2013). Informing Pedagogical Action: Aligning Learning Analytics With Learning Design. American Behavioral Scientist, XX(X), 1–21. doi:10.1177/0002764213479367
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Prinsloo, P., & Slade, S. (2013). An evaluation of policy frameworks for addressing ethical considerations in learning analytics. In Proceedings of the Third International Conference on Learning Analytics and Knowledge – LAK Õ13 (pp. 240–244).
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Siemens, G. (2013). Learning Analytics: The Emergence of a Discipline. American Behavioral Scientist, (August). doi:10.1177/0002764213498851
Siemens, George, & Long, P. (2011). Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE Review, 46(5).
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