Background

Intent

The Learning Analytics Readiness Instrument (LARI) is a survey instrument designed to be completed by multiple people within an institution as they deliberate the merits of engaging with an learning analytics (LA) project. Conceptually, the LARI could be used in the formative stages of planning to undertake a LA initiative. The LARI will ultimately provide an institutional profile with readiness indicators on several key elements necessary for LA success. The LARI has been designed to serve the purpose of a prescriptive diagnostic, meaning the instrument can be used to help determine strengths as well as potential foci that may need additional attention before a large-scale initiative is undertaken.

Framework

The LARI was created to firmly seat learning analytics (LA) at the intersection of “big data” and student success. When creating the framework for the LARI, the authors wanted to ensure it was fully situated in the existing learning analytics space by utilizing the existing definitions have been proffered by scholars and institutions in the LA literature.

As the LA space is still emergent, the LARI is framed using clearly delineated assumptions rather than a precise definition.The following principles were incorporated as a definition and broad consideration for the LARI: Institutions should examine

1. Rich, learning-related data sets,

2. as they are exposed to various analytics techniques,

3. in an effort to support teachers and/or learners,

4. as those populations move toward intervention, action, and increased success.

Citations

While not exhaustive, the LARI framework draws from the following scholars and prior projects in learning analytics:

• • Cooper, A. (2012). What is Analytics? Definition and Essential Characteristics. CETIS Analytics Series, 1(5). Available: http://publications.cetis.ac.uk/2012/521

  • EDUCAUSE Center for Applied Research (2012). Analytics Maturity Index. Available: http://www.educause.edu/ecar/research-publications/ecar-analytics-maturity-index-higher-education

  • EDUCAUSE Learning Initiative (2011). 7 things you should know about first-generation learning analytics. Louisville, CO: EDUCAUSE. Available: http://www.educause.edu/library/resources/7-things-you-should-know-about-first-generation-learning-analytics

• Krumm, A. E., Waddington, R. J., Lonn, S., & Teasley, S. D. (2012). Increasing academic success in undergraduate engineering education using learning analytics: A design-based research project. Available: http://hdl.handle.net/2027.42/106032

• Norris, D. M. & Baer, L. L. (2013) Building Organizational Capacity for Analytics. Louisville, CO: EDUCAUSE. Available: http://net.educause.edu/ir/library/pdf/PUB9012.pdf

• Prinsloo, P., & Slade, S. (2013). An evaluation of policy frameworks for addressing ethical considerations in learning analytics. In D. Suthers, K. Verbert, E. Duval., & X. Ochoa, Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 240-244), New York: ACM. Available: http://dl.acm.org/citation.cfm?doid=2460296.2460344

  • Society of Learning Analytics Research (n.d.) About [Webpage] Available: http://www.solaresearch.org/mission/about/

  • van Barneveld, A., Arnold, K. E., & Campbell, J. P. (2012, January). Analytics in higher education: Establishing a common language. ELI paper 1: 2012. Available: http://www.educause.edu/library/resources/analytics-higher-education-establishing-common-language

The Learning Analytics Readiness Instrument by Kimberly Arnold, Steven Lonn, and Matthew Pistilli is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.