Handbook of Learning Analytics
Addressing the Challenges
of Institutional Adoption
Cassandra Colvin, Shane Dawson,
Alexandra Wade & Dragan Gašević
Despite increased funding opportunities, research, and institutional investment, there remains a paucity of realized large-scale implementations of learning analytics strategies and activities in higher education. The lack of institutional exemplars denies the sector broad and nuanced understanding of the affordances and constraints of learning analytics implementations over time. This chapter explores the various models informing the adoption of large-scale learning analytics projects. In so doing, it highlights the limitations of current work and proposes a more empirically driven approach to identify the complex and interwoven dimensions impacting learning analytics adoption at scale.
Accard, P. (2015). Complex hierarchy: The strategic advantages of a trade-off between hierarchical supervision and self-organizing. European Management Journal, 33(2), 89–103.
Arnold, K. E., Lonn, S., & Pistilli, M. D. (2014). An exercise in institutional reflection: The learning analytics readiness instrument (LARI). Proceedings of the 4th International Conference on Learning Analytics and Knowledge (LAK ʼ14), 24–28 March 2014, Indianapolis, IN, USA (pp. 163–167). New York: ACM.
Arnold, K. E., Lynch, G., Huston, D., Wong, L., Jorn, L., & Olsen, C. W. (2014). Building institutional capacities and competencies for systemic learning analytics initiatives. Proceedings of the 4th International Conference on Learning Analytics and Knowledge (LAK ʼ14), 24–28 March 2014, Indianapolis, IN, USA (pp. 257–260). New York: ACM.
Baker, R., & Inventado, P. (2014). Educational data mining and learning analytics. In J. A. Larusson & B. White (Eds.), Learning analytics (pp. 61–75). New York: Springer.
Bichsel, J. (2012). Analytics in higher education: Benefits, barriers, progress and recommendations. Louisville, CO: EDUCAUSE Center for Applied Research.
Bolden, R. (2011). Distributed leadership in organizations: A review of theory and research. International Journal of Management Reviews, 13(3), 251–269.
Carbonell, K. B., Dailey-Hebert, A., & Gijselaers, W. (2013). Unleashing the creative potential of faculty to create blended learning. The Internet and Higher Education, 18, 29–37.
Colvin, C., Rogers, T., Wade, A., Dawson, S., Gašević, D., Buckingham Shum, S., . . . Fisher, J. (2015). Student retention and learning analytics: A snapshot of Australian practices and a framework for advancement. Canberra, ACT: Australian Government Office for Learning and Teaching.
Conde, M. Á., & Hernández-García, Á. (2015). Learning analytics for educational decision making. Computers in Human Behavior, 47, 1–3.
Daniel, B. (2015). Big data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904–920.
Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: The new science of winning. Boston, MA: Harvard Business School Press.
Dawson, S., Heathcote, L., & Poole, G. (2010). Harnessing ICT potential. International Journal of Educational Management, 24(2), 116–128.
Drachsler, H., & Greller, W. (2012). The pulse of learning analytics understandings and expectations from the stakeholders. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK ʼ12), 29 April–2 May 2012, Vancouver, BC, Canada (pp. 120–129). New York: ACM.
ECAR-ANALYTICS Working Group. (2015). The predictive learning analytics revolution: Leveraging learning data for student success ECAR working group paper. Louisville, CO: EDUCAUSE.
Ferguson, R., Macfadyen, L., Clow, D., Tynan, B., Alexander, S., & Dawson, S. (2015). Setting learning analytics in context: Overcoming the barriers to large-scale adoption. Journal of Learning Analytics, 1(3), 120–144.
Foreman, S. (2013a). Five steps to evaluate and select an LMS: Proven practices. Learning Solutions Magazine. http://www.learningsolutionsmag.com/articles/1181/five-steps-to-evaluate-and-select-an-lms-proven-practices.
Foreman, S. (2013b). The six proven steps for successful LMS implementation. Learning Solutions Magazine. http://www.learningsolutionsmag.com/articles/1214/the-six-proven-steps-for-successful-lms-implementation-part-1-of-2.
Graham, C. R., Woodfield, W., & Harrison, J. B. (2013). A framework for institutional adoption and implementation of blended learning in higher education. The Internet and Higher Education, 18, 4–14.
Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Journal of Educational Technology & Society, 15(3), 42–57.
Hazy, J. K., & Uhl-Bien, M. (2014). Changing the rules: The implications of complexity science for leadership research and practice. In D. V. Day (Ed.), Oxford Handbook of Leadership and Organizations. Oxford, UK: Oxford University Press.
Houchin, K., & MacLean, D. (2005). Complexity theory and strategic change: An empirically informed critique. British Journal of Management, 16(2), 149–166.
Kaski, T., Alamäki, A., & Moisio, A. (2014). A multi-discipline rapid innovation method. Interdisciplinary Studies Journal, 3(4), 163.
Kotter, J. P., & Schlesinger, L. A. (2008). Leading Change. Boston, MA: Harvard Business Review Press.
Laferrière, T., Hamel, C., & Searson, M. (2013). Barriers to successful implementation of technology integration in educational settings: A case study. Journal of Computer Assisted Learning, 29(5), 463–473.
Macfadyen, L., & Dawson, S. (2012). Numbers are not enough. Why e-learning analytics failed to inform an institutional strategic plan. Educational Technology & Society, 15(3), 149–163.
Macfadyen, L., Dawson, S., Pardo, A., & Gašević, D. (2014). Embracing big data in complex educational systems: The learning analytics imperative and the policy challenge. Research & Practice in Assessment, 9(2), 17–28.
Münch, J., Fagerholm, F., Johnson, P., Pirttilahti, J., Torkkel, J., & Jäarvinen, J. (2013). Creating minimum viable products in industry-academia collaborations. In B. Fitzgerald, K. Conboy, K. Power, R. Valerdi, L. Morgan, & K.-J. Stol (Eds.), Lean Enterprise Software and Systems (Vol. 167, pp. 137–151). Springer Berlin Heidelberg.
Norris, D., Baer, L., Leonard, J., Pugliese, L., & Lefrere, P. (2008). Action analytics: Measuring and improving performance that matters in higher education. EDUCAUSE Review, 43(1), 42.
Norris, D. M., & Baer, L. L. (2013). Building organizational capacity for analytics. Louisville, CO: EDUCAUSE.
Oster, M., Lonn, S., Pistilli, M. D., & Brown, M. G. (2016). The learning analytics readiness instrument. Proceedings of the 6th International Conference on Learning Analytics and Knowledge (LAK ʼ16), 25–29 April 2016, Edinburgh, UK (pp. 173–182). New York: ACM.
Owston, R. (2013). Blended learning policy and implementation: Introduction to the special issue. The Internet and Higher Education, 18, 1–3.
Ritchey, T. (2011). General morphological analysis: A general method for non-quantified modelling. In T. Ritchey (Ed.), Wicked Problems: Social Messes. http://www.swemorph.com/pdf/gma.pdf
Siemens, G., Dawson, S., & Lynch, G. (2013). Improving the quality and productivity of the higher education sector: Policy and strategy for systems-level deployment of learning analytics. Sydney, Australia: Australian Government Office for Teaching and Learning.
Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30.
Addressing the Challenges of Institutional Adoption
Handbook of Learning Analytics
Society for Learning Analytics Research
1. Teaching Innovation Unit, University of South Australia, Australia
2. School of Health Sciences, University of South Australia, Australia
3. Schools of Education and Informatics, The University of Edinburgh, United Kingdom
4. Teachers College, Columbia University, USA
5. LINK Research Lab, University of Texas at Arlington, USA
6. Learning Analytics Research Network, New York University, USA