The following post is about learning analytics…eventually. I think there are some interesting parallels between the challenges around learning analytics practice, and the broader challenges being faced by Australian universities – from an organisational/leadership perspective.
Universities, at least in Australia, are managed as corporate bureaucracies around key principles like efficiency, productivity and accountability (Connell, 2019; Kenny & Fluck, 2022; Murphy, 2013). Power and control in corporate bureaucracies sit almost exclusively with management (Uhl-Bien & Arena, 2018; Uhl‐Bien, 2021). Universities, like many organisations, still largely operate with and within outdated governance and political systems that are mired in bureaucracy and cronyism (Uhl-Bien & Arena, 2018). These organisations are structured hierarchically and consist of any number of silos that often consider the other silos as the ‘enemy’ (Cilliers & Greyvenstein, 2012). The hierarchical structures give rise to plan-based approaches and models of leadership that value productivity, efficiency and accountability at the expense of innovation and change (Kenny & Fluck, 2022; Uhl‐Bien, 2021). In a closed and stable system like a factory, these models of leadership make perfect sense. We can assume that tomorrow will be a clone of today so we can plan for tomorrow and we know who needs to do what, when and how. However, our universities operate in a web of open, complex systems that our current models of leadership are incapable of dealing with (Uhl‐Bien, 2021).
With our current leadership models, when faced with a problem, leaders will tend to react with an ordered response (NHS, 2020). That is, there will be a denial of the complexity and a fundamental assumption that the system will revert to the homeostasis that existed prior to the problem, and it is assumed that the problem can be managed using existing systems and structures (NHS, 2020). All of this is fine if the problem is linear – where there is clear cause and effect and the problem can be assigned to someone to resolve. The trouble is that many of the challenges that universities are grappling with have many interdependent, interconnected and autonomous parts. Hence the problems tend to be non-linear problems. These problems cannot be solved using traditional problem-solving methods (Beer & Lawson, 2016). Solving these types of problems requires adaptive leadership – leadership that enables exploration, innovation and different ways of doing things (Uhl‐Bien, 2021). Leadership in these complex environments is a “multi-faceted concept that uses a systems-level approach to design adaptive organisational structures, enabling networked interactions, nurturing innovation and providing leadership development that fosters collaboration (social capital) along with individual performance (human and intellectual capital)” (Uhl-Bien & Arena, 2018, p.89).
The example I like to use is the challenge of student attrition in the Australian Higher Education sector. This is basically where students leave university before finishing their qualifications – something the Government dislikes. The reasons that students leave before obtaining qualifications are dependent on individual circumstances and there is often very little that a university can do about it (Beer & Lawson, 2016). Attrition is a wicked problem that will not be solved by linear methods and within existing systems and structures (Beer & Lawson, 2016). Yet every year or two I get invited to participate in a newly formed committee or project group that has been tasked with “addressing our student attrition problem”. Invariably, this results in some variation of a be-seen-to-be-done exercise that makes little to no long-term impact on our operations or student attrition. My observations of a number of these cycles remind me of a phenomenon described in a book called “The Stupidity Paradox” – Managers think in the short term because they are evaluated by their superiors and colleagues on their short-term results. Making an impact on student attrition is the antithesis of a quick fix. It requires a different way of thinking about the problem and approaches to solving the problem outside of our current problem-solving norms. I think there is something similar happening with learning analytics.
We know that learning analytics is a bricolage field that requires collaboration between a network of stakeholders (Joksimović, Kovanović, & Dawson, 2019). We know that learning analytics is about providing representations of data about humans, to other humans, using technologies and representations that are never neutral (Munguia, Brennan, Taylor, & Lee, 2020). In other words, a typical learning analytics implementation involves dynamics that are too complex and interconnected to be managed or designed (Maric, Bass, Milosevic, & Uhl-Bien) – at least in a single pass. Learning analytics implementation involves cycles of applied research, experimentation, learning and design (Beer, Jones, & Lawson, 2019). I maintain that meso-level practitioners are a crucial component required for a successful learning analytics implementation. However, for the organisation to capitalise on the unique perspective afforded by meso-level practitioners, our leadership models need to evolve beyond our current industrial era bureaucratic models to adaptive models that recognise, enable and capitalise on our intellectual and social capital. This is why the concept of complexity leadership has captured my interest.
Beer, C., & Lawson, C. (2016). The problem of student attrition in higher education: An alternative perspective. Journal of Further and Higher Education, 41(6), 773-784. doi:10.1080/0309877x.2016.1177171
Beer, C., Jones, D., & Lawson, C. (2019). The challenge of leanring analytics implementation: Lessons learned. Paper presented at the Personalised Learning. Diverse Goals. One Heart, Singapore.
Cilliers, F., & Greyvenstein, H. (2012). The impact of silo mentality on team identity: An organisational case study. 2012, 38(2). doi:10.4102/sajip.v38i2.993
Connell, R. (2019). The good university: What universities actually do and why it’s time for radical change: Bloomsbury Publishing.
Joksimović, S., Kovanović, V., & Dawson, S. (2019). The journey of learning analytics. HERDSA Review of Higher Education, 6, 27-63.
Kenny, J., & Fluck, A. E. (2022). Emerging principles for the allocation of academic work in universities. Higher Education, 83(6), 1371-1388. doi:10.1007/s10734-021-00747-y
Maric, S., Bass, E., Milosevic, I., & Uhl-Bien, M. FUTURE OF LEADERSHIP IN HEALTHCARE: ENABLING COMPLEXITY DYNAMICS ACROSS LEVELS Organizer. Management, 615, 343-8094.
Munguia, P., Brennan, A., Taylor, S., & Lee, D. (2020). A learning analytics journey: Bridging the gap between technology services and the academic need. The internet and higher education, 46, 100744.
Murphy, P. (2013). The rise and fall of our bureaucratic universities. Quadrant, 57(5), 48-52.
NHS, H. (Producer). (2020). Mary Uhl-Bien in Conversation: COVID-19, complexity leadership and spread of innovation. Retrieved from https://www.youtube.com/watch?v=OGn0WgfyvEw
Uhl-Bien, M., & Arena, M. (2018). Leadership for organizational adaptability: A theoretical synthesis and integrative framework. The Leadership Quarterly, 29(1), 89-104. doi:10.1016/j.leaqua.2017.12.009
Uhl‐Bien, M. (2021). Complexity and COVID‐19: Leadership and Followership in a Complex World. Journal of Management studies, 10.1111/joms.12696. doi:10.1111/joms.12696