A colleague and I authored a paper last year that questioned some of the assumptions that Australian Higher Education Institutions (HEI) make about student retention/attrition (Beer & Lawson, 2016). We suggested that student attrition is a complex, non-linear problem; a wicked problem that is set within a complex social system and universities are making little headway with the issue (Beer & Lawson, 2016) . Despite the enormous interconnected complexity associated with student attrition, HEI still use traditional problem solving methods and mindsets when it comes to addressing their student attrition issues. We are now thinking about how we might convert our abstract writings on the topic of student attrition and retention into action. This post is intended to help get some of our thoughts down, writing as thinking if you like.
We know that students leave their universities based on a culmination of many factors, most of which fall outside the university’s ability to influence. This isn’t to say universities can’t do anything about it, far from it, but maybe we need to think about student attrition in a different way. Universities tend to treat attrition like it is a traditional problem that can be solved using classic approaches to problem solving based on a process of understanding the problem, gathering information, synthesizing information and formulating a solution (Ritchey, 2002). We would argue that this is an ontological misinterpretation of the actual nature of the system we are dealing with, so maybe we need a different approach.
It could be argued that the underlying system is being treated as an ordered, linear system whereby it makes sense to apply an approach based on detailed planning that aims to achieve an idealistic future state (ie most Australian universities mention increased retention in their strategic plans) (Boehm & Turner, 2003; Camillus, 2008). However, we suggest that the underlying system is (ontologically) an unordered system with its many interacting and interdependent variables, and behaves more like a complex adaptive system (CAS) (Davis & Sumara, 2007; Davis & Sumara, 2006; Mason, 2008a, 2008b). The following sections are not mutually exclusive and look at some of the differences between how universities are currently approaching attrition and an approach based on CAS. This might help us determine, where to from here.
Approach to implementing change
How HEI work at the moment (at least in my limited experience) is based around episodic change. This is where organisational change is stimulated by internal or external catalysts (Weick, 2012; Weick & Quinn, 1999; Weick, Sutcliffe, & Obstfeld, 2005). For example, new technologies, new managers, financial situations, restructuring and so on. These changes are intentional, infrequent and discontinuous. The organisational metaphor here is inertial and the emphasis is on short term adaptation (Weick & Quinn, 1999). When dealing with a CAS, unpredictability and disproportionate consequences are the norm. Change in these contexts is constant, always evolving, cumulative and endlessly reacting to small contingencies. The organisational metaphor here is based on agility and long term adaptation.
Communications, responsibility and accountability
HEI are, at least in Australia, rigidly organised as hierarchical bureaucracies. They are decomposed into organisational units where people are grouped by role. We often critically refer to these units as silos. Strategy is determined centrally by a small group of people and detailed plans are created, disseminated and deviation from the plan is strongly discouraged. Communications, responsibility around who does what, and accountability all flow from this rigid structure and acquiescence to the plan. A CAS approach recognises that institutional memory, cognition and the ability to solve problems is distributed across the network of agents in the organisation. Cross silo communications and collaboration in this case is crucial. CAS requires a network approach to organisational communications and collaboration.
Approach to problem solving and taking action
This is linked with the previous section but is another key difference worth mentioning. Currently, when universities are trying to address a complex issue like student attrition, they resort to detailed plans that aim to help the organisation achieve their desired future state; ie reduced attrition, increase enrolments etc. This plans include a range of key performance indicators (KPI) that are used to measure progress against the said plan. Detailed planning and strict adherence to the plan assumes that the interconnected array of systems involved are stable and fixed and won’t change as we implement the plan. An assumption that is almost universally wrong. CAS assume change, which then changes the approach to problem solving and action. Instead of targeting the idealistic future goals through detailed planning, the organisation adapts to the here and now, at the local level, addressing issues as they arise day-to-day, sharing what works and what doesn’t. In other words, the organisation applies a strategy centred upon learning, not planning.
Where to from here
These are just three broad areas whereby a CAS approach differs from the dominant approach, particularly how it pertains to addressing student retention. The challenge for us is to figure out how we can move towards a CAS approach, which we think has a greater chance of impacting upon student retention, given the dominant (for want of a better word) hierarchical approach. The reality is that our operating environment with its associated mindsets are rigidly hierarchical and this is not going to change anytime soon. The next step for us is to figure out how we apply and test some of the CAS principles within an environment that in many respects, contrasts markedly. So how can we apply an approach based on CAS principles to the application of CAS principles within a hierarchical environment?
Beer, C., & Lawson, C. (2016). The problem of student attrition in higher education: An alternative perspective. Journal of Further and Higher Education, 1-12. doi:10.1080/0309877X.2016.1177171
Boehm, B., & Turner, R. (2003). Using Risk to Balance Agile and Plan-Driven Methods. Computer, 36(6), 57.
Camillus, J. C. (2008). Strategy as a Wicked Problem. Harvard Business Review, 86(5), 98-106.
Davis, B., & Sumara, D. (2007). Complexity Science and Education: Reconceptualizing the Teacher’s Role in Learning. Interchange: A Quarterly Review of Education, 38(1), 53-67.
Davis, B., & Sumara, D. J. (2006). Complexity and education: Inquiries into learning, teaching, and research: Psychology Press.
Mason, M. (2008a). Complexity theory and the philosophy of education. Educational Philosophy and Theory, 40(1), 15. doi:10.1111/j.1469-5812.2007.00412.x
Mason, M. (2008b). What Is Complexity Theory and What Are Its Implications for Educational Change? Educational Philosophy and Theory, 40(1), 35-49.
Ritchey, T. (2002). Modelling complex socio-technical systems using morphological analysis. Adapted from an address to the Swedish Parliamentary IT Commission, Stockholm.
Weick, K. E. (2012). Making sense of the organization: Volume 2: The impermanent organization (Vol. 2): John Wiley & Sons.
Weick, K. E., & Quinn, R. E. (1999). Organizational change and development. Annual review of psychology, 50(1), 361-386.
Weick, K. E., Sutcliffe, K. M., & Obstfeld, D. (2005). Organizing and the process of sensemaking. Organization science, 16(4), 409-421.