Over the last six months I’ve been writing about student attrition and retention with a colleague from work. We’ve submitted a couple of journal articles that are currently in review about how universities continue to misinterpret the nature of their student attrition issue. To cut a long story short, we argue that attrition is a wicked problem, a problem in complexity. Climate change, deforestation and geopolitical conflicts are examples of wicked problems where there are no single solutions or even any obvious paths towards solution. Conceptualizing student attrition as a wicked problem occurring within non-linear, complex systems changes how we approach these types of issues. As an aside, even classifying attrition as a problem (or issue) the wrong way to think about it. It’s more like a symptom of a network of problems, a network where we can’t possible know what or where most of the nodes are.
While writing these papers I saw some similarities between how universities are approaching student attrition and how they are approach learning analytics adoption. In both cases they have mis-specified the nature of the organization. Their approaches are based on assumptions that the organisations are machine-like.
“Managers want workers to respond predictably to incentives and to accomplish goals defined by managers and to do this with little deviation from plans that management has developed to improve performance”(McDaniel, 2007)
The machine like model of organizations is associated with management approaches based on command, control and planning (McDaniel, 2007). This is a valid approach for managing in a linear, stable environment where future states can be anticipated. In fact these approaches depend on the ability of managers and workers to forecast future system states (McDaniel, 2007). However, if we view organisations and the environments in which they operate as complex adaptive systems, machine-model management no longer works. It is simply not possible to predict future states when the systems are made up of agents that are information processors with the capacity to modify their behavior based on information they receive (J. Holland, 2006; J. H. Holland, 1995). An important contrast between viewing an organization as a machine or as a complex adaptive system is the diversity of the agents within the system. Complex adaptive systems encourage diversity whereas the machine model tends to favor agent homogenization (McDaniel, 2007).
“Participation of clinicians in hospital strategic decision making is more helpful in terms of bottom line performance than the participation of middle managers”
(Ashmos, Duchon, McDaniel Jr, & Huonker, 2002)
“If we want workers to be able to improve performance in the face of unknowability, we must invest in efforts to help them make sense of the world in a way that enables the organisation to take action and to learn about the world from the actions that are taken”
So we appear have a mis-interpretation of the actual nature of organisations and the environments in which they operate. Organisations that are composed of information processing agents that change and adapt with the information they receive. And the rapid spread of hype around learning analytics, fueled by commercial entities, aimed at assisting decision-makers at many different levels of the academy. While I believe that learning analytics has enormous potential, I can’t help wondering if the fundamentally misunderstood nature of organisations is going to be its greatest limiting factor, especially when we are talking about information/action cycles.
Ashmos, D. P., Duchon, D., McDaniel Jr, R. R., & Huonker, J. W. (2002). What a mess! Participation as a simple managerial rule to ‘complexify’organizations. Journal of Management studies, 39(2), 189-206.
Holland, J. (2006). Studying Complex Adaptive Systems. Journal of Systems Science and Complexity, 19(1), 1-8. doi:10.1007/s11424-006-0001-z
Holland, J. H. (1995). Hidden order : how adaptation builds complexity / John H. Holland: Reading, Mass. : Addison-Wesley, c1995.
McDaniel, R. R., Jr. (2007). Management Strategies for Complex Adaptive Systems: Sensemaking, Learning, and Improvisation. Performance Improvement Quarterly, 20(2), 21-41. Retrieved from http://onlinelibrary.wiley.com/store/10.1111/j.1937-8327.2007.tb00438.x/asset/j.1937-8327.2007.tb00438.x.pdf?v=1&t=h672itdt&s=a7c341c21237351ad995bdb34074c98db94bb026