This post is some thinking around my PhD resulting from some conversations and presentations from this year’s wonderful ALASI2018 conference held recently in Melbourne.
In a recent post, I mentioned that technology or solutions to problems do not just spring into existence. They emerge from a complex network of interactions between people and technology (Hannon, 2013; Introna, 1996, 2007). This is especially true for learning analytics which aims to help people better understand some other people and their learning environments, using some technology, and with what is at best, incomplete data. So learning analytics does not just spring into existence, but results from cycles of interaction between people, technology (data), and educational objects (objects being units, assessments, students, staff, problems etc). A real example might help explain this better:
Some colleagues and I have been researching and tinkering with learning analytics since 2007. We noted that the information systems available to our teaching staff were woefully inadequate at providing the information and affordances that these staff needed, when they needed it, during their day-to-day activities. Our university has high proportions of online, low-socioeconomic and first-in-family students, so our initial focus around this information and affordance deficit was how do we provide teachers with an evidence-informed approach to interacting with their students? We also wanted to make it much easier for teachers to access engagement and performance information about their students, during the term when it could be acted upon. This lead to a series of research-development-feedback cycles that resulted in the EASICONNECT system back in 2014.
The EASI system is a basic risk/intervention type of learning analytics that allows academic staff to view their students’ activity and performance based around an indicator of success for each student. Staff can “nudge” (mailmerge) their students from the same web page, a facility that has proven to be very popular with teaching staff. To date, 1,077,732 EASI nudges have been delivered by teachers to 88.9% of all our HE students. While only 63% of units use EASI, and only 40% use it to nudge their students, this 40% represents 88.9% of all our students (perhaps pointing to EASI’s utility for teachers with larger undergraduate classes). We also noted a significant increase in activity (on average) by students who were sent an engagement nudge. EASI was not developed by a vendor, but was developed locally using an incremental, user-centered approach.
However, the point is that EASI did not just spring into existence back in 2014. It was the result of a whole range of interactions and adaptations prior to the formal implementation. Prior to, and during EASI’s implementation, the developers were conducting what I’d retrospectively suggest were cycles of interaction between people, technology and an education problem as per the following diagram.
Figure 1. Interaction Cycles
One of the workshops I attended at ALASI2018 was talking about complexity leadership and how there exists a tension between administrative/mainstream systems, processes and mindsets, and adaptive systems, process and mindsets. Innovation and problem solving (adaptive) in organisations always develops a tension with established, mainstream ways of doing things, and this is an area where leadership is often lacking. The following diagram builds upon what was shown at the conference.
Figure 2. Adapted from presentation slides by University of South Australia
The key point here is that there is an area of tension when these small scale innovations or solutions try to scale into the mainstream “business as usual” environment. This is where Siemens, Dawson & Eshleman (2018) say that leadership is required and complexity leadership provides a model that can help. Knowing that this tension exists and understanding its drivers, can potentially help us develop processes and policies that cater for this inevitable tension, and allows us to move forward with learning analytics. I think this is a vitally important concept for learning analytics where one-size-fits-all and single lens approaches simply don’t work. It also fits with our experience with EASI in this area of tension between bespoke, evidence-informed approaches to learning analytics and the orthodox preference for commercial-off-the-shelf, vendor-supplied solutions.
The above diagram also helps articulate the continuum between complicated and complex (Snowden & Boone, 2007). The left hand side is the domain of management where managers strive for predictability and order, while risk, change, information flow and diversity is shunned. The right hand side is the domain of leadership where leaders are comfortable with change, acceptable of failure, and strive to increase diversity and information flow (Freeburg, 2018). I would suggest that while organisations generally need both sides of this diagram, the left side can often abrogate the right. In an era of cascading complexity and change, the adaptations required for prosperity or even survival, will most likely come from the right side of the diagram (in my opinion).
From the perspective of learning analytics, I believe this diagram helps explain why learning analytics research is accelerating while learning analytics practice slowly pushes through this area of tension. From the perspective of my PhD, how does the meso-level practitioner, operating on the right hand side, help the “coal-face” teachers, navigate or work around this middle area of tension and conflict?
Freeburg, D. (2018). Leadership and innovation within a complex adaptive system: Public libraries. Journal of Librarianship and Information Science, 0(0), 0961000618810367. doi:10.1177/0961000618810367
Hannon, J. (2013). Incommensurate practices: Sociomaterial entanglements of learning technology implementation. Journal of Computer Assisted Learning, 29, 168-178.
Introna, L. D. (1996). Notes on ateleological information systems development. Information Technology & People, 9(4), 20-39.
Introna, L. D. (2007). Maintaining the reversibility of foldings: Making the ethics (politics) of information technology visible. Ethics and Information Technology, 9(1), 11-25.
Siemens, G., Dawson, S., & Eshleman, K. (2018). Complexity: A leader’s framework for understanding and managing change in higher education. Educause Review, 53(6), 27 – 42. Retrieved from Educause Review website:
Snowden, D., & Boone, M. E. (2007). A Leader’s Framework for Decision Making. Harvard Business Review, 85(11), 68-76