Learning analytics and the cascade of complexity

I recently read an interesting paper by Ruth Deakin Crick titled “Deep Engagement as a Complex System: Identify, Learning Power and Authentic Enquiry”. There are some elements of this paper that resonate with me with regards to my PHD and have particular relevance to the learning analytics field.

The paper is about student engagement and how it is best understood as a complex system that includes “a range of interrelated factors internal and external to the learner, in place and in time, which shape his or her engagement with learning opportunities” (Crick, 2012). This is something that I have been mulling for a while with regards to PHD, which includes elements of self-regulated learning (SRL) and student engagement as part of the design based research (DBR) cycle.

SRL is a metacognitive process where self-regulated learners plan, set goals, organize, self-monitor and self-evaluate at various points in the learning process (Zimmerman, 1990). SRL provides a framework by which student meta-cognitive processes can be assessed, knowing that high achieving students are more likely to employ systematic meta-cognitive, motivational and behavioral strategies (Zimmerman, 1990). Likewise, student engagement is well recognized within the research literature as being critical to student retention and success (Krause & Coates, 2008; Tinto, 1999; Urwin et al., 2010). A broad definition of student engagement describes a combination of time-on-task and their quality of effort that students devote to educationally purposeful activities (Krause & Coates, 2008; Stovall, 2003).

Both SRL and student engage are encapsulated within a cascade of contexts, which the Deakin Crick paper describes nicely. A student’s meta-cognitive processes about their learning, their engagement and their environments interact in unpredictable ways. For example, the paper suggests that the student’s personal context, which includes engagement and SRL, are part of their personal context, which is part of their social context, which is part of the global context (Crick, 2012). While I think this nicely highlights the cascade of contexts and portrays some of the complexity involved with student engagement, I also think it is difficult, perhaps impossible, to represent the complex array of factors that contribute to student success, or otherwise.

So what does this mean for learning analytics?

The important point in my mind is that it is not possible to capture all of the factors or variables that impact upon student learning. So no matter how much data we collect and analyse, we can never construct the full picture at any particular place or point in time. I cannot help wondering if:
a. we are assigning too much value on what is a very very narrow window on the world?
b. we are over-analyzing the data we currently collect?
c. we are overestimating the ability of inherently limited data to contribute to improved student learning outcomes?


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. Retrieved from

Crick, R. D. (2012). Deep engagement as a complex system: Identity, learning power and authentic enquiry Handbook of research on student engagement (pp. 675-694): Springer.

Krause, K.-L., & Coates, H. (2008). Students’ engagement in first-year university. Assessment & Evaluation in Higher Education, 33(5), 493 – 505. doi:10.1080/02602930701698892

Siemens, G., & Baker, R. S. J. d. (2012). Learning analytics and educational data mining: towards communication and collaboration. Paper presented at the Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, Vancouver, British Columbia, Canada. http://delivery.acm.org/10.1145/2340000/2330661/p252-siemens.pdf?ip= SERVICE&CFID=145100934&CFTOKEN=10069569&__acm__=1345679404_18f65a315d7b4ba9014a8f150ad6189c

Stovall, I. (2003). Engagement and Online Learning. UIS Community of Practice for E-Learning. Retrieved from http://otel.uis.edu/copel/EngagementandOnlineLearning.ppt

Tinto, V. (1999). Taking Student Retention Seriously: Rethinking the First Year of College. NACADA Journal, 19(2), 5-9.

Urwin, S., Stanley, R., Jones, M., Gallagher, A., Wainwright, P., & Perkins, A. (2010). Understanding student nurse attrition: Learning from the literature. Nurse Education Today, 30(2), 202-207.

Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational Psychologist, 25(1), 3-17.


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