Learning analytics

This blog post represents an exemplar for my students undertaking Learning in a Digital Age as part of their Graduate Certificate of Tertiary Education. It is based on the EASI system in place at CQUniversity and attempts to recreate/revisit our thinking when we saw an opportunity to adopt and integrate an emerging technology into our learning and teaching context.

Introduction to learning analytics
Associated with the almost universal adoption of digital technologies in higher education is their ability to store and track vast amounts of data on staff and student behaviour. The data captured by these digital systems can be analysed to improve decision making or to provide insight into the learning and teaching process. Learning analytics has been loosely defined as the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs (Gašević, Dawson, & Siemens, 2015). Learning analytics has been touted as a game-changer in higher education that can contribute to many areas within the academy.

There are two broad trajectories that universities tend to take with learning analytics. The use of learning analytics to help address student attrition and retention, and the use of learning analytics to contribute understanding to the learning and teaching process (Colvin et al., 2015). There is an argument to be made that learning analytics that contributes understanding to the learning and teaching process will also result in improved student attrition and retention. Despite this, there is evidence that suggests that these two trajectories are in fact diverging, despite their apparent complementary nature.

My context
As an educational developer attached to a central learning and teaching support department of a regional Australian university, my learning and teaching context is broader than is usual for a faculty academic. My role within a regional university that has high proportions of low SES and online students means that I am constantly on the lookout for new ways of helping my university retain more students. Learning analytics is recognised as an approach that can help with student attrition and retention by providing improved visibility over students who, with the advent of digital classrooms, have become less visible to their teachers when compared with face-to-face classrooms. The notion of the invisible student means that online students are not directly observable by their teachers and so alternative mechanisms are required to monitor student engagement in these online environments.

Evaluating learning analytics in this context?
There is extent literature and learning analytics projects aimed at addressing student attrition through the early identification of ‘at risk’ students and the facilitation of subsequent interventions (Liu, Rogers, & Pardo, 2015). However, the use of learning analytics for student attrition and retentions is not without criticism. Correlating variables in student behaviours with student success artificially creates an isolation from the real-world complexity of student life (Liu et al., 2015). It also promotes a deficit view of the student in that being ‘at risk’ suggests that there is ‘something wrong’ with the student, something that needs to be fixed (Liu et al., 2015; Macfadyen & Dawson, 2012). This raises questions of ethics and privacy around the intent behind the institutional use of student data (Prinsloo & Slade, 2015, 2017a, 2017b) such as might occur with learning analytics.

Like any evaluation of a new technology in any specific context, there are going to be pros and cons. In this case there is evidence that suggests that learning analytics can help universities with their student retention and many universities are investigating this approach. However, the research suggests that there has been a focus on the variables that contribute to student success or failure despite the absence of an established link between student success or failure and student attrition or retention. Student lives are simply too complex to categorise in such a manner. As such, I would suggest an evolutionary approach starting with the representation of student activity within the learning management system as a proxy indicator of student engagement. An approach that is less about the factors that contribute to student success and more about student activity.

Integration of the technology in my context
I believe that there is some potential for learning analytics to help unit coordinators better focus their attention in online classrooms. For example, there is anecdotal evidence that suggests that the engaged students demand and receive a disproportionate amount of attention from their unit coordinators. They are actively engaged in forums, seeking feedback on formative and summative activities and ask many questions of their teachers. With the increasing class sizes associated with online classrooms, this can mean that the less engaged and often lower achieving students can be overlooked or underserviced by their teachers.

My idea is to provide teaching staff with a view of their students’ activity in their Moodle sites. While there are not insignificant issues associated with clickstream information drawn from learning management systems, there appears to be an opportunity to take data that is already being collected and present it to teachers so as to highlight online students who may not be as engaged as others in their class. These students would otherwise be invisible to the busy and time-poor teacher who is struggling to keep abreast of the online forums and marking.

The following image is an example of the correlation between student activity on the Moodle learning management system and their resulting grade at CQUniversity. While this is a nice neat correlation it hides a great deal of the underpinning complexity and diversity across the student cohort.

Screen Shot 2017-08-22 at 11.18.05.png

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 http://www.olt.gov.au/project-student-retention-and-learning-analytics-snapshot-current-australian-practices-and-framework

Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends: Linking Research & Practice to Improve Learning, 59(1), 64-71. doi:10.1007/s11528-014-0822-x

Liu, D. Y.-T., Rogers, T., & Pardo, A. (2015). Learning analytics-are we at risk of missing the point. Paper presented at the Proceedings of the 32nd ascilite conference.
Macfadyen, L. P., & Dawson, S. (2012). Numbers Are Not Enough. Why e-Learning Analytics Failed to Inform an Institutional Strategic Plan. Journal of Educational Technology & Society, 15(3), 149-163.

Prinsloo, P., & Slade, S. (2015). Student privacy self-management: implications for learning analytics. Paper presented at the Fifth International Conference on Learning Analytics and Knowledge, Poughkeepsie, New York. Conference Publication retrieved from https://pdfs.semanticscholar.org/09d7/56d7a66f002f5c06b05237c3fc162b61a653.pdf

Prinsloo, P., & Slade, S. (2017a). Big Data, Higher Education and Learning Analytics: Beyond Justice, Towards an Ethics of Care Big Data and Learning Analytics in Higher Education (pp. 109-124): Springer International Publishing.

Prinsloo, P., & Slade, S. (2017b). An elephant in the learning analytics room: the obligation to act. Paper presented at the LAK’17 Proceedings of the Seventh International Learning Analytics & Knowledge Conference.




Relating to or denoting circumstances in which objective facts are less influential in shaping public opinion than appeals to emotion and personal belief”


I’m quite fascinated by this term that has been used in conjunction with politics and climate change. I find it fascinating and increasingly frustrating, that scientific evidence can be overcome through induced ignorance or doubt. Climate change denialism, homeopathy, big tobacco and GMO’s are all fields where ideology and self-interest drive efforts aimed at clouding or disputing scientific fact. Closer to home, I often find in conversations with colleagues and friends, that scientific proof or overwhelming evidence is not enough to shake what are essentially beliefs.

For example, I was talking to a person recently who firmly believes in the effectiveness of homeopathic remedies, despite overwhelming evidence that suggests otherwise. This person is very intelligent, very qualified and has experience in the health sciences, yet despite the evidence, they still believe that homeopathy treatments actually work. It never ceases to amaze me at just how irrational we humans really are, yet we all tend to believe that we can objectively evaluate without bias. This is especially interesting when you consider that these same flawed humans have created organisations like governments, corporations and universities; organisations that are obsessed with objectivity and quantitative measures of performance.

The trouble is, humans are not equipped to be truly objective, we have a blind spot when it comes to our own biases. Simply put, our view of the world is passed through our own cognitive filter and we not very good at processing and acting upon information received. I think this is worth thinking about as we try to employ learning analytics as a foundation for evidence-based learning and teaching.

How to take a complexity approach to attrition/retention

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.


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.

A little about nudges, cycles and the IRAC framework

One of the key characteristics of the EASI system is its ability to facilitate ‘nudges’. These nudges are small interventions conducted with students, students who are potentially struggling with their studies during the term. The nudge might take the form of an email to an individual or a mail-merge for a personalized message to multiple students. EASI allows academic staff to quickly identify students who might be struggling at any point during the term, and facilitates the execution of nudges so as to prompt students into re-engaging.


In a mechanical or simple system the response to a perturbation will generally be fairly easy to figure out as the results are determined by the perturbation. If a block of wood is nudged, knowledge of the conditions of the nudge (force, shape, mass, friction etcetera) is sufficient to both predict the result and explain the result (Davis & Sumara, 2006). The same is true for more complicated systems such as computers, mechanical and electrical systems. But such is not the case for complex systems. If you nudge a dog, the result will have nothing to do with Newtonian mechanics. The result in this case will be determined by the dog’s biological and experiential constitution. Humans are even more complex in this regard, as they have a broader repertoire of possible responses to the nudge (Davis & Sumara, 2006).

So the result arising from an action taken, an action based upon learning analytics provided information, is unpredictable. To me, this appears to suggest that a cyclical process is required for learning analytics. At least for learning analytics aimed at conducting interventions with ‘at risk’ students. There are some things to think about here with regards to IRAC framework. IRAC is a framework:

“that can be used to scaffold analysis of the complex array of, often competing, considerations associated with the institutional implementation of learning analytics” (Jones, Beer, & Clark, 2013).

The four components of the IRAC framework are:

  • Information – Is all the relevant and only the relevant information available?
  • Representation – Does the representation of the information aid the task being undertaken?
  • Affordances – Are their appropriate affordances for action?
  • Change – How will the information, its representation and affordances be changed or evolve?

One thing I think that we will need to explore further with regards to the IRAC framework is that it is a cycle. And I doubt that it is just a cycle with regards to the affordances part of the framework as the unpredictability of responses to nudges might indicate. The act of consuming analytics information even without any actions still has the potential to contribute to change in unpredictable ways. This has the potential to change the purpose or task that learning analytics was designed to address.



Davis, B., & Sumara, D. J. (2006). Complexity and education: Inquiries into learning, teaching, and research: Psychology Press.

Jones, D., Beer, C., & Clark, D. (2013). The IRAC framework: Locating the performance zone for learning analytics. Paper presented at the Electric Dreams., Sydney. http://www.ascilite.org.au/conferences/sydney13/program/papers/Jones.pdf

Learning analytics or crystal balls: Which one works best?



This post is a brief attempt at a “so what” from my previous post where I mentioned my growing skepticism about the predictive potential of learning analytics. It is also tying this post with some previous posts that I believe are relevant.

Broadly speaking, if we consider institutional learning and teaching environments as complex systems, or more correctly, complex adaptive systems, we change how these systems are managed. In linear systems cause and effect are evident and therefore, predicting the future states of these systems becomes possible. In complex systems, the agents are interacting with each other and the environment, which means the systems are in a constant state of evolution. It gets even more interesting when the individual human agents interacting within the system are known to have multiple identities and are temporally unpredictable. This is why prediction is almost impossible in complex systems; there are simply too many variables with unpredictable and disproportionate effects.

Much of the rhetoric around learning analytics is talking about its potential to use data about what has happened to predict what will happen. As I said in my previous post, this ignores the interactions and subsequent changes that occur after the prediction. This alone places real-world limits on the predictive potential of learning analytics, something that the commercial entities are unlikely to admit. Human beings feel threatened by uncertainty, which feeds our fears. Hence we strive to eliminate uncertainty by trying to predict the future so as to eliminate uncertainty.

“The study of the psychology of risk perception has found that one of the most powerful influences on fear is uncertainty.”

I have a number of concerns about learning analytics and predictive modeling. Firstly, our ability to use learning analytics for predictive modeling is inherently limited. Secondly, human nature compels us to try and reduce uncertainty by anticipating future states through prediction. Thirdly, commercial entities and consultants know about human nature and are playing to our fears by associating their products with an ability to make predictions. If their product X is so good at predictive modeling, why aren’t they making a killing on the share market?

All of this is a long way of saying that predictive modeling with learning analytics is interesting and potentially valuable but is not the only way that learning analytics can be applied. I live in fear of the one-off hegemonic approaches that organisations love to take. This comes back to my previous post on situation awareness whereby learning analytics has a role to play in better representing the present. A better map if you like, about what is the current state of the system and agents right now. This feeds into sense making where sense making is how we develop an understanding of what we are sensing to that we can take action. To me, this is where learning analytics can really make a difference with an increasingly complex higher education landscape. It’s also a lot easier than having to learn all those complicated statistics.

Learning analytics. Predictions? Understanding?

The following post is airing something that I am thinking about with regards to my PHD. It is some early thinking, so apologies in advance if it turns out to be nonsense.

Something that has concerned me for a while with regards to learning analytics, is the use of terms like ‘prediction’ and ‘understanding’. For example:

“Research in learning analytics and its closely related field of education data mining, has demonstrated much potential for understanding and optimizing the learning process”
(Siemens & Baker, 2012)

The Society for Learning Analytics Research defines Learning Analytics as: “… the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs”
(Siemens & Baker, 2012).

“… learning analytics (LA) offers the capacity to investigate the rising tide of learner data with the goal of understanding the activities and behaviors associated with effective learning”
(Macfadyen, Dawson, Pardo, & Gasevic, 2014)

“The intention is to develop models, algorithms, and processes that can be widely used. Transferability is a key factor here; analytic and predictive models need to be reliable and valid at a scale beyond the individual course or cohort”
(Ferguson et al., 2014)

“Learning analytics can penetrate the fog of uncertainty around how to allocate resources, develop competitive advantages, and most important, improve the quality and value of the learning experience” (Siemens & Long, 2011)

“Learning analytics is a technology on the rise in higher education. Adapted from business analytics, this approach is being used to track and predict student engagement and success in higher education.” (Lodge, 2011)

“There is evidence to suggest that learning analytics can be successfully used to predict students at risk of failing or withdrawing and allow for the provision of just in time intervention.”
(Lodge, 2011)

“While the use of learning analytics to track and predict student success in higher education is rapidly becoming mainstream practice in higher education institutions, it is predominantly being used to predict and prevent student attrition.”
(Lodge & Lewis, 2012)

“Sophisticated systems might even recommend learning activities, predict a student’s success or give advice”
(Dyckhoff, Lukarov, Muslim, Chatti, & Schroeder, 2013)

“… much of the early work has focused on the statistical prediction of outcomes, chiefly grades or retention, by relating these target variables to predictor variables harvested from students’ demographic and institutional variables, and their interaction with the LMS”
(Rogers, 2015)

These quotes are from a two-minute search through my Endnote library looking for terms like ‘prediction’ or ‘understanding’. There are a lot more but you get the point. The trouble is that I am becoming increasingly skeptical about that ability of learning analytics to contribute to prediction or even understanding. The following is an attempt to explain this skepticism based on my thinking, where learning analytics is data or information arising from interactions occurring within and between complex adaptive systems (Beer, Jones, & Clark, 2012).

Broadly speaking, there are assumptions inherent in terms like ‘prediction’ and ‘understanding’. They both, to some extent, assume that certainty and full knowledge can be attained, and that the agents and systems involved are fixed and will not evolve (Allen & Boulton, 2011). Likewise, prediction is based on a snapshot in time and cannot capture the impact of interactions between the agents and systems after the snapshot is taken. The snapshot is essentially only a view of something that is in transition (Allen & Boulton, 2011). There are also assumptions related to the stability, immovability or changelessness of:

  • The initial system’s starting state
  • The mechanisms that link the variables
  • The internal responses inside each agent or element
  • The system’s environment

The only way that prediction becomes possible is when the system or agent is isolated from external influences; something that can only ever occur in laboratory conditions (Allen & Boulton, 2011). Uncertainty is always present when we are talking about complex systems. The only way we can banish uncertainty is by making assumptions; something I am not sure is possible when we are talking about systems with many agents interacting in non-linear ways. For learning analytics, the power of prediction implicit in deterministic models can only be realised if the assumptions made in their creation are true (Allen & Boulton, 2011). My sense is that there are many people looking closely at the predictive potential of learning analytics, myself included. I am beginning to question why, especially when prediction, control and complete understanding are always an illusion, except in exceptional, controlled, closed and fixed situations (Allen & Boulton, 2011).

I am speculating, but I wonder if the predictive potential we perceive might be in learning analytics, how much of it is because we have constrained the system. For example, most universities have a single learning management system, a single student information system, some form of homogenized approach to course design, delivery so on and so forth. Have we suppressed the evolutionary potential to such an extent that we have created an environment that has made prediction and understanding possible?

My quick scan of the Endnote library also revealed a couple of articles that fit with some of the things I have mentioned here. The following from Doug Clow:

“Learning analytics is widely seen as entailing a feedback loop, where ‘actionable intelligence’ is produced from data about leaners and their contexts, and interventions are made with the aim of improving learning”
(Clow, 2014)

I intend to further explore this paper for a couple of reasons. It seems to align nicely with my thinking around the applicability of situation awareness and there also appears to be a, albeit limited, attempt at distributed cognition with regards to operationalization of learning analytics. And the following from Jason Lodge has some real gems that I need to look at more closely than I have to date:

“LA is unable to elucidate the student approach to learning, relationships between apparent levels of engagement online and overall student experiences, and is therefore limited as a measure of the process and pathways students may undertake to complete their learning, let alone for higher cognitive processes or ways of being” (Lodge & Lewis, 2012)


Allen, P., & Boulton, J. (2011). Complexity and limits to knowledge: The importance of uncertainty. The SAGE handbook of complexity and management, 164-181.

Beer, C., Jones, D., & Clark, D. (2012). Analytics and complexity: Learning and leading for the future. Paper presented at the ASCILITE2012 Future challenges, sustainable futures, Wellingtone, New Zealand.

Clow, D. (2014). Data wranglers: human interpreters to help close the feedback loop. Paper presented at the Proceedings of the Fourth International Conference on Learning Analytics And Knowledge.

Dyckhoff, A. L., Lukarov, V., Muslim, A., Chatti, M. A., & Schroeder, U. (2013). Supporting action research with learning analytics. Paper presented at the Proceedings of the Third International Conference on Learning Analytics and Knowledge.

Ferguson, R., Clow, D., Macfadyen, L., Essa, A., Dawson, S., & Alexander, S. (2014). Setting learning analytics in context: overcoming the barriers to large-scale adoption. Paper presented at the Proceedings of the Fourth International Conference on Learning Analytics And Knowledge.

Lodge, J. (2011). What if student attrition was treated like an illness? An epidemiological model for learning analytics. Paper presented at the ASCILITE – Australian Society for Computers in Learning in Tertiary Education Annual Conference 2011. http://www.editlib.org/p/43627

Lodge, J., & Lewis, M. (2012). Pigeon pecks and mouse clicks: Putting the learning back into learning analytics

. Paper presented at the ASCILITE 2012,, Wellington.

Macfadyen, L. P., Dawson, S., Pardo, A., & Gasevic, D. (2014). Big Data in Complex Educational Systems: The Learning Analytics Imperative and the Policy Challenge. Research & Practice in Assessment, 9(Winter, 2014), 11. Retrieved from http://go.galegroup.com/ps/i.do?action=interpret&v=2.1&u=cqu&it=JIourl&issn=2161-4210&authCount=1&p=AONE&sw=w&selfRedirect=true

Rogers, T. (2015). Critical realism and learning analytics research: epistemological implications of an ontological foundation. Paper presented at the Proceedings of the Fifth International Conference on Learning Analytics And Knowledge, Poughkeepsie, New York.

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

Siemens, G., & Long, P. (2011). Penetrating the Fog: Analytics in Learning and Education. Educause Review, 46(5), 9. Retrieved from http://www.educause.edu/ero/article/penetrating-fog-analytics-learning-and-education