Learning Analytics and Leading Activity (#LAK12)

I have been pondering the last couple of weeks what type of data or patterns might one look for from learning analytics. There are lots of different projects I have seen or been involved in that are emerging:

  • Retention focused – identifying learners at-risk of drop-out from the course;
  • Performance focused – predicting final exam success;
  • Activity focused – quantitative views of activity;
  • Course focused – usually linked to a bench-marking of staff performance;
  • Engagement focused – what types of things are people doing;

A further idea emerged for me when reading the NY Times article:  ‘How Companies Learn Your Secrets‘. The article details how supermarkets might gather information about you. The main goal for shopper analytics is to identify approaching periods when consumer’s patterns are subject to change. For example the number one period for this is when a new baby is born, or in marketing terms when parents are ‘exhausted, overwhelmed and their shopping patterns and brand loyalties are up for grabs’.  The approaching aspect is crucial here as the earlier an intervention occurs the more likely it is to beat other interventions, or in other words they more likely they are to switch to your store.

A similar model emerges in Vygotksy’s (1978) concept of the Zone of Proximal Development.  Vygotsky proposes that for effective pedagogical interventions one must calculate at least two development levels: actual development – that which the learner can achieve unaided (e.g. tests) and potential development – that which the learner can achieve with support. The zone of proximal development is the difference between the two. Vygotsky argues that ‘by using this method we can take account of not only the cycles and maturation processes that have already been completed but also those processes that are currently in a state of formation, that are just beginning to mature and develop‘ (pg. 87).

The zone of proximal development refers to the maturing functions that are relevant for the next development period, and Chaiklin (2003) further clarifies that social interaction does not create these functions but provides conditions for identifying their existence and the extent they are developed. Interaction and social network analysis alongside behavioural psychology may offer insights while prompting some further thought on the design of interactions.

Vygotsky further suggests this requires a re-examination of formal subject disciplines and their relation to overall development. For Vygotsky this cannot be solved in a single formula and diverse research around the zone of proximal development is required. Fortunately learner analytics isn’t about a single formula either (despite its philosopher stone appeal) – semantically linked data allows diverse sets to be explored and may be directed at revealing the ‘ripeness’ of maturing functions. It seems such an approach to learning analytics (one focused on development) may require new approaches to designing learning environments, in which analytics are an integral tool rather than a retrospective analysis of existing data.

Readings

Chaiklin, S. (2003). The Zone of Proximal Development in Vygotsky’s Analysis of Learning and Instruction. In A. Kozulin, B. Gindis, V. S. Ageyev, & S. M. Miller (Eds.), Vygotsky’s Educational Theory in Cultural Context (pp. 39-64). Cambridge: Cambridge University Press.

Vygotsky, L. (1978). Mind in Society. Cambridge, MA and London: Harvard University Press.

Educational Data Mining Taxonomy (#LAK12)

Learning Analytics – Week 1

For Baker and Yacef (2009) educational data is recognised to be different from other data sets due to multi-level hierarchy and non-independence. In their brief introduction to the state of Educational Data Mining (EDM), this rapidly growing research field originally emerged from exploration of student-computer interactions but have diversified into a broad spectrum of activities. The following taxonomy is presented as a summary of key activities in the field.

  1. Prediction
    • Classification
    • Regression
    • Density estimation
  2. Clustering
  3. Relationship mining
    • Association rule mining
    • Correlation mining
    • Sequential pattern mining
    • Causal data mining
  4. Distillation of data for human judgment
  5. Discovery with models

While items 1-4 are common to classical data mining the last item is unusal and has gained significant prominence within EDM. Item Response Theory, Bayes Nets, and Markov Decision Processes enter the field increasingly as psychometrics and student models merge into EDM.

Not being overly familiar with classical data mining I am drawn to learning analytics from Activity Theory, in particular Engestrom’s expansive research cycles. I am interested in data mining as a way to analyse activity to form new instruments for understanding the activity. My initial impression is that 1-3 would form part of the analysis, 4 would be instrument formation and 5 their application. My interest is how one can feed this back into the student learning experience which seems to fit nicely with the key applications identified by Baker and Yacef:
  1. Improvement of student models, such as the student’s current knowledge, motivation, meta-cognition, and attitudes;
  2. Improving models of a domain’s knowledge structure;
  3. Discovering which types of pedagogical support are most effective, either overall or for different groups of students or in different situations;
  4. Looking for empirical evidence to refine and extend educational theories and well-known educational phenomena.

Readings

Baker, R. S. J. D., & Yacef, K. (2009). The State of Educational Data Mining in 2009 : A Review and Future Visions. JEDM – Journal of Educational Data Mining, 1(1). Retrieved from http://www.educationaldatamining.org/JEDM/index.php?option=com_content&view=category&layout=blog&id=36&Itemid=55

Engeström, Y. (1987). Learning by expanding: An activity-theoretical approach to developmental research. Helsinki: Orienta-Konsultit. Retrieved from http://communication.ucsd.edu/MCA/Paper/Engestrom/expanding/toc.htm