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.
- Density estimation
- Relationship mining
- Association rule mining
- Correlation mining
- Sequential pattern mining
- Causal data mining
- Distillation of data for human judgment
- 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.
- Improvement of student models, such as the student’s current knowledge, motivation, meta-cognition, and attitudes;
- Improving models of a domain’s knowledge structure;
- Discovering which types of pedagogical support are most effective, either overall or for different groups of students or in different situations;
- Looking for empirical evidence to refine and extend educational theories and well-known educational phenomena.
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