An interesting paper from Dragan Gašević & colleagues presented at LAK15. This quantitative study looked at SRL behaviours – complementary to our qualitative approach.
Beheshitha, S.S., Gašević, D., & Hatala, M. (2015) A process mining approach to linking the study of aptitude and event facets of self-regulated learning. Proceedings of the Fifth International Conference on Learning Analytics and Knowledge, 16-20 March, 2015, Poughkeepsie, NY, USA http://dx.doi.org/10.1145/2723576.2723628
SRL can be thought of as aptitudes (observed differences in individuals – commonly measured by self-report) or events (the actions learners execute). We can analyse trace data and associate srl events with sequences/combinations of actions in online environments then relate these to SRL aptitudes (self-report). RQ1 Can we identify groups of learners with different aptitudes (looked specifically at deep/surface learning, and goal orientation). RQ2 Do these groups behave differently? Design: Filled in questionnaires, then used nStudy (tasks monitored included: bookmark and organise resources, highlight and quote key points, take notes, define terms, write report. These were then classified as rehearsal, elaboration, and organisation). N=20. Cluster analysis allowed classification of learners as deep or surface, but was not able to identify different types of goal orientation. Comparing surface and deep learners, different patterns were seen – deep learners chose a strategy and stuck to it, and their strategies were more focused on elaboration. Surface learners used more organisation, and were more likely to adopt different strategies.
Interesting complement to our PL-MOOC work, which also seeks to measure (self-reported) aptitude, then link it to SRL behaviours though in our case behaviours are also self-report and collected via interview (as opposed to mining of trace data). Very preliminary results, but shows promise as a method, and lit. review provides pointers to some interesting recent SRL research.
Hadwin, A.F., Nesbit, J.C., Jamieson-Noel, D., Code, J. and Winne, P. H. (2007) Examining trace data to explore self-regulated learning,” Metacognition & Learning, 2, (2–3), 107–124.
Bannert, M. Reimann, P., and Sonnenberg, C. (2014) Process mining techniques for analysing patterns and strategies in students’ self-regulated learning,” Metacognition & Learning, 9, 161–185.