Learning analytics at “small” scale: Exploring a complexity-grounded model for assessment automation

This study proposes a process-oriented,automatic,formative assessment model for small group learning based on complex systems theory using a small dataset from a technology-mediated,synchronous mathematics learning environment. We first conceptualize small group learning as a complex system and explain how group dynamics and interaction can be modeled via theoretically grounded,simple rules. These rules are then operationalized to build temporally-embodied measures,where varying weights are assigned to the same measures according to their significance during different time stages based on the golden ratio concept. This theory-based measure construction method in combination with a correlation-based feature subset selection algorithm reduces data dimensionality,making a complex system more understandable for people. Further,because the discipline of education often generates small datasets,a Tree-Augmented Naïve Bayes classifier was coded to develop an assessment model,which achieves the highest accuracy (95.8%) as compared to baseline models. Finally,we describe a web-based tool that visualizes time-series activities,assesses small group learning automatically,and also offers actionable intelligence for teachers to provide real-time support and intervention to students. The fundamental contribution of this paper is that it makes complex,small group behavior visible to teachers in a learning context quickly. Theoretical and methodological implications for technology mediated small group learning and learning analytics as a whole are then discussed.