Learning Analytics at "Small" Scale: Exploring a Complexity-Grounded Model for Assessment Automation

Sean Goggins (University of Missouri, USA)

Wanli Xing (University of Missouri, USA)

Xin Chen (Purdue University, USA)

Bodong Chen (University of Minnesota, USA)

Bob Wadholm (University of Missouri, USA)

Abstract: 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.

Keywords: assessment, complex systems, learning analytics, small group learning

Categories: E.0, L.0.0, L.1.1, L.3.6, L.6.2