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• W2261096117 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 Naive 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" @default.
• W2261096117 created "2016-06-24" @default.
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• W2261096117 date "2015-01-01" @default.
• W2261096117 modified "2023-09-23" @default.
• W2261096117 title "Learning Analytics at Small Scale: Exploring a Complexity-Grounded Model for Assessment Automation" @default.
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• W2261096117 doi "https://doi.org/10.3217/jucs-021-01-0066" @default.
• W2261096117 hasPublicationYear "2015" @default.
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