Stability and sensitivity of Learning Analytics based prediction models

Tempelaar, D. T.; Rienties, B. and Giesbers, B. (2015). Stability and sensitivity of Learning Analytics based prediction models. In: Proceedings of 7th International conference on Computer Supported Education (Helfert, Markus; Restivo, Maria Teresa; Zvacek, Susan and Uho, James eds.), 23-25 May 2015, Lisbon, Portugal, CSEDU, pp. 156–166.



Learning analytics seek to enhance the learning processes through systematic measurements of learning related data and to provide informative feedback to learners and educators. Track data from Learning Management Systems (LMS) constitute a main data source for learning analytics. This empirical contribution provides an application of Buckingham Shum and Deakin Crick’s theoretical framework of dispositional learning analytics: an infrastructure that combines learning dispositions data with data extracted from computer-assisted, formative assessments and LMSs. In two cohorts of a large introductory quantitative methods module, 2049 students were enrolled in a module based on principles of blended learning, combining face-to-face Problem-Based Learning sessions with e-tutorials. We investigated the predictive power of learning dispositions, outcomes of continuous formative assessments and other system generated data in modelling student performance and their potential to generate informative feedback. Using a dynamic, longitudinal perspective, computer-assisted formative assessments seem to be the best predictor for detecting underperforming students and academic performance, while basic LMS data did not substantially predict learning. If timely feedback is crucial, both use-intensity related track data from e-tutorial systems, and learning dispositions, are valuable sources for feedback generation.

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