Learning analytics seeks to enhance the learning process through systematic measurements of learning related data and to provide informative feedback to learners and teachers, so as to support the regulation of the learning. Track data from technology enhanced learning systems constitute the 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 [1]: an infrastructure that combines learning dispositions data with data extracted from computer assisted, formative assessments. In a large introductory quantitative methods module based on the principles of blended learning, combining face-to-face problem-based learning sessions with e-tutorials, we investigate the predictive power of learning dispositions, outcomes of continuous formative assessments and other system generated data in modeling 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.

blended learning, computer assisted assessment, dispositional learning analytics, e-tutorials, formative assessment, learning dispositions, student profiles
dx.doi.org/10.1007/978-3-319-08657-6_7, hdl.handle.net/1765/91453
Rotterdam School of Management (RSM), Erasmus University

Tempelaar, D.T, Rienties, B, & Giesbers, B. (2014). Computer Assisted, Formative Assessment and Dispositional Learning Analytics in Learning Mathematics and Statistics. doi:10.1007/978-3-319-08657-6_7