Predicting Student Success with Learning Analytics on Big Data Sets: Conditioning and Behavioural Factors

Wilde, Adriana (2014) Predicting Student Success with Learning Analytics on Big Data Sets: Conditioning and Behavioural Factors. University of Southampton, Masters Thesis.

Record type: Thesis (Masters)

Abstract

Advances in computing technologies have a profound impact in many areas of human concern, especially in education. Teaching and learning are undergoing a (digital) revolution , not only by changing the media and methods of delivery but by facilitating a conceptual shift from traditional face-to-face instruction towards a learner-centered paradigm with delivery increasingly becoming tailored to student needs. Educational institutions of the immediate future have the potential to predict (and even facilitate) student success by applying learning analytics techniques on the large amount of data they hold about their learners, which include a number of indicators that measure both the conditioning (under which students are subjected) and the behavioural factors (what students do) influencing whether a given student will be successful. More than ever before, key information about successful student habits and learning context can be discovered. Our hypothesis is that collective data can be used to construct a model of success for Higher Education students, which then can be used to identify students at risk. This is a complex issue which is receiving increased attention amongst e-learning communities (of which Massive Open Online Courses are an example), and administrators of learning management system alike. Smartphones, as sensor-rich, ubiquitous devices, are expected to become an important source of such data in the imminent future, increasing significantly the complexity of the problem of devising an accurate predictive model of success. This interim thesis presents the relevant issues in predicting student success using learning analytics approaches by incorporating both conditioning and behavioural factors with the ultimate goal of informing behavioural change interventions in the context of learning in Higher Education. It then discusses our work to date and concludes with a workplan to generate publishable results.

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