Learning Factor Models of Students at Risk of Failing in the Early Stage of Tertiary Education

Gray, Geraldine, McGuinness, Colm, Owende, Philip and Hofmann, Markus (2016) Learning Factor Models of Students at Risk of Failing in the Early Stage of Tertiary Education. Journal of Learning Analytics, 3 (2). pp. 330-372.


This paper reports on a study to predict students at risk of failing based on data available prior to commencement of first year. The study was conducted over three years, 2010 to 2012, on a student population from a range of academic disciplines, n=1,207. Data was gathered from both student enrollment data and an online, self-reporting, learner-profiling tool administered during first-year student induction. Factors considered included prior academic performance, personality, motivation, self-regulation, learning approaches, age, and gender. Models were trained on data from the 2010 and 2011 student cohort, and tested on data from the 2012 student cohort. A comparison of eight classification algorithms found k-NN achieved best model accuracy (72%), but results from other models were similar, including ensembles (71%), support vector machine (70%), and a decision tree (70%). However, improvements in model accuracy attributable to non-cognitive factors were not significant. Models of subgroups by age and discipline achieved higher accuracies, but were affected by sample size; n<900 underrepresented patterns in the dataset. Factors most predictive of academic performance in first year of study at tertiary education included age, prior academic performance, and self-efficacy. Early modelling of first-year students yielded informative, generalizable models that identified students at risk of failing.

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