This paper reports on a study to predict students at risk of failing based on data available prior to commencement of first year of study. 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 enrolment data maintained by college administration, 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%). Models of subgroups by age and discipline achieved higher accuracies, but were affected by sample size; n<900 underrepresented patterns in the dataset. Results showed that factors most predictive of academic performance in first year of study at tertiary education included age, prior academic performance and self-efficacy. This study indicated that early modelling of first year students yielded informative, generalisable models that identified students at risk of failing.
Developing a Model for Identifying Students at Risk of Failure in a First Year Accounting Unit