An Investigation of Psychometric Measures for Modelling Academic Performance in Tertiary Education

Increasing college participation rates, and a more diverse student population, is posing a challenge for colleges in facilitating all learners achieve their potential. This paper reports on a study to investigate the usefulness of data mining techniques in the analysis of factors deemed to be significant to academic performance in first year of college. Measures used include data typically available to colleges at the start of first year such as age, gender and prior academic performance. The study also explores the usefulness of additional psychometric measures that can be assessed early in semester one, specifically, measures of personality, motivation and learning strategies. A variety of data mining models are compared to assess the relative accuracy of each.