@inproceedings{af4cac21c9924ffe8c6bc8bfdd7a9e48,
title = "An investigation of psychometric measures for modelling academic performance in tertiary education",
abstract = "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.",
keywords = "Ability, Academic performance, Educational data mining, Learning style, Motivation, Personality, Self-regulated learning",
author = "Geraldine Gray and Colm McGuinness and Philip Owende",
note = "Publisher Copyright: {\textcopyright} 2013 International Educational Data Mining Society. All rights reserved.; 6th International Conference on Educational Data Mining, EDM 2013 ; Conference date: 06-07-2013 Through 09-07-2013",
year = "2013",
language = "English",
series = "Proceedings of the 6th International Conference on Educational Data Mining, EDM 2013",
publisher = "International Educational Data Mining Society",
editor = "D'Mello, \{Sidney K.\} and Calvo, \{Rafael A.\} and Andrew Olney",
booktitle = "Proceedings of the 6th International Conference on Educational Data Mining, EDM 2013",
}