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.

[1]  James C. Kaufman,et al.  The role of personality and motivation in predicting early college academic success in non-traditional students at a Hispanic-serving institution , 2008 .

[2]  Kristy J. Lauver,et al.  Do psychosocial and study skill factors predict college outcomes? A meta-analysis. , 2004, Psychological bulletin.

[3]  Toon Calders,et al.  Introduction to the special section on educational data mining , 2012, SKDD.

[4]  Oliver Mooney,et al.  A study of progression in Irish higher education , 2010 .

[5]  D. Dai,et al.  The relationship between the big-five model of personality and self-regulated learning strategies. , 2007 .

[6]  Dawn P. Flanagan,et al.  Interpreting Intelligence Tests from Contemporary Gf-Gc Theory: Joint Confirmatory Factor Analysis of the WJ-R and KAIT in a Non-White Sample , 1998 .

[7]  Neal Schmitt,et al.  Alternatives to GPA 1 Prediction of Four-Year College Student Performance using Cognitive and Noncognitive Predictors and the Impact on Demographic Status of Admitted Students , 2009 .

[8]  Ryan S. Baker,et al.  The State of Educational Data Mining in 2009: A Review and Future Visions. , 2009, EDM 2009.

[9]  L. R. Goldberg.  THE DEVELOPMENT OF MARKERS FOR THE BIG-FIVE FACTOR STRUCTURE , 1992 .

[10]  A. Furnham,et al.  Personality, intelligence and approaches to learning as predictors of academic performance , 2008 .

[11]  S. Dollinger,et al.  Which factors best account for academic success: Those which college students can control or those they cannot? , 2008 .

[12]  Sebastián Ventura,et al.  Educational data mining: A survey from 1995 to 2005 , 2007, Expert Syst. Appl..

[13]  Simone Volet,et al.  Cognitive and affective variables in academic learning: The significance of direction and effort in students' goals , 1997 .

[14]  Simon Cassidy,et al.  Exploring individual differences as determining factors in student academic achievement in higher education , 2012 .

[15]  J. Biggs,et al.  The revised two-factor Study Process Questionnaire: R-SPQ-2F. , 2001, The British journal of educational psychology.

[16]  R. Sternberg,et al.  Intelligence as Developing Expertise. , 1999, Contemporary educational psychology.

[17]  Kevin Downing,et al.  The reciprocal relationship between motivation and self-regulation: A longitudinal study on academic performance , 2010 .

[18]  Zachary A. Pardos,et al.  The sum is greater than the parts: ensembling models of student knowledge in educational software , 2012, SKDD.

[19]  P. Pintrich.  A Manual for the Use of the Motivated Strategies for Learning Questionnaire (MSLQ). , 1991 .

[20]  H. V. D. Flier,et al.  Using multiple and specific criteria to assess the predictive validity of the Big Five personality factors on academic performance. , 2010 .

[21]  Øyvind Lund Martinsen,et al.  Personality, approaches to learning and achievement , 2010 .

[22]  Timothy J. Pleskac,et al.  Prediction of 4-year college student performance using cognitive and noncognitive predictors and the impact on demographic status of admitted students. , 2009, The Journal of applied psychology.