A Review of Psychometric Data Analysis and Applications in Modelling of Academic Achievement in Tertiary Education

Abstract

Increasing college participation rates, and diversity in student population, is posing a challenge to colleges in their attempts to facilitate learners achieve their full academic potential. Learning analytics is an evolving discipline with capability for educational data analysis that could enable better understanding of learning process, and therefore mitigate these challenges. The outcome from such data analysis will be dependent on the range, type, and quality of available data and the type of analysis performed. This study reviewed factors that could be used to predict academic performance, but which are currently not systematically measured in tertiary education. It focused on psychometric factors of ability, personality, motivation, and learning strategies. Their respective relationships with academic performance are enumerated and discussed. A case is made for their increased use in learning analytics to enhance the performance of existing student models. It is noted that lack of independence, linear additivity, and constant variance in the relationships between psychometric factors and academic performance suggests increasing relevance of data mining techniques, which could be used to provide useful insights on the role of such factors in the modelling of learning process.