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

Type: Evidence | Proposition: A: Learning | Polarity: | Sector: | Country:

This study reviews factors that could be used to predict academic performance, but which are currently not systematically measured in tertiary education. It focuses on psychometric factors of ability, personality, motivation, and learning strategies. Learner ability, personality, motivation, and self‐regulation are shown to have significant relationships with academic performance.

The paper suggests that as such attributes can be measured prior to student engagement in course work, they could facilitate early recognition of learners at risk of failing, inform appropriate interventions, and provide early input to personalized learning environments.

This paper could inform research that would provide evidence for ‘Proposition A: Learning analytics improve learning outcomes’.

Past research

Research into personality traits, specifically the 'Big five' factors of openness, conscientiousness, extroversion, agreeableness, and neuroticism, suggests some are indicative of potential academic achievement. Personality attributes measured using the 'Big five' construct accounted for up to 30% of the variance in academic performance at tertiary level. Motivation has also been correlated with academic performance.

Citation: Gray, Geraldine, McGuinness, Colm, Owende, Philip, & Carthy, Aiden. (2014). A review of psychometric data analysis and applications in modelling of acaemic achievement in tertiary education. Journal of Learning Analytics, 1(1), 175-106. | Url: http://epress.lib.uts.edu.au/journals/index.php/JLA/issue/view/307

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