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


  • Geraldine Gray Insitute of Technology Blanchardstown




Learning analytics, educational data mining, psychometrics, classification, academic performance, ability, personality, Big five, motivation, learning style, self regulated learning, learning disposition


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. 

Author Biography

Geraldine Gray, Insitute of Technology Blanchardstown

Geraldine Gray is a lecturer with the Informatics Department at the Institute of Technology Blanchardstown(ITB) Dublin,  Ireland, specialising in the design and delivery of modules in the areas of data mining, text mining and enterprise application development at levels 7, 8 and 9, and is course co-ordintaor for ITB’s online masters in Business Intelligence and Data Mining. Prior to joining ITB, Geraldine lectured in IT Tallaght, and also has a number of years of industrial experience developing software for distribution and inventory management. Research interests and publications include educational data mining, data mining techniques for computer forensics, mining unstructured data, semantic web technologies and web services.  She is currently completing her PhD in the field of Educational Data Mining, based on ongoing research in partnership with the National Learning Network to investigate the relationships between a range of psychometric factors and student performance.




How to Cite

Gray, G. (2014). A Review of Psychometric Data Analysis and Applications in Modelling of Academic Achievement in Tertiary Education. Journal of Learning Analytics, 1(1), 75-106. https://doi.org/10.18608/jla.2014.11.5



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