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

Autor/inn/en	Gray, Geraldine; McGuinness, Colm; Owende, Philip; Carthy, Aiden
Titel	A Review of Psychometric Data Analysis and Applications in Modelling of Academic Achievement in Tertiary Education
Quelle	In: Journal of Learning Analytics, 1 (2014) 1, S.75-106 (32 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1929-7750
Schlagwörter	Psychometrics; Data Analysis; Academic Achievement; Postsecondary Education; Educational Research; Data Collection; Learning Processes; Learning Strategies; Cognitive Ability; Electronic Learning; Motivation; Correlation; Grade Point Average; Hierarchical Linear Modeling; Regression (Statistics); Bayesian Statistics; College Students; Intelligent Tutoring Systems; Self Efficacy + Suchen Sie Ihr Suchwort? Psychometry; Psychometrie; Auswertung; Schulleistung; Post-secondary education; Tertiäre Bildung; Bildungsforschung; Pädagogische Forschung; Data capture; Datensammlung; Learning process; Lernprozess; Learning methode; Learning techniques; Lernmethode; Lernstrategie; Denkfähigkeit; psychologische; Motivation (psychologisch); Korrelation; Regression; Regressionsanalyse; Collegestudent; Intelligentes Tutorsystem; Self-efficacy; Selbstwirksamkeit
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. (As Provided).
Anmerkungen	Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: http://learning-analytics.info/journals/index.php/JLA/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01