The Potential for Student Performance Prediction in Small Cohorts with Minimal Available Attributes

Autor/inn/en	Wakelam, Edward; Jefferies, Amanda; Davey, Neil; Sun, Yi
Titel	The Potential for Student Performance Prediction in Small Cohorts with Minimal Available Attributes
Quelle	In: British Journal of Educational Technology, 51 (2020) 2, S.347-370 (24 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Wakelam, Edward) ORCID (Jefferies, Amanda)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0007-1013
DOI	10.1111/bjet.12836
Schlagwörter	Academic Achievement; At Risk Students; Data Analysis; Identification; Academic Failure; Dropouts; Computer Assisted Instruction; Student Characteristics; Computer Simulation; Grades (Scholastic); Prediction; Attendance; College Seniors; Lecture Method + Suchen Sie Ihr Suchwort? Schulleistung; Auswertung; Identifikation; Identifizierung; Drop-out; Drop-outs; Dropout; Early leavers; Schulversagen; Computer based training; Computerunterstützter Unterricht; Computergrafik; Computersimulation; Notenspiegel; Vorhersage; Anwesenheit; College; Colleges; Senior; Hochschule; Fachhochschule
Abstract	The measurement of student performance during their progress through university study provides academic leadership with critical information on each student's likelihood of success. Academics have traditionally used their interactions with individual students through class activities and interim assessments to identify those "at risk" of failure/withdrawal. However, modern university environments, offering easy on-line availability of course material, may see reduced lecture/tutorial attendance, making such identification more challenging. Modern data mining and machine learning techniques provide increasingly accurate predictions of student examination assessment marks, although these approaches have focussed upon large student populations and wide ranges of data attributes per student. However, many university modules comprise relatively small student cohorts, with institutional protocols limiting the student attributes available for analysis. It appears that very little research attention has been devoted to this area of analysis and prediction. We describe an experiment conducted on a final-year university module student cohort of 23, where individual student data are limited to lecture/tutorial attendance, virtual learning environment accesses and intermediate assessments. We found potential for predicting individual student interim and final assessment marks in small student cohorts with very limited attributes and that these predictions could be useful to support module leaders in identifying students potentially "at risk." (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01