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

Ed Wakelam, Amanda Jefferies, Neil Davey, Yi Sun

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
56 Downloads (Pure)


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.”.

Original languageEnglish
Article numberBJET12836
JournalBritish Journal of Educational Technology
Early online date25 Jun 2019
Publication statusE-pub ahead of print - 25 Jun 2019


  • Learning Analytics, Educational Data Mining, Academic Intervention, Small Datasets, Decision Trees, Random Forest, K-Nearest Neighbours


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