Success profiling: A methodological perspective on the interactive nature of success predictors on student performance at an open and distance learning institution

  • H Muller
  • E Swanepoel
  • A de Beer
Keywords: ODL, blended learning intervention, Business Management, success profile, satellite class attendance, student performance, throughput, decision trees, data partitioning, CHAID analysis, population group, matriculation certificate, age, at-risk profile


The drive to improve the academic performance of students at an open and distance learning (ODL) institution has resulted in the incorporation of a blended learning component, namely satellite classes, in the learning strategy to enhance the academic performance of first year diploma students in Business Management and Management. Monitoring this intervention to justify implementation costs (Mathur & Oliver, 2007:3) and effectiveness in relation to student performance is essential. Whereas an initial study confirmed a statistically significant relationship between satellite class attendance and academic performance, this study evaluated the interaction effect of satellite classes and additional, potential success predictors on academic performance by applying the Chi-square Automatic Interaction Detector (CHAID) methodology. This decision tree methodology described the interactive driving forces that impacted on student success. Satellite class intervention and biographical student attributes constituted the driving forces. The CHAID analysis enabled the profiling of successful and at-risk students. The decision tree algorithm mimics true life situations where various effects interactively and jointly influence and predict an outcome. The results showed that satellite class intervention as such was an effective and significant predictor of performance, but that the critical interacting nature of satellite class attendance and additional co-predictors, such as population group and type of matriculation certificate, considerably strengthened performance prediction.

Journal Identifiers

print ISSN: 0258-2236