Open distance learning (ODL), by the definition, subscribes to the limited direct engagement of the students with the learning institution. The University of South Africa (UNISA), Western Cape, has placed key emphasis on providing an array of electronic support platforms to assist students with their needs in quantitative literacy skills. Consequently, as a part of this additional learning support, an online environment was designed in which the students, enrolled for various mathematics modules, were able to access an array of educational resources. Findings from similar studies suggest, that the utilisation of employment of additional learning support could show a positive effect on students' academic achievement in distance learning. Therefore, a quasiexperimental research study was conducted to investigate this supposition by comparing the assessment outcomes among various student groups subjected to the additional learning support interventions. It has been found that online intervention might affect the success rate of the students, however, not necessarily in a simple or in the anticipated way.

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The use of data mining within the higher education context has, increasingly, been gaining traction.A parallel examination of the accuracy, robustness and utility of the algorithms applied to data mining is argued as a necessary step toward entrenching the use of EDM. This article provides a comparative analysis of various classification algorithms within an Open Distance Learning institution in South Africa. The study compares the performance of the ZeroR, OneR, Naïve Bayes, IBk, Simple Logistic Regression and the J48 in classifying students within a cohort over an eightyear time span. The initial results appear to show that, given the data quality and structure of the institution under study, the J48 most consistently performed with the highest levels of accuracy.