  The purpose of this paper is to determine potential identifiers of students'
academic success in foundation mathematics course from the data logs of an
intelligent tutor. A cross-sectional study design was used. A sample of 58
records was extracted from the data-logs of an intelligent tutor, Assessment
for Learning using Knowledge Spaces (ALEKS). This data was triangulated with
the data collected from surveys. Two-step clustering, regression analysis,
paired-sample t-tests were applied to address the research questions. The
data-logs of ALEKS include information about number of topics practiced and
number of topics mastered. A derived attribute, which is the ratio of number of
topics mastered to number of topics practiced is found to be a predictor of
final marks. Students' prior knowledge and this derived attribute are
predictors of final grades with R square=42%. Students were asked to report
their preferred way of selecting topics as either sequential or random. Results
of paired sample t-test demonstrated that the students who selected topics in
sequential manner were able to retain their mastery of learning after the
summative assessment whereas the students who chose topics randomly were not
able to retain their mastery of learning.
