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

Students' patterns of interaction with a mathematics intelligent tutor: Learning analytics application