STUDENTS'PATTERNS OF INTERACTION WITH A MATHEMATICS INTELLIGENT TUTOR:LEARNING ANALYTICS APPLICATION

Purpose: The purpose of this paper is to determine potential identifiers of students’ academic success in foundation mathematics course by analyzing the data logs of an intelligent tutor. Design/ methodology/approach: A cross-sectional study design was used. A sample of 58 records was extracted from the data-logs of the intelligent tutor, ALEKS. This data was triangulated with the data collected from surveys. Two-step clustering, correlation and regression analysis, Chi-square analysis and paired sample t-tests were applied to address the research questions. Findings: The data-logs of ALEKS include information about number of topics practiced and number of topics mastered by each student. Prior knowledge and derived attribute, which is the ratio of number of topics mastered to number of topics practiced(denoted by the variable m top in this paper) are found to be predictors of final marks in the foundation mathematics course with = 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 a 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. Originality and value: This research has established three indicators of academic success in the course of foundation mathematics which is delivered using the intelligent tutor ALEKS. Instructors can monitor students’ progress and detect students at-risk who are not able to attain desired pace of learning and guide them to choose the correct sequence of topics.