Using non-identifiable data to predict student course selections

  • Ognjanovic I
  • Gasevic D
  • Dawson S
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Abstract

The ability to predict what university course a student may select has important quality assurance and economic imperatives. The capacity to determine future course load and student interests provides for increased accuracy in the allocation of resources including curriculum and learning support and career counselling services. Prior research in data mining has identified several models that can be applied to predict course selection based on the data residing in institutional information systems. However, these models only aim to predict the total number of students that may potentially enrol in a course. This prior work has not examined the prediction of the course enrolments with respect to the specific academic term and year in which the students will take those courses in the future. Moreover, these prior models operate under the assumption that all data stored within institutional information systems can be directly associated with an individual student's identity. This association with student identity is not always feasible due to government regulations (e.g.; student evaluations of teaching and courses). In this paper, we propose an approach for extracting student preferences from sources available in institutional student information systems. The extracted preferences are analysed using the Analytical Hierarchy Process (AHP), to predict student course selection. The AHP-based approach was validated on a dataset collected in an undergraduate degree program at a Canadian research-intensive university (N = 1061). The results demonstrate that the accuracy of the student course predictions was high and equivalent to that of previous data mining approaches using fully identifiable data. The findings suggest that a students' grade point average relative to the grades of the courses they are considering for enrolment was the most important factor in determining future course selections. This finding is consistent with theories of modern counseling psychology that acknowledges self-efficacy as a critical factor in career planning.

Author-supplied keywords

  • Course enrolment prediction
  • Decision science
  • Higher education
  • Learning analytics
  • Student information systems

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