Abstract
Student performance modelling (SPM) is a critical step to assessing and improving students performances in their learning discourse. However, most existing SPM are based on statistical approaches, which on one hand are based on probability, depicting that results are based on estimation; and on the other hand, actual influences of hidden factors that are peculiar to students, lecturers, learning environment and the family, together with their overall effect on student performance have not been exhaustively investigated. In this paper, Student Performance Models (SPM) for improving students performance in programming courses were developed using M5P Decision Tree (MDT) and Linear Regression Classifier (LRC). The data used was gathered using a structured questionnaire from 295 students in 200 and 300 levels of study who offered Web programming, C or JAVA at Federal University, Oye-Ekiti, Nigeria between 2012 and 2016. Hidden factors that are significant to students performance in programming were identified. The relevant data gathered, normalized, coded and prepared as variable and factor datasets, and fed into the MDT algorithm and LRC to develop the predictive models. The evaluation results obtained indicate that the variable-based LRC produced the best model in terms of MAE, RMSE, RAE and the RRSE having yielded the least values in all the evaluations conducted. Further results obtained established the strong significance of attitude of students and lecturers, fearful perception of students, erratic power supply, university facilities, student health and students attendance to the performance of students in programming courses. The variable-based LRC model presented in this paper could provide baseline information about students performance thereby offering better decision making towards improving teaching/learning outcomes in programming courses.
Highlights
Computer programming courses are a fundamental part of many Universities’ curricula and among the most important subjects for computer science and information technology students
The variable-based Linear Regression Classifier produced the best model in terms of mean absolute error, root mean squared error, relative absolute error and the root relative squared error having yielded the least values in all these metrics
This is followed by the variable-based M5P decision tree, factor-based M5P Decision Tree and the factorbased linear regression classifiers in decreasing order of performance
Summary
Computer programming courses are a fundamental part of many Universities’ curricula and among the most important subjects for computer science and information technology students. This requires the knowledge of programming tools and languages, problem-solving skills and effective strategies for program design and implementation [1]. The high level of abstraction and very complex language syntax and semantic structures induced in programming makes it a much dreaded task in which most students fail [2]. The perception of the complexity ascribed to programming courses can be described as one of the main reasons that may have attributed to the decline in number of undergraduates who offer or intend to offer computer science in various institutions [4]
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