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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  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  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 developed models were obtained, validated and afterwards implemented in an Android 1.0.1 Studio environment. Extended Markup Language (XML) and Java were used for the design of the Graphical User Interface (GUI) and the logical implementation of the developed models as a mobile calculator, respectively. However, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE) and the Root Relative Squared Error (RRSE) were the metrics used to evaluate the robustness of MDT and LRC 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  courses.

Development of Mobile-Interfaced Machine Learning-Based Predictive Models for Improving Students’ Performance in Programming Courses