  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.
