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Development of Multi-Factored Predictive Models for Students’ Performance in Programming Courses in a Nigerian Tertiary Institution

Temitayo Matthew Fagbola, Ibrahim Adepoju Adeyanju, Olatayo Moses Olaniyan, Ayodele Oloyede

Abstract


The academic performance of higher institution students in programming courses, especially in developing countries, has been observed to be poor. However, the fundamental factors that affect students’ performance in programming courses have not been exhaustively investigated. This paper investigates the factors influencing the performance of students in programming courses and developed models for predicting students’ performance in these courses using data from a Nigerian higher institution.

A structured questionnaire was designed and used to gather relevant data from second and third year students between 2014 and 2016 who offered programming courses. The data was normalized, coded and organized as variable and factor datasets. Using statistical approaches such as standard deviation, correlation and multiple regression analysis, the datasets were descriptively analyzed while predictive models were developed and validated.

Extensive analysis of responses from participants showed that the major factors affecting the performance of students in programming courses are erratic power supply, family income, inadequate university facilities, and students’ attendance at lectures. Each of these factors were statistical significant in terms of correlation to students’ performances. Three multi-linear regression models called Student Controllable Performance Model, Student Uncontrollable Performance Model and Hybrid Student Performance Model were developed to adequately predict the expected performance of any student.

Synthesis and applications: The developed predictive models could facilitate better decision making and situational awareness by University administrators. It would also be useful to students on how to respond to instructional practices more appropriately in order to achieve better grades in programming courses.


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