Comparative Analysis of ND II Computer Science Students’ Programming Performance Across Python, Java, and C++ Programming Languages

Author

Mustapha Ibrahim

Abstract

 Programming competence is a fundamental requirement in Computer Science education; however, students often demonstrate varying levels of achievement when learning and applying different programming languages. This study investigated the comparative performance of ND II Computer Science students across three commonly used programming languages: Python, Java, and C++. The study was conducted at the Department of Computer Science, Federal Polytechnic, Damaturu, Yobe State, Nigeria. A quantitative comparative research design was adopted, involving 350 ND II Computer Science students selected through a purposive sampling technique. Data were collected using a programming performance assessment and a structured questionnaire. Students’ performance was evaluated based on programming accuracy, task completion time, error frequency, code efficiency, and overall achievement scores. Data were analysed using descriptive statistics, Analysis of Variance (ANOVA), Tukey post hoc analysis, Pearson correlation, and multiple regression analysis. The findings revealed significant differences in students’ programming performance across the selected programming languages. Students recorded the highest mean performance score in Python (M = 78.45), followed by Java (M = 72.31) and C++ (M = 69.76). The ANOVA results showed a statistically significant difference in students’ performance across Python, Java, and C++ (p < 0.05). Further analysis revealed a significant positive relationship between students’ prior programming experience and their programming performance. Regression analysis indicated that programming experience, language familiarity, and frequency of practice significantly contributed to predicting students’ programming achievement. The study concludes that programming language characteristics and learner-related factors influence students’ programming performance. The findings provide useful insights for Computer Science educators and curriculum developers in selecting appropriate programming languages and designing effective programming instruction strategies within polytechnic education settings.

Keywords

Programming languages, Computer Science education, student performance, Python, Java, C++, programming assessment, comparative analysis.

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References

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