The Impact of Credits on Student Performance: A Case Study of Sri Lanka

A well-designed curriculum is essential to ensure that students can maintain a healthy academic life, whether at school, college, or university. Such a curriculum would help students balance their academic work and extracurricular activities effectively. This study used performance data of computer science students from a university to examine the impact of credit load on semester-wise academic performance. The data was subjected to feature engineering, and various machine learning algorithms, including decision trees, random forest, and support vector machines, were applied to the data to investigate the effect of credit load on students’ performance (semester end Grade Point Average), considering the inclusion and exclusion of credit load per semester. The study revealed a weak correlation between credit load and semester-wise academic performance. This result may be attributed to the fact that students become accustomed to the heavy academic workload from their schooling years. Therefore, conducting a similar study using school students’ data is recommended. Moreover, it is advisable to balance the academic workload so that there is little or no difference in the number of credits registered per semester. This way, students can engage in other activities while also maintaining their academic performance levels.