Analysis of Coding Stress Impact on Students Programming Skills with Random Forest and C4.5 Algorithms

Abstract

Students' stress often impedes their advancement in programming, which demands logical reasoning, an understanding of algorithms, and a firm grasp of basic concepts. This research intends to pinpoint the elements that affect students' programming abilities, explore their connection to stress levels, and assess the effectiveness of the Random Forest and C4.5 algorithms in classifying data. Information was gathered through an online questionnaire involving 744 students in 2024 at various leading universities in Islamabad, Pakistan. The dataset used in this study was sourced from Kaggle, which provides insights into factors affecting students' programming performance and stress levels. The analysis utilized a Confusion Matrix and evaluation metrics like accuracy, precision, recall, and F1-Score. The analysis results indicate that the C4.5 algorithm has a higher accuracy of 68.04% compared to Random Forest, which achieved 65.54%. Additionally, C4.5 outperforms Random Forest in terms of precision, scoring 71.7% versus 65.2%. However, in terms of recall, Random Forest performs better with a score of 66.3%, while C4.5 only reaches 59.6%. This study confirms that interest in programming, debugging skills, mathematical and analytical abilities, and perceptions of programming significantly impact students' performance and stress levels. Students with strong logical abilities and adequate support demonstrate better performance and lower stress levels, whereas those with weak technical skills and negative perceptions are more vulnerable to stress, which adversely affects their performance. These findings emphasize the importance of creating a positive learning environment through interactive methods, structured problem-solving, and additional support.