Improving students Python coding skills using automated tools in a learning management system

This study examined the effect of integrating automated assessment tools within a Learning Management System on Grade 10 students’ Python coding skills at Jakar Higher Secondary School, Bhutan. Although Python has been prioritised in the national Information and Communication Technology curriculum, students demonstrated limited proficiency in Python functions and debugging, partly due to delayed feedback from manual assessment and limited access to guided practice. A one-group pre-test–post-test quasi-experimental design within a mixed-methods framework was employed over four weeks with 31 students. The intervention incorporated automated quizzes, coding activities, real-time feedback, and progress-tracking features embedded in the LMS developed by the Ministry of Education and Skills Development. Quantitative data from achievement tests and survey questionnaires were analysed using descriptive statistics and a paired-samples t-test, while qualitative data from semi-structured interviews and a research diary were analysed thematically. The results showed a statistically significant improvement in students’ post-test scores, with a large effect size, indicating substantial improvement in Python functions and debugging performance. Survey findings also revealed high levels of student engagement, motivation, self-regulated learning, foundational knowledge acquisition, and perceived academic improvement. The qualitative findings supported these results by highlighting the value of instant feedback, self-paced learning, repeated practice, and access to Learning Management System-based learning resources. The study concludes that Learning Management System-based automated assessment may support the development of Python coding skills and promote learner-centred practices in resource-constrained Bhutanese classrooms.