Enhancing Students' Performance in Computer Science Through Tailored Instruction Based on their Programming Background

Computer science including data analytics is a widely popular field, boasting promising career opportunities in the future. Proficiency in programming stands as a fundamental requirement for success in this domain. However, students entering MSc programs in data analytics often possess varying levels of programming background, which can impact their performance in assignments. Recognising and addressing these differences through tailored instruction can improve students’ outcomes. This paper explores the importance of considering students' programming backgrounds in the data analytics field and highlights strategies to enhance their performance based on prior knowledge. This study was carried out on two different modules in two different pathways. We have chosen two distinct cohorts and pathways to ensure unbiased conclusions in our study. The initial research was applied to the Database and Programming Fundamentals module for an MSc data analytics cohort, and then we utilized a Deep Learning module for final year computer science undergraduates as a validation cohort. As a conclusion, this study successfully demonstrated a significant increase in student assignment performance through the implementation of tailored instruction based on students' programming backgrounds. Despite receiving positive student feedback and observing excellent and improved performances, it is crucial to acknowledge instances of unsatisfactory student performance as well. Both studies were conducted by the School of Electronics, Electrical Engineering, and Computer Science (EEECS) at Queen's University Belfast (QUB) during the academic year 2021/2022.