In the ever-evolving landscape of STEM education, understanding how students navigate the complex terrain of learning programming has become a paramount challenge. Programming is widely acknowledged as a critical skill in numerous scientific, technological, and mathematical domains. However, how novices from diverse academic backgrounds acquire and refine these skills remains insufficiently understood. A groundbreaking study published in 2025 in the International Journal of STEM Education offers an unprecedented deep dive into this very enigma. Led by Gao, Yan, and Liu, the research meticulously traces and compares the distinct learning trajectories of novice computer science and mathematics students during an introductory programming course through a sophisticated sequential analysis of their scores, engagement levels, and code-quality metrics.
At the heart of this research lies a fundamental question: how do learners from different academic disciplines progress and evolve when introduced to programming? While computer science students are traditionally expected to excel given their background, mathematics students bring their own sets of strengths, such as mathematical rigor and analytical thinking. The study exploits modern computational methods—especially sequence analysis—to unravel these learning processes, providing insights that transcend simplistic, one-time assessments. This multi-dimensional longitudinal approach places the spotlight on temporal patterns, engagement behaviors, and technical output, capturing the subtle and dynamic nature of learning programming.
Employing a cohort-based approach, the researchers analyzed data from two groups: novice computer science majors and mathematics majors enrolled in a compulsory programming course. The dataset included detailed
