The Perceived Learning Behaviors and Assessment Techniques of First-Year Students in Computer Science: An Empirical Study
Manuela Andreea Petrescu, Tudor Dan Mihoc
TL;DR
The paper investigates what first-year computer science students perceive as their main learning motivators, fair assessment methods, and effective learning strategies in the post-pandemic educational landscape. It uses a two-week online survey of 43 freshmen CS students, with open and accountable items and thematic analysis guided by ACM standards. Findings indicate a strong preference for in-person, exercise-based, and collaborative learning, and for practical and written assessments over oral or purely theoretical evaluations, with stress and fear influencing performance. The study provides actionable insights for educators to design more engaging, real-world-oriented curricula that reduce stress and improve learning outcomes in the early stages of CS education.
Abstract
The objective of our study is to ascertain the present learning behaviors, driving forces, and assessment techniques as perceived by first-year students, and to examine them through the lens of the most recent developments (pandemic, shift to remote instruction, return to in-person instruction). Educators and educational institutions can create a more accommodating learning environment that takes into account the varied needs and preferences of students by recognizing and implementing these findings, which will ultimately improve the quality of education as a whole. Students believe that in-person instruction is the most effective way to learn, with exercise-based learning, group instruction, and pair programming. Our research indicates that, for evaluation methods, there is a preference for practical and written examinations. Our findings also underscore the importance of incorporating real-world scenarios, encouraging interactive learning approaches, and creating engaging educational environments.