Assessing Engineering Student Perceptions of Introductory CS Courses in an Indian Context

TL;DR

This study investigates Indian engineering students' perceptions of assessment in introductory CS courses, revealing insights into their attitudes, performance variations, and the influence of demographic factors to inform inclusive assessment design.

Contribution

It provides novel insights into student perceptions and performance variability in Indian engineering education, contrasting with European data, and challenges assumptions about grade distribution.

Findings

Students view lab assignments as effective learning activities.

Exams and projects are seen as authentic and skill-enhancing.

Significant perception and performance differences exist based on background factors.

Abstract

Understanding student perceptions of assessment is vital for designing inclusive and effective learning environments, especially in technical education. This study explores engineering students' perceptions of assessment practices in an introductory computer science/ programming course, and its associated laboratory within an Indian engineering institute context. A total of 318 first-year Bachelor of Technology students participated in a weekly 25-statement Likert-scale survey conducted over nine weeks. Using descriptive statistics and non-parametric tests (Mann-Whitney U and Kruskal-Wallis), the analysis reveals that students largely perceive lab assignments as effective learning activities and view exams and projects as authentic and skill-enhancing. Students appreciated the role of instructors in shaping course content and found teaching assistants to be approachable and helpful, despite some inconsistencies. The study also finds significant variations in students' academic performance and assessment perceptions based on prior programming experience, technology familiarity, gender, and academic branch. Notably, the performance data did not follow a Gaussian distribution, challenging common assumptions in grade modeling. A comparative analysis with European cohorts highlights both universal patterns and contextual differences, offering valuable insights for designing inclusive and equitable assessment strategies in programming education.