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

2025-08-06

AI Summary

This study investigates first-year engineering students’ perceptions of the “Introduction to Computer Science” course and its assessment practices in India, aiming to foster more inclusive and equitable learning environments. Method: A nine-week Likert-scale survey (N=XXX) was administered, with data analyzed using descriptive statistics and nonparametric tests (Mann–Whitney U, Kruskal–Wallis). Results: Prior programming experience, gender, academic specialization, and technical familiarity significantly influenced students’ perceptions; assessment outcomes exhibited non-normal distribution, challenging conventional parametric grading assumptions. Cross-cultural cohort comparisons revealed strong consensus on the authenticity of laboratory tasks and effectiveness of teaching assistant support; pedagogical design received positive feedback on inclusivity and fairness dimensions. Contribution: The study uncovers a multifaceted sociotechnical framework shaping assessment perception and provides empirically grounded evidence to inform context-sensitive, equity-oriented assessment reform in non-Western engineering education.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Evaluation and Analysis
• Application Domains: Humanities & Computational Social Science

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

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.

Problem

• Explore engineering students' perceptions of CS course assessments in India
• Analyze impact of prior experience and demographics on assessment views
• Compare Indian and European student perceptions for inclusive strategies

Innovation

• Used Likert-scale surveys for student feedback
• Applied non-parametric statistical analysis methods
• Compared perceptions across diverse demographic factors