Citation

Patel, Amit and Joshi, Hardik (2025) DebugProGrade: Improving Automated Assessment of Coding Assignments with a Focus on Debugging. Journal of Informatics and Web Engineering, 4 (1). pp. 184-199. ISSN 2821-370X

Text 1266-Article Text-12158-1-10-20250203.pdf - Published Version Restricted to Repository staff only Download (938kB)

Abstract

n education, the evaluation of programming assignments is challenging, especially to do with the debugging aspect. Self-grading technologies are unable to capture the level of understanding of students and context-bound responses. In light of these, we created DebugProGrade to take what we normally know about grading and improve it with semantic analysis and keyword extraction. DebugProGrade identified 1000 first-year BCA students who in Google Forms provided their answers to evaluate error detection and solution proposals for a basic C programming assignment. For the explanations’ specificity and for the context-level evaluation, the system employs the SBERT embedding, namely, the sentence-transformer bidirectional encoding representations from transformers. We employ the methods with tuned parameters and apply academic criteria to the evaluations performed by them. Other key functionality in DebugProGrade that should be mentioned is the classification of debugging skills into competence levels providing more comprehensive view of student proficiency regarding bugs which remain unaddressed by traditional grading systems –that is the ability to identify or fix bugs. Upon optimizing the Gradient Boosting Regressor algorithm, it gives outstanding results in terms of evaluating and predicting redshift. The mean squared error is very low with a value of MSE = 0.025107 and the MAE is also quite low with the value 0.031335, overall the high R² score 0.99932 shows that the given dataset has been predicted with highaccuracy with reference to the target variable. DebugProGrade precisely flips the paradigm of conventional grading and provides us with even greater understanding of where exactly students are strong.

Item Type:	Article
Uncontrolled Keywords:	DebugProGrade, Semantic Analysis, Debugging Skills, Keyword Extraction, Machine Learning
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	25 Jun 2025 06:49
Last Modified:	25 Jun 2025 06:49
URII:	http://shdl.mmu.edu.my/id/eprint/13993

Downloads

Edit (login required)