.png){kind=icon}
返回
Automated Code Assessment and Feedback: A Comprehensive Model for Improved Programming Education
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2025.3554838.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Programming skills are essential in nearly every job today. To prepare students for the growing demand for programming expertise, they must be proficient in coding. This poses a challenge for educators who must ensure students are well-equipped to meet these demands. In Today's time, most of the schools and universities have introduced advanced curricula and syllabi with programming courses, thus an effective and conventional model to evaluate the codes submitted by students in a timely manner is very much required. Traditional grading systems were often slow, biased, and lacked the ability to provide meaningful feedback, which is crucial for student improvement. This study proposes an extensive automated code assessment and feedback model that addresses these issues and provides timely feedback to students. The model evaluates different aspects of the codes like originality, accuracy, structure, and syntax. By using the Abstract Syntax Tree (AST) analysis for code structure, plagiarism detection, and syntax error identification, the model offers an improved approach to evaluating student's coding skills. To evaluate the model, a group of 60 engineering students participated, and the model demonstrated a 40% increase in grading efficiency, reducing grading time while maintaining quality. The feedback on scores provided students with valuable insights about their understanding of programming concepts. This model not only supports skill development through actionable feedback but also promotes larger educational goals of accessibility and inclusivity by offering a sustainable solution for quality learning.
Keyword:
Codes
Programming profession
Syntactics
Encoding
Education
Accuracy
Semantics
Plagiarism
Testing
Sustainable development
Automated assessment
code evaluation
plagiarism detection
programming education
student feedback
sustainable education
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
暂无论文信息