Bringing Machine Learning to Automated Assessment of Programming Assignments
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".
Deadline for manuscript submissions: 26 February 2025 | Viewed by 372
Special Issue Editors
Interests: automated assessment; programming education; machine learning; gamification
Interests: automated assessment; programming education; technology enhanced learning; web adaptability; semantic web; gamification
Interests: computer science; generative adversarial networks; synthetic data; NPL; fake news identification; data mining; text mining; machine learning; social network analysis; data visualization; eLearning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Learning how to program requires intense and diverse hands-on practice, supported by timely, accurate, and personalized feedback to enable progression after facing learning barriers and improve coding skills. Manual grading of programming assignments is a time-consuming and subjective process, which hinders its use in these contexts. Thus, automated assessment is an essential asset in programming education. These systems can analyze code syntax, logic, and execution, and offer insights into areas for improvement, potential bugs, and best coding practices. Machine learning (ML) brings the ability to adapt and personalize feedback based on individual student needs to automated assessment. By training the models on vast amounts of programming data, ML models can learn patterns and common errors made by students, enabling them to provide targeted feedback tailored to each learner. This personalized approach not only helps students understand their mistakes but also guides them towards specific areas of improvement, fostering a more efficient and effective learning experience. However, ensuring the reliability and fairness of automated grading systems is crucial. Models need to be trained on diverse datasets, encompassing different programming languages, problem domains, and difficulty levels, to mitigate biases and ensure accurate evaluation. Furthermore, transparency and interpretability of machine learning models are paramount, as educators and students alike need to understand how the assessment process works and trust its outcomes.
The aim of this Special Issue is to collect high-quality submissions on research bringing machine learning to automated assessment of programming assignments. Research outcomes presenting novel ML techniques and tools to automatically generate feedback or improve other aspects of the automated assessment of programming assignments are welcome. Additionally, discussing the challenges associated with fairness, bias, and interpretability and present strategies to address them is also within the scope of this work.
Dr. José Carlos Paiva
Dr. José Paulo Leal
Dr. Alvaro Figueira
Guest Editors
Manuscript Submission Information
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Keywords
- automated assessment
- programming assignments
- machine learning
- feedback
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