| Title: | Automated grading through contrastive learning : a gradient analysis and feature ablation approach |
|---|
| Authors: | ID Sokač, Mateo (Author)
ID Fabijanić, Mario (Author)
ID Mekterović, Igor (Author)
ID Mršić, Leo (Author) |
|---|
| Files: |  URL - Source URL, visit https://www.mdpi.com/2504-4990/7/2/41
 PDF - Presentation file, download (4,12 MB)
MD5: DB4D2106BCD7DABC77590E5E83493782
|
|---|
| Language: | English |
|---|
| Typology: | 1.01 - Original Scientific Article |
|---|
| Organization: |  RUDOLFOVO - Rudolfovo - Science and Technology Centre Novo Mesto
|
|---|
| Abstract: | As programming education becomes increasingly complex, grading student code has become a challenging task. Traditional methods, such as dynamic and static analysis, offer foundational approaches but often fail to provide granular insights, leading to inconsistencies in grading and feedback. This study addresses the limitations of these methods by integrating contrastive learning with explainable AI techniques to assess SQL code submissions. We employed contrastive learning to differentiate between student and correct SQL solutions, projecting them into a high-dimensional latent space, and used the Frobenius norm to measure the distance between these representations. This distance was used to predict the percentage of points deducted from each student’s solution. To enhance interpretability, we implemented feature ablation and integrated gradients, which provide insights into the specific tokens in student code that impact the grading outcomes. Our findings indicate that this approach improves the accuracy, consistency, and transparency of automated grading, aligning more closely with human grading standards. The results suggest that this framework could be a valuable tool for automated programming assessment systems, offering clear, actionable feedback and making machine learning models in educational contexts more interpretable and effective. |
|---|
| Keywords: | automated programming assessment systems (APASs), contrastive learning, explainable AI, feature ablation, integrated gradients, machine learning in education, natural language processing (NLP) |
|---|
| Publication version: | Version of Record |
|---|
| Publication date: | 29.04.2025 |
|---|
| Publisher: | MDPI |
|---|
| Year of publishing: | 2025 |
|---|
| Number of pages: | str. 1-24 |
|---|
| Numbering: | Vol. 7, iss. 2 |
|---|
| PID: | 20.500.12556/DiRROS-22642  |
|---|
| UDC: | 004.85:37 |
|---|
| ISSN on article: | 2504-4990 |
|---|
| DOI: | 10.3390/make7020041 |
|---|
| COBISS.SI-ID: | 239074051 |
|---|
| Copyright: | © 2025 by the authors |
|---|
| Note: | Nasl. z nasl. zaslona;
Opis vira z dne 11. 6. 2025;
Soavtorji: Mario Fabijanić, Igor Mekterović and Leo Mršić;
|
|---|
| Publication date in DiRROS: | 09.09.2025 |
|---|
| Views: | 302 |
|---|
| Downloads: | 149 |
|---|
| Metadata: |    |
|---|
|
:
|
Copy citation |
|---|
| | | | Share: |  |
|---|
Hover the mouse pointer over a document title to show the abstract or click
on the title to get all document metadata. |
