Application of Artificial Intelligence using K-Means for Programming Question Assessment
The manual assessment of programming assignments remains a significant challenge in educational settings due to its time-consuming nature and susceptibility to human error. Observational studies of course instructors reveal that over 40% have made grading mistakes, often due to fatigue or inconsiste...
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| Format: | Article |
| Language: | Indonesian |
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Islamic University of Indragiri
2025-07-01
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| Series: | Sistemasi: Jurnal Sistem Informasi |
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| Online Access: | https://sistemasi.ftik.unisi.ac.id/index.php/stmsi/article/view/5360 |
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| author | Waliyyudin Waliyyudin Ichsan Ibrahim |
| author_facet | Waliyyudin Waliyyudin Ichsan Ibrahim |
| author_sort | Waliyyudin Waliyyudin |
| collection | DOAJ |
| description | The manual assessment of programming assignments remains a significant challenge in educational settings due to its time-consuming nature and susceptibility to human error. Observational studies of course instructors reveal that over 40% have made grading mistakes, often due to fatigue or inconsistent evaluation standards. This study aims to develop an automated assessment system using artificial intelligence to enhance both objectivity and efficiency in the evaluation process. The method employed is the K-Means clustering algorithm, chosen for its ability to group answers based on similarities in logic and code structure rather than mere textual similarity. Five assessment categories were used as clustering standards: Logic and Algorithm, Data Structures, Object-Oriented Programming (OOP), Implementation, and Error Handling. The system was developed using an Agile Development approach and evaluated with student responses from programming courses. System performance was validated quantitatively by comparing cluster results against ground truth labels from manual grading. The system achieved 87% clustering accuracy, reduced the average grading time to 4.5 seconds per answer (compared to 13 seconds manually—representing a 65% efficiency gain), and decreased the inter-rater score standard deviation from 7.5 to 2.8 points. The results indicate that the system can deliver accurate real-time feedback. This study focused on programming questions ranging from easy to hard difficulty levels. In the future, the system could be enhanced by integrating advanced syntax analysis and expanding the evaluation criteria to support large-scale deployment. |
| format | Article |
| id | doaj-art-dcc059aba7654a7e92fac7199a406fbe |
| institution | DOAJ |
| issn | 2302-8149 2540-9719 |
| language | Indonesian |
| publishDate | 2025-07-01 |
| publisher | Islamic University of Indragiri |
| record_format | Article |
| series | Sistemasi: Jurnal Sistem Informasi |
| spelling | doaj-art-dcc059aba7654a7e92fac7199a406fbe2025-08-20T02:39:51ZindIslamic University of IndragiriSistemasi: Jurnal Sistem Informasi2302-81492540-97192025-07-011441920193610.32520/stmsi.v14i4.53601153Application of Artificial Intelligence using K-Means for Programming Question AssessmentWaliyyudin Waliyyudin0Ichsan Ibrahim1STMIK Indonesia MandiriSTMIK Indonesia MandiriThe manual assessment of programming assignments remains a significant challenge in educational settings due to its time-consuming nature and susceptibility to human error. Observational studies of course instructors reveal that over 40% have made grading mistakes, often due to fatigue or inconsistent evaluation standards. This study aims to develop an automated assessment system using artificial intelligence to enhance both objectivity and efficiency in the evaluation process. The method employed is the K-Means clustering algorithm, chosen for its ability to group answers based on similarities in logic and code structure rather than mere textual similarity. Five assessment categories were used as clustering standards: Logic and Algorithm, Data Structures, Object-Oriented Programming (OOP), Implementation, and Error Handling. The system was developed using an Agile Development approach and evaluated with student responses from programming courses. System performance was validated quantitatively by comparing cluster results against ground truth labels from manual grading. The system achieved 87% clustering accuracy, reduced the average grading time to 4.5 seconds per answer (compared to 13 seconds manually—representing a 65% efficiency gain), and decreased the inter-rater score standard deviation from 7.5 to 2.8 points. The results indicate that the system can deliver accurate real-time feedback. This study focused on programming questions ranging from easy to hard difficulty levels. In the future, the system could be enhanced by integrating advanced syntax analysis and expanding the evaluation criteria to support large-scale deployment.https://sistemasi.ftik.unisi.ac.id/index.php/stmsi/article/view/5360automated assessmentartificial intelligencek-means clusteringprogramming problemsagile development |
| spellingShingle | Waliyyudin Waliyyudin Ichsan Ibrahim Application of Artificial Intelligence using K-Means for Programming Question Assessment Sistemasi: Jurnal Sistem Informasi automated assessment artificial intelligence k-means clustering programming problems agile development |
| title | Application of Artificial Intelligence using K-Means for Programming Question Assessment |
| title_full | Application of Artificial Intelligence using K-Means for Programming Question Assessment |
| title_fullStr | Application of Artificial Intelligence using K-Means for Programming Question Assessment |
| title_full_unstemmed | Application of Artificial Intelligence using K-Means for Programming Question Assessment |
| title_short | Application of Artificial Intelligence using K-Means for Programming Question Assessment |
| title_sort | application of artificial intelligence using k means for programming question assessment |
| topic | automated assessment artificial intelligence k-means clustering programming problems agile development |
| url | https://sistemasi.ftik.unisi.ac.id/index.php/stmsi/article/view/5360 |
| work_keys_str_mv | AT waliyyudinwaliyyudin applicationofartificialintelligenceusingkmeansforprogrammingquestionassessment AT ichsanibrahim applicationofartificialintelligenceusingkmeansforprogrammingquestionassessment |