Multigraph-based Deep Programming Ability Tracing Method for Students
Programming has become a crucial ability with the development of artificial intelligence, making it increasingly important to track and improve programming proficiency of students. However, most programming ability tracing methods rely primarily on the final outcomes of programming exercises to meas...
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| Format: | Article |
| Language: | English |
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Tamkang University Press
2025-01-01
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| Series: | Journal of Applied Science and Engineering |
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| Online Access: | http://jase.tku.edu.tw/articles/jase-202508-28-08-0019 |
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| author | Yue Wang Guanwen Zhang |
| author_facet | Yue Wang Guanwen Zhang |
| author_sort | Yue Wang |
| collection | DOAJ |
| description | Programming has become a crucial ability with the development of artificial intelligence, making it increasingly important to track and improve programming proficiency of students. However, most programming ability tracing methods rely primarily on the final outcomes of programming exercises to measure the programming
proficiency, and neglect the rich behavioral structure information derived from the programming process, leading to a suboptimal solution in the programming ability tracing. To this end, we propose a multigraphbased deep programming ability tracing method for students (MDPAT), which consists of four components. Specifically, MDPAT devises the multigraph programming modelling via conducting the program knowledge graph and the program iteration graph to generate the submission representations of programming exercises, which effectively captures static structure information and dynamic structure information within the programming process. Then, MDPAT designs the programming knowledge gated update and the programming ability
gated update to aggregate information of the programming knowledge and the programming ability that are derived from submission representations, which achieves a balanced and comprehensive tracking of evolving programming proficiency. Meanwhile, MDPAT utilizes the programming answer prediction to generate final programming proficiency measurement of students. Four components of MDPAT collaborate organically to achieve maximum performance in the programming ability tracing. Finally, extensive results on two real world
datasets, especially on the Atcoder_C dataset, ACC shows a 5.06% improvement compared to the second best result, verify that MDPAT conducts a new standard baseline in the programming ability tracing. |
| format | Article |
| id | doaj-art-b67bfa242ee245c48562449c54492c8a |
| institution | Kabale University |
| issn | 2708-9967 2708-9975 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Tamkang University Press |
| record_format | Article |
| series | Journal of Applied Science and Engineering |
| spelling | doaj-art-b67bfa242ee245c48562449c54492c8a2025-01-31T15:39:09ZengTamkang University PressJournal of Applied Science and Engineering2708-99672708-99752025-01-012881827183510.6180/jase.202508_28(8).0019Multigraph-based Deep Programming Ability Tracing Method for StudentsYue Wang0Guanwen Zhang1Faculty of education, Shandong Normal University, Jinan, Shandong, China 250014School of journalism and communication, Shandong Normal University, Jinan, Shandon, China 250014Programming has become a crucial ability with the development of artificial intelligence, making it increasingly important to track and improve programming proficiency of students. However, most programming ability tracing methods rely primarily on the final outcomes of programming exercises to measure the programming proficiency, and neglect the rich behavioral structure information derived from the programming process, leading to a suboptimal solution in the programming ability tracing. To this end, we propose a multigraphbased deep programming ability tracing method for students (MDPAT), which consists of four components. Specifically, MDPAT devises the multigraph programming modelling via conducting the program knowledge graph and the program iteration graph to generate the submission representations of programming exercises, which effectively captures static structure information and dynamic structure information within the programming process. Then, MDPAT designs the programming knowledge gated update and the programming ability gated update to aggregate information of the programming knowledge and the programming ability that are derived from submission representations, which achieves a balanced and comprehensive tracking of evolving programming proficiency. Meanwhile, MDPAT utilizes the programming answer prediction to generate final programming proficiency measurement of students. Four components of MDPAT collaborate organically to achieve maximum performance in the programming ability tracing. Finally, extensive results on two real world datasets, especially on the Atcoder_C dataset, ACC shows a 5.06% improvement compared to the second best result, verify that MDPAT conducts a new standard baseline in the programming ability tracing.http://jase.tku.edu.tw/articles/jase-202508-28-08-0019programming ability tracingmultigraph programming modellinggated update learning |
| spellingShingle | Yue Wang Guanwen Zhang Multigraph-based Deep Programming Ability Tracing Method for Students Journal of Applied Science and Engineering programming ability tracing multigraph programming modelling gated update learning |
| title | Multigraph-based Deep Programming Ability Tracing Method for Students |
| title_full | Multigraph-based Deep Programming Ability Tracing Method for Students |
| title_fullStr | Multigraph-based Deep Programming Ability Tracing Method for Students |
| title_full_unstemmed | Multigraph-based Deep Programming Ability Tracing Method for Students |
| title_short | Multigraph-based Deep Programming Ability Tracing Method for Students |
| title_sort | multigraph based deep programming ability tracing method for students |
| topic | programming ability tracing multigraph programming modelling gated update learning |
| url | http://jase.tku.edu.tw/articles/jase-202508-28-08-0019 |
| work_keys_str_mv | AT yuewang multigraphbaseddeepprogrammingabilitytracingmethodforstudents AT guanwenzhang multigraphbaseddeepprogrammingabilitytracingmethodforstudents |