Multigraph-based Deep Programming Ability Tracing Method for Students

Yue  Wang; Guanwen Zhang

doi:10.6180/jase.202508_28(8).0019

Multigraph-based Deep Programming Ability Tracing Method for Students

Computer Science and Information Engineering

Yue Wang¹ and Guanwen Zhang²This email address is being protected from spambots. You need JavaScript enabled to view it.

¹Faculty of education, Shandong Normal University, Jinan, Shandong, China 250014

²School of journalism and communication, Shandong Normal University, Jinan, Shandon, China 250014

Received: October 9, 2024
Accepted: October 23, 2024
Publication Date: November 24, 2024

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202508_28(8).0019

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 multigraph based 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 program ming 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.

Keywords: Programming ability tracing; multigraph programming modelling; gated update learning

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