From Sparse to Smart: Leveraging AI for Effective Online Judge Problem Classification in Programming Education

Filipe Dwan Pereira; Maely Moraes; Marcelo Henklain; Arto Hellas; Elaine Oliveira; Dragan Gasevic; Raimundo Barreto; Rafael Mello

doi:10.1007/978-3-031-72315-5_25

From Sparse to Smart: Leveraging AI for Effective Online Judge Problem Classification in Programming Education

Filipe Dwan Pereira^*, Maely Moraes, Marcelo Henklain, Arto Hellas, Elaine Oliveira, Dragan Gasevic, Raimundo Barreto, Rafael Mello

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

Abstract

Online Judges (OJs) have gained substantial traction in programming education due to their ability to simultaneously present problem-solving challenges to students while offering instant feedback and correction. Such technologies are also essential to allow students in remote areas to access quality and equitable education. Nonetheless, OJ systems often lack sufficient amounts of annotated data (i.e., labelled data) about the topics of the problems that they aim to support, which makes choosing appropriate problems hard. Topic annotations hold significant value for instructors when selecting problems for assignments and for novice students seeking independent use of OJ systems. In this work, we propose and evaluate a pre-trained deep learning architecture and an active learning methodology to automatically annotate OJ problems in the context of introductory programming. Our results show that, when using a smaller amount of data, the methodology demonstrates performance comparable to those of the existing state-of-the-art methods for the identical task.

Original language	English
Title of host publication	Technology Enhanced Learning for Inclusive and Equitable Quality Education - 19th European Conference on Technology Enhanced Learning, EC-TEL 2024, Proceedings
Editors	Rafael Ferreira Mello, Nikol Rummel, Ioana Jivet, Gerti Pishtari, José A. Ruipérez Valiente
Publisher	Springer
Pages	359-374
Number of pages	16
ISBN (Print)	978-3-031-72314-8
DOIs	https://doi.org/10.1007/978-3-031-72315-5_25
Publication status	Published - 2024
MoE publication type	A4 Conference publication
Event	European Conference on Technology Enhanced Learning - Krems, Austria Duration: 16 Sept 2024 → 20 Sept 2024 Conference number: 19

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	15159 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	European Conference on Technology Enhanced Learning
Abbreviated title	EC-TEL
Country/Territory	Austria
City	Krems
Period	16/09/2024 → 20/09/2024

Keywords

Computer Education
Educational Text Mining
Online Judges
Resource-limited

Access to Document

10.1007/978-3-031-72315-5_25

Cite this

Pereira, F. D., Moraes, M., Henklain, M., Hellas, A., Oliveira, E., Gasevic, D., Barreto, R., & Mello, R. (2024). From Sparse to Smart: Leveraging AI for Effective Online Judge Problem Classification in Programming Education. In R. Ferreira Mello, N. Rummel, I. Jivet, G. Pishtari, & J. A. Ruipérez Valiente (Eds.), Technology Enhanced Learning for Inclusive and Equitable Quality Education - 19th European Conference on Technology Enhanced Learning, EC-TEL 2024, Proceedings (pp. 359-374). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 15159 LNCS). Springer. https://doi.org/10.1007/978-3-031-72315-5_25

Pereira, Filipe Dwan ; Moraes, Maely ; Henklain, Marcelo et al. / From Sparse to Smart: Leveraging AI for Effective Online Judge Problem Classification in Programming Education. Technology Enhanced Learning for Inclusive and Equitable Quality Education - 19th European Conference on Technology Enhanced Learning, EC-TEL 2024, Proceedings. editor / Rafael Ferreira Mello ; Nikol Rummel ; Ioana Jivet ; Gerti Pishtari ; José A. Ruipérez Valiente. Springer, 2024. pp. 359-374 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "From Sparse to Smart: Leveraging AI for Effective Online Judge Problem Classification in Programming Education",

abstract = "Online Judges (OJs) have gained substantial traction in programming education due to their ability to simultaneously present problem-solving challenges to students while offering instant feedback and correction. Such technologies are also essential to allow students in remote areas to access quality and equitable education. Nonetheless, OJ systems often lack sufficient amounts of annotated data (i.e., labelled data) about the topics of the problems that they aim to support, which makes choosing appropriate problems hard. Topic annotations hold significant value for instructors when selecting problems for assignments and for novice students seeking independent use of OJ systems. In this work, we propose and evaluate a pre-trained deep learning architecture and an active learning methodology to automatically annotate OJ problems in the context of introductory programming. Our results show that, when using a smaller amount of data, the methodology demonstrates performance comparable to those of the existing state-of-the-art methods for the identical task.",

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Pereira, FD, Moraes, M, Henklain, M, Hellas, A, Oliveira, E, Gasevic, D, Barreto, R & Mello, R 2024, From Sparse to Smart: Leveraging AI for Effective Online Judge Problem Classification in Programming Education. in R Ferreira Mello, N Rummel, I Jivet, G Pishtari & JA Ruipérez Valiente (eds), Technology Enhanced Learning for Inclusive and Equitable Quality Education - 19th European Conference on Technology Enhanced Learning, EC-TEL 2024, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 15159 LNCS, Springer, pp. 359-374, European Conference on Technology Enhanced Learning, Krems, Austria, 16/09/2024. https://doi.org/10.1007/978-3-031-72315-5_25

From Sparse to Smart: Leveraging AI for Effective Online Judge Problem Classification in Programming Education. / Pereira, Filipe Dwan; Moraes, Maely; Henklain, Marcelo et al.
Technology Enhanced Learning for Inclusive and Equitable Quality Education - 19th European Conference on Technology Enhanced Learning, EC-TEL 2024, Proceedings. ed. / Rafael Ferreira Mello; Nikol Rummel; Ioana Jivet; Gerti Pishtari; José A. Ruipérez Valiente. Springer, 2024. p. 359-374 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 15159 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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AU - Moraes, Maely

AU - Henklain, Marcelo

AU - Hellas, Arto

AU - Oliveira, Elaine

AU - Gasevic, Dragan

AU - Barreto, Raimundo

AU - Mello, Rafael

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AB - Online Judges (OJs) have gained substantial traction in programming education due to their ability to simultaneously present problem-solving challenges to students while offering instant feedback and correction. Such technologies are also essential to allow students in remote areas to access quality and equitable education. Nonetheless, OJ systems often lack sufficient amounts of annotated data (i.e., labelled data) about the topics of the problems that they aim to support, which makes choosing appropriate problems hard. Topic annotations hold significant value for instructors when selecting problems for assignments and for novice students seeking independent use of OJ systems. In this work, we propose and evaluate a pre-trained deep learning architecture and an active learning methodology to automatically annotate OJ problems in the context of introductory programming. Our results show that, when using a smaller amount of data, the methodology demonstrates performance comparable to those of the existing state-of-the-art methods for the identical task.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Pereira FD, Moraes M, Henklain M, Hellas A, Oliveira E, Gasevic D et al. From Sparse to Smart: Leveraging AI for Effective Online Judge Problem Classification in Programming Education. In Ferreira Mello R, Rummel N, Jivet I, Pishtari G, Ruipérez Valiente JA, editors, Technology Enhanced Learning for Inclusive and Equitable Quality Education - 19th European Conference on Technology Enhanced Learning, EC-TEL 2024, Proceedings. Springer. 2024. p. 359-374. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-72315-5_25