Personalised programming education with knowledge tracing
| dc.contributor.author | Shaka, Martha | en |
| dc.contributor.author | Carraro, Diego | en |
| dc.contributor.author | Brown, Kenneth N. | en |
| dc.contributor.funder | Science Foundation Ireland | en |
| dc.date.accessioned | 2024-01-23T12:37:12Z | |
| dc.date.available | 2024-01-23T12:37:12Z | |
| dc.date.issued | 2023-12-14 | en |
| dc.description.abstract | In traditional programming education, addressing diverse student needs and providing effective and scalable learning experiences is challenging. Conventional methods struggle to adapt to varying learning styles and offer personalised feedback. AI-based Programming Tools (AIPTs) have shown promise in automating feedback, simplifying programming concepts, and guiding students. Their widespread adoption is hindered by limitations related to accuracy, explanation, and personalisation. Conversely, AIPTs tailored for expert programmers, such as ChatGPT and Copilot, have gained popularity for their productivity-enhancing capabilities, but they still fall short in terms of personalisation, neglecting individual students’ unique knowledge and skills. Our research aims to leverage AI to create AIPTs that offer personalised feedback through adaptive learning, accommodating diverse student backgrounds and proficiency levels. In particular, we explore using Knowledge Tracing (KT) to anticipate specific syntax errors in programming assignments, addressing the challenges novices face in acquiring syntactical knowledge. The findings suggest the KT’s potential to transform programming education by enabling timely interventions for students dealing with specific errors or misconceptions, automating personalised feedback, and informing tailored instructional strategies | en |
| dc.description.status | Peer reviewed | en |
| dc.description.version | Accepted Version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Shaka, M., Carraro, D. and Brown, K.N. (2023) ‘Personalised programming education with knowledge tracing’, in Proceedings of the 2023 Conference on Human Centered Artificial Intelligence: Education and Practice. Dublin Ireland: ACM, pp. 47–47. https://doi.org/10.1145/3633083.3633220. | en |
| dc.identifier.doi | 10.1145/3633083.3633220 | en |
| dc.identifier.startpage | 47 | en |
| dc.identifier.uri | https://hdl.handle.net/10468/15415 | |
| dc.language.iso | en | en |
| dc.publisher | ACM, Association for Computing Machinery | en |
| dc.relation.ispartof | Proceedings of the 2023 Conference on Human Centered Artificial Intelligence: Education and Practice | en |
| dc.relation.project | info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/ | en |
| dc.rights | © 2023 Copyright held by the owner/author(s). Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). | en |
| dc.subject | Knowledge tracing | en |
| dc.subject | Syntax errors | en |
| dc.subject | Programming assignments | en |
| dc.subject | Personalisation | en |
| dc.subject | Automated feedback | en |
| dc.subject | Artificial intelligence | en |
| dc.subject | AI | en |
| dc.subject | Interactive learning environments | en |
| dc.subject | Applied computing | en |
| dc.title | Personalised programming education with knowledge tracing | en |
| dc.type | Article (peer-reviewed) | en |
| dc.type | proceedings-article | en |
