{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T02:28:16Z","timestamp":1772159296899,"version":"3.50.1"},"publisher-location":"Cham","reference-count":11,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031362712","type":"print"},{"value":"9783031362729","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-36272-9_79","type":"book-chapter","created":{"date-parts":[[2023,6,25]],"date-time":"2023-06-25T23:03:19Z","timestamp":1687734199000},"page":"830-835","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Training Language Models for\u00a0Programming Feedback Using Automated Repair Tools"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2272-2763","authenticated-orcid":false,"given":"Charles","family":"Koutcheme","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,26]]},"reference":[{"key":"79_CR1","doi-asserted-by":"publisher","unstructured":"Azcona, D., Smeaton, A.: +5 Million Python & Bash Programming Submissions for 5 Courses & Grades for Computer-Based Exams Over 3 Academic Years (2020). https:\/\/doi.org\/10.6084\/m9.figshare.12610958.v1","DOI":"10.6084\/m9.figshare.12610958.v1"},{"key":"79_CR2","doi-asserted-by":"publisher","unstructured":"Chen, M., et al.: Evaluating large language models trained on code (2021). https:\/\/doi.org\/10.48550\/ARXIV.2107.03374","DOI":"10.48550\/ARXIV.2107.03374"},{"key":"79_CR3","unstructured":"Cleuziou, G., Flouvat, F.: Learning student program embeddings using abstract execution traces. In: 14th International Conference on Educational Data Mining, pp. 252\u2013262 (2021)"},{"key":"79_CR4","doi-asserted-by":"crossref","unstructured":"Gulwani, S., Radi\u010dek, I., Zuleger, F.: Automated clustering and program repair for introductory programming assignments (2018). http:\/\/arxiv.org\/abs\/1603.03165, arXiv:1603.03165 [cs]","DOI":"10.1145\/3192366.3192387"},{"key":"79_CR5","doi-asserted-by":"crossref","unstructured":"Hu, Y., Ahmed, U.Z., Mechtaev, S., Leong, B., Roychoudhury, A.: Re-factoring based program repair applied to programming assignments. In: 2019 34th IEEE\/ACM International Conference on Automated Software Engineering (ASE), pp. 388\u2013398. IEEE\/ACM (2019)","DOI":"10.1109\/ASE.2019.00044"},{"issue":"2","key":"79_CR6","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1080\/08993400802114581","volume":"18","author":"R McCauley","year":"2008","unstructured":"McCauley, R., et al.: Debugging: a review of the literature from an educational perspective. Comput. Sci. Educ. 18(2), 67\u201392 (2008). https:\/\/doi.org\/10.1080\/08993400802114581","journal-title":"Comput. Sci. Educ."},{"key":"79_CR7","doi-asserted-by":"crossref","unstructured":"Pu, Y., Narasimhan, K., Solar-Lezama, A., Barzilay, R.: sk_p: a neural program corrector for MOOCs. arXiv:1607.02902 [cs] (2016). http:\/\/arxiv.org\/abs\/1607.02902","DOI":"10.1145\/2984043.2989222"},{"key":"79_CR8","doi-asserted-by":"publisher","unstructured":"Singh, R., Gulwani, S., Solar-Lezama, A.: Automated feedback generation for introductory programming assignments. In: Proceedings of the 34th ACM SIGPLAN Conference on Programming Language Design and Implementation, pp. 15\u201326. PLDI 2013, Association for Computing Machinery, New York, NY, USA (2013). https:\/\/doi.org\/10.1145\/2491956.2462195","DOI":"10.1145\/2491956.2462195"},{"key":"79_CR9","doi-asserted-by":"crossref","unstructured":"Wang, K., Singh, R., Su, Z.: Data-driven feedback generation for introductory programming exercises. arXiv:1711.07148 [cs] (2017). http:\/\/arxiv.org\/abs\/1711.07148","DOI":"10.1145\/3192366.3192384"},{"key":"79_CR10","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, W., Joty, S.R., Hoi, S.C.H.: Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation. In: EMNLP, pp. 8696\u20138708. Association for Computational Linguistics (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.685"},{"key":"79_CR11","doi-asserted-by":"publisher","unstructured":"Zhang, J., et al.: Repairing bugs in python assignments using large language models (2022). https:\/\/doi.org\/10.48550\/ARXIV.2209.14876","DOI":"10.48550\/ARXIV.2209.14876"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Education"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-36272-9_79","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:14:23Z","timestamp":1710260063000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-36272-9_79"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031362712","9783031362729"],"references-count":11,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-36272-9_79","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"26 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIED","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence in Education","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tokyo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aied2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.aied2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"311","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"53","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"26","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}