{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T18:32:00Z","timestamp":1779906720252,"version":"3.53.1"},"reference-count":26,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2024,3,23]],"date-time":"2024-03-23T00:00:00Z","timestamp":1711152000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems: Applications in Engineering and Technology"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:p>\n                    Emotion detection in educational dialogues, particularly within student-teacher interactions, has become a crucial research area for improving the learning experience. In this paper, we employ two models, one generic Bidirectional Encoder Representations from Transformers (BERT) and the Emotion detection model Robustly Optimized BERT Approach (EmoRoBERTa), to automatically classify emotions in a corpus of student-teacher chat interactions. Then subsequently, we validate these classifications using a scheme based on oracles, employing two generative large language models (ChatGPT and Bard). Experiments on emotion detection in dialogues between students and teachers revealed that EmoRoBERTa exhibited a reasonable level of agreement with the oracles, while ChatGPT demonstrated the highest consistency with EmoRoBERTa\u2019s predictions. Furthermore, we identified the impact of specific words on emotion classification, offering insights into the decision-making process of these models. The results not only highlight the prominent presence of emotions like\n                    <jats:italic toggle=\"yes\">approval, gratitude, curiosity, disapproval, amusement, confusion, remorse, joy<\/jats:italic>\n                    , and\n                    <jats:italic toggle=\"yes\">surprise<\/jats:italic>\n                    but also provide substantial support for the utilization of the proposed emotion detection model to enhance the student learning environment. Exploring the emotional aspects of educational dialogues holds the potential to enhance instruction methods, provide timely assistance to students in need, and create an improved learning atmosphere.\n                  <\/jats:p>","DOI":"10.3233\/jifs-219340","type":"journal-article","created":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T12:01:00Z","timestamp":1711454460000},"page":"241-251","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":7,"title":["Emotion detection in educational dialogues by transfer learning"],"prefix":"10.1177","volume":"50","author":[{"given":"Aurelio","family":"L\u00f3pez-L\u00f3pez","sequence":"first","affiliation":[{"name":"Computational Sciences Department, Instituto Nacional de Astrof\u00edsica, \u00d3ptica y Electr\u00f3nica, Tonantzintla, Puebla, M\u00e9xico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jes\u00fas Miguel","family":"Garc\u0131a-Gorrostieta","sequence":"additional","affiliation":[{"name":"Department of Computer Systems Engineering, Universidad de la Sierra, Moctezuma, Sonora, M\u00e9xico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel","family":"Gonz\u00e1lez-L\u00f3pez","sequence":"additional","affiliation":[{"name":"Department of Postgraduate Studies and Research, Instituto Tecnol\u00f3gico de Nogales, Sonora, M\u00e9xico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2024,3,23]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"crossref","unstructured":"CainesA.YannakoudakisH.EdmondsonH.AllenH.P\u00e9rez-ParedesP.ByrneB.ButteryP. The teacher-student chatroom corpus in: Proceedings of the 9th Workshop on NLP for Computer Assisted Language Learning Gothenburg Sweden LiU Electronic Press (2020) 10\u201320.","DOI":"10.3384\/ecp2017510"},{"key":"e_1_3_2_3_1","unstructured":"CalmaA.LeimeisterJ.M.LukowiczP.Oeste-Rei\u00dfS.ReitmaierT.SchmidtA.SickB.StummeG.ZweigK.A. From active learning to dedicated collaborative interactive learning in: ARCS 2016; 29th International Conference on Architecture of Computing Systems VDE (2016) 1\u20138."},{"key":"e_1_3_2_4_1","doi-asserted-by":"crossref","unstructured":"DemszkyD.Movshovitz-AttiasD.KoJ.CowenA.NemadeG.RaviS. GoEmotions:Adataset of fine-grained emotions in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics Association for Computational Linguistics (2020) 4040\u20134054.","DOI":"10.18653\/v1\/2020.acl-main.372"},{"key":"e_1_3_2_5_1","unstructured":"DevlinJ.ChangM.-W.LeeK.ToutanovaK. BERT: Pre-training of deep bidirectional transformers for language understanding in: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies Volume 1 (Long and Short Papers) pages 4171\u20134186 Minneapolis Minnesota Association for Computational Linguistics (2019) 4171\u20134186."},{"key":"e_1_3_2_6_1","unstructured":"GoetzT.ZirngiblA.PekrunR.HallN. Emotions learning and achievement from an educationalpsychological perspective (2003)."},{"key":"e_1_3_2_7_1","doi-asserted-by":"crossref","unstructured":"KamathR.GhoshalA.EswaranS.HonnavalliP. An enhanced context-based emotion detection model using roberta in: 2022 IEEE International Conference on Electronics Computing and Communication Technologies (CONECCT) IEEE (2022) 1\u20136.","DOI":"10.1109\/CONECCT55679.2022.9865796"},{"key":"e_1_3_2_8_1","doi-asserted-by":"crossref","unstructured":"KimC.PekrunR. 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