The effectiveness of group and online learning environments has been widely recognized. Hence, supporting teachers monitoring of group activities in online learning environments is increasingly important. In this study, we propose algorithms for analyzing discussion transitions and their main themes. The first algorithm aims to help teachers quickly identify groups that require intervention. It uses entities and Wikipedia data in the discussion texts to create models that capture continuously changing discussion content over time. The second algorithm aims to instantaneously identify the discussion topic of the group prior to teacher intervention. From the Wikipedia categories obtained regarding discussion transition analysis, the algorithm selects those with the highest probability of being appropriate for the discussion text. To evaluate the effectiveness of the proposed algorithms, we tested them on the W2E dataset, which includes topics comprising multiple events, and an actual discussion dataset. The results confirmed that the algorithms detected the discussion transitions and main themes with high accuracies.
AhmadM.JunusK.SantosoH. (2022). Automatic content analysis of asynchronous discussion forum transcripts: A systematic literature review. Education and Information Technologies, 27, 11355–11410. https://doi.org/10.1007/s10639-022-11065-w
2.
AshikawaM.KawamuraT.OhsugaA. (2019). Proposal of grade training method for quality improvement in microtask crowdsourcing. Web Intelligence, 17(4), 313–326. https://doi.org/10.3233/WEB-190421
3.
AtapattuT.FalknerK. (2016). A framework for topic generation and labeling from MOOC discussions. In Proceedings of the Third (2016) ACM conference on learning @ Scale, L@S ’16 (pp. 201–204). New York, NY, USA: Association for Computing Machinery. ISBN 9781450337267. https://doi.org/10.1145/2876034.2893414
4.
BleiD. M.NgA. Y.JordanM. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(null), 993–1022.
5.
CarbonellJ.GoldsteinJ. (1998). The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proceedings of the 21st Annual international ACM SIGIR conference on research and development in information retrieval, SIGIR ’98 (pp. 335–336). New York, NY, USA: Association for Computing Machinery. ISBN 1581130155. https://doi.org/10.1145/290941.291025
6.
ChangM. W.RatinovL.RothD.SrikumarV. (2008). Importance of semantic representation: Dataless classification. In Proceedings of the 23rd National conference on artificial intelligence—Volume 2 (AAAI’08, pp. 830–835). AAAI Press. ISBN 9781577353683.
7.
ChatterjeeS.DietzL. (2021). Entity retrieval using fine-grained entity aspects. In Proceedings of the 44th International ACM SIGIR conference on research and development in information retrieval, SIGIR ’21 (pp. 1662–1666). New York, NY, USA: Association for Computing Machinery. ISBN 9781450380379. https://doi.org/10.1145/3404835.3463035
8.
ChenB. (2014). Visualizing semantic space of online discourse: The knowledge forum case. In Proceedings of the Fourth international conference on learning analytics and knowledge, LAK ’14 (pp. 271–272). New York, NY, USA: Association for Computing Machinery. ISBN 9781450326643. https://doi.org/10.1145/2567574.2567595
9.
ChuZ.WangS.XieJ.ZhuT.YanY.YeJ.ZhongA.HuX.LiangJ.YuP. S.WenQ. (2025). LLM agents for education: Advances and applications. In C. Christodoulopoulos, T. Chakraborty, C. Rose, & V. Peng (Eds.), Findings of the association for computational linguistics: EMNLP 2025 (pp. 13782–13810). Suzhou, China: Association for Computational Linguistics. ISBN 979-8-89176-335-7. https://doi.org/10.18653/v1/2025.findings-emnlp.743
10.
DeerwesterS. C.DumaisS. T.LandauerT. K.FurnasG. W.HarshmanR. A. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391–407.
11.
EbbinghausH. (1987). Memory: A contribution to experimental psychology. Dover Publications.
12.
Ezen-CanA.BoyerK. E.KelloggS.BoothS. (2015). Unsupervised modeling for understanding mooc discussion forums: A learning analytics approach. In Proceedings of the Fifth international conference on learning analytics and knowledge, LAK ’15 (pp. 146–150). New York, NY, USA: Association for Computing Machinery. ISBN 9781450334174. https://doi.org/10.1145/2723576.2723589
13.
FerraginaP.ScaiellaU. (2010). TAGME: On-the-fly annotation of short text fragments (by Wikipedia entities). In Proceedings of the 19th ACM international conference on information and knowledge management, CIKM ’10 (pp. 1625–1628). New York, NY, USA: Association for Computing Machinery. ISBN 9781450300995. https://doi.org/10.1145/1871437.1871689
14.
HoangT. A.VoK. D.NejdlW. (2018). W2E: A worldwide-event benchmark dataset for topic detection and tracking. In Proceedings of the 27th ACM international conference on information and knowledge management, CIKM ’18 (pp. 1847–1850). New York, NY, USA: Association for Computing Machinery. ISBN 9781450360142. https://doi.org/10.1145/3269206.3269309
15.
HoffartJ.YosefM. A.BordinoI.FürstenauH.PinkalM.SpaniolM.TanevaB.ThaterS.WeikumG. (2011). Robust disambiguation of named entities in text. In R. Barzilay & M. Johnson (Eds.), Proceedings of the 2011 conference on empirical methods in natural language processing (pp. 782–792). Edinburgh, Scotland, UK: Association for Computational Linguistics. https://aclanthology.org/D11-1072/
16.
HsiaoI. H.AwasthiP. (2015). Topic facet modeling: Semantic visual analytics for online discussion forums. In Proceedings of the Fifth international conference on learning analytics and knowledge, LAK ’15 (pp. 231–235). New York, NY, USA: Association for Computing Machinery. ISBN 9781450334174. https://doi.org/10.1145/2723576.2723613
17.
LeQ.MikolovT. (2014). Distributed representations of sentences and documents. In Proceedings of the 31st international conference on international conference on machine learning—Volume 32, ICML’14 (pp. II–1188–II–1196). JMLR.org.
18.
LiebA.GoelT. (2024). Student interaction with newtbot: An LLM-as-tutor chatbot for secondary physics education. In Extended abstracts of the CHI conference on human factors in computing systems, CHI EA ’24. New York, NY, USA: Association for Computing Machinery. ISBN 9798400703317. https://doi.org/10.1145/3613905.3647957
19.
LiuB.HanF. X.NiuD.KongL.LaiK.XuY. (2020). Story Forest: Extracting events and telling stories from breaking news. ACM Transactions on Knowledge Discovery From Data, 14(3), Article No. 31. https://doi.org/10.1145/3377939
20.
MachizawaD.SawahataN.IkejiriR.SumikawaY. (2026). StoryNetworks: An annotated dataset of event dependencies from short descriptions. In New trends in theory and practice of digital libraries (pp. 46–56). Cham: Springer Nature Switzerland. ISBN 978-3-032-06136-2.
21.
MendesP. N.JakobM.García-SilvaA.BizerC. (2011). DBpedia spotlight: Shedding light on the web of documents. In Proceedings of the 7th International conference on semantic systems, I-Semantics ’11 (pp. 1–8). New York, NY, USA: Association for Computing Machinery. ISBN 9781450306218. https://doi.org/10.1145/2063518.2063519
22.
Ministry of Education, Culture, Sports, Science and Technology (2018) Koutou gakkou gakushuu sidou youryou kaisetsu chiri rekishi hen.
23.
MullaS.ShaikhN. F. (2024). Over comparative study of text summarization techniques based on graph neural networks. Web Intelligence, 22(2), 231–248. https://doi.org/10.3233/WEB-230014
24.
NanniF.PonzettoS. P.DietzL. (2018). Entity-aspect linking: Providing fine-grained semantics of entities in context. In Proceedings of the 18th ACM/IEEE on joint conference on digital libraries, JCDL ’18 (pp. 49–58). New York, NY, USA: Association for Computing Machinery. ISBN 9781450351782. https://doi.org/10.1145/3197026.3197047
25.
PrasojoR. E.KacimiM.NuttW. (2015). Entity and aspect extraction for organizing news comments. In Proceedings of the 24th ACM International on conference on information and knowledge management, CIKM ’15 (pp. 233–242). New York, NY, USA: Association for Computing Machinery. ISBN 9781450337946. https://doi.org/10.1145/2806416.2806576
26.
QiY.ZhouL.SiH.WanJ.JinT. (2017). An approach to news event detection and tracking based on stream of online news. In 2017 9th International conference on intelligent human–machine systems and cybernetics (IHMSC) (Vol. 2, pp. 193–196). https://doi.org/10.1109/IHMSC.2017.158
27.
RadinskyK.DavidovichS. (2012). Learning to predict from textual data. Journal of Artificial Intelligence Research, 45(1), 641–684.
28.
RaganatoA.Camacho-ColladosJ.NavigliR. (2017). Word sense disambiguation: A unified evaluation framework and empirical comparison. In M. Lapata, P. Blunsom, & A. Koller (Eds.), Proceedings of the 15th Conference of the European chapter of the Association for Computational Linguistics: Volume 1, Long Papers (pp. 99–110). Valencia, Spain: Association for Computational Linguistics. https://aclanthology.org/E17-1010/
29.
RamsdellJ.DietzL. (2020). A large test collection for entity aspect linking. In Proceedings of the 29th ACM international conference on information & knowledge management, CIKM ’20 (pp. 3109–3116). New York, NY, USA: Association for Computing Machinery. ISBN 9781450368599. https://doi.org/10.1145/3340531.3412875
30.
SawahataN.MachizawaD.IkejiriR.SumikawaY. (2026). LLM-based dependency tracking for short event descriptions. In Linking theory and practice of digital libraries (pp. 71–89). Cham: Springer Nature Switzerland. ISBN 978-3-032-05409-8.
31.
ShenC.WangD.LiT. (2010). Topic aspect analysis for multi-document summarization. In Proceedings of the 19th ACM international conference on information and knowledge management, CIKM ’10 (pp. 1545–1548). New York, NY, USA: Association for Computing Machinery. ISBN 9781450300995. https://doi.org/10.1145/1871437.1871668
32.
ShochchoM. I.RahmanM. A. U.RohanS.IslamA.HeickalH.RahmanA. M.AminM. A.AliA. A. (2025). Improving user engagement and learning outcomes in LLM-based Python tutor: A study of pace. In Proceedings of the Extended Abstracts of the CHI conference on human factors in computing systems, CHI EA ’25. New York, NY, USA: Association for Computing Machinery. ISBN 9798400713958. https://doi.org/10.1145/3706599.3720240
33.
SinghJ.NejdlW.AnandA. (2016). History by diversity: Helping historians search news archives. In Proceedings of the 2016 ACM on conference on human information interaction and retrieval, CHIIR ’16 (pp. 183–192). New York, NY, USA: Association for Computing Machinery. ISBN 9781450337519. https://doi.org/10.1145/2854946.2854959
34.
SumikawaY.TakadaA.IchinoseA.MurakamiA.ToyonoY.IkejiriR.SakasegawaM.SekineK.YamauchiY. (2022). Online discussion transition analysis for group learning support. In 2022 IEEE/WIC/ACM International joint conference on web intelligence and intelligent agent technology (WI-IAT) (pp. 249–255). https://doi.org/10.1109/WI-IAT55865.2022.00043
35.
TanZ.ZhangP.TanJ.GuoL. (2014). A multi-layer event detection algorithm for detecting global and local hot events in social networks. Procedia Computer Science, 29, 2080–2089. https://doi.org/10.1016/j.procs.2014.05.192
36.
WangT.ZhanY.LianJ.HuZ.YuanN. J.ZhangQ.XieX.XiongH. (2025a). LLM-powered multi-agent framework for goal-oriented learning in intelligent tutoring system. In Companion Proceedings of the ACM on web conference 2025, WWW ’25 (pp. 510–519). New York, NY, USA: Association for Computing Machinery. ISBN 9798400713316. https://doi.org/10.1145/3701716.3715244
37.
WangX. J.LeeC. P.MutluB. (2025b). LearnMate: Enhancing online education with LLM-powered personalized learning plans and support. In Proceedings of the extended abstracts of the CHI conference on human factors in computing systems, CHI EA ’25. New York, NY, USA: Association for Computing Machinery. ISBN 9798400713958. https://doi.org/10.1145/3706599.3719857
38.
YangC. C.ShiX.WeiC. P. (2009). Discovering event evolution graphs from news corpora. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, 39(4), 850–863. https://doi.org/10.1109/TSMCA.2009.2015885
39.
YangY.UrgoK.ArguelloJ.CapraR. (2025). Search+chat: Integrating search and GenAI to support users with learning-oriented search tasks. In Proceedings of the 2025 ACM SIGIR conference on human information interaction and retrieval, CHIIR ’25 (pp. 57–70). New York, NY, USA: Association for Computing Machinery. ISBN 9798400712906. https://doi.org/10.1145/3698204.3716446
40.
ZarraT.ChihebR.FaiziR.El AfiaA. (2018). Student interactions in online discussion forums: Visual analysis with LDA topic models. In Proceedings of the international conference on learning and optimization algorithms: Theory and applications, LOPAL ’18. New York, NY, USA: Association for Computing Machinery. ISBN 9781450353045. https://doi.org/10.1145/3230905.3230920
41.
ZhangC.LyuJ.XuK. (2022a). A Storytree-based model for inter-document causal relation extraction from news articles. Knowledge and Information Systems, 65(2), 827–853. https://doi.org/10.1007/s10115-022-01781-7
42.
ZhangG.ZhuZ.ZhuS.LiangR.SunG. (2022b). Towards a better understanding of the role of visualization in online learning: A review. Visual Informatics, 6(4), 22–33. https://doi.org/10.1016/j.visinf.2022.09.002
43.
ZhouX.GururajanR.LiY.VenkataramanR.TaoX.BargshadyG.BaruaP. D.Kondalsamy-ChennakesavanS. (2020). A survey on text classification and its applications. Web Intelligence, 18(3), 205–216. https://doi.org/10.3233/WEB-200442
