---
title: "The TalkMoves Dataset — paper by Suresh et al. (2022)"
description: "The authors release the TalkMoves dataset to fill this gap. 32 references, cited by 29 papers here."
authors: ["Abhijit Suresh", "Jennifer Jacobs", "Charis Harty", "Margaret Perkoff", "James H. Martin", "Tamara Sumner"]
year: 2022
venue: "Proceedings of the Language Resources and Evaluation Conference"
source: "https://arxiv.org/abs/2204.09652"
doi: "10.63317/2azjpibjyj5e"
arxiv: "2204.09652"
licence: "CC BY-NC-SA 4.0 (arXiv)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/suresh-2022-talkmoves-dataset-k-12-mathematics/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/suresh-2022-talkmoves-dataset-k-12-mathematics/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2022](https://md.research.elamur.ai/free/en/papers/2022/page/5/) / The TalkMoves Dataset: K-12 Mathematics Lesson Transcripts Annotated for Teacher and Student Discursive Moves

# The TalkMoves Dataset: K-12 Mathematics Lesson Transcripts Annotated for Teacher and Student Discursive Moves

**Authors:** [Abhijit Suresh](https://md.research.elamur.ai/free/en/authors/abhijit-suresh/), [Jennifer Jacobs](https://md.research.elamur.ai/free/en/authors/jennifer-jacobs/), Charis Harty, [Margaret Perkoff](https://md.research.elamur.ai/free/en/authors/margaret-perkoff/), [James H. Martin](https://md.research.elamur.ai/free/en/authors/james-h-martin/) and [Tamara Sumner](https://md.research.elamur.ai/free/en/authors/tamara-sumner/) · **Year:** 2022 · **Venue:** [Proceedings of the Language Resources and Evaluation Conference](https://md.research.elamur.ai/free/en/venues/lrec/) ([LREC 2022](https://md.research.elamur.ai/free/en/venues/lrec/2022/)) · **Licence:** [CC BY-NC-SA 4.0 (arXiv)](https://creativecommons.org/licenses/by-nc-sa/4.0/)

**Links:** [Publisher's page](https://doi.org/10.63317/2azjpibjyj5e) · [PDF](https://arxiv.org/pdf/2204.09652) · [DOI 10.63317/2azjpibjyj5e](https://doi.org/10.63317/2azjpibjyj5e) · [arXiv 2204.09652](https://arxiv.org/abs/2204.09652) · [OpenAlex](https://openalex.org/W3203068943)

**Citations:** 14 citations (OpenAlex, 2026-10-14) · [29](https://md.research.elamur.ai/free/en/papers/suresh-2022-talkmoves-dataset-k-12-mathematics/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Privacy and practical constraints mean classroom recordings are rarely shared, so researchers building NLP tools for education have little public data. Existing dialogue-act corpora (Switchboard, ICSI/MRDA, MultiWOZ, EMOTyDA) also do not capture multi-party talk between teachers and students. The authors release the TalkMoves dataset to fill this gap. It contains 567 human-transcribed K-12 mathematics lesson transcripts. Most are complete in-person lessons with whole-class or small-group discussion, and a few are online sessions. The material comes from Inside Mathematics, the TIMSS 1999 video study, Video Mosaic, and anonymized transcripts gathered through the TalkMoves application. Each sentence is segmented by speaker role (teacher or student). Annotators hand-coded each one as one of ten accountable talk moves or as "none." Utterances also carry automatically assigned dialogue acts based on the Switchboard scheme. Two annotators reached high Cohen's kappa agreement, measured before coding and again about halfway through. The corpus and its coding protocol give educators and NLP researchers a theory-grounded resource for studying classroom discourse and training classifiers. Parts of it already power the TalkMoves feedback tool for teachers.

## Key points
- Size: 567 transcripts, 174,186 teacher utterances, 59,874 student utterances, about 1.8M words (15,830 unique). Lessons typically run about 55 minutes.
- Label set: six teacher talk moves (e.g., Keeping everyone together, Revoicing, Pressing for reasoning) and four student moves (e.g., Making a claim, Providing evidence or reasoning). These were chosen for their frequency, reliability and expert guidance.
- Sources: Inside Mathematics (Dana Center, UT Austin), TIMSS 1999 video study, Video Mosaic (Rutgers), and the TalkBack studies run through talkmoves.com.
- Preprocessing: raw transcripts were converted to standardized CSV files, and transcription artifacts such as "[background noise]" were stripped.
- The closest related resource is the Educational Uptake dataset (Demszky et al., 2021). The released corpus supports the deployed TalkMoves application, which gives teachers automated feedback.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Colorado Boulder](https://md.research.elamur.ai/free/en/universities/university-of-colorado-boulder/) — University, Boulder, United States · 76 works · 51,396 citations
- [University of Colorado System](https://md.research.elamur.ai/free/en/universities/university-of-colorado-system/) — University, Boulder, United States · 11 works · 1,747 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 19,503 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 18,805 works · [🤝 Social Sciences](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · 2,009 works · [🎓 Education](https://md.research.elamur.ai/free/en/shelves/social-sciences/education/) · 221 works

## Cite

Cite as: Abhijit Suresh, Jennifer Jacobs, Charis Harty, Margaret Perkoff, James H. Martin and Tamara Sumner. 2022. The TalkMoves Dataset: K-12 Mathematics Lesson Transcripts Annotated for Teacher and Student Discursive Moves. Proceedings of the Language Resources and Evaluation Conference.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{suresh2022talkmoves,
  title = {{The TalkMoves Dataset: K-12 Mathematics Lesson Transcripts Annotated for Teacher and Student Discursive Moves}},
  author = {Abhijit Suresh, Jennifer Jacobs, Charis Harty, Margaret Perkoff, James H. Martin and Tamara Sumner},
  booktitle = {Proceedings of the Language Resources and Evaluation Conference},
  year = {2022},
  doi = {10.63317/2azjpibjyj5e},
  eprint = {2204.09652},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.63317/2azjpibjyj5e}
}
```

- [← previous SciNLI: A Corpus for Natural Language Inference on Scientific Text](https://md.research.elamur.ai/free/en/papers/sadat-2022-scinli-corpus-natural-language-inference/)
- [next → ArMIS - The Arabic Misogyny and Sexism Corpus with Annotator Subjective Disagreements](https://md.research.elamur.ai/free/en/papers/almanea-2022-armis-arabic-misogyny-sexism-corpus/)
- [↑ back to papers of 2022](https://md.research.elamur.ai/free/en/papers/2022/page/5/)

## References

32 references, 7 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Association, N. G. et al. (2010). Common core state standards. Washington, DC. [find](https://scholar.google.com/scholar?q=Association%2C+N%2E+G%2E+et+al%2E+%282010%29%2E+Common+core+state+standards%2E+Washington%2C+DC%2E)
2. Boston, M. (2012). Assessing instructional quality in mathematics. The Elementary School Journal, 113(1):76–104. [find](https://scholar.google.com/scholar?q=Boston%2C+M%2E+%282012%29%2E+Assessing+instructional+quality+in+mathematics%2E+The+Elementary+School+Journal%2C+113%281%29%3A76%E2%80%93104%2E)
3. Budzianowski, P., Wen, T.-H., Tseng, B.-H., Casanueva, I., Ultes, S., Ramadan, O., and Gašić, M. (2018). [Multiwoz–a large-scale multi-domain wizard-of-oz dataset for task-oriented dialogue modelling](https://md.research.elamur.ai/free/en/papers/budzianowski-2018-multiwoz-large-scale-multi-domain/). arXiv preprint arXiv:1810.00278.
4. Candela, A. G., Boston, M. D., and Dixon, J. K. (2020). Discourse actions to promote student access. Mathematics Teacher: Learning and Teaching PK-12, 113(4):266–277. [find](https://scholar.google.com/scholar?q=Candela%2C+A%2E+G%2E%2C+Boston%2C+M%2E+D%2E%2C+and+Dixon%2C+J%2E+K%2E+%282020%29%2E+Discourse+actions+to+promote+student+access%2E+Mathematics+Teacher%3A+Learning+and+Teaching+PK%2D12%2C+113%284%29%3A266%E2%80%93277%2E)
5. Clark, K., Luong, M.-T., Le, Q. V., and Manning, C. D. (2020). Electra: Pre-training text encoders as discriminators rather than generators. arXiv preprint arXiv:2003.10555. [find](https://scholar.google.com/scholar?q=Clark%2C+K%2E%2C+Luong%2C+M%2E%2DT%2E%2C+Le%2C+Q%2E+V%2E%2C+and+Manning%2C+C%2E+D%2E+%282020%29%2E+Electra%3A+Pre%2Dtraining+text+encoders+as+discriminators+rather+than+generators%2E+arXiv+preprint+arXiv%3A2003%2E10555%2E)
6. Demszky, D., Liu, J., Mancenido, Z., Cohen, J., Hill, H., Jurafsky, D., and Hashimoto, T. (2021). [Measuring conversational uptake: A case study on student-teacher interactions](https://md.research.elamur.ai/free/en/papers/demszky-2021-measuring-conversational-uptake-case-study/). arXiv preprint arXiv:2106.03873.
7. Derry, S. J., Pea, R. D., Barron, B., Engle, R. A., Erickson, F., Goldman, R., Hall, R., Koschmann, T., Lemke, J. L., Sherin, M. G., et al. (2010). Conducting video research in the learning sciences: Guidance on selection, analysis, technology, and ethics. The journal of the learning sciences, 19(1)… [find](https://scholar.google.com/scholar?q=Derry%2C+S%2E+J%2E%2C+Pea%2C+R%2E+D%2E%2C+Barron%2C+B%2E%2C+Engle%2C+R%2E+A%2E%2C+Erickson%2C+F%2E%2C+Goldman%2C+R%2E%2C+Hall%2C+R%2E%2C+Koschmann%2C+T%2E%2C+Lemke%2C+J%2E+L%2E%2C+Sherin%2C+M%2E+G%2E%2C+et+al%2E+%282010%29%2E+Conducting+video+research+in+the+learning+sciences%3A+)
8. Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). [Bert: Pre-training of deep bidirectional transformers for language understanding](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/). arXiv preprint arXiv:1810.04805.
9. Franke, M. L., Turrou, A. C., Webb, N. M., Ing, M., Wong, J., Shin, N., and Fernandez, C. (2015). Student engagement with others’ mathematical ideas: The role of teacher invitation and support moves. The Elementary School Journal, 116(1):126–148. [find](https://scholar.google.com/scholar?q=Franke%2C+M%2E+L%2E%2C+Turrou%2C+A%2E+C%2E%2C+Webb%2C+N%2E+M%2E%2C+Ing%2C+M%2E%2C+Wong%2C+J%2E%2C+Shin%2C+N%2E%2C+and+Fernandez%2C+C%2E+%282015%29%2E+Student+engagement+with+others%E2%80%99+mathematical+ideas%3A+The+role+of+teacher+invitation+and+support+moves%2E+)
10. Ganesh, A., Palmer, M., and Kann, K. (2021). What would a teacher do? predicting future talk moves. arXiv preprint arXiv:2106.05249. [find](https://scholar.google.com/scholar?q=Ganesh%2C+A%2E%2C+Palmer%2C+M%2E%2C+and+Kann%2C+K%2E+%282021%29%2E+What+would+a+teacher+do%3F+predicting+future+talk+moves%2E+arXiv+preprint+arXiv%3A2106%2E05249%2E)
11. Janin, A., Baron, D., Edwards, J., Ellis, D., Gelbart, D., Morgan, N., Peskin, B., Pfau, T., Shriberg, E., Stolcke, A., et al. (2003). The icsi meeting corpus. In 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings.(ICASSP’03)., volume 1, pages I–I. IEEE… [find](https://scholar.google.com/scholar?q=Janin%2C+A%2E%2C+Baron%2C+D%2E%2C+Edwards%2C+J%2E%2C+Ellis%2C+D%2E%2C+Gelbart%2C+D%2E%2C+Morgan%2C+N%2E%2C+Peskin%2C+B%2E%2C+Pfau%2C+T%2E%2C+Shriberg%2C+E%2E%2C+Stolcke%2C+A%2E%2C+et+al%2E+%282003%29%2E+The+icsi+meeting+corpus%2E+In+2003+IEEE+International+Conference+on)
12. Jennifer, J., Scornavacco, K., Harty, C., Suresh, A., Lai, V., and Sumner, T. (in press). Promoting rich discussions in mathematics classrooms: Using personalized, automated feedback to support reflection and instructional change. Teaching and Teacher Education. [find](https://scholar.google.com/scholar?q=Jennifer%2C+J%2E%2C+Scornavacco%2C+K%2E%2C+Harty%2C+C%2E%2C+Suresh%2C+A%2E%2C+Lai%2C+V%2E%2C+and+Sumner%2C+T%2E+%28in+press%29%2E+Promoting+rich+discussions+in+mathematics+classrooms%3A+Using+personalized%2C+automated+feedback+to+support+reflec)
13. Jurafsky, D. (1997). Switchboard swbd-damsl shallow-discourse-function annotation coders manual. Institute of Cognitive Science Technical Report. [find](https://scholar.google.com/scholar?q=Jurafsky%2C+D%2E+%281997%29%2E+Switchboard+swbd%2Ddamsl+shallow%2Ddiscourse%2Dfunction+annotation+coders+manual%2E+Institute+of+Cognitive+Science+Technical+Report%2E)
14. Krawczyk, B. (2016). Learning from imbalanced data: open challenges and future directions. Progress in Artificial Intelligence, 5(4):221–232. [find](https://scholar.google.com/scholar?q=Krawczyk%2C+B%2E+%282016%29%2E+Learning+from+imbalanced+data%3A+open+challenges+and+future+directions%2E+Progress+in+Artificial+Intelligence%2C+5%284%29%3A221%E2%80%93232%2E)
15. Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019). [Roberta: A robustly optimized bert pretraining approach](https://md.research.elamur.ai/free/en/papers/liu-2019-roberta-robustly-optimized-bert-pretraining/). arXiv preprint arXiv:1907.11692.
16. Major, L. and Watson, S. (2018). Using video to support in-service teacher professional development: the state of the field, limitations and possibilities. Technology, Pedagogy and Education, 27(1):49–68. [find](https://scholar.google.com/scholar?q=Major%2C+L%2E+and+Watson%2C+S%2E+%282018%29%2E+Using+video+to+support+in%2Dservice+teacher+professional+development%3A+the+state+of+the+field%2C+limitations+and+possibilities%2E+Technology%2C+Pedagogy+and+Education%2C+27%281%29%3A49)
17. McHugh, M. L. (2012). Interrater reliability: the kappa statistic. Biochemia medica, 22(3):276–282. [find](https://scholar.google.com/scholar?q=McHugh%2C+M%2E+L%2E+%282012%29%2E+Interrater+reliability%3A+the+kappa+statistic%2E+Biochemia+medica%2C+22%283%29%3A276%E2%80%93282%2E)
18. Michaels, S., O’Connor, M. C., Hall, M. W., and Resnick, L. B. (2010). Accountable talk® sourcebook. Pittsburg, PA: Institute for Learning University of Pittsburgh. Murphy, PK, Wilkinson, IAG, Soter, AO, Hennessey, MN, & Alexander, JF. [find](https://scholar.google.com/scholar?q=Michaels%2C+S%2E%2C+O%E2%80%99Connor%2C+M%2E+C%2E%2C+Hall%2C+M%2E+W%2E%2C+and+Resnick%2C+L%2E+B%2E+%282010%29%2E+Accountable+talk%C2%AE+sourcebook%2E+Pittsburg%2C+PA%3A+Institute+for+Learning+University+of+Pittsburgh%2E+Murphy%2C+PK%2C+Wilkinson%2C+IAG%2C+Soter%2C+)
19. O’Connor, C. and Michaels, S. (2019). Supporting teachers in taking up productive talk moves: The long road to professional learning at scale. International Journal of Educational Research, 97:166– 175. [find](https://scholar.google.com/scholar?q=O%E2%80%99Connor%2C+C%2E+and+Michaels%2C+S%2E+%282019%29%2E+Supporting+teachers+in+taking+up+productive+talk+moves%3A+The+long+road+to+professional+learning+at+scale%2E+International+Journal+of+Educational+Research%2C+97%3A166%E2%80%93+17)
20. O’Connor, C., Michaels, S., and Chapin, S. (2015). Scaling down” to explore the role of talk in learning: From district intervention to controlled classroom study. Socializing intelligence through academic talk and dialogue, pages 111–126. [find](https://scholar.google.com/scholar?q=O%E2%80%99Connor%2C+C%2E%2C+Michaels%2C+S%2E%2C+and+Chapin%2C+S%2E+%282015%29%2E+Scaling+down%E2%80%9D+to+explore+the+role+of+talk+in+learning%3A+From+district+intervention+to+controlled+classroom+study%2E+Socializing+intelligence+through+aca)
21. Ravi, S. and Kozareva, Z. (2018). Self-governing neural networks for on-device short text classification. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 887–893. [find](https://scholar.google.com/scholar?q=Ravi%2C+S%2E+and+Kozareva%2C+Z%2E+%282018%29%2E+Self%2Dgoverning+neural+networks+for+on%2Ddevice+short+text+classification%2E+In+Proceedings+of+the+2018+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2C+pag)
22. Resnick, L. B., Asterhan, C. S., and Clarke, S. N. (2018). Accountable talk: Instructional dialogue that builds the mind. Geneva, Switzerland: The International Academy of Education (IAE) and the International Bureau of Education (IBE) of the United Nations Educational, Scientific and Cultural Organ… [find](https://scholar.google.com/scholar?q=Resnick%2C+L%2E+B%2E%2C+Asterhan%2C+C%2E+S%2E%2C+and+Clarke%2C+S%2E+N%2E+%282018%29%2E+Accountable+talk%3A+Instructional+dialogue+that+builds+the+mind%2E+Geneva%2C+Switzerland%3A+The+International+Academy+of+Education+%28IAE%29+and+the+Inte)
23. Saha, T., Patra, A., Saha, S., and Bhattacharyya, P. (2020). [Towards emotion-aided multi-modal dialogue act classification](https://md.research.elamur.ai/free/en/papers/saha-2020-emotion-aided-multi-modal-dialogue/). In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4361–4372.
24. Scornavacco, K., Jacobs, J., and Harty, C. (accepted). Automated feedback on discourse moves teachers’ perceived utility of a big data tool. Annual conference of the American Educational Research Association. [find](https://scholar.google.com/scholar?q=Scornavacco%2C+K%2E%2C+Jacobs%2C+J%2E%2C+and+Harty%2C+C%2E+%28accepted%29%2E+Automated+feedback+on+discourse+moves+teachers%E2%80%99+perceived+utility+of+a+big+data+tool%2E+Annual+conference+of+the+American+Educational+Research+Asso)
25. Shriberg, E., Dhillon, R., Bhagat, S., Ang, J., and Carvey, H. (2004). [The icsi meeting recorder dialog act (mrda) corpus](https://md.research.elamur.ai/free/en/papers/shriberg-2004-icsi-meeting-recorder-dialog-act/). Technical report, INTERNA-TIONAL COMPUTER SCIENCE INST BERKE-LEY CA.
26. Stolcke, A., Ries, K., Coccaro, N., Shriberg, E., Bates, R., Jurafsky, D., Taylor, P., Martin, R., Ess-Dykema, C. V., and Meteer, M. (2000). [Dialogue act modeling for automatic tagging and recognition of conversational speech](https://md.research.elamur.ai/free/en/papers/stolcke-2000-dialogue-act-modeling-automatic-tagging/). Computational linguistics, 26(3):339–373.
27. Suresh, A., Sumner, T., Huang, I., Jacobs, J., Foland, B., and Ward, W. (2018). Using deep learning to automatically detect talk moves in teachers’ mathematics lessons. In 2018 IEEE International Conference on Big Data (Big Data), pages 5445–5447. IEEE. [find](https://scholar.google.com/scholar?q=Suresh%2C+A%2E%2C+Sumner%2C+T%2E%2C+Huang%2C+I%2E%2C+Jacobs%2C+J%2E%2C+Foland%2C+B%2E%2C+and+Ward%2C+W%2E+%282018%29%2E+Using+deep+learning+to+automatically+detect+talk+moves+in+teachers%E2%80%99+mathematics+lessons%2E+In+2018+IEEE+International+Conf)
28. Suresh, A., Sumner, T., Jacobs, J., Foland, B., and Ward, W. (2019). Automating analysis and feedback to improve mathematics teachers’ classroom discourse. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 9721–9728. [find](https://scholar.google.com/scholar?q=Suresh%2C+A%2E%2C+Sumner%2C+T%2E%2C+Jacobs%2C+J%2E%2C+Foland%2C+B%2E%2C+and+Ward%2C+W%2E+%282019%29%2E+Automating+analysis+and+feedback+to+improve+mathematics+teachers%E2%80%99+classroom+discourse%2E+In+Proceedings+of+the+AAAI+conference+on+art)
29. Suresh, A., Jacobs, J., Clevenger, C., Lai, V., Tan, C., Martin, J. H., and Sumner, T. (2021a). Using ai to promote equitable classroom discussions: The talkmoves application. In International Conference on Artificial Intelligence in Education, pages 344–348. Springer. [find](https://scholar.google.com/scholar?q=Suresh%2C+A%2E%2C+Jacobs%2C+J%2E%2C+Clevenger%2C+C%2E%2C+Lai%2C+V%2E%2C+Tan%2C+C%2E%2C+Martin%2C+J%2E+H%2E%2C+and+Sumner%2C+T%2E+%282021a%29%2E+Using+ai+to+promote+equitable+classroom+discussions%3A+The+talkmoves+application%2E+In+International+Confere)
30. Suresh, A., Jacobs, J., Lai, V., Tan, C., Ward, W., Martin, J. H., and Sumner, T. (2021b). Using transformers to provide teachers with personalized feedback on their classroom discourse: The talkmoves application. [find](https://scholar.google.com/scholar?q=Suresh%2C+A%2E%2C+Jacobs%2C+J%2E%2C+Lai%2C+V%2E%2C+Tan%2C+C%2E%2C+Ward%2C+W%2E%2C+Martin%2C+J%2E+H%2E%2C+and+Sumner%2C+T%2E+%282021b%29%2E+Using+transformers+to+provide+teachers+with+personalized+feedback+on+their+classroom+discourse%3A+The+talkmoves)
31. AAAI 2021 Spring Symposium on Artificial Intelligence for K-12 Education. [find](https://scholar.google.com/scholar?q=AAAI+2021+Spring+Symposium+on+Artificial+Intelligence+for+K%2D12+Education%2E)
32. Xu, L., Aranda, G., Widjaja, W., and Clarke, D. (2018). Video-based Research in Education: Cross-disciplinary Perspectives. Routledge. [find](https://scholar.google.com/scholar?q=Xu%2C+L%2E%2C+Aranda%2C+G%2E%2C+Widjaja%2C+W%2E%2C+and+Clarke%2C+D%2E+%282018%29%2E+Video%2Dbased+Research+in+Education%3A+Cross%2Ddisciplinary+Perspectives%2E+Routledge%2E)

*Updated 3 November 2026*