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Towards Speech-Based Collaboration Detection in a Noisy Classroom

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Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium (AIED 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13356))

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Abstract

An Intelligent Orchestration System, such as our FACT [1], should act like an automated teaching assistant that helps teachers provide relevant, timely help. To do so, it needs to know what the students are doing and thus who needs help more than the others. This is especially important when students work in small groups and the teacher’s ability to monitor every group frequently diminishes. This project is an attempt to investigate the feasibility and challenges of only using the students’ speech to predict each group’s collaboration status. We are using machine-learning techniques to build models that agree with our human annotator’s collaboration status judgments.

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References

  1. VanLehn, K., et al.: Can an orchestration system increase collaborative, productive struggle in teaching-by-eliciting classrooms? Interact. Learn. Environ. 29, 987–1005 (2021). https://doi.org/10.1080/10494820.2019.1616567

    Article  Google Scholar 

  2. Dillenbourg, P.: Design for classroom orchestration. Comput. Educ. 69, 485–492 (2013). https://doi.org/10.1016/j.compedu.2013.04.013

    Article  Google Scholar 

  3. Dillenbourg, P., Prieto, L.P., Olsen, J.K.: Classroom orchestration. In: International Handbook of the Learning Sciences, pp. 180–190. Routledge, New York (2018). https://doi.org/10.4324/9781315617572-18

  4. Ramakrishnan, A., Ottmar, E., LoCasale-Crouch, J., Whitehill, J.: Toward automated classroom observation: predicting positive and negative climate. In: 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), pp. 1–8. IEEE (2019). https://doi.org/10.1109/FG.2019.8756529

  5. Dillenbourg, P., Jermann, P.: Technology for Classroom Orchestration. In: Khine, M., Saleh, I. (eds.) New Science of Learning, pp. 525–552. Springer, New York (2010). https://doi.org/10.1007/978-1-4419-5716-0_26

  6. Martinez-Maldonado, R.: A handheld classroom dashboard: teachers’ perspectives on the use of real-time collaborative learning analytics. Int. J. Comput.-Support. Collab. Learn. 14(3), 383–411 (2019). https://doi.org/10.1007/s11412-019-09308-z

    Article  Google Scholar 

  7. C. Graesser, A., Foltz, P.W., Rosen, Y., Shaffer, D.W., Forsyth, C., Germany, M.-L.: Challenges of assessing collaborative problem solving. In: Care, E., Griffin, P., Wilson, M. (eds.) Assessment and Teaching of 21st Century Skills. EAIA, pp. 75–91. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-65368-6_5

    Chapter  Google Scholar 

  8. Laal, M., Laal, M., Kermanshahi, Z.K.: 21st century learning; learning in collaboration. Procedia. Soc. Behav. Sci. 47, 1696–1701 (2012). https://doi.org/10.1016/j.sbspro.2012.06.885

    Article  Google Scholar 

  9. Chi, M.T.H., Wylie, R.: The ICAP framework: linking cognitive engagement to active learning outcomes. Educ. Psychol. 49, 219–243 (2014). https://doi.org/10.1080/00461520.2014.965823

    Article  Google Scholar 

  10. Dillenbourg, P., Järvelä, S., Fischer, F.: The evolution of research on computer-supported collaborative learning: from design to orchestration. In: Technology-Enhanced Learning: Principles and Products (2009). https://doi.org/10.1007/978-1-4020-9827-7_1

  11. Segal, A., et al.: Keeping the teacher in the loop: technologies for monitoring group learning in real-time. In: André, E., Baker, R., Hu, X., Rodrigo, M.M.T., du Boulay, B. (eds.) AIED 2017. LNCS (LNAI), vol. 10331, pp. 64–76. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61425-0_6

    Chapter  Google Scholar 

  12. Rodríguez-Triana, M.J., Martínez-Monés, A., Asensio-Pérez, J.I., Dimitriadis, Y.: Scripting and monitoring meet each other: aligning learning analytics and learning design to support teachers in orchestrating CSCL situations. Br. J. Edu. Technol. 46, 330–343 (2015). https://doi.org/10.1111/BJET.12198

    Article  Google Scholar 

  13. Martinez-Maldonado, R., Yacef, K., Kay, J.: TSCL: a conceptual model to inform understanding of collaborative learning processes at interactive tabletops. Int. J. Hum. Comput. Stud. 83, 62–82 (2015). https://doi.org/10.1016/J.IJHCS.2015.05.001

    Article  Google Scholar 

  14. Sümer, Ö., Goldberg, P., D’Mello, S., Gerjets, P., Trautwein, U., Kasneci, E.: Multimodal engagement analysis from facial videos in the classroom (2021)

    Google Scholar 

  15. Subburaj, S.K., Stewart, A.E.B., Ramesh Rao, A., D’Mello, S.K.: Multimodal, multiparty modeling of collaborative problem solving performance. In: Proceedings of the 2020 International Conference on Multimodal Interaction, pp. 423–432. ACM, New York (2020). https://doi.org/10.1145/3382507.3418877

  16. Martinez, R., Wallace, J.R., Kay, J., Yacef, K.: Modelling and identifying collaborative situations in a collocated multi-display groupware setting. In: Biswas, G., Bull, S., Kay, J., Mitrovic, A. (eds.) AIED 2011. LNCS (LNAI), vol. 6738, pp. 196–204. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21869-9_27

    Chapter  Google Scholar 

  17. Gweon, G., Jain, M., McDonough, J., Raj, B., Rosé, C.P.: Measuring prevalence of other-oriented transactive contributions using an automated measure of speech style accommodation. Int. J. Comput.-Support. Collab. Learn. 8, 245–265 (2013)

    Article  Google Scholar 

  18. Richey, C., et al.: The SRI speech-based collaborative learning corpus. In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 08–12 September, pp. 1550–1554 (2016). https://doi.org/10.21437/Interspeech.2016-1541

  19. 1Bassiou, N., et al.: Privacy-preserving speech analytics for automatic assessment of student collaboration. In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 08–12 September, pp. 888–892 (2016). https://doi.org/10.21437/Interspeech.2016-1569

  20. Viswanathan, S.A., VanLehn, K.: Collaboration detection that preserves privacy of students’ speech. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds.) AIED 2019. LNAI, vol. 11625, pp. 507--517. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-23204-7_42

  21. Zhang, B., Provost, E.M., Essl, G.: Cross-corpus acoustic emotion recognition with multi-task learning: seeking common ground while preserving differences. IEEE Trans. Affect. Comput. 10, 85–99 (2019). https://doi.org/10.1109/TAFFC.2017.2684799

    Article  Google Scholar 

  22. FACT. https://fact.asu.edu/. Accessed 26 Oct 2021

  23. VanLehn, K.: How can fact encourage collaboration and self-correction. In: Deep Comprehension: Multi-Disciplinary Approaches to Understanding, Enhancing, and Measuring Comprehension, pp. 114–124 (2018). https://doi.org/10.4324/9781315109503

  24. Mathematics Assessment Project. http://map.mathshell.org/index.php. Accessed 26 Oct 2021

  25. Nijmegen: Max Planck Institute for Psycholinguistics, T.L.Archive.: ELAN (Version 6.2). https://archive.mpi.nl/tla/elan

  26. Lausberg, H., Sloetjes, H.: Coding gestural behavior with the NEUROGES-ELAN system. Behav. Res. Methods 41, 841–849 (2009). https://doi.org/10.3758/BRM.41.3.841

    Article  Google Scholar 

  27. Audacity® software is copyright © 1999–2021 Audacity Team. The name Audacity® is a registered trademark.: Audacity. http://audacityteam.org

  28. Eyben, F., Schuller, B.: openSMILE:). ACM SIGMultimedia Records. 6, 4–13 (2015). https://doi.org/10.1145/2729095.2729097

    Article  Google Scholar 

  29. Schuller, B., Steidl, S., Batliner, A., Burkhardt, F., Devillers, L., Müller, C., Narayanan, S.S.: The INTERSPEECH 2010 paralinguistic challenge. In: Eleventh Annual Conference of the International Speech Communication Association (2010)

    Google Scholar 

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Acknowledgments

This research was supported by grant NSF FW-HTF 1840051.

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Authors and Affiliations

  1. School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, 85287, USA

    Bahar Shahrokhian & Kurt VanLehn

Authors
  1. Bahar Shahrokhian
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  2. Kurt VanLehn
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Corresponding author

Correspondence to Bahar Shahrokhian .

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Editors and Affiliations

  1. Ateneo De Manila University, Quezon, Philippines

    Maria Mercedes Rodrigo

  2. Department of Computer Science, North Carolina State University, Raleigh, NC, USA

    Noburu Matsuda

  3. Durham University, Durham, UK

    Alexandra I. Cristea

  4. University of Leeds, Leeds, UK

    Vania Dimitrova

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Shahrokhian, B., VanLehn, K. (2022). Towards Speech-Based Collaboration Detection in a Noisy Classroom. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_11

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  • DOI: https://doi.org/10.1007/978-3-031-11647-6_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-11646-9

  • Online ISBN: 978-3-031-11647-6

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