Abstract
This work considers group discussion data, as recorded in video conferencing settings, and demonstrates the ability to use readily available computational tools to glean important information characterizing the dynamics of the group discussion. In particular, our toolbox reveals a number of important characteristics of a discussion, including who is speaking when and each participant’s relative sentiment throughout the discussion. We also identify the moments of the discussion where specific events occur, such as reading or quoting from source material or when questions are being asked, etc. Finally, we conduct a topic analysis to characterize the amount of time spent on various themes in the discussion. We then demonstrate that these tools are reasonably accurate at locating the desired content and can, in fact, provide a unique window to into the dynamics of group discussions.










Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data Availability
Due to the proprietary nature of the data and the request for anonymity of the sponsoring organization, the dataset specifically used in this research is not available publicly.
References
Adedjouma, M., Sabetzadeh, M., Briand, L. C. (2014). Automated detection and resolution of legal cross references: Approach and a study of Luxembourg’s legislation. In 2014 IEEE 22nd International Requirements Engineering Conference (RE), Karlskrona, Sweden, 2014, 63–72. https://doi.org/10.1109/RE.2014.6912248
Almarzooq, Z. I., Lopes, M., & Kochar, A. (2020). Virtual learning during the covid-19 pandemic: A disruptive technology in graduate medical education. Journal of the American College of Cardiology, 75(20), 2635–2638.
Al-Zoube, M. (2009). E-learning on the cloud. The International Arab Journal of Information Technology, 1(2), 58–64.
Arnett ,T. (2013). Will computers replace teachers? 8. Accessed from https://www.christenseninstitute.org/blog/will-computers-replace-teachers/ 12/13/2021
Aronson, E., et al. (1978). The jigsaw classroom. Sage.
Bachour, K., Kaplan, F., & Dillenbourg, P. (2010). An interactive table for supporting participation balance in face-to-face collaborative learning. IEEE Transactions on Learning Technologies, 3(3), 203–213.
Bartolini, I., Ciaccia, P., Patella, M. (2002). String matching with metric trees using an approximate distance. In A. H. F. Laender, A. L. Oliveira (Eds.), String Processing and Information Retrieval. SPIRE 2002. Lecture notes in computer science (vol 2476). Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45735-6_24
Basu, S., Yu, Y., Zimmermann, R. (2016). Fuzzy clustering of lecture videos based on topic modeling. In 2016 14th international workshop on content-based multimedia indexing (CBMI), Bucharest, Romania, pp. 1–6. https://doi.org/10.1109/CBMI.2016.7500264
Blanchard, N., D’Mello, S., Olney, A. M., and Nystrand, M. (2015). Automatic classification of question & answer discourse segments from teacher’s speech in classrooms. International Educational Data Mining Society.
Blanchard, N., Donnelly, P. J., Olney, A. M., Samei, B., Ward, B., Sun, X., Kelly, S., Nystrand, M., and D’Mello, S. K. (2016). Semi-automatic detection of teacher questions from human-transcripts of audio in live classrooms. International Educational Data Mining Society
Chacon, S., & Straub, B. (2014). Pro git. Apress.
Chen, Y., Yu, B., Zhang, X., Yu, Y. (2016). Topic modeling for evaluating students’ reflective writing: A case study of pre-service teachers’ journals. In Proceedings of the sixth international conference on learning analytics & knowledge. Association for Computing Machinery, New York, NY, USA, p1–5. https://doi.org/10.1145/2883851.2883951
Chung, J. S., Zisserman, A. (2017). Out of time: Automated lip sync in the wild. In C. S. Chen, J. Lu, K. K. Ma (Eds.), Computer vision – Asian conference on computer vision 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science (vol. 10117). Springer, Cham. https://doi.org/10.1007/978-3-319-54427-4_19
Cohen, E. G. (1994). Restructuring the classroom: Conditions for productive small groups. Review of Educational Research, 64(1), 1–35.
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46.
Cooper, K. M., Schinske, J. N., & Tanner, K. D. (2021). Reconsidering the share of a think–pair–share: Emerging limitations, alternatives, and opportunities for research. CBE-Life Sciences Education, 20(1), fe1.
Dascalu, M., Trausan-Matu, Ş., Dessus, P. (2014). Validating the automated assessment of participation and of collaboration in chat conversations. In: S. Trausan-Matu, K. E. Boyer, M. Crosby, K. Panourgia (Eds.), Intelligent Tutoring Systems. ITS 2014 (pp 230–235). https://doi.org/10.1007/978-3-319-07221-0_27
De Maat, E., Winkels, R., Van Engers, T. (2006). Automated detection of reference. In Proceedings of the 2006 Conference on Legal Knowledge and Information Systems: JURIX 2006: The Nineteenth Annual Conference, 152, 41. IOS Press
DiMicco, J. M., Pandolfo, A., Bender, W. (2004). Influencing group participation with a shared display. In Proceedings of the 2004 ACM conference on Computer supported cooperative work. Association for computing machinery, New York, NY, USA, pp 614–623. https://doi.org/10.1145/1031607.1031713
Dong, W., Mani, A., Pentland, A., Lepri, B., Pianesi, F. (2011). Modeling group discussion dynamics. Submitted to IEEE Transactions on Autonomous Mental Development
Donnelly, P. J., Blanchard, N., Samei, B., Olney, A. M., Sun, X., Ward, B., Kelly, S., Nystran, M., D’Mello, S. K. (2016). Automatic teacher modeling from live classroom audio. In Proceedings of the 2016 conference on user modeling adaptation and personalization. Association for Computing Machinery, New York, NY, USA, pp 45–53. https://doi.org/10.1145/2930238.2930250
Douthit, R. P. (1961). A historical study of group discussion principles and techniques developed by’the inquiry’ 1922–1933. Louisiana State University and Agricultural & Mechanical College.
Eslamian, H., Saeedi, R. M., & Fatehi, Y. (2013). Comparison of the effectiveness of teaching methods of group discussion and lecture on learning and satisfaction of students in teaching of religion and life courses in the secondary school students. Curriculum Planning Knowledge & Research in Educational Sciences, 10(11(38)), 13–23.
Farnsworth, W. (2021). The Socratic method: a practitioner’s handbook. Godine.
Filippidou, F., Moussiades, L. A. (2020). Benchmarking of IBM, google and wit automatic speech recognition systems. In IFIP International Conference on Artificial Intelligence Applications and Innovations 2020, 583, pp 73–82. Springer Nature - PMC COVID-19 Collection. https://doi.org/10.1007/978-3-030-49161-1_7
Fischer, C. A. (2018). The power of the socratic classroom: students, questions, dialogue learning. Sienna Books.
Galanes, G. J., Adams, K. H., & Brilhart, J. K. (2007). Effective group discussion: Theory and practice. McGraw-Hill.
Gall, M. D., & Gillett, M. (1980). The discussion method in classroom teaching. Theory Into Practice, 19(2), 98–103.
Goldschmid, B., & Goldschmid, M. L. (1976). Peer teaching in higher education: A review. Higher Education, 5(1), 9–33.
Google LLC. (2021). Google cloud speech-to-text. Accessed 9–22–2020 from https://cloud.google.com/speech-to-text/docs/libraries
Hankins, J. (2007). Humanism, scholasticism, and renaissance philosophy. The Cambridge Companion to Renaissance Philosophy, 1, 30–48.
Harari, Y. N. (2014). Sapiens: A brief history of humankind. Random House.
Hegstrom, T. G. (2008). Group discussion and democracy: The status of our attempts to teach productive participation in public policy decision-making. Submitted to Communication and Public Policy: Proceedings of the 2008 International Colloquium on Communication
Hmelo-Silver, C. E. (2004). Problem-based learning: What and how do students learn? Educational Psychology Review, 16(3), 235–266.
Hoppe, H., Doberstein, D., Hecking, T. (2020). Using sequence analysis to determine the well-functioning of small groups in large online courses. International Journal of Artificial Intelligence in Education, 31. https://doi.org/10.1007/s40593-020-00229-9
Horton, W. (2011). E-learning by design. Wiley.
Horvath, W. J. (1965). A mathematical model of participation in small group discussions. Behavioral Science, 10(2):164. Apr 01 1965. Last updated - 2013–02–24
Huang, G. Y., Chen, J., Liu, H., Fu, W., Ding, W., Tang, J., Yang, S., Li, G., Liu, Z. (2020). Neural multi-task learning for teacher question detection in online classrooms. In International Conference on Artificial Intelligence in Education 2020; 12163, pp 269–281. Springer Nature - PMC COVID-19 Collection. https://doi.org/10.1007/978-3-030-52237-7_22
Johnson, J. P., & Mighten, A. (2005). A comparison of teaching strategies: Lecture notes combined with structured group discussion versus lecture only. Journal of Nursing Education, 44(7), 319–322.
Judson, L. S. (1936). A manual of group discussion. Circular, vol. 446, University of Illinois College of Agriculture Agricultural Experiment Station and Extension Service in Agriculture and Home Economics
Kaddoura, M. (2013). Think pair share: A teaching learning strategy to enhance students’ critical thinking. Educational Research Quarterly, 36(4), 3–24.
Kelly, S. (2007). Classroom discourse and the distribution of student engagement. Social Psychology of Education, 10(3), 331–352.
Kherwa, P., & Bansal, P. (2019). Topic modeling: A comprehensive review. EAI Endorsed Transactions on Scalable Information Systems, 7(24), European Union Digital Library. https://doi.org/10.4108/eai.13-7-2018.159623
Kopp, W. (2013). Computers can’t replace real teachers, 4. Accessed from https://www.cnn.com/2013/04/08/opinion/kopp-kids-real-teachers/index.html 12/13/2021
Krämer, N. C., & Bente, G. (2010). Personalizing e-learning the social effects of pedagogical agents. Educational Psychology Review, 22(1), 71–87.
Kristeller, P. O. (1944). Humanism and scholasticism in the italian renaissance. Byzantion, 17, 346–374.
Lander, R. (2002). Scored group discussion: an assessment tool. Curriculum Services.
Larson, B. E. (2000). Classroom discussion: A method of instruction and a curriculum outcome. Teaching and Teacher Education, 16(5–6), 661–677.
Li, C., & Xing, W. (2021). Natural language generation using deep learning to support mooc learners. International Journal of Artificial Intelligence in Education, 31, 186–214.
Loria, S. (2020). Textblob api. Accessed 9–22–2021 from https://textblob.readthedocs.io/en/dev/quickstart.html#sentiment-analysis.
Lyons, J. S. (2009). Communimetrics: A communication theory of measurement in human service settings. Springer Science & Business Media.
Mabrito, M. (2006). A study of synchronous versus asynchronous collaboration in an online business writing class. The American Journal of Distance Education, 20(2), 93–107.
Meeker, B. F. (2020). Nonlinear models of distribution of talking in small groups. Social Science Research, 85, 102367.
Misuraca, M., Forciniti, A., Scepi, G., Spano, M. (2020). Sentiment analysis for education with r: packages, methods and practical applications. arXiv preprint arXiv:2005.12840
Mite-Baidal, K., Delgado-Vera, C., Solís-Avilés, E., Espinoza, A.H., Ortiz-Zambrano, J., Varela-Tapia, E. (2018). Sentiment analysis in education domain: A systematic literature review. In R. Valencia-García, G. Alcaraz-Mármol, J. Del Cioppo-Morstadt, N. Vera-Lucio, M. Bucaram-Leverone (Eds.), Technologies and Innovation. CITI 2018. Communications in Computer and Information Science (vol. 883, pp. 285–297). Springer, Cham. https://doi.org/10.1007/978-3-030-00940-3_21
Mühlenbrock, M. (2001). Action-based collaboration analysis for group learning, In Dissertations in Artificial Intelligence (No. 244). Ios Press.
Pal, S., Pramanik, P. K. D., Majumdar, T., & Choudhury, P. (2019). A semi-automatic metadata extraction model and method for video-based e-learning contents. Education and Information Technologies, 24(6), 3243–3268.
Pollock, P. H., Hamann, K., & Wilson, B. M. (2011). Learning through discussions: Comparing the benefits of small-group and large-class settings. Journal of Political Science Education, 7(1), 48–64.
Pozzi, F. (2010). Using jigsaw and case study for supporting online collaborative learning. Computers & Education, 55(1), 67–75.
Prahl, K. (2017). Best practices for the think-pair-share active-learning technique. The American Biology Teacher, 79(1), 3–8.
Rani, S., & Kumar, P. (2017). A sentiment analysis system to improve teaching and learning. Computer, 50(5), 36–43.
Rose, M. R., Diamond, S. S., & Powers, D. A. (2020). Inequality in talk and group size effects: An analysis of measures. Group Processes and Intergroup Relations, 23(5), 778–798.
Sannier, N., Adedjouma, M., Sabetzadeh, M., & Briand, L. (2017). An automated framework for detection and resolution of cross references in legal texts. Requirements Engineering, 22(2), 215–237.
Saraceno, C. (1999). Video content extraction and representation using a joint audio and video processing. In 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No. 99CH36258), Phoenix, AZ, USA, 1999, vol. 6, pp 3033–3036. https://doi.org/10.1109/ICASSP.1999.757480
Serhan, D. (2020). Transitioning from face-to-face to remote learning: Student’ attitudes and perceptions of using zoom during covid-19 pandemic. International Journal of Technology in Education and Science, 4(4), 335–342.
Stefanile, A. (2020). The transition from classroom to zoom and how it has changed education. Journal of Social Science Research, 16, 33–40.
Thies, J., Stappen, L., Hagerer, G., Schuller, B. W., Groh, G. (2021). Graphtmt: Unsupervised graph-based topic modeling from video transcripts. In 2021 IEEE Seventh International Conference on Multimedia Big Data (BigMM), Taichung, Taiwan, 2021, pp 1–8. IEEE, https://doi.org/10.1109/BigMM52142.2021.00009
Thompson, A. (2021). Can computer learning ever replace classroom learning? Accessed from https://educatorsusa.org/can-computer-learning-ever-replace-classroom-learning/ 12/13/2021
Tilk, O., and Alumäe, T. (2015). LSTM for punctuation restoration in speech transcripts. In Sixteenth Annual Conference of the International Speech Communication Association, Interspeech 2015, Dresden, Germany. https://doi.org/10.21437/Interspeech.2015-240
Tran, O. T., Ngo, B. X., Nguyen, M. L., & Shimazu, A. (2014). Automated reference resolution in legal texts. Artificial Intelligence and Law, 22(1), 29–60.
Tsai, M.-J. (2009). The model of strategic e-learning: Understanding and evaluating student e-learning from metacognitive perspectives. Journal of Educational Technology & Society, 12(1), 34–48.
Turkle, S. (2016). Reclaiming conversation: The power of talk in a digital age. Penguin.
Utterback, W. E. (1947). Political significance of group discussion. The Annals of the American Academy of Political and Social Science, 250(1), 32–40.
Wales, C. E., & Stager, R. A. (1978). The guided design approach. Instructional design library vol. 9. Educational Technology Publications, 9780877781134.
Wang, J. T. (2021). Navigating video-based learning in science–how do we close the gap between online and physical classrooms? In Proceedings of The Australian Conference on Science and Mathematics Education, page 6. Accessed from https://openjournals.library.sydney.edu.au/index.php/IISME 9/29/2021
Whitman, N. A., & Fife, J. D. (1988). Peer teaching: To teach is to learn twice. ASHE-ERIC Higher Education Report No. 4, 1988.
Wileden, A. F., & Ewbank, H. L. (1935). How to conduct group discussion. Circular (vol. 276). University of Wisconsin College of Agriculture. Extension Service of the College of agriculture, the University of Wisconsin, Wisconsin.
Wood, D. F. (2003). Problem based learning. Bmj, 326(7384), 328–330.
Yang, Y.-T.C., Newby, T. J., & Bill, R. L. (2005). Using socratic questioning to promote critical thinking skills through asynchronous discussion forums in distance learning environments. American Journal of Distance Education, 19(3), 163–181.
Young, K. S., Wood, J. T., Phillips, G. M., & Pedersen, D. J. (2021). Group discussion: A practical guide to participation and leadership (5th ed.). Waveland Press.
Zhou, J., and Ye, J.,-m. (2020). Sentiment analysis in education research: A review of journal publications. Interactive Learning Environments, 31(3), 1252–1264.
Acknowledgements
We gratefully acknowledge the support of our sponsoring organization who wishes to remain anonymous. The data used in the paper is proprietary, but we have their permission to publish the methods and tools we developed for them using their data so long as strict confidentiality measures are followed to protect proprietary information and PII.
Funding
The affiliated authors have received research support from Brigham Young University, Provo, UT, USA.
Author information
Authors and Affiliations
Contributions
Michael DeBuse, Dallin Clayton, and Brooks Butler contributed to the study conception and design with Sean Warnick performing the role of supervision and advisor. System development, construction, and analysis were performed by Michael DeBuse, Dallin Clayton, and Brooks Butler. The first draft of the manuscript was written by Michael DeBuse, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding authors
Ethics declarations
Competing Interests
The authors have no relevant financial or non-financial interests to disclose.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
DeBuse, M., Clayton, D., Butler, B. et al. A Toolbox for Understanding the Dynamics of Small Group Discussions. Int J Artif Intell Educ 34, 586–615 (2024). https://doi.org/10.1007/s40593-023-00360-3
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40593-023-00360-3