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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.

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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.

    Article  Google Scholar 

  • Al-Zoube, M. (2009). E-learning on the cloud. The International Arab Journal of Information Technology, 1(2), 58–64.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Book  Google Scholar 

  • 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.

    Article  MathSciNet  Google Scholar 

  • Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 Conference152, 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • Farnsworth, W. (2021). The Socratic method: a practitioner’s handbook. Godine.

    Google Scholar 

  • 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.

    Google Scholar 

  • Galanes, G. J., Adams, K. H., & Brilhart, J. K. (2007). Effective group discussion: Theory and practice. McGraw-Hill.

    Google Scholar 

  • Gall, M. D., & Gillett, M. (1980). The discussion method in classroom teaching. Theory Into Practice, 19(2), 98–103.

    Article  Google Scholar 

  • Goldschmid, B., & Goldschmid, M. L. (1976). Peer teaching in higher education: A review. Higher Education, 5(1), 9–33.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Harari, Y. N. (2014). Sapiens: A brief history of humankind. Random House.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Book  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Kelly, S. (2007). Classroom discourse and the distribution of student engagement. Social Psychology of Education, 10(3), 331–352.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Larson, B. E. (2000). Classroom discussion: A method of instruction and a curriculum outcome. Teaching and Teacher Education, 16(5–6), 661–677.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Book  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Meeker, B. F. (2020). Nonlinear models of distribution of talking in small groups. Social Science Research, 85, 102367.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Pozzi, F. (2010). Using jigsaw and case study for supporting online collaborative learning. Computers & Education, 55(1), 67–75.

    Article  Google Scholar 

  • Prahl, K. (2017). Best practices for the think-pair-share active-learning technique. The American Biology Teacher, 79(1), 3–8.

    Article  Google Scholar 

  • Rani, S., & Kumar, P. (2017). A sentiment analysis system to improve teaching and learning. Computer, 50(5), 36–43.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Stefanile, A. (2020). The transition from classroom to zoom and how it has changed education. Journal of Social Science Research, 16, 33–40.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Turkle, S. (2016). Reclaiming conversation: The power of talk in a digital age. Penguin.

    Google Scholar 

  • Utterback, W. E. (1947). Political significance of group discussion. The Annals of the American Academy of Political and Social Science, 250(1), 32–40.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Zhou, J., and Ye, J.,-m. (2020). Sentiment analysis in education research: A review of journal publications. Interactive Learning Environments, 31(3), 1252–1264.

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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.

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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.

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Correspondence to Michael DeBuse, Dallin Clayton, Brooks Butler or Sean Warnick.

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DeBuse, M., Clayton, D., Butler, B. et al. A Toolbox for Understanding the Dynamics of Small Group Discussions. Int J Artif Intell Educ (2023). https://doi.org/10.1007/s40593-023-00360-3

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