Abstract
Collaboration is an important 21st century skill; it can take place in a remote or co-located setting. Co-located collaboration (CC) is a very complex process which involves subtle human interactions that can be described with multimodal indicators (MI) like gaze, speech and social skills. In this paper, we first give an overview of related work that has identified indicators during CC. Then, we look into the state-of-the-art studies on feedback during CC which also make use of MI. Finally, we describe a Wizard of Oz (WOz) study where we design a privacy-preserving research prototype with the aim to facilitate real-time collaboration in-the-wild during three co-located group PhD meetings (of 3–7 members). Here, human observers stationed in another room act as a substitute for sensors to track different speech-based cues (like speaking time and turn taking); this drives a real-time visualization dashboard on a public shared display. With this research prototype, we want to pave way for design-based research to track other multimodal indicators of CC by extending this prototype design using both humans and sensors.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
An integrated sensor tracking simultaneously infrared, depth, audio and video.
- 2.
An electronic sensing device worn around the neck that can collect and analyze social dynamics.
References
Anastasiou, D., Ras, E.: A questionnaire-based case study on feedback by a tangible interface. In: Proceedings of the 2017 ACM WS on Intelligent Interfaces for Ubiquitous and Smart Learning, pp. 39–42. ACM (2017)
Bachour, K., Kaplan, F., Dillenbourg, P.: An interactive table for supporting participation balance in face-to-face collaborative learning. IEEE Trans. Learn. Technol. 3(3), 203–213 (2010)
Balaam, M., Fitzpatrick, G., Good, J., Harris, E.: Enhancing interactional synchrony with an ambient display. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 867–876. ACM (2011)
Bassiou, N., Tsiartas, A., Smith, J., Bratt, H., Richey, C., Shriberg, E., D’Angelo, C., Alozie, N.: Privacy-preserving speech analytics for automatic assessment of student collaboration. In: INTERSPEECH, pp. 888–892 (2016)
Bergstrom, T., Karahalios, K.: Conversation clock: visualizing audio patterns in co-located groups. In: 40th Annual Hawaii International Conference on System Sciences, p. 78. IEEE (2007)
Chikersal, P., Tomprou, M., Kim, Y.J., Woolley, A.W., Dabbish, L.: Deep structures of collaboration: physiological correlates of collective intelligence and group satisfaction. In: CSCW, pp. 873–888 (2017)
Cukurova, M., Luckin, R., Mavrikis, M., Millán, E.: Machine and human observable differences in groups’ collaborative problem-solving behaviours. In: Lavoué, É., Drachsler, H., Verbert, K., Broisin, J., Pérez-Sanagustín, M. (eds.) EC-TEL 2017. LNCS, vol. 10474, pp. 17–29. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66610-5_2
Cukurova, M., Luckin, R., Millán, E., Mavrikis, M.: The nispi framework: analysing collaborative problem-solving from students’ physical interactions. Comput. Educ. 116, 93–109 (2018)
Davidsen, J., Ryberg, T.: This is the size of one meter: childrens bodily-material collaboration. Int. J. CSCL 12(1), 65–90 (2017)
Dede, C.: Comparing frameworks for 21st century skills. In: 21st Century Skills: Rethinking How Students Learn, vol. 20, pp. 51–76 (2010)
Di Mitri, D., Schneider, J., Drachsler, H., Specht, M.: From signals to knowledge. A conceptual model for multimodal learning analytics. J. Comput. Assist. Learn. 34(4), 338–349 (2018)
Dillenbourg, P.: What do you mean by collaborative learning? (1999)
DiMicco, J.M., Pandolfo, A., Bender, W.: Influencing group participation with a shared display. In: Proceedings of the 2004 ACM Conference on CSCW, pp. 614–623. ACM (2004)
Dyckhoff, A.L.: Action research and learning analytics in higher education. eleed 10(1) (2014)
Grover, S., Bienkowski, M., Tamrakar, A., Siddiquie, B., Salter, D., Divakaran, A.: Multimodal analytics to study collaborative problem solving in pair programming. In: Proceedings of the 6th International Conference on LAK, pp. 516–517. ACM (2016)
Jermann, P., Mullins, D., Nüssli, M.A., Dillenbourg, P.: Collaborative gaze footprints: correlates of interaction quality. In: CSCL 2011 Conference Proceedings, vol. 1, pp. 184–191. International Society of the Learning Sciences (2011)
Kim, T., Chang, A., Holland, L., Pentland, A.S.: Meeting mediator: enhancing group collaboration using sociometric feedback. In: Proceedings of the 2008 ACM Conference on CSCW, pp. 457–466. ACM (2008)
Kulyk, O., Wang, J., Terken, J.: Real-time feedback on nonverbal behaviour to enhance social dynamics in small group meetings. In: Renals, S., Bengio, S. (eds.) MLMI 2005. LNCS, vol. 3869, pp. 150–161. Springer, Heidelberg (2006). https://doi.org/10.1007/11677482_13
Lubold, N., Pon-Barry, H.: Acoustic-prosodic entrainment and rapport in collaborative learning dialogues. In: Proceedings of the 2014 ACM WS on Multimodal Learning Analytics Workshop and Grand Challenge, pp. 5–12. ACM (2014)
Madan, A., Caneel, R., Pentland, A.S.: Groupmedia: distributed multi-modal interfaces. In: Proceedings of the 6th International Conference on Multimodal Interfaces, pp. 309–316. ACM (2004)
Martinez-Maldonado, R., Clayphan, A., Yacef, K., Kay, J.: Mtfeedback: providing notifications to enhance teacher awareness of small group work in the classroom. IEEE Trans. Learn. Technol. 8(2), 187–200 (2015)
Meier, A., Spada, H., Rummel, N.: A rating scheme for assessing the quality of computer-supported collaboration processes. Int. J. CSCL 2(1), 63–86 (2007)
O’Donnell, A.M.: The role of peers and group learning (2006)
Pijeira-Daz, H.J., Drachsler, H., Kirschner, P.A., Järvelä, S.: Profiling sympathetic arousal in a physics course: how active are students? J. Comput. Assist. Learn. 34(4), 397–408 (2018)
Richardson, D.C., Dale, R.: Looking to understand: the coupling between speakers’ and listeners’ eye movements and its relationship to discourse comprehension. Cogn. Sci. 29(6), 1045–1060 (2005)
Scherr, R.E., Hammer, D.: Student behavior and epistemological framing: examples from collaborative active-learning activities in physics. Cogn. Instr. 27(2), 147–174 (2009)
Schneider, B., Blikstein, P.: Unraveling students interaction around a tangible interface using multimodal learning analytics. J. Educ. Data Min. 7(3), 89–116 (2015)
Schneider, B., Pea, R.: Real-time mutual gaze perception enhances collaborative learning and collaboration quality. Int. J. CSCL 8(4), 375–397 (2013)
Schneider, B., Pea, R.: Toward collaboration sensing. Int. J. CSCL 9(4), 371–395 (2014)
Schneider, B., Sharma, K., Cuendet, S., Zufferey, G., Dillenbourg, P., Pea, R.D.: 3d tangibles facilitate joint visual attention in dyads. International Society of the Learning Sciences, Inc. [ISLS] (2015)
Schneider, J., Di Mitri, D., Limbu, B., Drachsler, H.: Multimodal learning hub: a tool for capturing customizable multimodal learning experiences. In: Drachsler, H., et al. (eds.) EC-TEL 2018. LNCS, vol. 11082, pp. 45–58. Springer, AG (2018)
Shih, P.C., Nguyen, D.H., Hirano, S.H., Redmiles, D.F., Hayes, G.R.: Groupmind: supporting idea generation through a collaborative mind-mapping tool. In: Proceedings of the ACM 2009 International Conference on Supporting Group Work, pp. 139–148. ACM (2009)
Spikol, D., Ruffaldi, E., Dabisias, G., Cukurova, M.: Supervised machine learning in multimodal learning analytics for estimating success in project-based learning. J. Comput. Assist. Learn. 34(4), 366–377 (2018)
Spikol, D., Ruffaldi, E., Landolfi, L., Cukurova, M.: Estimation of success in collaborative learning based on multimodal learning analytics features. In: Proceedings of the 17th ICALT, pp. 269–273. IEEE (2017)
Stahl, G., Law, N., Hesse, F.: Reigniting CSCL flash themes. Int. J. CSCL 8(4), 369–374 (2013)
Stiefelhagen, R., Zhu, J.: Head orientation and gaze direction in meetings. In: CHI 2002 Extended Abstracts on Human Factors in Computing Systems, pp. 858–859. ACM (2002)
Tausch, S., Hausen, D., Kosan, I., Raltchev, A., Hussmann, H.: Groupgarden: supporting brainstorming through a metaphorical group mirror on table or wall. In: Proceedings of the 8th Nordic Conference on Human-Computer Interaction, pp. 541–550. ACM (2014)
Terken, J., Sturm, J.: Multimodal support for social dynamics in co-located meetings. Pers. Ubiquitous Comput. 14(8), 703–714 (2010)
Triglianos, V., Praharaj, S., Pautasso, C., Bozzon, A., Hauff, C.: Measuring student behaviour dynamics in a large interactive classroom setting. In: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, pp. 212–220. ACM (2017)
Worsley, M., Blikstein, P.: Leveraging multimodal learning analytics to differentiate student learning strategies. In: Proceedings of the 5th International Conference on LAK, pp. 360–367. ACM (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Praharaj, S., Scheffel, M., Drachsler, H., Specht, M. (2018). Multimodal Analytics for Real-Time Feedback in Co-located Collaboration. In: Pammer-Schindler, V., Pérez-Sanagustín, M., Drachsler, H., Elferink, R., Scheffel, M. (eds) Lifelong Technology-Enhanced Learning. EC-TEL 2018. Lecture Notes in Computer Science(), vol 11082. Springer, Cham. https://doi.org/10.1007/978-3-319-98572-5_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-98572-5_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-98571-8
Online ISBN: 978-3-319-98572-5
eBook Packages: Computer ScienceComputer Science (R0)