Skip to main content
SpringerLink
Log in

EduBrowser: A Multimodal Automated Monitoring System for Co-located Collaborative Learning

  • Conference paper
  • First Online:
Learning Technology for Education Challenges (LTEC 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1011))

Included in the following conference series:

Abstract

Majority of learning analytics systems are designed to monitor and analyze students’ online interactions during collaborative learning. In the case of co-located collaborative learning, student interactions take place in the physical space as well as online. While existing learning management systems provide specific logs and snapshots of students’ online responses that are automatically captured, the potential of insights that can be derived from students’ non-digital face-to-face interactions during collaborative discourse remains untapped. In this paper, we propose an architecture for data acquisition and processing from co-located face-to-face collaborative learning, designed to be scalable beyond dyadic and triadic collaborative learning and across different curricula. We outline the system design, current experience of deployment across 4 sessions of co-located collaborative learning sessions, as well as brief examples of acquired data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. 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)

    Article  Google Scholar 

  2. Bereiter, C., Scardamalia, M.: Education for the knowledge age. In: Alexander, P.A., Winne, P.H. (eds.) Handbook of Educational Psychology, 2nd edn, pp. 695–713. Lawrence Erlbaum Associates, Mahwah (2006)

    Google Scholar 

  3. Burgoon, J., Dunbar, N.E., Giles, H.: Interaction coordination and adaptation. In: Social Signal Processing, pp. 78–96. Cambridge University Press, Cambridge (2017)

    Google Scholar 

  4. Chai, C.S., Lim, W.Y., So, H.J., Cheah, H.M.: Advancing Collaborative Learning with ICT: Conception, Cases and Design. Ministry of Education, Singapore (2011)

    Google Scholar 

  5. Chartrand, T.L., Lakin, J.L.: The antecedents and consequences of human behavioral mimicry. Annu. Rev. Psychol. 64, 285–308 (2013)

    Article  Google Scholar 

  6. Chua, Y.H.V., Dauwels, J., Tan, S.C.: Technologies for automated analysis of co-located, real-life physical learning spaces: where are we now? In: Proceedings of the International Conference on Learning Analytics and Knowledge (LAK 2019), pp. 11–20. ACM (2019)

    Google Scholar 

  7. Dillenbourg, P.: Design for classroom orchestration. Comput. Edu. 69, 485–492 (2013)

    Article  Google Scholar 

  8. Cukurova, M., Luckin, R., Millán, E., Mavrikis, M.: The NISPI framework: analyzing collaborative problem-solving from students’ physical interactions. Comput. Edu. 116, 93–109 (2018)

    Article  Google Scholar 

  9. D’Mello, S., Graesser, A.: Dynamics of affective states during complex learning. Learn. Instr. 22(2), 145–157 (2012)

    Article  Google Scholar 

  10. D’Mello, S., Lehman, B., Pekrun, R., Graesser, A.: Confusion can be beneficial for learning. Learn. Instr. 29, 153–170 (2014)

    Article  Google Scholar 

  11. Eyben, F., Wollmer, M., Schuller, B.: Opensmile: the munich versatile and fast open-source audio feature extractor. In: Proceedings of the 18th ACM International Conference on Multimedia (MM 2010), pp. 1459–1462. ACM (2010)

    Google Scholar 

  12. 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 Learning Analytics and Knowledge (LAK 2016), pp. 516–517. ACM (2016)

    Google Scholar 

  13. 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. Comp-Supp. Coll. 8(2), 245–265 (2013)

    Google Scholar 

  14. La France, M.: Postural mirroring and intergroup relations. Pers. Soc. Psychol. Bull. 77(11), 207–217 (1985)

    Article  Google Scholar 

  15. Lubold, N., Pon-Barry, H.: Acoustic-prosodic entrainment and rapport in collaborative learning dialogues. In: Proceedings of the 2014 ACM Workshop on Multimodal Learning Analytics Workshop and Grand Challenge (MLA 2014), pp. 5–12. ACM (2014)

    Google Scholar 

  16. 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)

    Article  Google Scholar 

  17. Mercer, N.: Talk and the development of reasoning and understanding. Hum. Dev. 51, 90–100 (2008)

    Article  Google Scholar 

  18. Orozco-Arroyave, J.R., Vsquez-Correa, J.C., et al.: NeuroSpeech: an open-source software for Parkinson’s speech analysis. Digit. Signal Process. 77, 207–221 (2017)

    Article  Google Scholar 

  19. Oviatt, S., Hang, K., Zhou, J., Chen, F.: Spoken interruptions signal productive problem solving and domain expertise in mathematics. In: Proceedings of 2015 ACM on International Conference on Multimodal Interaction (ICMI 2015), pp. 311–318. ACM (2015)

    Google Scholar 

  20. Parmelee, D., Michaelsen, L.K., Cook, S., Hudes, P.D.: Team-based learning a practical guide: AMEE guide no. 65. Med. Teach. 34(5), 275–287 (2012)

    Article  Google Scholar 

  21. Pennebaker, J.W., Boyd, R.L., Jordan, K., Blackburn, K.: The development and psychometric properties of LIWC2015 (2015)

    Google Scholar 

  22. Praharaj, S., Scheffel, M., Drachsler, H., Specht, M.: Multimodal analytics for real-time feedback in co-located collaboration. In: Pammer-Schindler, V., Pérez-Sanagustín, M., Drachsler, H., Elferink, R., Scheffel, M. (eds.) EC-TEL 2018. LNCS, vol. 11082, pp. 187–201. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98572-5_15

    Chapter  Google Scholar 

  23. Rajalingam, P., Rotgans, J.I., Zary, N., Ferenczi, M.A., Gagnon, P., Low-Beer, N.: Implementation of team-based learning on a large scale: three factors to keep in mind. Med. Teach. 40(6), 1–7 (2018)

    Article  Google Scholar 

  24. Rasheed, U., Tahir, Y., Dauwels, S., Dauwels, J., Thalmann, D., Magnenat-Thalmann, N.: Real-time comprehensive sociometrics for two-person dialogs. In: Salah, A.A., Hung, H., Aran, O., Gunes, H. (eds.) HBU 2013. LNCS, vol. 8212, pp. 196–208. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-02714-2_17

    Chapter  Google Scholar 

  25. Ruffaldi, E., Dabisias, G., Landolfi, L., Spikol, D.: Data collection and processing for a multimodal learning analytic system. In: Proceedings of 2016 SAI Computing Conference, pp. 858–863. IEEE (2016)

    Google Scholar 

  26. Scherer, S., Weibel, N., Morency, L.P., Oviatt, S.: Multimodal prediction of expertise and leadership in learning groups. In: Proceedings of the 1st International Workshop on Multimodal Learning Analytics (MLA 2012). ACM (2012)

    Google Scholar 

  27. Schneider, B., Blikstein, P.: Unraveling students’ interaction around a tangible interface using multimodal learning analytics. J. Educ. Data Min. 7(3), 89–116 (2015)

    Google Scholar 

  28. Schneider B., Sharma, K., Cuendet, S., Zufferey, G., Dillenbourg, P., Pea, R.: Detecting collaborative dynamics using mobile eye-trackers. In: Proceedings of the 12th International Conference of the Learning Sciences, pp. 522–529 (2016)

    Google Scholar 

  29. 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)

    Article  Google Scholar 

  30. 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 IEEE International Conference on Advanced Learning Technologies (ICALT 2017), pp. 269–273. IEEE (2017)

    Google Scholar 

  31. Stöckli, S., Schulte-Mecklenbeck, M., Borer, S., Samson, A.C.: Facial expression analysis with AFFDEX and FACET: a validation study. Behav. Res. Methods 50(4), 1446–1460 (2018)

    Article  Google Scholar 

  32. Tahir, Y., et al.: Real-time sociometrics from audio-visual features for two-person dialogs. In: 2015 IEEE International Conference on Digital Signal Processing, pp. 823–827. IEEE (2015)

    Google Scholar 

  33. Tan, J.P.-L., Caleon, I., Ng, H.L., Poon, C.L., Koh, E.: Collective creativity competencies and collaborative problem-solving outcomes: insights from the dialogic interactions of singapore student teams. In: Care, E., Griffin, P., Wilson, M. (eds.) Assessment and Teaching of 21st Century Skills. EAIA, pp. 95–118. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-65368-6_6

    Chapter  Google Scholar 

  34. Taylor, R.: The multimodal texture of engagement: prosodic language, gaze and posture in engaged, creative classroom interaction. Think. Skills Creat. 20, 83–96 (2016)

    Article  Google Scholar 

  35. Villar, A.: Response bias. In: Lavrakas, P.J. (ed.) Encyclopedia of Survey Research Methods, pp. 752–753. Sage Publications Inc., Thousand Oaks (2011)

    Google Scholar 

  36. Weimar, E., Nugroho, A., Visser, J., Plaat, A., Goudbeek, M., Schouten, A.P.: The Influence of teamwork quality on software team performance. arXiv preprint arXiv:1701.06146 (2017)

  37. Woolley, A.W., Gerbasi, M.E., Chabris, C.F., Kosslyn, S.M., Hackman, J.R.: Bringing in the experts: how team composition and collaborative planning jointly shape analytic effectiveness. Small Group Res. 39(3), 352–371 (2008)

    Article  Google Scholar 

  38. Woolley, A.W., Aggarwal, I., Malone, T.W.: Collective intelligence in teams and organizations. In: Handbook of Collective Intelligence, pp. 143–168 (2015)

    Google Scholar 

  39. Worsley, M., Blikstein, P.: Leveraging multimodal learning analytics to differentiate student learning strategies. In: Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK 2015), pp. 360–367. ACM (2015)

    Google Scholar 

  40. Worsley, M., Blikstein, P.: Using learning analytics to study cognitive disequilibrium in a complex learning environment. In: Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK 2015), pp. 426–427. ACM (2015)

    Google Scholar 

  41. Zapata, J., Andreas S.K.: Assessing the performance of automatic speech recognition systems when used by native and non-native speakers of three major languages in dictation workflows. In: Proceedings of the 20th Nordic Conference of Computational Linguistics (NODALIDA 2015), pp. 201–210. Linköping University Electronic Press (2015)

    Google Scholar 

Download references

Acknowledgements

This project is supported by grants M4081917 and M4081918 from the Centre for Research and Development in Learning at NTU (CRADLE@NTU).

Author information

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Yi Han Victoria Chua & Justin Dauwels

  2. Medical Education Research and Scholarship Unit (MERSU), Lee Kong Chian School of Medicine, Singapore, Singapore

    Preman Rajalingam

  3. National Institute of Education, Singapore, Singapore

    Seng Chee Tan

Authors
  1. Yi Han Victoria Chua
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Preman Rajalingam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Seng Chee Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Justin Dauwels
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yi Han Victoria Chua .

Editor information

Editors and Affiliations

  1. Staffordshire University, Stoke-on-Trent, Steiermark, UK

    Lorna Uden

  2. Universidad Tecnica Federico Santa Maria, Valparaiso, Chile

    Dario Liberona

  3. Universidad de Salamanca, Salamanca, Spain

    Galo Sanchez

  4. Universidad de Salamanca, Salamanca, Spain

    Sara Rodríguez-González

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chua, Y.H.V., Rajalingam, P., Tan, S.C., Dauwels, J. (2019). EduBrowser: A Multimodal Automated Monitoring System for Co-located Collaborative Learning. In: Uden, L., Liberona, D., Sanchez, G., Rodríguez-González, S. (eds) Learning Technology for Education Challenges. LTEC 2019. Communications in Computer and Information Science, vol 1011. Springer, Cham. https://doi.org/10.1007/978-3-030-20798-4_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-20798-4_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20797-7

  • Online ISBN: 978-3-030-20798-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation