Abstract
This paper seeks to contribute to the emerging field of Quantitative Ethnography (QE) by demonstrating its utility to solve a complex challenge in Learning Analytics: the provision of timely feedback to collocated teams and their coaches. We define two requirements that extend the QE concept in order to operationalise it such a design process, namely, the use of co-design methodologies, and the availability of automated analytics workflow to close the feedback loop. We introduce the Multimodal Matrix as a data modelling approach that can integrate theoretical concepts about teamwork with contextual insights about specific work practices, enabling the analyst to map between higher order codes and low-level sensor data, with the option add the results of manually performed analyses. This is implemented in software as a workflow for rapid data modelling, analysis and interactive visualisation, demonstrated in the context of nursing teamwork simulations. We propose that this exemplifies how a QE methodology can underpin collocated activity analytics, at scale, with in-principle applications to embodied, collocated activities beyond our case study.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Pozyx developer kit and a multitag-positioning system: https://www.pozyx.io.
- 2.
Laerdal simulation manikins: https://www.laerdal.com/nz/products/simulation-training/emergency-care-trauma/simman-3g.
- 3.
Empatica wristbands: https://www.empatica.com/en-int/research/e4.
References
Alhadad, S.S.J.: Visualizing data to support judgement, inference, and decision making in learning analytics: insights from cognitive psychology and visualization science. J. Learn. Anal. 5(2), 60–85 (2018)
Bakhshinategh, B., Zaiane, O.R., Elatia, S., Ipperciel, D.: Educational data mining applications and tasks: a survey of the last 10 years. Educ. Inf. Technol. 23(1), 537–553 (2018)
Behrens, J.T., Dicerbo, K.E., Foltz, P.W.: Assessment of complex performances in digital environments. Ann. Am. Acad. Polit. Soc. Sci. 683(1), 217–232 (2019)
Bodily, R., Verbert, K.: Trends and issues in student-facing learning analytics reporting systems research. In: Proceedings of the International Learning Analytics and Knowledge Conference, LAK 2017, pp. 309–318 (2017)
Braithwaite, J.J., Watson, D.G., Jones, R., Rowe, M.: A guide for analysing electrodermal activity (EDA) & skin conductance responses (SCRs) for psychological experiments. Psychophysiology 49(1), 1017–1034 (2013)
Buckingham Shum, S., Crick, R.D.: Learning analytics for 21st century competencies. J. Learn. Anal. 3(2), 6–21 (2016)
Chandrasegaran, S., Bryan, C., Shidara, H., Chuang, T.-Y., Ma, K.-L.: TalkTraces: real-time capture and visualization of verbal content in meetings. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2019, pp. 577:571–577:514 (2019)
Chappel, S.E., Verswijveren, S.J.J.M., Aisbett, B., Considine, J., Ridgers, N.D.: Nurses’ occupational physical activity levels: a systematic review. Int. J. Nurs. Stud. 73(August), 52–62 (2017)
Csanadi, A., Eagan, B., Kollar, I., Shaffer, D.W., Fischer, F.: When coding-and-counting is not enough: using epistemic network analysis (ENA) to analyze verbal data in CSCL research. Int. J. Comput.-Support. Collab. Learn. 13(4), 419–438 (2018)
Di Mitri, D., Schneider, J., Klemke, R., Specht, M., Drachsler, H.: Read between the lines: an annotation tool for multimodal data for learning. In: Proceedings of the International Conference on Learning Analytics and Knowledge, LAK 2019, pp. 51–60 (2019)
Echeverria, V.: Designing and validating automated feed-back for collocated teams using multimodal learning analytics. Ph.D. in Learning Analytics, University of Technology Sydney (UTS), Sydney, Australia (in preparation)
Echeverria, V., Martinez-Maldonado, R., Buckingham Shum, S.: Towards collaboration translucence: giving meaning to multimodal group data. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2019, p. 39 (2019)
Echeverria, V., Martinez-Maldonado, R., Chiluiza, K., Buckingham Shum, S.: DBCollab: automated feedback for face-to-face group database design. In: Proceedings of the International Conference on Computers in Education, ICCE 2017, pp. 156–165 (2017)
Echeverria, V., Martinez-Maldonado, R., Power, T., Hayes, C., Buckingham Shum, S.: Where is the nurse? Towards automatically visualising meaningful team movement in healthcare education. In: Penstein Rosé, C., et al. (eds.) AIED 2018. LNCS (LNAI), vol. 10948, pp. 74–78. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93846-2_14
Gašević, D., Dawson, S., Siemens, G.: Let’s not forget: learning analytics are about learning. TechTrends 59(1), 64–71 (2015)
Gašević, D., Joksimović, S., Eagan, B.R., Shaffer, D.W.: SENS: network analytics to combine social and cognitive perspectives of collaborative learning. Comput. Hum. Behav. 92, 562–577 (2018)
Gibson, A., Martinez-Maldonado, R.: That dashboard looks nice, but what does it mean?: towards making meaning explicit in learning analytics design. In: Proceedings of the Australian Conference on Computer-Human Interaction, OzCHI 2017, pp. 528–532 (2017)
Green, A., Stawicki, S.P., Firstenberg, M.S.: Medical error and associated harm-the the critical role of team communication and coordination. In: Vignettes in Patient Safety, pp. 1–13. IntechOpen, London (2018)
Herder, T., et al.: Supporting teachers’ intervention in students’ virtual collaboration using a network based model. In: Proceedings of the International Conference on Learning Analytics and Knowledge, LAK 2018, pp. 21–25 (2018)
Jivet, I., Scheffel, M., Specht, M., Drachsler, H.: License to evaluate: preparing learning analytics dashboards for educational practice. In: Proceedings of the International Learning Analytics and Knowledge Conference, LAK 2018, pp. 31–40 (2018)
Martinez-Maldonado, R.: I spent more time with that team: making spatial pedagogy visible using positioning sensors. In: Proceedings of the International Conference on Learning Analytics & Knowledge, LAK 2019, pp. 21–25 (2019)
Martinez-Maldonado, R., Dimitriadis, Y., Martinez-Monés, A., Kay, J., Yacef, K.: Capturing and analyzing verbal and physical collaborative learning interactions at an enriched interactive tabletop. Int. J. Comput.-Support. Collab. Learn. 8(4), 455–485 (2013)
Martinez-Maldonado, R., Echeverria, V., Santos, O.C., Dos Santos, A.D.P., Yacef, K.: Physical learning analytics: a multimodal perspective. In: Proceedings of the International Conference on Learning Analytics and Knowledge, LAK 2018, pp. 375–379 (2018)
Martinez-Maldonado, R., Goodyear, P., Kay, J., Thompson, K., Carvalho, L.: An actionable approach to understand group experience in complex, multi-surface spaces. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2016, pp. 2062–2074 (2016)
Matcha, W., Gasevic, D., Pardo, A.: A systematic review of empirical studies on learning analytics dashboards: a self-regulated learning perspective. IEEE Trans. Learn. Technol. (2019, in press)
Mislevy, R.J., Behrens, J.T., Dicerbo, K.E., Levy, R.: Design and discovery in educational assessment: evidence-centered design, psychometrics, and educational data mining. J. Educ. Data Mining 4(1), 11–48 (2012)
Müller, L., Rivera-Pelayo, V., Kunzmann, C., Schmidt, A.: From stress awareness to coping strategies of medical staff: supporting reflection on physiological data. In: Salah, A.A., Lepri, B. (eds.) HBU 2011. LNCS, vol. 7065, pp. 93–103. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25446-8_11
Ochoa, X.: Multimodal learning analytics. In: The Handbook of Learning Analytics, pp. 129–141. SOLAR, Alberta (2017)
Ochoa, X., Chiluiza, K., Granda, R., Falcones, G., Castells, J., Guamán, B.: Multimodal transcript of face-to-face group-work activity around interactive tabletops. In: Proceedings of the CROSS-MMLA Workshop on Multimodal Learning Analytics Across Spaces, pp. 1–6 (2018)
Perera, D., Kay, J., Koprinska, I., Yacef, K., Zaïane, O.R.: Clustering and sequential pattern mining of online collaborative learning data. IEEE Trans. Knowl. Data Eng. 21(6), 759–772 (2008)
Prieto-Alvarez, C., Martinez-Maldonado, R., Shum, S.B.: Mapping learner-data journeys: evolution of a visual co-design tool. In: Proceedings of the ACM Australian Computer-Human Interaction Conference, OzCHI 2018, pp. 205–214 (2018)
Prieto-Alvarez, C.G., Martinez-Maldonado, R., Anderson, T.D.: Co-designing learning analytics tools with learners. In: Learning Analytics in the Classroom: Translating Learning Analytics for Teachers, pp. 93–110. Routledge, London (2018)
Sarter, N.B.: Multimodal information presentation: design guidance and research challenges. Int. J. Ind. Ergon. 36(5), 439–445 (2006)
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)
Shaffer, D.W.: Epistemic frames for epistemic games. Comput. Educ. 46(3), 223–234 (2006)
Shaffer, D.W.: Quantitative Ethnography. Cathcart Press, Madison (2017)
Shute, V.J., Ventura, M.: Stealth Assessment: Measuring and Supporting Learning in Video Games. MIT Press, Cambridge (2013)
Taylor, S., Jaques, N., Chen, W., Fedor, S., Sano, A., Picard, R.: Automatic identification of artifacts in electrodermal activity data. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015, pp. 1934–1937 (2015)
Wise, A., Knight, S., Buckingham Shum, S.: Collaborative learning analytics. In: Cress, U., Rosé, C., Wise, A., Oshima, J. (eds.) International Handbook of Computer-Supported Collaborative Learning. Springer, Cham (in press)
Worsley, M., Blikstein, P.: A multimodal analysis of making. Int. J. Artif. Intell. Educ. 28(3), 385–419 (2018)
Zhang, Z., Sarcevic, A.: Constructing awareness through speech, gesture, gaze and movement during a time-critical medical task. In: Boulus-Rødje, N., Ellingsen, G., Bratteteig, T., Aanestad, M., Bjørn, P. (eds.) ECSCW 2015: Proceedings of the 14th European Conference on Computer Supported Cooperative Work, 19-23 September 2015, Oslo, Norway, pp. 163–182. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20499-4_9
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Buckingham Shum, S., Echeverria, V., Martinez-Maldonado, R. (2019). The Multimodal Matrix as a Quantitative Ethnography Methodology. In: Eagan, B., Misfeldt, M., Siebert-Evenstone, A. (eds) Advances in Quantitative Ethnography. ICQE 2019. Communications in Computer and Information Science, vol 1112. Springer, Cham. https://doi.org/10.1007/978-3-030-33232-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-33232-7_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33231-0
Online ISBN: 978-3-030-33232-7
eBook Packages: Computer ScienceComputer Science (R0)