1. Bowman, D., et al.: The mathematical foundations of epistemic network analysis. In: Ruis, A.R., Lee, S.B. (eds.) ICQE 2021. CCIS, vol. 1312, pp. 91–105. Springer, Cham (2021).https://doi.org/10.1007/978-3-030-67788-6_7

   Chapter  Google Scholar 

2. Chng, E., Seyam, M.R., Yao, W., Schneider, B.: Using motion sensors to understand collaborative interactions in digital fabrication labs. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12163, pp. 118–128. Springer, Cham (2020).https://doi.org/10.1007/978-3-030-52237-7_10

   Chapter  Google Scholar 

3. Cukurova, M., Giannakos, M., Martinez-Maldonado, R.: The promise and challenges of multimodal learning analytics. BJET51(5), 1441–1449 (2020)

   Google Scholar 

4. Delahunty, J., Verenikina, I., Jones, P.: Socio-emotional connections: identity, belonging and learning in online interactions. A literature review. Technol. Pedag. Educ.23(2), 243–265 (2014)

   Article  Google Scholar 

5. Echeverria, V., Martinez-Maldonado, R., Power, T., Hayes, C., Shum, S.B.: 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

   Chapter  Google Scholar 

6. Fan, Y., et al.: Dissecting learning tactics in MOOC using ordered network analysis. J. Comput. Assist. Learn.39, 154–166 (2022)

   Article  Google Scholar 

7. Fernandez-Nieto, G.M., Martinez-Maldonado, R., Kitto, K., Shum, S.B.: Modelling spatial behaviours in clinical team simulations using epistemic network analysis: methodology and teacher evaluation. In: LAK 2021, pp. 386–396 (2021)

   Google Scholar 

8. Gašević, D., Dawson, S., Rogers, T., Gasevic, D.: Learning analytics should not promote one size fits all: the effects of instructional conditions in predicting academic success. Internet High. Educ.28, 68–84 (2016)

   Article  Google Scholar 

9. Giannakos, M., Cukurova, M., Papavlasopoulou, S.: Sensor-based analytics in education: lessons learned from research in multimodal learning analytics. In: Giannakos, M., Spikol, D., Di Mitri, D., Sharma, K., Ochoa, X., Hammad, R. (eds.) The Multimodal Learning Analytics Handbook, pp. 329–358. Springer, Cham (2022).https://doi.org/10.1007/978-3-031-08076-0_13

   Chapter  Google Scholar 

10. Greenberg, S., Boring, S., Vermeulen, J., Dostal, J.: Dark patterns in proxemic interactions: a critical perspective. In: DIS 2014, pp. 523–532 (2014)

    Google Scholar 

11. Hall, E.T.: The Hidden Dimension, vol. 609. Doubleday, Garden City (1966)

    Google Scholar 

12. Järvelä, S., Dindar, M., Sobocinski, M., Nguyen, A.: Multimodal research for studying collaborative learning in higher education. In: Handbook of Digital Higher Education, pp. 199–210. Edward Elgar Publishing (2022)

    Google Scholar 

13. Kinkead, K.J., Miller, H., Hammett, R.: Adult perceptions of in-class collaborative problem solving as mitigation for statistics anxiety. J. Contin. High. Educ.64(2), 101–111 (2016)

    Article  Google Scholar 

14. Luciano, M.M., Mathieu, J.E., Park, S., Tannenbaum, S.I.: A fitting approach to construct and measurement alignment: the role of big data in advancing dynamic theories. Organ. Res. Methods21(3), 592–632 (2018)

    Article  Google Scholar 

15. Marquart, C., Tan, Y., Cai, Z., Shaffer, D.W.: Ordered network analysis (2022).https://epistemic-analytics.gitlab.io/qe-packages/ona/cran/

16. Martinez-Maldonado, R., Echeverria, V., Fernandez Nieto, G., Buckingham Shum, S.: From data to insights: a layered storytelling approach for multimodal learning analytics. In: CHI, pp. 1–15 (2020)

    Google Scholar 

17. Martinez-Maldonado, R., Echeverria, V., Schulte, J., Shibani, A., Mangaroska, K., Buckingham Shum, S.: Moodoo: indoor positioning analytics for characterising classroom teaching. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12163, pp. 360–373. Springer, Cham (2020).https://doi.org/10.1007/978-3-030-52237-7_29

    Chapter  Google Scholar 

18. Martínez-Maldonado, R., Yan, L., Deppeler, J., Phillips, M., Gašević, D.: Classroom analytics: telling stories about learning spaces using sensor data. In: Gil, E., Mor, Y., Dimitriadis, Y., Köppe, C. (eds.) Hybrid Learning Spaces. Understanding Teaching-Learning Practice, pp. 185–203. Springer, Cham (2022).https://doi.org/10.1007/978-3-030-88520-5_11

    Chapter  Google Scholar 

19. Pozyx: Unleash the power of real-time location solutions (2022).https://www.pozyx.io/. Accessed 21 Nov 2022

20. Prifti, R.: Self-efficacy and student satisfaction in the context of blended learning courses. Open Learn.37(2), 111–125 (2022)

    Article  Google Scholar 

21. Riquelme, F., et al.: Where are you? Exploring micro-location in indoor learning environments. IEEE Access8, 125776–125785 (2020)

    Article  Google Scholar 

22. Saquib, N., Bose, A., George, D., Kamvar, S.: Sensei: sensing educational interaction. PACM IMWUT1(4), 1–27 (2018)

    Google Scholar 

23. Shaffer, D.W., Collier, W., Ruis, A.R.: A tutorial on epistemic network analysis: analyzing the structure of connections in cognitive, social, and interaction data. J. Learn. Anal.3(3), 9–45 (2016)

    Article  Google Scholar 

24. Shaffer, D.W., et al.: Epistemic network analysis: a prototype for 21st-century assessment of learning. IJLM1(2), 33–53 (2009)

    MathSciNet  Google Scholar 

25. Siebert-Evenstone, A.L., Irgens, G.A., Collier, W., Swiecki, Z., Ruis, A.R., Shaffer, D.W.: In search of conversational grain size: modeling semantic structure using moving stanza windows. J. Learn. Anal.4(3), 123–139 (2017)

    Google Scholar 

26. Swiecki, Z., et al.: Assessment in the age of artificial intelligence. Comput. Educ.: Artif. Intell. (2022). In press

    Google Scholar 

27. Tan, Y., Ruis, A.R., Marquart, C., Cai, Z., Knowles, M.A., Shaffer, D.W.: Ordered network analysis. In: Damşa, C., Barany, A. (eds.) Advances in Quantitative Ethnography. Communications in Computer and Information Science, vol. 1785, pp. 101–116. Springer, Cham (2022).https://doi.org/10.1007/978-3-031-31726-2_8

    Chapter  Google Scholar 

28. Vrzakova, H., Amon, M.J., Stewart, A., Duran, N.D., D’Mello, S.K.: Focused or stuck together: multimodal patterns reveal triads’ performance in collaborative problem solving. In: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, pp. 295–304 (2020)

    Google Scholar 

29. Yan, L., et al.: Footprints at school: modelling in-class social dynamics from students’ physical positioning traces. In: LAK 2021, pp. 43–54 (2021)

    Google Scholar 

30. Yan, L., et al.: The role of indoor positioning analytics in assessment of simulation-based learning. Br. Educ. Res. J. (2022). In press

    Google Scholar 

31. Yan, L., Zhao, L., Gasevic, D., Martinez-Maldonado, R.: Scalability, sustainability, and ethicality of multimodal learning analytics. In: LAK 2022, pp. 13–23. Association for Computing Machinery, New York (2022)

    Google Scholar 

32. Zhao, L., et al.: METS: multimodal learning analytics of embodied teamwork learning. In: 13th International Learning Analytics and Knowledge Conference (2023). In press.https://doi.org/10.1145/3576050.3576076

33. Zhao, L., et al.: Modelling co-located team communication from voice detection and positioning data in healthcare simulation. In: LAK 2022, pp. 370–380 (2022)

    Google Scholar 

Download references