FATE in MMLA
A Student-Centred Exploration of Fairness, Accountability, Transparency, and Ethics in Multimodal Learning Analytics
DOI:
https://doi.org/10.18608/jla.2024.8351Keywords:
multimodal learning analytics, fairness, accountability, transparency, ethics, research paperAbstract
Multimodal learning analytics (MMLA) integrates novel sensing technologies and artificial intelligence algorithms, providing opportunities to enhance student reflection during complex, collaborative learning experiences. Although recent advancements in MMLA have shown its capability to generate insights into diverse learning behaviours across various learning settings, little research has been conducted to evaluate these systems in authentic learning contexts, particularly regarding students’ perceived fairness, accountability, transparency, and ethics (FATE). Understanding these perceptions is essential to using MMLA effectively without introducing ethical complications or negatively affecting how students learn. This study aimed to address this gap by assessing the FATE of MMLA in an authentic, collaborative learning context. We conducted semi-structured interviews with 14 undergraduate students who used MMLA visualizations for post-activity reflection. The findings highlighted the significance of accurate and comprehensive data representation to ensure visualization fairness, the need for different levels of data access to foster accountability, the imperative of measuring and cultivating transparency with students, and the necessity of transforming informed consent from dichotomous to continuous and measurable scales. While students value the benefits of MMLA, they also emphasize the importance of ethical considerations, highlighting a pressing need for the LA and MMLA communities to investigate and address FATE issues actively.
References
Alfredo, R., Echeverria, V., Jin, Y., Swiecki, Z., Gaševìc, D., & Martinez-Maldonado, R. (2024). SLADE: A method for designing human-centred learning analytics systems. In Proceedings of the 14th International Conference on Learning Analytics and Knowledge (LAK 2024), 18–22 March 2024, Kyoto, Japan (pp. 24–34, Vol. 32). ACM. https://doi.org/10.1145/3636555.3636847
Alwahaby, H., Cukurova, M., Papamitsiou, Z., & Giannakos, M. (2022). The evidence of impact and ethical considerations of multimodal learning analytics: A systematic literature review. In M. Giannakos, D. Spikol, D. Di Mitri, K. Sharma, X. Ochoa, & R. Hammad (Eds.), The multimodal learning analytics handbook (pp. 289–325). Springer International Publishing. https://doi.org/10.1007/978-3-031-08076-0_12
Ando, H., Cousins, R., & Young, C. (2014). Achieving saturation in thematic analysis: Development and refinement of a codebook. Comprehensive Psychology, 3. https://doi.org/10.2466/03.cp.3.4
Azevedo, R., & Gašević, D. (2019). Analyzing multimodal multichannel data about self-regulated learning with advanced learning technologies: Issues and challenges. Computers in Human Behavior, 96, 207–210. https://doi.org/https://doi.org/10.1016/j.chb.2019.03.025
Barbierato, E., Vedova, M. L. D., Tessera, D., Toti, D., & Vanoli, N. (2022). A methodology for controlling bias and fairness in synthetic data generation. Applied Sciences, 12(9), 4619. https://doi.org/10.3390/app12094619
Beardsley, M., Martínez Moreno, J., Vujovic, M., Santos, P., & Hernández-Leo, D. (2020). Enhancing consent forms to support participant decision making in multimodal learning data research. British Journal of Educational Technology, 51(5), 1631–1652. https://doi.org/https://doi.org/10.1111/bjet.12983
Bezuidenhout, L., & Ratti, E. (2021). What does it mean to embed ethics in data science? An integrative approach based on microethics and virtues. AI & Society, 36(3), 939–953. https://doi.org/10.1007/s00146-020-01112-w
Blikstein, P. (2013). Multimodal learning analytics. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (LAK 2013), 8–13 April 2013, Leuven, Belgium (pp. 102–106). ACM. https://doi.org/10.1145/2460296.2460316
Bose, J., & Rengel, Z. (2009). A model formative assessment strategy to promote student-centered self-regulated learning in higher education. US-China Education Review A, 6(12), 29–35. https://www.davidpublisher.com/index.php/Home/Article/index?id=13795.html
Carless, D. (2009). Trust, distrust and their impact on assessment reform. Assessment & Evaluation in Higher Education, 34(1), 79–89. https://doi.org/10.1080/02602930801895786
Chaudhry, M. A., Cukurova, M., & Luckin, R. (2022). A transparency index framework for AI in education. In M. Rodrigo, N. Matsuda, A. Cristea, & V. Dimitrova (Eds.), Artificial intelligence in education. Posters and late breaking results, workshops and tutorials, industry and innovation tracks, practitioners’ and doctoral consortium. AIED 2022. Lecture notes in computer science (pp. 195–198). Springer. https://doi.org/10.35542/osf.io/bstcf
Chejara, P., Kasepalu, R., Prieto, L. P., Rodr´ıguez-Triana, M. J., Ruiz-Calleja, A., & Shankar, S. K. (2023). Multimodal learning analytics research in the wild: Challenges and their potential solutions. In Proceedings of the Sixth Workshop on Leveraging Multimodal Data for Generating Meaningful Feedback (CROSSMMLA 2023) at the 13th International Conference on Learning Analytics and Knowledge (LAK 2023), 13–17 March 2023, Arlington, Texas, USA (pp. 36–42). CEUR. https://ceur-ws.org/Vol-3439/
Crescenzi-Lanna, L. (2020). Multimodal learning analytics research with young children: A systematic review. British Journal of Educational Technology, 51(5), 1485–1504. https://doi.org/10.1111/bjet.12959
Cukurova, M., Giannakos, M., & Martinez-Maldonado, R. (2020). The promise and challenges of multimodal learning analytics. British Journal of Educational Technology, 51(5), 1441–1449. https://doi.org/10.1111/bjet.13015
Darabi Bazvand, A., & Rasooli, A. (2022). Students’ experiences of fairness in summative assessment: A study in a higher education context. Studies in Educational Evaluation, 72, 101118. https://doi.org/10.1016/j.stueduc.2021.101118
Deho, O. B., Zhan, C., Li, J., Liu, J., Liu, L., & Duy Le, T. (2022). How do the existing fairness metrics and unfairness mitigation algorithms contribute to ethical learning analytics? British Journal of Educational Technology, 53(4), 822–843. https://doi.org/10.1111/bjet.13217
D’Mello, S. K., Olney, A. M., Blanchard, N., Samei, B., Sun, X., Ward, B., & Kelly, S. (2015). Multimodal capture of teacher-student interactions for automated dialogic analysis in live classrooms. In Proceedings of the 2015 ACM International Conference on Multimodal Interaction (ICMI 2015), 9–13 November 2015, Seattle, Washington, USA. ACM. https://doi.org/10.1145/2818346.2830602
Dolan, B. M., Arnold, J., & Green, M. M. (2019). Establishing trust when assessing learners: Barriers and opportunities. Academic Medicine, 94(12), 1851–1853. https://doi.org/10.1097/acm.0000000000002982
Fereday, J., & Muir-Cochrane, E. (2006). Demonstrating rigor using thematic analysis: A hybrid approach of inductive and deductive coding and theme development. International Journal of Qualitative Methods, 5(1), 80–92. https://doi.org/10.1177/160940690600500107
Friend Wise, A., Knight, S., & Buckingham Shum, S. (2021). Collaborative learning analytics. In U. Cress, C. Rose, A. Friend Wise, & J. Oshima (Eds.), International handbook of computer-supported collaborative learning (pp. 425–443). Springer International Publishing. https://doi.org/10.1007/978-3-030-65291-3_23
Gašević, D., Dawson, S., & Siemens, G. (2014). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71. https://doi.org/10.1007/s11528-014-0822-x
General Data Protection Regulation. (2016). Regulation (EU) 2016/679 of the European Parliament and of the council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A02016R0679-20160504
Giannakos, M., Cukurova, M., & Papavlasopoulou, S. (2022). Sensor-based analytics in education: Lessons learned from research in multimodal learning analytics. In M. Giannakos, D. Spikol, D. Di Mitri, K. Sharma, X. Ochoa, & R. Hammad (Eds.), The multimodal learning analytics handbook (pp. 329–358). Springer International Publishing. https://doi.org/10.1007/978-3-031-08076-0_13
Giannakos, M. N., Sharma, K., Pappas, I. O., Kostakos, V., & Velloso, E. (2019). Multimodal data as a means to understand the learning experience. International Journal of Information Management, 48, 108–119. https://doi.org/10.1016/j.ijinfomgt.2019.02.003
Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough?: An experiment with data saturation and variability. Field Methods, 18(1), 59–82. https://doi.org/10.1177/1525822x05279903
Hakami, E., & Leo, D. H. (2020). How are learning analytics considering the societal values of fairness, accountability, transparency and human well-being? A literature review. Paper presented at LASI-SPAIN 2020: Learning Analytics Summer Institute Spain 2020, 15–16 June 2020, Valladolid, Spain. https://repositori.upf.edu/handle/10230/47267
Hall, E. T. (1971). Proxemics and design. Design and Environment, 2(4), 24–25, 58. https://eric.ed.gov/?id=EJ052960
Holstein, K., & Doroudi, S. (2019). Fairness and equity in learning analytics systems (FairLAK). In J. Cunningham, N. Hoover, S. Hsiao, G. Lynch, K. McCarthy, C. Brooks, R. Ferguson, & U. Hoppe (Eds.), Companion Proceedings of the Ninth International Conference on Learning Analytics and Knowledge (LAK 2019), 4–8 March 2019, Tempe, Arizona, USA (pp. 500–503). SoLAR. https://www.solaresearch.org/core/companion-proceedings-of-the-9th-international-learning-analytics-and-knowledge-conference-lak19/
Islam, S. R., Russell, I., Eberle, W., & Dicheva, D. (2022). Incorporating the concepts of fairness and bias into an undergraduate computer science course to promote fair automated decision systems. In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education (SIGCSE 2022), 3–5 March 2022, Providence, Rhode Island, USA (pp. 1075–1075, Vol. 2). ACM. https://doi.org/10.1145/3478432.3499043
Jiang, W., & Pardos, Z. A. (2021). Towards equity and algorithmic fairness in student grade prediction. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (AIES 2021), 19–21 May 2021, online (pp. 608–617). ACM. https://doi.org/10.1145/3461702.3462623
Junghans, C., Feder, G., Hemingway, H., Timmis, A., & Jones, M. (2005). Recruiting patients to medical research: Double blind randomised trial of “opt-in” versus “opt-out” strategies. BMJ, 331(7522), 940. https://doi.org/10.1136/bmj.38583.625613.ae
Kasepalu, R., Chejara, P., Prieto, L. P., & Ley, T. (2021). Do teachers find dashboards trustworthy, actionable and useful? A vignette study using a logs and audio dashboard. Technology, Knowledge and Learning, 27(3), 971–989. https://doi.org/10.1007/s10758-021-09522-5
Kasinidou, M., Kleanthous, S., Orphanou, K., & Otterbacher, J. (2021). Educating computer science students about lgorithmic fairness, accountability, transparency and ethics. In Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE 2021), 29 June–1 July 2021, online (pp. 484–490, Vol. 1). ACM. https://doi.org/10.1145/3430665.3456311
Khalil, M., Prinsloo, P., & Slade, S. (2023). Fairness, trust, transparency, equity, and responsibility in learning analytics. Journal of Learning Analytics, 10(1), 1–7. https://doi.org/10.18608/jla.2023.7983
Khosravi, H., Buckingham Shum, S., Chen, G., Conati, C., Tsai, Y.-S., Kay, J., Knight, S., Martinez-Maldonado, R., Sadiq, S., & Gašević, D. (2022). Explainable artificial intelligence in education. Computers and Education: Artificial Intelligence, 3, 100074. https://doi.org/10.1016/j.caeai.2022.100074
Kim, J.- Y., & Cho, S.- B. (2022). An information theoretic approach to reducing algorithmic bias for machine learning. Neurocomputing, 500, 26–38. https://doi.org/10.1016/j.neucom.2021.09.081
Li, W., Brooks, C., & Schaub, F. (2019). The impact of student opt-out on educational predictive models. In Proceedings of the Ninth International Conference on Learning Analytics and Knowledge (LAK 2019), 4–8 March 2019, Tempe, Arizona, USA. ACM. https://doi.org/10.1145/3303772.3303809
Li, W., Sun, K., Schaub, F., & Brooks, C. (2022). Disparities in students’ propensity to consent to learning analytics. International Journal of Artificial Intelligence in Education, 32(3), 564–608. https://doi.org/10.35542/osf.io/vnc9b
Liu, Q., & Khalil, M. (2023). Understanding privacy and data protection issues in learning analytics using a systematic review. British Journal of Educational Technology, 54(6), 1715–1747. https://doi.org/10.1111/bjet.13388
Mangaroska, K., Martinez-Maldonado, R., Vesin, B., & Gašević, D. (2021). Challenges and opportunities of multimodal data in human learning: The computer science students’ perspective. Journal of Computer Assisted Learning, 37(4), 1030–1047. https://doi.org/10.1111/jcal.12542
Martinez-Maldonado, R., Echeverria, V., Fernandez Nieto, G., & Buckingham Shum, S. (2020). From data to insights: A layered storytelling approach for multimodal learning analytics. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI 2020), 25–30 April 2020, Honolulu, Hawaii, USA. ACM. https://doi.org/10.1145/3313831.3376148
Martinez-Maldonado, R., Echeverria, V., Fernandez-Nieto, G., Yan, L., Zhao, L., Alfredo, R., Li, X., Dix, S., Jaggard, H., Wotherspoon, R., Osborne, A., Shum, S. B., & Ga ˇsevi ´c, D. (2023). Lessons learnt from a multimodal learning analytics deployment in-the-wild. ACM Transactions on Computer-Human Interaction, 31(1), 1–41. https://doi.org/10.1145/3622784
Martinez-Maldonado, R., Pardo, A., Mirriahi, N., Yacef, K., Kay, J., & Clayphan, A. (2016). LATUX: An iterative workflow for designing, validating and deploying learning analytics visualisations. Journal of Learning Analytics, 2(3), 9–39. https://doi.org/10.18608/jla.2015.23.3
McDonald, N., Schoenebeck, S., & Forte, A. (2019). Reliability and inter-rater reliability in qualitative research: Norms and guidelines for CSCW and HCI practice. Proceedings of the ACM on Human-Computer Interaction, 3, 1–23. https://doi.org/10.1145/3359174
Memarian, B., & Doleck, T. (2023). Fairness, accountability, transparency, and ethics (FATE) in artificial intelligence (AI), and higher education: A systematic review. Computers and Education: Artificial Intelligence, 5, 100152. https://doi.org/10.1016/j.caeai.2023.100152
Ochoa, X. (2022). Multimodal learning analytics: Rationale, process, examples, and direction. In C. Lang, G. Siemens, A. Friend Wise, D. Gašević, & A. Merceron (Eds.), The handbook of learning analytics (pp. 54–65). SoLAR. https://doi.org/10.18608/hla22.006
Ochoa, X., & Dominguez, F. (2020). Controlled evaluation of a multimodal system to improve oral presentation skills in a real learning setting. British Journal of Educational Technology, 51(5), 1615–1630. https://doi.org/10.1111/bjet.12987
Ouhaichi, H., Spikol, D., & Vogel, B. (2023). Rethinking MMLA: Design considerations for multimodal learning analytics systems. In Proceedings of the 10th ACM Conference on Learning @ Scale (L@S 2023), 20–22 July 2023, Copenhagen, Denmark (pp. 354–359). ACM. https://doi.org/10.1145/3573051.3596186
Pagallo, U. (2017, August). From automation to autonomous systems: A legal phenomenology with problems of accountability. In C. Sierra (Ed.), Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017),19–25 August 2017, Melbourne, Australia (pp. 17–23). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2017/3
Pardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45(3), 438–450. https://doi.org/10.1111/bjet.12152
Prinsloo, P., & Slade, S. (2016). Student vulnerability, agency and learning analytics: An exploration. Journal of Learning Analytics, 3(1), 159–182. https://doi.org/10.18608/jla.2016.31.10
Prinsloo, P., & Slade, S. (2017). An elephant in the learning analytics room: The obligation to act. In Proceedings of the Seventh International Conference on Learning Analytics and Knowledge (LAK 2017), 13–17 March 2017, Vancouver, British Columbia, Canada. ACM. https://doi.org/10.1145/3027385.3027406
Prinsloo, P., Slade, S., & Khalil, M. (2023). Multimodal learning analytics—In-between student privacy and encroachment: A systematic review. British Journal of Educational Technology, 54(6), 1566–1586. https://doi.org/10.1111/bjet.13373
Rubel, A., & Jones, K. M. L. (2016). Student privacy in learning analytics: An information ethics perspective. The Information Society, 32(2), 143–159. https://doi.org/10.1080/01972243.2016.1130502
Scheffel, M., Drachsler, H., Toisoul, C., Ternier, S., & Specht, M. (2017). The proof of the pudding: Examining validity and reliability of the evaluation framework for learning analytics. In E. Lavoué, H. Drachsler, K. Verbert, J. Broisin, & M. Pérez-Sanagustín (Eds.), Data driven approaches in digital education. EC-TEL 2017. Lecture notes in computer science (pp. 194–208, Vol. 10474). Springer International Publishing. https://doi.org/10.1007/978-3-319-66610-5_15
Schneider, B., Davis, R., Martinez-Maldonado, R., Biswas, G., Worsley, M., & Rummel, N. (2024). Stepping outside the ivory tower: How can we implement multimodal learning analytics in ecological settings, and turn complex temporal data sources into actionable insights? In J. Clarke-Midura, I. Kollar, X. Gu, & C. D’Angelo (Eds.), Proceedings of the 17th International Conference on Computer-Supported Collaborative Learning (CSCL 2024), 10–14 June 2024, Buffalo, New York, USA (pp. 323–330). International Society of the Learning Sciences. https://doi.org/10.22318/cscl2024.259119
Schneider, B., Sharma, K., Cuendet, S., Zufferey, G., Dillenbourg, P., & Pea, R. (2018). Leveraging mobile eye-trackers to capture joint visual attention in co-located collaborative learning groups. International Journal of Computer-Supported Collaborative Learning, 13(3), 241–261. https://doi.org/10.1007/s11412-018-9281-2
Shaffer, D. W., Collier, W., & Ruis, A. R. (2016). A tutorial on epistemic network analysis: Analyzing the structure of connections in cognitive, social, and interaction data. Journal of Learning Analytics, 3(3), 9–45. https://doi.org/10.18608/jla.2016.33.3
Sharma, K., & Giannakos, M. (2020). Multimodal data capabilities for learning: What can multimodal data tell us about learning? British Journal of Educational Technology, 51(5), 1450–1484. https://doi.org/10.1111/bjet.12993
Shin, D., Rasul, A., & Fotiadis, A. (2021). Why am I seeing this? Deconstructing algorithm literacy through the lens of users. Internet Research, 32(4), 1214–1234. https://doi.org/10.1108/intr-02-2021-0087
Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529. https://doi.org/10.1177/0002764213479366
Spikol, D., Ruffaldi, E., Landolfi, L., & Cukurova, M. (2017). Estimation of success in collaborative learning based on multimodal learning analytics features. In M. Chang, N.-S. Chen, R. Huang, Kinshuk, D. G. Sampson, & R. Vasiu (Eds.), Proceedings of the 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT 2017), 3–7 July 2017, Timisoara, Romania. IEEE. https://doi.org/10.1109/icalt.2017.122
Sümer, Ö., Goldberg, P., D’Mello, S., Gerjets, P., Trautwein, U., & Kasneci, E. (2023). Multimodal engagement analysis from facial videos in the classroom. IEEE Transactions on Affective Computing, 14(2), 1012–1027. https://doi.org/10.1109/taffc.2021.3127692
Sun, K., Mhaidli, A. H., Watel, S., Brooks, C. A., & Schaub, F. (2019). It’s my data! Tensions among stakeholders of a learning analytics dashboard. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI 2019), 4–9 May 2019, Glasgow, UK. ACM. https://doi.org/10.1145/3290605.3300824
Tsai, Y. - S., Perrotta, C., & Gašević, D. (2019). Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics. Assessment & Evaluation in Higher Education, 45(4), 554–567. https://doi.org/10.1080/02602938.2019.1676396
Tsai, Y.- S., Whitelock-Wainwright, A., & Gašević, D. (2020). The privacy paradox and its implications for learning analytics. In Proceedings of the 10th International Conference on Learning Analytics and Knowledge (LAK 2020), 23–27 March 2020, Frankfurt, Germany. ACM. https://doi.org/10.1145/3375462.3375536
Tzimas, D., & Demetriadis, S. (2021). Ethical issues in learning analytics: A review of the field. Educational Technology Research and Development, 69(2), 1101–1133. https://doi.org/10.1007/s11423-021-09977-4
Ungerer, L., & Slade, S. (2022). Ethical considerations of artificial intelligence in learning analytics in distance education contexts. In P. Prinsloo, S. Slade, & M. Khalil (Eds.), Learning analytics in open and distributed learning (pp. 105–120). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-0786-9_8
Verbert, K., Ochoa, X., De Croon, R., Dourado, R. A., & De Laet, T. (2020). Learning analytics dashboards: The past, the present and the future. In Proceedings of the 10th International Conference on Learning Analytics and Knowledge (LAK 2020), 23–27 March 2020, Frankfurt, Germany (pp. 35–40). ACM. https://doi.org/10.1145/3375462.3375504
Worsley, M., & Blikstein, P. (2015). Leveraging multimodal learning analytics to differentiate student learning strategies. In Proceedings of the Fifth International Conference on Learning Analytics and Knowledge (LAK 2015), 16–20 March 2015, Poughkeepsie, New York, USA (pp. 360–367, Vol. 1). ACM. https://doi.org/10.1145/2723576.2723624
Worsley, M., Martinez-Maldonado, R., & D’Angelo, C. (2021). A new era in multimodal learning analytics: Twelve core commitments to ground and grow MMLA. Journal of Learning Analytics, 8(3), 10–27. https://doi.org/10.18608/jla.2021.7361
Yan, L., Echeverria, V., Jin, Y., Fernandez-Nieto, G., Zhao, L., Li, X., Alfredo, R., Swiecki, Z., Gašević, D., & Martinez-Maldonado, R. (2024). Evidence-based multimodal learning analytics for feedback and reflection in collaborative learning. British Journal of Educational Technology, 55(5), 1900–1925. https://doi.org/10.1111/bjet.13498
Yan, L., Martinez-Maldonado, R., Zhao, L., Dix, S., Jaggard, H., Wotherspoon, R., Li, X., & Gašević, D. (2023). The role of indoor positioning analytics in assessment of simulation-based learning. British Journal of Educational Technology, 54(1), 267–292. https://doi.org/https://doi.org/10.1111/bjet.13262
Yan, L., Zhao, L., Gašević, D., & Martinez-Maldonado, R. (2022). Scalability, sustainability, and ethicality of multimodal learning analytics. In Proceedings of the 12th International Conference on Learning Analytics and Knowledge (LAK 2022), 21–25 March 2022, online (pp. 13–23, Vol. 3). ACM. https://doi.org/10.1145/3506860.3506862
Zhao, L., Swiecki, Z., Gasevic, D., Yan, L., Dix, S., Jaggard, H., Wotherspoon, R., Osborne, A., Li, X., Alfredo, R., & Martinez-Maldonado, R. (2023). METS: Multimodal learning analytics of embodied teamwork learning. In Proceedings of the 13th International Conference on Learning Analytics and Knowledge (LAK 2023), 13–17 March 2023, Arlington, Texas, USA. ACM. https://doi.org/10.1145/3576050.3576076
Zhao, L., Yan, L., Gašević, D., Dix, S., Jaggard, H., Wotherspoon, R., Alfredo, R., Li, X., & Martinez-Maldonado, R. (2022). Modelling co-located team communication from voice detection and positioning data in healthcare simulation. In Proceedings of the 12th International Conference on Learning Analytics and Knowledge (LAK 2022), 21–25 March 2022, online (pp. 370–380, Vol. 6). ACM. https://doi.org/10.1145/3506860.3506935
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