Designing a Human-centred Learning Analytics Dashboard In-use
DOI:
https://doi.org/10.18608/jla.2024.8487Keywords:
design-in-use, instrumental genesis, HCLA, AI-powered, co-design, dashboard, nursing education, research paperAbstract
Despite growing interest in applying human-centred design methods to create learning analytics (LA) systems, most efforts have concentrated on initial design phases, with limited exploration of how LA tools and practices can co-evolve during the actual learning and teaching activities. This paper examines how a human-centred LA dashboard can be further refined and adapted by teachers while actively using it in a real-world scenario (i.e., design-in-use), beyond its intended design (i.e., design-for-use). We use instrumental genesis as a theoretical lens to analyze the temporary and permanent instrumentalization of design features and individual and collective instrumentation of the LA dashboard. The analysis of semi-structured individual interviews with five nursing teachers who used an LA dashboard to guide team reflections with 224 students (56 teams) revealed technical and pedagogical changes that occurred in both the system’s features (instrumentalization) and teaching practices (instrumentation). We found that teachers adopted the LA dashboard beyond initially intended ways by (i) providing emotional support with the analytics, (ii) reducing details in AI-powered data visualizations for easier comprehension, (iii) creating data narratives to address data limitations, and (iv) collectively developing new practices to use the LA dashboard for co-teaching. Therefore, teachers’ design-in-use of the LA dashboard highlights the ongoing need for design improvements to address challenges posed by dynamic data and complex algorithms underlying AI and analytics interfaces.
References
Aleven, V., Blankestijn, J., Lawrence, L., Nagashima, T., & Taatgen, N. (2022). A dashboard to support teachers during students’ self-paced AI-supported problem-solving practice. In I. Hilliger, P. Mu˜noz-Merino, T. De Laet, A. Ortega-Arranz, & T. Farrell (Eds.), Educating for a New Future: Making Sense of Technology-Enhanced Learning Adoption (pp. 16–30). Springer. https://doi.org/10.1007/978-3-031-16290-92
Alfredo, R., Echeverria, V., Jin, Y., Swiecki, Z., Gasevic, 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). ACM. https://doi.org/10.1145/3636555.3636847
Alfredo, R., Echeverria, V., Jin, Y., Yan, L., Swiecki, Z., Gasevic, D., & Martinez-Maldonado, R. (2024). Human-centred learning analytics and AI in education: A systematic literature review. Computers and Education: Artificial Intelligence, 6, 100215. https://doi.org/10.1016/j.caeai.2024.100215
Alfredo, R. D., Nie, L., Kennedy, P., Power, T., Hayes, C., Chen, H., McGregor, C., Swiecki, Z., Gasevic, D., & Martinez- Maldonado, R. (2023). “That student should be a lion tamer!” StressViz: Designing a stress analytics dashboard for teachers. In Proceedings of the 13th International Conference on Learning Analytics and Knowledge (LAK 2023), 13–17 March 2023, Arlington, Texas, USA (pp. 57–67). ACM. https://doi.org/10.1145/3576050.3576058
Alhadad, S. S. J., Thompson, K., Knight, S., Lewis, M., & Lodge, J. M. (2018). Analytics-enabled teaching as design: Reconceptualisation and call for research. In Proceedings of the Eighth International Conference on Learning Analytics and Knowledge (LAK 2018), 7–9 March 2018, Sydney, Australia (pp. 427–435). ACM. https://doi.org/10.1145/3170358.3170390
Amarasinghe, I., Hern´andez-Leo, D., & Ulrich Hoppe, H. (2021). Deconstructing orchestration load: Comparing teacher support through mirroring and guiding. International Journal of Computer-Supported Collaborative Learning, 16(3), 307-338. https://doi.org/10.1007/s11412-021-09351-9
Baldauf, M., Adegeye, F., Alt, F., & Harms, J. (2016). Your browser is the controller: Advanced web-based smartphone remote controls for public screens. In Proceedings of the Fifth ACM International Symposium on Pervasive Displays (PerDis 2016), 20–26 June 2016, Oulu, Finland (pp. 175–181). ACM. https://doi.org/10.1145/2914920.2915026
Becerra, Á ., Mohseni, Z., Sanz, J., & Cobos, R. (2024). A generative AI-based personalized guidance tool for enhancing the feedback to MOOC learners. In Proceedings of the 2024 IEEE Global Engineering Education Conference (EDUCON 2024), 8–11 May 2024, Kos, Greece (pp. 1–8). IEEE. https://doi.org/10.1109/EDUCON60312.2024.10578809
Bingham, A. J. (2023). From data management to actionable findings: A five-phase process of qualitative data analysis. International Journal of Qualitative Methods, 22, 16094069231183620. https://doi.org/10.1177/16094069231183620
Brinkmann, S. (2020). Unstructured and semistructured interviewing. In P. Leavy (Ed.), Oxford handbook of qualitative research. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780190847388.013.22
Buckingham Shum, S., Ferguson, R., & Martinez-Maldonado, R. (2019). Human-centred learning analytics. Journal of Learning Analytics, 6(2), 1–9. https://doi.org/10.18608/jla.2019.62.1
Buckingham Shum, S., Mart´ınez-Maldonado, R., Dimitriadis, Y., & Santos, P. (2024). Human-centred learning analytics: 2019–24. British Journal of Educational Technology, 55(3), 755–768. https://doi.org/10.1111/bjet.13442
Buckingham Shum, S. J., & Luckin, R. (2019). Learning analytics and AI: Politics, pedagogy and practices. British Journal of Educational Technology, 50(6), 2785–2793. https://doi.org/10.1111/bjet.12880
Carvalho, L., Martinez-Maldonado, R., & Goodyear, P. (2019). Instrumental genesis in the design studio. International Journal of Computer-Supported Collaborative Learning, 14(1), 77–107. https://doi.org/10.1007/s11412-019-09294-2
Cerratto Pargman, T., Nouri, J., & Milrad, M. (2018). Taking an instrumental genesis lens: New insights into collaborative mobile learning. British Journal of Educational Technology, 49(2), 219–234. https://doi.org/10.1111/bjet.12585
Chen, S., Liu, Y., Lu, R., Zhou, Y., Lee, Y.-C., & Huang, Y. (2022). “Mirror, mirror, on the wall”—Promoting self-regulated learning using affective states recognition via facial movements. In Proceedings of the 2022 Designing Interactive Systems Conference (DIS 2022), 13–17 June 2022, online (pp. 1300–1314). ACM. https://doi.org/10.1145/3532106.3533500
Cheng, A., Eppich, W., Grant, V., Sherbino, J., Zendejas, B., & Cook, D. A. (2014). Debriefing for technology-enhanced simulation: A systematic review and meta-analysis. Medical Education, 48(7), 657–666. https://doi.org/10.1111/medu.12432
Core, C., Conci, A., De Angeli, A., Masu, R., & Morreale, F. (2017). Designing a musical playground in the kindergarten. In Proceedings of the 2017 Conference on Electronic Visualisation and the Arts (EVA 2017), 13–17 July 2017, London, UK (pp. 1–12). BCS Learning & Development Ltd. https://doi.org/10.14236/ewic/HCI2017.41
de Bayser, M., Azevedo, L. G., & Cerqueira, R. (2015, May). ResearchOps: The case for devops in scientific applications. In R. Badonnel, J. Xiao, S. Ata, F. De Turk, V. Groza, & C. Raniery P. dos Santos (Eds.), Proceedings of the 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM 2015), 11–15 May 2015, Ottawa, Ontario, Canada (pp. 1398–1404). https://doi.org/10.1109/INM.2015.7140503
Decuyper, A., Tack, H., Vanblaere, B., Simons, M., & Vanderlinde, R. (2023). Collaboration and shared responsibility in team teaching: A large-scale survey study. Education Sciences, 13(9), 896. https://doi.org/10.3390/educsci13090896
Dollinger, M., Liu, D., Arthars, N., & Lodge, J. (2019). Working together in learning analytics towards the co-creation of value. Journal of Learning Analytics, 6(2), 10–26. https://doi.org/10.18608/jla.2019.62.2
Dollinger, M., & Lodge, J. M. (2018). Co-creation strategies for learning analytics. In Proceedings of the Eighth International Conference on Learning Analytics and Knowledge (LAK 2018), 7–9 March 2018, Sydney, Australia (pp. 97–101). ACM. https://doi.org/10.1145/3170358.3170372
Echeverria, V., Yan, L., Zhao, L., Abel, S., Alfredo, R., Dix, S., Jaggard, H., Wotherspoon, R., Osborne, A., Buckingham Shum, S., Gasevic, D., & Martinez-Maldonado, R. (2024). TeamSlides: A multimodal teamwork analytics dashboard for teacher-guided reflection in a physical learning space. In Proceedings of the 14th International Conference on Learning Analytics and Knowledge (LAK 2024), 18–22 March 2024, Kyoto, Japan. ACM. https://doi.org/10.1145/3636555.3636857
Fernandez Nieto, G. M., Kitto, K., Buckingham Shum, S., & Martinez-Maldonado, R. (2022). Beyond the learning analytics dashboard: Alternative ways to communicate student data insights combining visualisation, narrative and storytelling. In Proceedings of the 12th International Conference on Learning Analytics and Knowledge (LAK 2022), 21–25 March 2022, online (pp. 219–229). ACM. https://doi.org/10.1145/3506860.3506895
Fernandez-Nieto, G., An, P., Zhao, J., Buckingham Shum, S., & Martinez-Maldonado, R. (2022). Classroom Dandelions: Visualising Participant Position, Trajectory and Body Orientation Augments Teachers’ Sensemaking. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, 1–17. https://doi.org/10.1145/3491102.3517736
Fernandez-Nieto, G. M., Martinez-Maldonado, R., Echeverria, V., Kitto, K., Gasevic, D., & Buckingham Shum, S. (2024). Data storytelling editor: A teacher-centred tool for customising learning analytics dashboard narratives. In Proceedings of the 14th International Conference on Learning Analytics and Knowledge (LAK 2024), 18–22 March 2024, Kyoto, Japan (pp. 678–689). ACM. https://doi.org/10.1145/3636555.3636930
Fernandez-Nieto, G. M., Martinez-Maldonado, R., Kitto, K., & Buckingham Shum, S. (2021). Modelling spatial behaviours in clinical team simulations using epistemic network analysis: Methodology and teacher evaluation. In Proceedings of the 11th International Conference on Learning Analytics and Knowledge (LAK 2021), 12–16 April 2021, Irvine, California, USA (pp. 386–396). ACM. https://doi.org/10.1145/3448139.3448176
Folcher, V. (2003). Appropriating artifacts as instruments: When design-for-use meets design-in-use. Interacting with Computers, 15(5), 647–663. https://doi.org/10.1016/S0953-5438(03)00057-2
Friend Wise, A., Brackett, A., & Maddox, B. (2023, March 12). Flexible coupling of learning analytics research and practice in the university: A collective strengths approach. In Companion Proceedings of the 13th International Conference on Learning Analytics and Knowledge (LAK 2023), 13–17 March 2023, Arlington, Texas, USA (pp. 25–28). https://www.solaresearch.org/wp-content/uploads/2023/03/LAK23CompanionProceedings.pdf
Hara, T., Shimada, S., & Arai, T. (2013). Design-of-use and design-in-use by customers in differentiating value creation. CIRP Annals, 62(1), 103–106. https://doi.org/10.1016/j.cirp.2013.03.080
Holstein, K., Harpstead, E., Gulotta, R., & Forlizzi, J. (2020). Replay enactments: Exploring possible futures through historical data. In Proceedings of the 2020 ACM Designing Interactive Systems Conference (DIS 2020), 6–10 July 2020, Eindhoven, Netherlands (pp. 1607–1618). ACM. https://doi.org/10.1145/3357236.3395427
Holstein, K., McLaren, B. M., & Aleven, V. (2019). Co-designing a real-time classroom orchestration tool to support teacher-AI complementarity. Journal of Learning Analytics, 6(2), 27–52. https://doi.org/10.18608/jla.2019.62.3
Kaliisa, R., & Dolonen, J. A. (2023). CADA: A teacher-facing learning analytics dashboard to foster teachers’ awareness of students’ participation and discourse patterns in online discussions. Technology, Knowledge and Learning, 28(3), 937–958. https://doi.org/10.1007/s10758-022-09598-7
Kaliisa, R., Jivet, I., & Prinsloo, P. (2023). A checklist to guide the planning, designing, implementation, and evaluation of learning analytics dashboards. International Journal of Educational Technology in Higher Education, 20(1), 28. https://doi.org/10.1186/s41239-023-00394-6
Kaliisa, R., Misiejuk, K., López-Pernas, S., Khalil, M., & Saqr, M. (2024, March). Have learning analytics dashboards lived up to the hype? A systematic review of impact on students’ achievement, motivation, participation and attitude. In Proceedings of the 14th International Conference on Learning Analytics and Knowledge (LAK 2024), 18–22 March 2024, Kyoto, Japan (pp. 295–304). ACM. https://doi.org/10.1145/3636555.3636884
Kim, H., & Lim, Y.-K. (2023, July). Investigating how users design everyday intelligent systems in use. In D. Byrne, N. Martelaro, A. Boucher, D. Chatting, S. F. Alaoui, S. Fox, I. Nicenboim, & C. MacArthur (Eds.), Proceedings of the 2023 ACM Designing Interactive Systems Conference (DIS 2023), 10–14 July 2023, Pittsburgh, Pennsylvania, USA (pp. 702–711). ACM. https://doi.org/10.1145/3563657.3596039
Kloos, C. D., Dimitriadis, Y., Hernández-Leo, D., Alario-Hoyos, C., Martínez-Monés, A., Santos, P., Muñoz-Merino, P. J., Asensio-Pérez, J. I., & Safont, L. V. (2022). H2O Learn—Hybrid and human-oriented learning: Trustworthy and human-centered learning analytics (TaHCLA) for hybrid education. In I. Kallel, H. M. Kammoun, & L. Hsairi (Eds.), Proceedings of the 2022 IEEE Global Engineering Education Conference (EDUCON 2022), 28–31 March 2022, Tunis, Tunisia (pp. 94–101). IEEE. https://doi.org/10.1109/EDUCON52537.2022.9766770
Knowles, B., Bull, C. N., Davies, N., Simm, W., Bates, O., & Hayes, N. (2019). Examining interdependencies and constraints in co-creation. In Proceedings of the 2019 Conference on Designing Interactive Systems (DIS 2019), 23–28 June 2019, San Diego, California, USA (pp. 291–302). ACM. https://doi.org/10.1145/3322276.3322317
Lagrange, J.-B., & Richard, P. R. (2022). Instrumental genesis in the theory of MWS: Insight from didactic research on digital artifacts. In A. Kuzniak, E. Montoya-Delgadillo, & P. R. Richard (Eds.), Mathematical work in educational context (pp. 211–228, Vol. 18). Springer International Publishing. https://doi.org/10.1007/978-3-030-90850-8_9
Larrabee Sønderlund, A., Hughes, E., & Smith, J. (2019). The efficacy of learning analytics interventions in higher education: A systematic review. British Journal of Educational Technology, 50(5), 2594–2618. https://doi.org/10.1111/bjet.12720
Lawrence, L., Echeverria, V., Yang, K., Aleven, V., & Rummel, N. (2023). How teachers conceptualise shared control with an AI co-orchestration tool: A multiyear teacher-centred design process. British Journal of Educational Technology, 55(3), 823–844. https://doi.org/10.1111/bjet.13372
Lee, H. H., & Gargroetzi, E. (2023). “It’s like a double-edged sword”: Mentor perspectives on ethics and responsibility in a learning analytics–supported virtual mentoring program. Journal of Learning Analytics, 10(1), 85–100. https://doi.org/10.18608/jla.2023.7787
Lee-Cultura, S., Sharma, K., & Giannakos, M. (2023). MultiModal teacher dashboards: Challenges and opportunities of enhancing teacher insights through a case study. IEEE Transactions on Learning Technologies, 17, 181–201. https://doi.org/10.1109/TLT.2023.3276848
Lonchamp, J. (2012). An instrumental perspective on CSCL systems. International Journal of Computer-Supported Collaborative Learning, 7(2), 211–237. https://doi.org/10.1007/s11412-012-9141-4
Martinez-Maldonado, R. (2023). Human-centred learning analytics: Four challenges in realising the potential. Learning Letters, 1, 6. https://doi.org/10.59453/FIZJ7007
Martinez-Maldonado, R., Carvalho, L., & Goodyear, P. (2018). Collaborative design-in-use: An instrumental genesis lens in multi-device environments. Proceedings of the ACM on Human-Computer Interaction, 2(CSCW), 118:1–118:24. https://doi.org/10.1145/3274387
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., & Gaˇsevi´c, D. (2023). Lessons learnt from a multimodal learning analytics deployment in-the-wild. ACM Transactions on Computer-Human Interaction, 31(1), 8:1–8:41. 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(CSCW), 72:1–72:23. https://doi.org/10.1145/3359174
Miller, K., Riley, W., & Davis, S. (2009). Identifying key nursing and team behaviours to achieve high reliability. Journal of Nursing Management, 17(2), 247–255. https://doi.org/https://doi.org/10.1111/j.1365-2834.2009.00978.x
Mohseni, Z. A., Masiello, I., & Martins, R. M. (2023). Codeveloping an easy-to-use learning analytics dashboard for teachers in primary–secondary education: A human-centered design approach. Education Sciences, 13(12), 1190. https://doi.org/10.3390/educsci13121190
Moltudal, S. H., Krumsvik, R. J., & Høydal, K. L. (2022). Adaptive learning technology in primary education: Implications for professional teacher knowledge and classroom management. Frontiers in Education, 7. https://doi.org/10.3389/feduc.2022.830536
Muller, M. J. (2002). Participatory design: the third space in HCI. In J. A. Jacko & A. Sears (Eds.), The human-computer interaction handbook: Fundamentals, evolving technologies and emerging applications (pp. 1051–1068). L. Erlbaum Associates Inc. https://dl.acm.org/doi/10.5555/772072.772138
Nazaretsky, T., Ariely, M., Cukurova, M., & Alexandron, G. (2022). Teachers’ trust in AI-powered educational technology and a professional development program to improve it. British Journal of Educational Technology, 53(4), 914–931. https://doi.org/10.1111/bjet.13232
Nelson, J., Buisine, S., & Aoussat, A. (2009). Design in use: Some methodological considerations. In CIRP MS’09, 42nd CIRP Conference on Manufacturing Systems (pp. 3–5). https://api.semanticscholar.org/CorpusID:167215398
Nicholson, R., Bartindale, T., Kharrufa, A., Kirk, D., & Walker-Gleaves, C. (2022). Participatory design goes to school: Co-teaching as a form of co-design for educational technology. In S. Barbosa, C. Lampe, C. Appert, D. A. Shamma, S. Drucker, J. Williamson, & K. Yatani (Eds.), CHI Conference on Human Factors in Computing Systems (CHI 2022), 29 April–5 May 2022, New Orleans, Louisiana, USA (pp. 1–17). ACM. https://doi.org/10.1145/3491102.3517667
Ouhaichi, H., Spikol, D., & Vogel, B. (2023). Research trends in multimodal learning analytics: A systematic mapping study. Computers and Education: Artificial Intelligence, 4, 100136. https://doi.org/10.1016/j.caeai.2023.100136
Overdijk, M., Van Diggelen, W., Tomadaki, E., & Scott, P. (2006). Technology appropriation in face-to-face collaborative learning. In Innovative Approaches for Learning and Knowledge Sharing, EC-TEL 2006 Workshops Proceedings, 1 October 2006, Crete, Greece (pp. 89–96). https://www.researchgate.net/publication/221549900_Technology_Appropriation_in_Face-to-Face_Collaborative_Learning
Paniagua, A., & Istance, D. (2018). Teachers as designers of learning environments: The importance of innovative pedagogies. OECD. https://doi.org/10.1787/9789264085374-en
Rabardel, P., & Bourmaud, G. (2003). From computer to instrument system: A developmental perspective. Interacting with Computers, 15(5), 665–691. https://doi.org/10.1016/S0953-5438(03)00058-4
Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., & Sutskever, I. (2022). Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356.
Riley, W., Hansen, H., G¨urses, A. P., Davis, S., Miller, K., & Priester, R. (2008). The nature, characteristics and patterns of perinatal critical events teams. In K. Henriksen, J. B. Battles, M. A. Keyes, & M. L. Grady (Eds.), Advances in patient safety: New directions and alternative approaches (Vol. 3: Performance and tools). Agency for Healthcare Research; Quality (US). https://www.ncbi.nlm.nih.gov/books/NBK43664
Schmitz, M., Scheffel, M., Bemelmans, R., & Drachsler, H. (2022). FoLA2—A method for co-creating learning analytics– supported learning design. Journal of Learning Analytics, 9(2), 265–281. https://doi.org/10.18608/jla.2022.7643
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
Slade, S., Prinsloo, P., & Khalil, M. (2023). “Trust us,” they said. Mapping the contours of trustworthiness in learning analytics. Information and Learning Sciences, 124(9/10), 306–325. https://doi.org/10.1108/ILS-04-2023-0042
Susnjak, T., Ramaswami, G. S., & Mathrani, A. (2022). Learning analytics dashboard: A tool for providing actionable insights to learners. International Journal of Educational Technology in Higher Education, 19(1), 1–23. https://doi.org/10.1186/s41239-021-00313-7
Tchounikine, P. (2017). Designing for appropriation: A theoretical account. Human–Computer Interaction, 32(4), 155–195. https://doi.org/10.1080/07370024.2016.1203263
Tessier, S. (2012). From field notes, to transcripts, to tape recordings: Evolution or combination? International Journal of Qualitative Methods, 11(4), 446–460. https://doi.org/10.1177/160940691201100410
Topali, P., Ortega-Arranz, A., Rodríguez-Trianab, M. J., Er, E., Khalil, M., & Akçapınare, G. (2024). Designing human-centered learning analytics and artificial intelligence in education solutions: A systematic literature review. Behaviour & Information Technology. https://doi.org/10.1080/0144929X.2024.2345295
Vágová, R. (2021). Designing combinations of physical and digital manipulatives to develop students’ visualisation. Open Education Studies, 3(1), 56–75. https://doi.org/10.1515/edu-2020-0140
Van Schoors, R., Elen, J., Raes, A., & Depaepe, F. (2023). Tinkering the teacher–technology nexus: The case of teacher- and technology-driven personalisation. Education Sciences, 13(4), 349. https://doi.org/10.3390/educsci13040349
Wiley, K., Dimitriadis, Y., & Linn, M. (2024). A human-centred learning analytics approach for developing contextually scalable K-12 teacher dashboards. British Journal of Educational Technology, 55(3), 845–885. https://doi.org/10.1111/bjet.13383
Williamson, K., & Kizilcec, R. (2022). A review of learning analytics dashboard research in higher education: Implications for justice, equity, diversity, and inclusion. In Proceedings of the 12th International Conference on Learning Analytics and Knowledge (LAK 2022), 21–25 March 2022, online (pp. 260–270). ACM. https://doi.org/10.1145/3506860.3506900
Yan, L., Martinez-Maldonado, R., & Gasevic, D. (2024). Generative artificial intelligence in learning analytics: Contextualising opportunities and challenges through the learning analytics cycle. In Proceedings of the 14th International Conference on Learning Analytics and Knowledge (LAK 2024), 18–22 March 2024, Kyoto, Japan (pp. 101–111). ACM. https://doi.org/10.1145/3636555.3636856
Yan, L., Zhao, L., Echeverria, V., Jin, Y., Alfredo, R., Li, X., Gasevic, D., & Martinez-Maldonado, R. (2024). VizChat: Enhancing learning analytics dashboards with contextualised explanations using multimodal generative AI chatbots. In A. M. Olney, I.-A. Chounta, Z. Liu, O. C. Santos, & I. I. Bittencourt (Eds.), Artificial intelligence in education (pp. 180–193). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-64299-913
Yang, K. B., Echeverria, V., Lu, Z., Mao, H., Holstein, K., Rummel, N., & Aleven, V. (2023). Pair-Up: Prototyping human-AI co-orchestration of dynamic transitions between individual and collaborative learning in the classroom. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI 2023), 23–28 April 2023, Hamburg, Germany (pp. 1–17). ACM. https://doi.org/10.1145/3544548.3581398
Yang, Q., Steinfeld, A., Ros´e, C., & Zimmerman, J. (2020). Re-examining whether, why, and how human-AI interaction is uniquely difficult to design. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI 2020), 25–30 April 2020, Honolulu, Hawaii, USA (pp. 1–13). ACM. https://doi.org/10.1145/3313831.3376301
Zhao, L., Echeverria, V., Swiecki, Z., Yan, L., Alfredo, R., Li, X., Gasevic, D., & Martinez-Maldonado, R. (2024). Epistemic network analysis for end-users: Closing the loop in the context of multimodal analytics for collaborative team learning. In Proceedings of the 14th International Conference on Learning Analytics and Knowledge (LAK 2024), 18–22 March 2024, Kyoto, Japan (pp. 90–100). ACM. https://doi.org/10.1145/3636555.3636855
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 (pp. 186–196). ACM. https://doi.org/10.1145/3576050.3576076
Zhao, L., Yan, L., Gasevic, 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). ACM. https://doi.org/10.1145/3506860.3506935
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Journal of Learning Analytics
This work is licensed under a Creative Commons Attribution 4.0 International License.
