Abstract
There has been a proliferation of learning analytics (LA) interfaces designed to support teachers, such as LA dashboards. However, although teacher dashboards have been extensively studied, there is limited understanding of the relationship between single-page or multi-page dashboard designs and the cognitive demands placed on teachers to use them. Additionally, teachers typically have varying levels of visualisation literacy (VL), which may make it easier or more difficult for them to engage with single-page versus multi-page dashboard designs. In this paper, we explore how teachers, with varying VL, use single-page and multi-page LA dashboards. We conducted a quasi-experimental study with 23 higher education teachers of varied VL inspecting single and multi-page LA dashboards. We used an eye-tracking device to measure cognitive load while teachers inspected the LA dashboards in online group work. We investigated how proxy metrics derived from eye-tracking data related to teachers’ cognitive load varied depending on the type of the dashboard teacher used and the level of VL teachers have. Our findings suggest that the design of the LA dashboard had an impact on the cognitive load experienced by the teachers. Post-hoc analysis revealed that teachers with low VL had a marginally lower cognitive load when using single-page dashboards. We argue that the LA dashboard design for teachers should account for teachers’ levels of VL and provide design recommendations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Cohen’s d effect sizes are interpreted as follows: \(>=0.2\) (small), \(>=0.5\) (medium), and \(>=0.8\) (large).
References
Ahn, J., Nguyen, H., Campos, F.: From visible to understandable: designing for teacher agency in education data visualizations. Contemp. Issues Technol. Teach. Educ. 21(1), 155–186 (2021)
Bach, B., et al.: Dashboard design patterns, August 2022. arXiv:2205.00757 [cs]
Bao, H., Li, Y., Su, Y., Xing, S., Chen, N.S., Rosé, C.P.: The effects of a learning analytics dashboard on teachers’ diagnosis and intervention in computer-supported collaborative learning. Technol. Pedagog. Educ. 30(2), 287–303 (2021)
Bartindale, T., Chen, P., Marshall, H., Pozdniakov, S., Richardson, D.: ZoomSense: a scalable infrastructure for augmenting zoom. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 3771–3774 (2021)
Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc.: Ser. B (Methodol.) 57(1), 289–300 (1995)
Burns, A., Xiong, C., Franconeri, S., Cairo, A., Mahyar, N.: How to evaluate data visualizations across different levels of understanding, September 2020. arXiv:2009.01747 [cs]
Knoop-van Campen, C.A., Wise, A., Molenaar, I.: The equalizing effect of teacher dashboards on feedback in K-12 classrooms. Interact. Learn. Environ. 1–17 (2021)
Campos, F., Ahn, J., DiGiacomo, D.K., Nguyen, H., Hays, M.: Making sense of sensemaking: understanding how K-12 teachers and coaches react to visual analytics. J. Learn. Anal. 1–21 (2021)
Charleer, S., Gerling, K., Gutiérrez, F., Cauwenbergh, H., Luycx, B., Verbert, K.: Real-time dashboards to support esports spectating. In: Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play, pp. 59–71 (2018)
Chen, Q., Li, Z., Pong, T.C., Qu, H.: Designing narrative slideshows for learning analytics. In: 2019 IEEE pacific visualization symposium (PacificVis), pp. 237–246 (2019)
Dipace, A., Fazlagic, B., Minerva, T.: The design of a learning analytics dashboard: Eduopen MOOC platform redefinition procedures. J. E-learn. Knowl. Soc. 15(3), 29–47 (2019)
Donohoe, D., Costello, E.: Data visualisation literacy in higher education: an exploratory study of understanding of a learning dashboard tool. Int. J. Emerg. Technol. Learn. (iJET) 15(17), 115 (2020)
Dourado, R.A., Rodrigues, R.L., Ferreira, N., Mello, R.F., Gomes, A.S., Verbert, K.: A teacher-facing learning analytics dashboard for process-oriented feedback in online learning. In: LAK21: 11th International Learning Analytics and Knowledge Conference, pp. 482–489 (2021)
Echeverria, V., Martinez-Maldonado, R., Buckingham Shum, S., Chiluiza, K., Granda, R., Conati, C.: Exploratory versus explanatory visual learning analytics: driving teachers’ attention through educational data storytelling. J. Learn. Anal. 5(3) (2018)
Fernandez Nieto, G.M., Kitto, K., Buckingham Shum, S., Martinez-Maldonado, R.: Beyond the learning analytics dashboard: alternative ways to communicate student data insights combining visualisation, narrative and storytelling. In: LAK22: 12th International Learning Analytics and Knowledge Conference, pp. 219–229. Online , March 2022
Firat, E.E., Joshi, A., Laramee, R.S.: Interactive visualization literacy: the state-of-the-art. Inf. Vis. 21(3), 285–310 (2022)
Herodotou, C., Rienties, B., Boroowa, A., Zdrahal, Z., Hlosta, M.: A large-scale implementation of predictive learning analytics in higher education: The teachers’ role and perspective. Educ. Technol. Res. Dev. 67(5), 1273–1306 (2019)
Kaliisa, R., Mørch, A.I., Kluge, A.: ‘My Point of Departure for Analytics is Extreme Skepticism’: implications derived from an investigation of university teachers’ learning analytics perspectives and design practices. Technol. Knowl. Learn. 27(2), 505–527 (2022)
Krejtz, K., Duchowski, A.T., Niedzielska, A., Biele, C., Krejtz, I.: Eye tracking cognitive load using pupil diameter and microsaccades with fixed gaze. PLoS ONE 13(9), e0203629 (2018)
Kuznetsova, A., Brockhoff, P.B., Christensen, R.H.B.: lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82(13), 1–26 (2017)
Lalle, S., Toker, D., Conati, C.: Gaze-driven adaptive interventions for magazine-style narrative visualizations. IEEE Trans. Vis. Comput. Graph. 27(6), 2941–2952 (2021)
Lawrence, L.E.M., et al.: Teachers’ orchestration needs during the shift to remote learning. In: De Laet, T., Klemke, R., Alario-Hoyos, C., Hilliger, I., Ortega-Arranz, A. (eds.) EC-TEL 2021. LNCS, vol. 12884, pp. 347–351. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86436-1_33
Lee, S., Kim, S.H., Kwon, B.C.: VLAT: development of a visualization literacy assessment test. IEEE Trans. Vis. Comput. Graph. 23(1), 551–560 (2017)
van Leeuwen, A., Knoop-van Campen, C.A., Molenaar, I., Rummel, N.: How teacher characteristics relate to how teachers use dashboards: results from two case studies in K-12. J. Learn. Anal. 8(2), 6–21 (2021)
Lenth, R.V.: emmeans: Estimated Marginal Means, aka Least-Squares Means (2022), r package version 1.8.2
Mandinach, E.B., Abrams, L.M.: Data literacy and learning analytics. In: Lang, C., Siemens, G., Wise, A.F., GaÅ¡eviÄ\(\ddagger \), D., Merceron, A. (eds.) The Handbook of Learning Analytics, 2 edn, pp. 196–204. SoLAR (2017)
Martinez-Maldonado, R., Echeverria, V., Fernandez Nieto, G., Buckingham Shum, S.: 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, pp. 1–15. Honolulu HI USA, April 2020
Matcha, W., Ahmad Uzir, N., Gasevic, D., Pardo, A.: A systematic review of empirical studies on learning analytics dashboards: a self-regulated learning perspective. IEEE Trans. Learn. Technol. 13(2), 226–245 (2020)
Mathôt, S., Vilotijević, A.: Methods in cognitive pupillometry: design, preprocessing, and statistical analysis. Behav. Res. Methods (2022)
Molenaar, I., Knoop-van Campen, C.A.N.: How teachers make dashboard information actionable. IEEE Trans. Learn. Technol. 12(3), 347–355 (2019)
Molenaar, I., Knoop-van Campen, C.: Teacher dashboards in practice: usage and impact. In: Lavoué, É., Drachsler, H., Verbert, K., Broisin, J., Pérez-Sanagustín, M. (eds.) EC-TEL 2017. LNCS, vol. 10474, pp. 125–138. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66610-5_10
Ndukwe, I.G., Daniel, B.K.: Teaching analytics, value and tools for teacher data literacy: a systematic and tripartite approach. Int. J. Educ. Technol. High. Educ. 17(1), 1–31 (2020)
Pozdniakov, S., et al.: The Question-driven Dashboard: How Can We Design Analytics Interfaces Aligned to Teachers’ Inquiry? p. 11 (2022)
Pozdniakov, S., Martinez-Maldonado, R., Tsai, Y.S., Echeverria, V., Srivastava, N., Gasevic, D.: How do teachers use dashboards enhanced with data storytelling elements according to their data visualisation literacy skills? In: LAK23: 13th International Learning Analytics and Knowledge Conference. LAK2023, New York, NY, USA, pp. 89–99 (2023)
Sahin, M., Ifenthaler, D.: Visualizations and dashboards for learning analytics: a systematic literature review. In: Sahin, M., Ifenthaler, D. (eds.) Visualizations and Dashboards for Learning Analytics. AALT, pp. 3–22. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-81222-5_1
Salas-Pilco, S.Z., Xiao, K., Hu, X.: Artificial intelligence and learning analytics in teacher education: a systematic review. Educ. Sci. 12(8), 569 (2022). Multidisciplinary Digital Publishing Institute
Schwendimann, B.A., et al.: Perceiving learning at a glance: a systematic literature review of learning dashboard research. IEEE Trans. Learn. Technol. 10(1), 30–41 (2017)
Sergis, S., Sampson, D.G.: Teaching and learning analytics to support teacher inquiry: a systematic literature review. In: Learning Analytics: Fundaments, Applications, and Trends, pp. 25–63 (2017)
Sousa, E.B.d., Alexandre, B., Ferreira Mello, R., Pontual Falcão, T., Vesin, B., Gašević, D.: Applications of learning analytics in high schools: a systematic literature review. Front. Artif. Intell. 4, 737891 (2021). publisher: Frontiers Media SA
Tsai, Y.S., Gasevic, D.: Learning analytics in higher education - challenges and policies: a review of eight learning analytics policies. In: Proceedings of the Seventh International Learning Analytics and Knowledge Conference, pp. 233–242. Vancouver British Columbia Canada, March 2017
Van Leeuwen, A., Rummel, N.: Comparing teachers’ use of mirroring and advising dashboards. In: Proceedings of the Tenth International Conference on Learning Analytics and Knowledge, pp. 26–34 (2020)
Voithofer, R., Golan, A.M.: Data sources for educators: mining meaningful data for course and program decision making. In: Responsible Analytics and Data Mining in Education, pp. 83–100 (2018)
van der Wel, P., van Steenbergen, H.: Pupil dilation as an index of effort in cognitive control tasks: a review. Psychonomic Bull. Rev. 25(6), 2005–2015 (2018)
Wise, A.F., Jung, Y.: Teaching with analytics: towards a situated model of instructional decision-making. J. Learn. Anal. 6(2), 53–69 (2019)
Acknowledgements
This research was at least in part funded by the Australian Research Council (DP210100060) and Jacobs Foundation (Research Fellowship).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Pozdniakov, S., Martinez-Maldonado, R., Tsai, YS., Srivastava, N., Liu, Y., Gasevic, D. (2023). Single or Multi-page Learning Analytics Dashboards? Relationships Between Teachers’ Cognitive Load and Visualisation Literacy. In: Viberg, O., Jivet, I., Muñoz-Merino, P., Perifanou, M., Papathoma, T. (eds) Responsive and Sustainable Educational Futures. EC-TEL 2023. Lecture Notes in Computer Science, vol 14200. Springer, Cham. https://doi.org/10.1007/978-3-031-42682-7_23
Download citation
DOI: https://doi.org/10.1007/978-3-031-42682-7_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-42681-0
Online ISBN: 978-3-031-42682-7
eBook Packages: Computer ScienceComputer Science (R0)