Skip to main content

Single or Multi-page Learning Analytics Dashboards? Relationships Between Teachers’ Cognitive Load and Visualisation Literacy

  • Conference paper
  • First Online:
Responsive and Sustainable Educational Futures (EC-TEL 2023)

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    Cohen’s d effect sizes are interpreted as follows: \(>=0.2\) (small), \(>=0.5\) (medium), and \(>=0.8\) (large).

References

  1. 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)

    Google Scholar 

  2. Bach, B., et al.: Dashboard design patterns, August 2022. arXiv:2205.00757 [cs]

  3. 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)

    Article  Google Scholar 

  4. 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)

    Google Scholar 

  5. 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)

    MathSciNet  MATH  Google Scholar 

  6. 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]

  7. 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)

    Google Scholar 

  8. 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)

    Google Scholar 

  9. 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)

    Google Scholar 

  10. 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)

    Google Scholar 

  11. 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)

    Google Scholar 

  12. 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)

    Article  Google Scholar 

  13. 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)

    Google Scholar 

  14. 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)

    Google Scholar 

  15. 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

    Google Scholar 

  16. Firat, E.E., Joshi, A., Laramee, R.S.: Interactive visualization literacy: the state-of-the-art. Inf. Vis. 21(3), 285–310 (2022)

    Article  Google Scholar 

  17. 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)

    Article  Google Scholar 

  18. 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)

    Article  Google Scholar 

  19. 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)

    Article  Google Scholar 

  20. 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)

    Article  Google Scholar 

  21. 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)

    Article  Google Scholar 

  22. 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

    Chapter  Google Scholar 

  23. 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)

    Article  Google Scholar 

  24. 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)

    Article  Google Scholar 

  25. Lenth, R.V.: emmeans: Estimated Marginal Means, aka Least-Squares Means (2022), r package version 1.8.2

    Google Scholar 

  26. 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)

    Google Scholar 

  27. 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

    Google Scholar 

  28. 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)

    Article  Google Scholar 

  29. Mathôt, S., Vilotijević, A.: Methods in cognitive pupillometry: design, preprocessing, and statistical analysis. Behav. Res. Methods (2022)

    Google Scholar 

  30. Molenaar, I., Knoop-van Campen, C.A.N.: How teachers make dashboard information actionable. IEEE Trans. Learn. Technol. 12(3), 347–355 (2019)

    Google Scholar 

  31. 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

    Chapter  Google Scholar 

  32. 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)

    Article  Google Scholar 

  33. Pozdniakov, S., et al.: The Question-driven Dashboard: How Can We Design Analytics Interfaces Aligned to Teachers’ Inquiry? p. 11 (2022)

    Google Scholar 

  34. 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)

    Google Scholar 

  35. 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

    Chapter  Google Scholar 

  36. 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

    Google Scholar 

  37. 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)

    Article  Google Scholar 

  38. 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)

    Google Scholar 

  39. 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

    Google Scholar 

  40. 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

    Google Scholar 

  41. 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)

    Google Scholar 

  42. 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)

    Google Scholar 

  43. 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)

    Article  Google Scholar 

  44. Wise, A.F., Jung, Y.: Teaching with analytics: towards a situated model of instructional decision-making. J. Learn. Anal. 6(2), 53–69 (2019)

    Google Scholar 

Download references

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

Authors

Corresponding author

Correspondence to Stanislav Pozdniakov .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

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)

Publish with us

Policies and ethics