What Could a Non-Mechanizing Dashboard Look Like?

Nearly every aspect of this project was conceived of, or analyzed through, the educational designs and designers introduced in this course. Mumford’s conception of the clock as an instrument of mechanization served as early inspiration for our thoughts on LADs and their perpetuation of mechanization (Strate & Lum, 2000). Subsequent material, such as Latour’s delegation/prescription and Barad’s intra-action all served as guidance in the development of our “idealized” LAD. Outside of course materials, research specific to dashboard development proved influential, such as Alfredo (2024) who discusses teachers’ clever manipulations of LADs to support student emotions, and Michos et al.’s (2023) analysis of teacher data-literacy.

The dashboard itself integrated many stock assets. Figma offers a wide variety of user-created component libraries. These provided both inspiration and groundwork for some of our components, where we took bits and pieces of libraries and transformed them into something new (high degree of intra-action here, dare I say). Jordan also had access to stock image libraries, which is where our more detailed images of fish, aquatics, and emojis came from.

While Figma was certainly the primary “technology” we used, I think “Figma skills” fails to capture the skills we developed over the course of this project. Jordan absolutely led the Figma development, but ensured there was space for me to develop my skills through component design and building. In turn, I was able to dive deeper into the pedagogical/design justifications for our dashboard, with Jordan’s contributions. In reality, I would say that Figma only served as the medium for our group’s increased skill in design-thinking. Our project meetings mostly involved analyzing the failures and dangers of classic dashboard design, and dissecting these failures (and their solutions) through the lens of whatever course-reading we had just completed. I can confidently say that this project has served as a major shift in how I perceive LADs and the many nuances of their designs and components.

I believe the idealized nature of our design was well-suited for Figma. Figma’s collaboration features and flexibility allowed us to experiment and create a reasonably complex design without needing to go down a rabbit-hole of software development. We had no pretense that our tool and its design would actually work - only that it theoretically could.

The majority of our design was intended to subvert the mechanical discriminatory potential of the typical LAD. Not only in a Latourian ‘prescriptive’ sense, but also in the ways it mechanizes the learning process. Any persons who’s recorded learning output doesn’t align with the dashboard’s preconceived norms (Wilson et al., 2017), be they socioeconomically disadvantaged, students with different learning abilities, students using assistive technologies, students with culturally different approaches to learning, non-native speakers, etc., are prone to discriminatory representation by many LADs. Our perspective was that much of this discrimination resulted from the overemphasis on quantification in the typical LAD, and thus our proposed approach attempts to emphasize the individual learner and their qualitative nuances.

Metatheory

I would posit that the act of design is the curation of agential cuts, and thus our tool is rife with them (Toohey, 2018). In attempting to humanize the LAD, we excluded swaths of potentially valuable metrics, ‘cuts’ that likely impact its utility to educators. Our LAD’s qualitative emphasis would mean that it could be delegated a degree of emotional intelligence, and social awareness of students, and in turn prescribe onto educators the data literacy and cognitive-load capacity necessary to interpret its nuanced reporting (Latour, 1992). Above all, our LAD looks to combat the mechanization of learning, but I have to question whether a dashboard that promises to impart any insight into the learning process is merely perpetuating mechanization regardless of how it conveys information (Strate & Lum, 2000).

References

Alfredo, R. D., Echeverria, V., Zhao, L., Lawrence, L., Fan, J. X., Yan, L., Li, X., Swiecki, Z., Gašević, D., & Martinez-Maldonado, R. (2024). Designing a Human-centred Learning Analytics Dashboard In-use. Journal of Learning Analytics, 11(3), 62–81. https://doi.org/10.18608/jla.2024.8487

Latour, B. (1992) ‘Where are the missing masses? The sociology of a few mundane artifacts’, in Bijker, W. E. and Law, J. (eds) Shaping Technology/Building Society: Studies in Sociotechnical Change, Cambridge, MA, MIT Press, pp. 225-58.

Wilson, A., Watson, C., Thompson, T. L., Drew, V., & Doyle, S. (2017). Learning analytics: Challenges and limitations. Teaching in Higher Education, 22(8), 991–1007. https://doi.org/10.1080/13562517.2017.1332026

Michos, K., Schmitz, M.-L., & Petko, D. (2023). Teachers’ data literacy for learning analytics: A central predictor for digital data use in upper secondary schools. Education and Information Technologies, 28(11), 14453–14471. https://doi.org/10.1007/s10639-023-11772-y

Monforte, J. (2018). What is new in new materialism for a newcomer? Qualitative Research in Sport, Exercise and Health, 10(3), 378–390. https://doi.org/10.1080/2159676X.2018.1428678

Strate, L., & Lum, C. M. K. (2000). Lewis Mumford and the ecology of technics. New Jersey Journal of Communication, 8(1), 56–78. https://doi.org/10.1080/15456870009367379

Toohey, K. (2018). New Materialism and Language Learning. In Learning English at School: Identity, Socio-material Relations and Classroom Practice (pp. 25–44). Multilingual Matters. https://www.degruyterbrill.com/document/doi/10.21832/9781788920094-004/html

AI Usage

I used Anthropic’s Claude for research during the ideation phase of this project. It was helpful for learning analytics dashboard research, interpreting complex theoretical frameworks (particularly Barad’s new materialism), and exploring potential approaches to “humanizing” learning analytics dashboards.