Block 2 Reflection

Education has increasingly become datafield – reducing and quantifying complex learning processes and teacher-student and student-student relationships. Student behavior and performance are measured and analyzied, marketed as insights unobtainable without the data (Knox, 2020). These insights are often presented to educators in the form of learning dashboards where student learning is summarized and personalized learning paths (or other interventions) are recommended (Brown, 2020).

Who decides what aspects of student learning is datafied and how that data is collected, analyzed, and visualized is, unfortunately, not decided by every instructor in every classroom but by the developers behind the algorithms of the learning dashboard (Raffaghelli, 2020; van Dijck, 2018). Consequently, instructors can be uncertain on how data was collected and what meaning can be drawn from it (Brown, 2020). This is especially concerning as predictive capabilities of these systems could “radically affect” a students educational career (Williamson, 2020) and educators need to possess a critical understanding of the reductive and instrumental nature of data (Raffaghelli, 2020).

Dashboards direct an instructors attention to specific areas that are perceived by the developers as ‘learning’ (Williamson, 2020; Raffaghelli, 2020) possibly distracting the instructor from other aspects of their students learning. Both Williamson (2020) and Brown (2020) note that instructors may also use these dashboards as a way to classify and categorize students to give targeted interventions, but this could possibly lead to preferential treatment (or dismissal) of certain classes of students. Algorithmic culture already reinforces the digital inequalities which may be unknown to systems dependent on these algorithms.

Instructors, too, are becoming subjected to these datafied systems. Measures of student performance are often treated as proxy measures of instructor performance (Williamson, 2020). Good instructors, then, are the producers of ‘good’ student data. Data becomes the focus and instructors urge students toward particular outcomes rather than on the learning process itself (Bulger, 2016). Instructors become to know themselves and their practice through data (Harrison, 2020).

Subjecting instructors to these datafied systems risks their autonomy as there pedagogy may be (forcibly) reshaped to be dashboard friendly (Williamson, 2020). Interestingly, Brown (2020) comments that the instructors he was surveying did not rely on the dashboard to plan their teaching. Rather, they could not identify productive strategies to incorporate them into their teaching and relegating the dashboards to a glorified polling system and an identification tool for unfamiliar students (ibid). Similarly, the role of the instructor is shifting – personalized intervention and assessing students are outsourced to the algorithm (and developers) and the teacher is assuming the role of a dashboard monitor (van Dijck, 2018).


Bulger, M. 2016. Personalized Learning: The Conversations We’re Not Having. Data & Society working paper. Available: https://datasociety.net/pubs/ecl/PersonalizedLearning_primer_2016.pdf

Brown, M. 2020. Seeing students at scale: how faculty in large lecture courses act upon learning analytics dashboard data. Teaching in Higher Education. 25(4), pp. 384-400

Harrison, M.J., Davies, C., Bell, H., Goodley, C., Fox, S & Downing, B. 2020. (Un)teaching the ‘datafied student subject’: perspectives from an education-based masters in an English university, Teaching in Higher Education, 25:4, 401-417, DOI: 10.1080/13562517.2019.1698541

Raffaghelli, J.E. & Stewart, B. 2020. Centering complexity in ‘educators’ data literacy’ to support future practices in faculty development: a systematic review of the literature, Teaching in Higher Education, 25:4, 435-455, DOI: 10.1080/13562517.2019.1696301

van Dijck, J., Poell, T., & de Waal, M. 2018. Chapter 6: Education, In The Platform Society, Oxford University Press

Williamson, B. Bayne, S. Shay, S. 2020. The datafication of teaching in Higher Education: critical issues and perspectives. Teaching in Higher Education. 25(4), pp. 351-365.

1 thought on “Block 2 Reflection

  1. This is an excellent summary of the ‘teaching with data’ block, in which you draw on the literature really well.

    ‘Who decides what aspects of student learning is datafied and how that data is collected, analyzed, and visualized is, unfortunately, not decided by every instructor in every classroom but by the developers behind the algorithms of the learning dashboard’

    I agree that this is a central issue. If data-driven systems can have such a profound impact on education (as the developers of such platforms often claim) then it seems strange to me that teachers wouldn’t be involved in deciding how that impact happens. But teachers aren’t entirely powerless?

    ‘Algorithmic culture already reinforces the digital inequalities which may be unknown to systems dependent on these algorithms.’

    This is a key point as well, I think. Because these systems are imbued with authority, we’re not really encouraged to see them as revealing only very specific views of the world, and concealing others.

    Nice work here, you’ve identified a lot of really crucial issues related to teaching in a succinct way.

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