Block 2 Summary – Teaching with Data

Over the past three weeks, I have explored themes of mastery learning and Blooms 2-sigma problem, synchronous and asynchronous synergy, the spectrum of pedagogy, andragogy and heutagogy, and growth mindset.

The first observation is there is the term ‘pedagogy’ seems to be used in the course reading material in situations that would otherwise be termed ‘andragogy’ or even ‘heutagogy’ (e.g. (Brown, 2020); the distinctions are vital if we are to discuss the role of educator and how it is influenced by the data (Blaschke, 2012). It was also very interesting to learn of the limitations of a pedagogical model for remote learning during COVID, leading to emergency heutagogy (Moore, 2020). My subject was a student I worked directly with using the Hegarty maths site. The design of Hegarty Maths is set-up for mastery-based, self-paced learning allowing for synchronous intervention as and when needed. The synchronous intervention seemed to have a more meaningful impact than asynchronous videos for the student as it allowed more scope for dialogue and making more bespoke ‘corrective measures (Bloom, 1984). Using Hegarty on a mass scale wouldn’t be as effective for synchronous intervention-based teaching and a platform such as Learning By Questions may be more suitable for it. LBQ however, has the pedagogical constrain of not having video content for effective modelling (Rosenshine, 2012) for self-paced learning and for students to complete tasks outside a 24-hour period. For class sizes of 30 or more, replicating the two standard deviation success Bloom observed in 1984 can potentially involve constructivist approaches of gamification (Benn, 2013) or the Peer Instruction method (Mazur, 2009). The limitations of quantitative data due to the ‘clicker method Mazure (2009) has suggested and also limitations of data being able to represent student diagrams to explore physics concepts (Brown, 2020), can potentially be resolved with new technologies such as Wooclap which allow for a greater variety of student responses.

Wooclap allows for a variety of student inputs and participation (Wooclap, 2018)

As part of the Learning Analytics Dashboard for a lecture which is based on the Peer Instruction approach (Brown, 2020), it would be interesting to learn more about how data can be represented to demonstrate the impact of PI as misconceptions are identified and explored through peer-discussion, as Pekka Peura, Finland’s most famous teacher has demonstrated (TEDx Talks, 2018).

Pekka Peura’s example of peer-instruction (TEDxTalks, 2018)

Brown (2020) makes a valid point about digital tools being open and configurable; I disagree about pedagogy necessarily driving tools design, as innovation in the later can shape pedagogy and subsequently scope for andragogy and heutagogy.

Areas that involved more qualitative fields such as growth mindset and Freirean dialogue as part of the interdependent teaching-learning process, for the data-visualisation diagram were captured in only very elementary representations. Such qualitative fields can’t remain truly meaningful when students’ experiences are shaped by neoliberalist approaches to education in which ‘datafication of education’ serves to measure performance (Williamson, Bayne and Shay, 2020) and in so doing reduces students to ‘datafied objects’ and hence impacts pedagogy requiring the ‘unteaching’ of its subjects (Harrison et al., 2020).


Benn, B. (2013) The 2-Sigma Problem: Ben Betts at TEDxWarwickED – YouTube. Available at: (Accessed: 28 February 2021).

Blaschke, L. M. (2012) ‘Heutagogy and lifelong learning: A review of heutagogical practice and self-determined learning’, The International Review of Research in Open and Distributed Learning, 13(1), pp. 56–71. doi: 10.19173/irrodl.v13i1.1076.

Bloom, B. S. (1984) ‘The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring’, Educational Researcher, 13(6), pp. 4–16. doi: 10.2307/1175554.

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. doi: 10.1080/13562517.2019.1698540.

Harrison, M. J. et al. (2020) ‘(Un)teaching the “datafied student subject”: perspectives from an education-based masters in an English university’, Teaching in Higher Education, 25(4), pp. 401–417. doi: 10.1080/13562517.2019.1698541.

Mazur, E. (2009) ‘Farewell, Lecture?’, Science, 323(5910), pp. 50–51.

Moore, R. L. (2020) ‘Developing lifelong learning with heutagogy: contexts, critiques, and challenges’, Distance Education, 41(3), pp. 381–401. doi: 10.1080/01587919.2020.1766949.

Rosenshine, B. (2012) ‘Principles of instruction: research-based strategies that all teachers should know’, American Educator, 36(1), pp. 12–21.

TEDx Talks (2018) Natural Born Learners | Alex Beard | TEDxYouth@Manchester. Available at: (Accessed: 14 March 2021).

Williamson, B., Bayne, S. and Shay, S. (2020) ‘The datafication of teaching in Higher Education: critical issues and perspectives’, Teaching in Higher Education, 25(4), pp. 351–365. doi: 10.1080/13562517.2020.1748811.

Wooclap (2018) Wooclap, the web-based app that makes learning awesome. Available at: (Accessed: 14 March 2021).

One Reply to “Block 2 Summary – Teaching with Data”

  1. ‘The first observation is there is the term ‘pedagogy’ seems to be used in the course reading material in situations that would otherwise be termed ‘andragogy’ or even ‘heutagogy’ (e.g. (Brown, 2020); the distinctions are vital if we are to discuss the role of educator and how it is influenced by the data (Blaschke, 2012).’

    Good point to bring in this literature, and highlight potential differences in the definition of teaching.

    Excellent to see some wider literature brought in to the discussion here. However, it would have been better to demonstrate more in the way of systematic engagement with the core course literature, and the sociological and political arguments being made about the influence of datafication on teaching. I think your focus on ideas related to ‘learning how to learn’ are really relevant here though, and I think that should include not taking data at ‘face value’, but rather involve questioning how and why they have been produced.

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