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This week I continued with the learning journey from Data Visualisation 4 – Bloom’s 2-Sigma Problem and Synchronous Teaching. I decided to explore an asynchronous and synchronous synergetic model in which the two can leverage and support each other for effective student-centred learning Lemov (2021). In this week’s approach, once student A had engaged with the material herself and requested help, I supported student A asynchronously with self-created video solutions using FlipGrid; this also allowed for greater levels of personalised teaching based on explanations adapted to the learner’s cognitive preference e.g. representing various stages of the function before an algebraic solution is considered.
Student A then attempted the topic questions again and if needed, I then supported student A, synchronously. The questions being attempted are not tests for each topic but adaptive mathematical questions that require the application of skills and understanding. One can also arguably see the progress through such activities and ‘collecting points’ as a process of gamification (Benn, 2013). It remains to question if mastery learning is more suitable for an outcomes-based education system by offering greater levels of personalisation of the curriculum given the greater impact over constructivist approaches to learning and if the two are mutually exclusive (Gokalp, 2017).
Fig 1. Learning journey of student A
Fig 2. Key
The data visualisation produced is a co-constructed visual representation of the learning journey of the student, where the vertical hight represents the number of ‘building blocks’ or ‘gaps’ that have been addressed. The horizontal blocks are the topics student A is working through at her own pace rather than that of an externally dictating one. The sequence of colours offer a way of representing, to some degree, Vygotsky’s zone of proximal development of the student for the topic, ‘to determine what a student can do with help and without help’, as well as the potential for determining student resilience over time; such a constructivist approach and learner autonomy allows for a shift from pedagogy to andragogy along the pedagogy-heutagogy spectrum (Halupa, 2015).
The data visualisation produced is significantly different from a teacher’s mastery-learning grade book, which wouldn’t necessarily provide a visualisation of a personalised learning journey (CanvasLMS, 2020).
Fig 2: Mastery gradebook example (CanvasLMS, 2020)
A potential problem with data visualisation-5 is defining terms and what we mean by terms such as ‘mastery’ and to what extent a score on a task is reflective of that (Spiegelhalter, 2019). In the case of student A, despite having achieved 90% in topic 4 for example, there was still more work required to deepen her understanding. Also, the remaining 10% may not represent a skill or comprehension gap if it is addressed through ‘corrective measures’ or ‘self-learning’ through subsequent activities or questions in the very task.
References
Benn, B. (2013) The 2-Sigma Problem: Ben Betts at TEDxWarwickED – YouTube. Available at: https://www.youtube.com/watch?v=wqLiLH6Sjnw (Accessed: 28 February 2021).
CanvasLMS (2020). Available at: https://community.canvaslms.com/t5/Instructor-Guide/How-do-I-use-the-Learning-Mastery-Gradebook-to-view-outcome/ta-p/775 (Accessed: 28 February 2021).
Gokalp, M. (no date) ‘COMPARE THE EFFECT OF MASTERY LEARNING AND CONSTRUCTIVIST APPROACHES TO THE ACADEMIC ACHIEVEMENTS OF TEACHERS’, 3(1), p. 6.
Halupa, C. (2015) ‘Pedagogy, Andragogy, and Heutagogy’, in, pp. 143–158. doi: 10.4018/978-1-4666-8571-0.ch005.
Spiegelhalter, D. J. (2019) The art of statistics: learning from data. UK: Pelican, an imprint of Penguin Books (Pelican book).
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