Data Visualisation 4 – Bloom’s 2 Sigma Problem and Synchronous Teaching

This week I decided to capture data to explore Bloom’s 2 -Sigma Problem (Bloom, 1984), in which he demonstrated students who received 1-1 tuition with a mastery learning approach, outperform students in a class size of 30 by two standard deviations (2-sigma) in exam performance.

The post below is from my blog on data visualisation, which was created as part of the Critical Data in Education MSc module in Digital Education.

This week I decided to capture data to explore Bloom’s 2 -Sigma Problem (Bloom, 1984), in which he demonstrated students who received 1-1 tuition with a mastery learning approach, outperform students in a class size of 30 by two standard deviations (2-sigma) in exam performance.

Thirty-five years later, with the advent of EdTech, AI and data-driven platforms, we can be on the cusp of solving the problem of how this level of mastery can be replicated on a larger scale. What if the cognitive distribution between human-technology assemblage, which lead to ammeter chess players using technology to beat grandmasters and more power machines (Beard, 2018), can also lead to more effective teaching?

I chose a student for this pilot, who I will refer to as student A and gave synchronous live teaching as we worked through topics on the Hegarty Maths site. The six topics listed in figure 2b together form a unit on Solving Equations. Student A was asked to watch the video for the topic, make notes before answering 12 questions on the site. The video solutions offer good models of worked examples to offer cognitive support (Rosenshine, 2012). At each stage, the student could ask for help if needed and go directly to the part of the video which demonstrates the concept which needs to be applied. Students receive two attempts to answer a question before it is given a red light and move on. I recorded the number of times I offered ‘corrective measures’ as suggested by Bloom as a means of direct support and intervention to a student-centred learning model; taking this approach and intervening only when student A was stuck, allowed me to get a more accurate picture of strengths and weaknesses. For topic 3, student A received an amber light and before continuing to the next topic, we decided to work through a few of the ‘building block’ topics suggested by Hegarty maths before re-attempting topic 3 again; these were not personalised to the needs of the student but based on the pre-requisites of the topic. The data visualisation diagram (fig 2a) represents no fixed structure and would offer a visual-map through the horizontal and vertical flights of the student’s journey through mastery learning of the topic.