Page 13 - R_EdQuire White Paper Nov 2017 v3.4

EdQuire White Paper: Computer learning behaviour in K-12
Nov 2017 V3.4
Page 13 of 15

Figure 10 Proportions of On-Task and Off-Task per minutes during the pre- and post-stage of self-regulation.

Discussion
Although limited in time and numbers, this improvement in computer engagement is nevertheless highly
significant both statistically and in effect. Interestingly, these self-selected students had a significantly higher
Off-Task times of 33% compared to 10% for the whole group, suggesting students’ awareness or meta-
cognition of their distracted study habits, and seeking confirmation from our data and then exercising
effective self-governance. We will analyse the data from the students who did not use the feedback as a
further control measure.

More data is required to confirm this effect and, of course, its persistence. Experience shows that students
will tend to game parameters presented to them, particularly if they affect their scores. Great care will need
to be taken to ensure that such feedback is true and constructive. While reduction of Off-Task behaviour and
task switching seems likely to lead to improved educational outcomes, all data-based interventions will
require validation against educational outcomes, in whatever form they may be defined by experts, before
scaling implementation.

A particularly exciting application of this feedback and self-selection of students is in the context of the
second digital divide, separating students with ICT learning skills from those lacking such skills and being
distracted by ICT. In the latter group of students, not only can our data identify their lack of skill and enable
teacher intervention, but the self regulatory effect might would automatically apply most benefit to those
students needing it most.

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