I was fortunate to be able to attend Professor Diana Laurillard’s seminar on Learning ‘number sense’ through digital games with intrinsic feedback. The main presentation was the use of a specially designed game as an intervention method to support students with ‘dyscalculia’, however this was positioned in the broader context of bringing together the fields of neuroscience and educational research through educational technology.
Dyscalculia is a core deficit in numerosity that typically manifests through a lack of understanding of relationships between numbers and a reliance on counting to solve numerical problems. While this bears no relation to overall intelligence it persists through life and may cause issues and anxieties through schooling and present challenges to everyday tasks such as counting change or measurement. Individuals will typically develop compensatory strategies. Neuroscience identifies abnormalities in the intraparietal sulci which usually results in less grey matter and a reduced possibility for connections in the region. Cognitive testing in educational settings is used to distinguish low numeracy from dyscalculia and cognitive science theorises learning process based on self-regulation, constructionism and design research.
The intervention designed is a constructivist game that provides the learner with scaffolded progress through levels. The first level uses colour beads that can be sliced or added together to form the target number. The next level adds numbers (symbols) to the beads, the next removes colour and the final level uses the number symbols only.
The following principles are embedded through the design
- Making the task goal meaningful through targets and levels
- Allows learners to act to achieve the goal(s)
- Feeds back action in relation to the goal
- Motivates revisions to improve
These are consistent with how Special Educational Needs (SEN) teachers design activities who have had success but limited to local contexts.
Using technology may support reaching a wider audience and offers some key affordance advatnages:
- No wrong answer
- Learners can move at their own pace
- Adaptive to learner performance
- Private and
- Available at home
The technology also affords data capture of actions within the game and video of the learner participation to supplement interviews, observations and assessment results. For example it is possible to detect the number of actions taken to complete the task goal and the overall time taken, both of which provide indications of how successful the learner was in understanding the task goals and performing the task.
Relating this to my own research on engagement it was interesting to see the video of the young girl and her facial expressions while participating as a sign of affective engagement. When presented with the beads there was a grimace and some lip biting as a strategy formed. This was followed by a sideways glance to the teacher whose acknowledgement of success led to a huge smile. While the game itself made certain goals explicit, there was an indication of the social presence within success recognition through a human mediator that went beyond what the game offered. The video of the actions was insightful in revealing techniques or strategies adopted by learners. in the example shown the tactic used was largely to trim single beads from a larger block and then adding these individually to another bead block – this may have suggested a counting strategy rather than a fuller grasp of the numbers. Other strategies mentioned included creating larger stacks in order to break them into smaller ones – a destructive twist on the game. Capturing and comparing different sequential strategies may provide an insight into cognitive engagement whether this be struggling to grasp the concepts or disrupting the expected patterns.
A key theme wove throughout the seminar was the evolving relationship between educational research and neuroscience. Thomas and Laurillard (2012) provide a mapping of terminology and process between the domains such that neuroscience identifies where to focus teaching and cognitive science identifies what it takes to long, while educational technology allows for unsupervised learning. The potential of overlapping these to build educational neuroscience has a wide range of applications.
Thomas, M. S. C., & Laurillard, D. (2012). Computational modelling of learning and teaching. In D. Mareschal, A. Tolmie, & B. Butterworth (Eds.), Handbook of Educational Neuroscience. Oxford: Wiley-Blackwell.