Translational Specifications of Neural-Informed Game-Based Interventions for Mathematical Cognitive Development of Low-Progress Learners
This Project aims to address the challenge of levelling up low progress (LP) learners in mathematics, particularly those who continue to struggle despite multi-pronged behavioural intervention approaches in schools. In recognising there could be a range of reasons why LP learners may have limited progression in math, we begin with a characterisation study of the LP target population to identify underlying causes and mechanisms for persistent low achievement.
Another key issue for this study is the translation of basic neuroscience research into effective classroom interventions (Jamaludin, 2017). In recognising teachers as the key stakeholders for utility of neuroscience knowledge to be effectively translated into remediating interventions (Lovat & Smith, 2003), our intervention design and experimental implementations entail collaborative partnerships with LP Math teachers.
Our data-driven approach in LP classification constitute twofold aims:
- To investigate the characteristics of learners with persistent low achievement in math as a primer for targeted interventions of different LP sub-types
- To provide the science of learning math in relation to learners’ difficulties and core problems through empirical investigations of neural and behavioural performance changes on designed tasks and learning activities.
Through this, we seek to develop an account of the role targeted interventions play, if any, for learners’ foundational conceptual understanding.
In the first intervention design phase, we will implement a prior developed constructionist neural-informed digital game (NumberBeads) with the LP learners in the target population. Prior studies have shown that (i) learners do many more tasks in a given period than is possible in a typical mathematics lesson during curriculum time, and (ii) within a lesson period they improve in accuracy and reaction times (Butterworth, Varma, & Laurillard, 2010). This platform supports the automated online collection and analysis of data while learners play the game.
In the second intervention design phase, through the analysis of data being collected, we will design and develop a constructionist and practice-based version game for an identified key concept that scientific literature and teacher feedback suggest as foundational for the primary school curriculum. The design and development of the game will be in collaboration with the team who developed NumberBeads.
We are cognizant of translational issues of neuroscience informed knowledge uptake in curriculum work and in implementation in classroom practices (e.g., Ansari & Lyons, 2016; Bowers, 2016; Gabrieli, 2016; Howard-Jones et al, 2016). Arising from our experience in interventions research in schools (Jamaludin & Hung, 2016; Jamaludin & Hung, 2017), we posit the role of teachers, in ‘brokering’ optimal translations of basic neuroscience research into effective learning interventions, as critical for remediation of learning deficits in Math. We propose the partnership with teachers to be mediated by an online MOOC platform that functions as a both a dialogic and development space for LP Math teachers.
The MOOC will be in the form of an open online site, where participating teachers will have access to:
- Video case studies of learners working on mathematics tasks
- Screencasts of brain images, talking through what they mean in terms of how the brain is developing differently from different learners
- Links to online games, such as NumberBeads (Butterworth & Laurillard, 2016), with video explanations of the brain science and pedagogical science that underpins it
- Lesson plans for how the game might be introduced into a class with different underlying reasons for LP Math (teachers having access to the data collected)
- Activities in which teachers report back on their trials, and compare their experiences in online discussion forums and peer review activities
Teachers will also have the opportunities to reflect on their own learning designs in Math, interpreting local data from students’ use of the games, and learning collaboratively from the results, with support from the research team as well as the Expert Consultants (Laurillard, 2016).
We posit that the work on translating knowledge arising from this research into downstream gains need to take into account the indigenous contextual factors of Singapore classrooms (Hung, Jamaludin, & Toh, 2015), such as class size and teacher workload. We argue that proposed translational efforts-such as that in the form of pipeline organization of educational neuroscience (e.g. see Gabrieli, 2016)-may not achieve the degree of ‘depth’ of remediating deficits in Math learning. Without partnering teachers in the design and implementation of the intervention strategy, the value of the games in remedying, for example, distal causes of too little experience of numbers, may not be maximized, if teachers do not use the games for supporting independent learning through the provision of intensive practice, alongside the classroom syllabus.