Translational Specifications of Neural-Informed Game-Based Interventions for Mathematical Cognitive Development of Low-Progress Learners

Project Number
NRF2016-SOL002-003

Project Duration
November 2017 - October 2020

Status
In-Progress

Abstract
The team aims to address the challenge of levelling up low progress (LP) learners in mathematics, particularly those who continue to struggle despite multi-pronged behavioral intervention approaches in schools. In recognizing there would be a range of reasons why LP learners may have low or limited progression in math, we begin with a characterization study of the LP target population to identify underlying causes and mechanisms for persistent low achievement. A data-driven approach in LP classification constitute twofold aims; i) overview the characteristics of learners with persistent low achievement in math as primer for targeted interventions of different LP subtypes, and ii) provide the science of learning math in relation to learners' difficulties and core problems through empirical investigations of neural and behavioral performance changes on designed tasks and learning activities, thereby developing an account of the role targeted interventions play, if any, for learners' foundational conceptual understanding. Targeted interventions are personalized and predictive. By personalized, we refer to the fundamental and widely acknowledged challenge of improving education in recognizing that students differ one from another in substantial ways. Currently, these differences most often become apparent after education failure. By predictive, we refer to the importance of identifying which students with particular learning differences respond substantially to current curriculum and those who are predicted to fail (Gabrieli, 2016). The latter ought to be offered alternative curricula that attenuates prolonged low attainments in math. At an overarching level, our research is part of a systematic approach to education improvement (Hung, Jamaludin, & Toh, 2015) aimed at bridging achievement gaps and shortening the lower tail-end of achievement bell curves. In doing so, we pay careful attention to the translational impact pathways from basic neuroscience to classroom applications, feeding back into cognitive neuroscience theories of learning mathematics, within a context of Singapore's education landscape.

Funding Source
NRF

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