Personalized learning: From neurogenetics of behaviors to designing optimal language training

Abstract

Variability in drug responsivity has prompted the development of Personalized Medicine, which has shown great promise in utilizing genotypic information to develop safer and more effective drug regimens for patients. Similarly, individual variability in learning outcomes has puzzled researchers who seek to create optimal learning environments for students. "Personalized Learning" seeks to identify genetic, neural and behavioral predictors of individual differences in learning and aims to use predictors to help create optimal teaching paradigms. Evidence for Personalized Learning can be observed by connecting research in pharmacogenomics, cognitive genetics and behavioral experiments across domains of learning, which provides a framework for conducting empirical studies from the laboratory to the classroom and holds promise for addressing learning effectiveness in the individual learners. Evidence can also be seen in the subdomain of speech learning, thus providing initial support for the applicability of Personalized Learning to language.

Keywords: Individual differences; Language learning; Neurogenetics; Personalized learning.

Copyright © 2016 Elsevier Ltd. All rights reserved.

Figures

Figure 1. BDNF val66met polymorphism is associated with responsivity to dosage of fine motor learning

(A) In one study, subjects performed exercises to improve movements of the first dorsal interosseous muscle (FDI) (Kleim et al., 2006). Cortical plasticity in the form of cortical motor map expansion was measured using transcranial magnetic stimulation. Subjects were trained for 30 minutes. Only subjects with the val/val genotype showed significant cortical map expansion: average data (*p

Figure 2. Applying Personalized Learning to Language

(A) Individual differences in tone learning success: In our studies, we taught native English-speaking adults to incorporate pitch in lexically meaningful contexts. We found a range of learning success in these adults (Deng et al., in press). (B) Genetic association between tone perception and load of ASPM-G allele (Wong et al., 2013). (C) Neural markers of tone learning: Brain activation revealed by successful versus less successful learners in pre-training contrast (Wong et al., 2007; upper panel). HG volume of successful versus less successful learners in the left and right hemispheres (Wong et al., 2008; lower panel, **p < 0.007 and *p < 0.05). (D) Using predictors for optimizing tone learning: High variability training significantly enhanced learning for better pitch perceivers (HAL), whereas poorer pitcher perceivers (LAL) were significantly impaired by increased stimulus variability (Perrachione et al., 2011; left panel, ordinate values have been arcsine transformed). Lexical pitch-pattern training given before lexical training improved learning more than lexical training alone, and more so for poorer pitch perceivers (Ingvalson et al., 2013; right panel, arcsine-transformed accuracy, the dashed line indicates perfect identification performance, error bars represent SEM).