From Hand to Eye: a Meta-Analysis of the Benefit from Handwriting Training in Visual Graph Recognition

Abstract

Handwriting (HW) training seems to boost recognition of visual graphs and learning to read more than other learning experiences. However, effects across studies appear to be variable and the underlying cognitive mechanism has been elusive. We thus conducted a meta-analysis on 50 independent experiments (with 1525 participants) to determine the magnitude of this HW benefit in visual graph recognition, while enlightening the underlying cognitive mechanism, by investigating four types of moderators: training program (type of control training, presence/absence of phonological training, and HW tasks adopted); set size and training regime (duration and frequency of session and total amount of training); granularity of visual discrimination and perceptual learning tasks; and age of participants. The benefit from HW training was moderate-to-large and significant (Hedge’s g = 0.58, SE = .09) and was also modulated by the type of control training (larger relative to motor, g = 0.78, than to visual control, g = 0.37), phonological training (larger when it was absent, g = 0.79, than present, g = 0.47), and granularity of visual discrimination (larger for fine-grained, g = 0.93, than coarse-grained, g = 0.19). These results seem consistent with symbolic accounts that hold that the advantage from HW training in visual graph recognition is about perceptual learning rather than the motor act. Multiple meta-regressions also revealed that training regime moderated the HW benefit. We conclude that HW training is effective to improve visual graph recognition, and hence is still relevant for literacy instruction in the present digital era.