Dyslexia-Early Identification and Prevention: Highlights from the Jyväskylä Longitudinal Study of Dyslexia - PubMed (original) (raw)

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Dyslexia-Early Identification and Prevention: Highlights from the Jyväskylä Longitudinal Study of Dyslexia

Heikki Lyytinen et al. Curr Dev Disord Rep. 2015.

Abstract

Over two decades of Finnish research, monitoring children born with risk for dyslexia has been carried out in the Jyväskylä Longitudinal Study of Dyslexia (JLD). Two hundred children, half at risk, have been assessed from birth to puberty on hundreds of measures. The aims were to identify measures of prediction of later reading difficulty and to instigate appropriate and earliest diagnosis and intervention. We can identify at-risk children from newborn electroencephalographic brain recordings (Guttorm et al., J Neural Transm 110:1059-1074, 2003). Predictors are also apparent from late-talking infants who have familial background of dyslexia (Lyytinen and Lyytinen, Appl Psycolinguistics 25:397-411, 2004). The earliest easy-to-use predictive measure to identify children who need help to avoid difficulties in learning to read is letter knowledge (Lyytinen et al., Merrill-Palmer Q 52:514-546, 2006). In response, a purpose-engineered computer game, GraphoGame™, provides an effective intervention tool (Lyytinen et al., Scand J Psychol 50:668-675, 2009). In doubling as a research instrument, GraphoGame provides bespoke intervention/reading instruction for typical/atypically developing children. Used extensively throughout Finland, GraphoGame is now crossing the developed and developing world to assist children, irrespective of the cause (environmental or genetic) of their failing to learn to read (Ojanen et al., Front Psychol 6(671):1-13, 2015).

Keywords: Dyslexia; Finnish; GraphoGame; Intervention; Longitudinal; Prediction.

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Figures

Fig. 1

Fig. 1

In the game (left-hand-side), the learner chooses, from the alternatives on the screen, the letter that corresponds to the sound heard through headphones. The illustration (right-hand-side) shows how the game data can be analyzed. The illustration shows the sound /N/ (in the center) which the learner has heard in the game more than 100 times by the time of this analysis, and the incorrect alternative letters (distractors) shown on the game screen at the same time as letter N. The numbers on the outer circle tell the number of times that a certain distractor was present on the game screen at the same time with target letter N. From the illustration, it can be seen that, in the beginning, the learner has chosen letter M or letter R when N was asked for (shown in red) but has later learned to differentiate N from these letters (shown in green). Similar improvement can be seen in other letters (for more information about this method, see Lyytinen et al. ) [52]

References

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