Supporting Acquisition of Spelling Skills in Different Orthographies Using an Empirically Validated Digital Learning Environment.

Heikki Juhani Lyytinen,Margaret Semrud-Clikeman,Ulla Richardson,Hong Li,Kenneth Pugh

doi:10.3389/fpsyg.2021.566220

Abstract

This paper discusses how the association learning principle works for supporting acquisition of basic spelling and reading skills using digital game-based learning environment with the Finland-based GraphoLearn (GL) technology. This program has been designed and validated to work with early readers of different alphabetic writing systems using repetition and reinforcing connections between spoken and written units. Initially GL was developed and found effective in training children at risk of reading disorders in Finland. Today GL training has been shown to support learning decoding skills among children independent of whether they face difficulties resulting from educational, social, or biological reasons.

Highlights

Our experience with teaching alphabetic reading and spelling in a transparent writing system (Finnish) provides a basic model of how digital training can be used when instructing less transparent writing systems such as English
Our observations based on a very early recording of event-related potentials (ERPs) revealed that when newborns were exposed to streams of sinusoidal sound pips which contained repeated standard stimuli with infrequently (12%) presented deviant sounds revealing socalled mismatch negativity (MMN)-type response, we found a clear difference
In non-transparent writing environments, such a connection-building approach between spoken and written units was shown to be more effective than has been achieved via the published traditional reading interventions offered by expert remedial teachers

Summary

INTRODUCTION

Our experience with teaching alphabetic reading and spelling in a transparent writing system (Finnish) provides a basic model of how digital training can be used when instructing less transparent writing systems such as English. These bottlenecks can be overcome with intensive use of GL (i.e., sufficient repetitive practice) as the learners are repeatedly taught to differentiate acoustically similar speech sounds (such as those represented by letters l, m, and n) as the logs reveal (Niemelä et al, 2020) and connect these sounds reliably to their corresponding letters This necessary first step is required to learn the basic reading/spelling skill in any transparent alphabetic orthography where the sizes of to-be-connected spoken and written items are small (i.e., single sounds/phonemes and letters/graphemes). Because reading skill in non-transparent orthographies is acquired by relying on larger units stored in memory and requires acquiring orthographic word knowledge, learning becomes more demanding due to a larger number of the to-bestored connections This difficulty is naturally reflected in the time needed for training the skill using GraphoLearn (GL) technology-based learning environments. This is a good example of how the content of the to-be-trained connections defines what needs to be chosen for training

A DETAILED DESCRIPTION OF THE GL-TECHNOLOGY LEARNING GAME

Findings

CONCLUDING REMARKS