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Productive vocabulary composition is investigated in 17-month-old children who are participating in an ongoing longitudinal dyslexia research project in the Netherlands. The project is searching for early precursors for dyslexia and follows a group of children who are genetically at risk for dyslexia and a control group during the first 10 years of their lives. Among other measures, the Dutch version of the MacArthur Communicative Development Inventory: Words and Sentences (N-CDI) is used to investigate early vocabulary development. In this article, the first N-CDI results from the 2 groups of 17-month-old children are compared with each other, with other cross-sectional, cross-linguistic studies, and with a similar Finnish longitudinal dyslexia project. The Dutch children show the same general acquisition pattern as documented for other languages, but there are significant differences between the two groups of 17-month-old children in total number of words produced and in the linguistic composition of their productive vocabulary.

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There is increasing interest in the application of cognitive neuroscience in educational thinking and practice, and here we review findings from neuroscience that demonstrate its potential relevance to technology-enhanced learning (TEL). First, we identify some of the issues in integrating neuroscientific concepts into TEL research. We caution against seeking prescriptive neuroscience solutions for TEL and emphasize the need, instead, to conceptualize TEL at several different levels of analysis (brain, mind and behaviour, including social behaviour). Our review emphasizes the possibility of combining TEL and neuroscience concepts in adaptive educational systems, and we consider instances of interdisciplinary technology-based interventions drawing on neuroscience and aimed at remediating developmental disorders. We also consider the potential relevance of findings from neuroscience for the development of artificial agency, creativity, collaborative learning and neural insights into how different types of multimodality may influence learning, which may have implications for the future developments of tangibles. Finally, we identify a range of reasons why dialogue between neuroscience and the communities involved with technology and learning is likely to increase in the future.