Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

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In Germany (similar to other countries), 30 % of students demonstrate insufficient spelling skills at the end of primary school – partly owing to the challenge for teachers to manage a variety of students' learning needs. Digital tools using Machine Learning can enable teachers to individualise students' learning. However, there are still no suitable approaches for demographics of students who are not yet proficient in spelling.With an aim to adapt Machine Learning for students of all proficiencies, we investigate how accurately specific spelling errors can be predicted across different skill levels, and what the content-related reasons for incorrect predictions are.To that end, we...

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