Spelling in Computer-Assisted Language Learning

Rimrott & Heift 2005
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Spelling in Computer-Assisted Language Learning - Background
Spell Checking in CALL
• The use of word processors has become an integral part of the language learning
classroom
• spell checkers have turned into a highly desirable tool for non-native writers
• however, the success of generic spell checkers in correcting misspellings by non-native
writers has not been studied extensively
Generic spell checkers
• are designed for native speakers
• yet in CALL, we are dealing with non-native writers
• assume that most misspellings are performance-based (typos)
• yet non-native writers also make competence-related errors because they do not
know the foreign language that well (e.g. for )
• correction rate for native speakers’ misspellings is above 90%
• no comparable studies for non-native speakers’ misspellings
The algorithms of generic spell checkers are based on empirical findings
• most (80 – 95%) misspellings contain only a single error of omission, addition,
substitution or transposition (e.g. , , , )
• the first letter of a misspelled word is usually correct