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 developed a web application to record the spelling efforts of N = 685 first- and second-graders
in Bavaria, Germany. A total of 18,133 different misspellings were recorded. Using this dataset, we trained six
Machine Learning models and compared their performances to predict misspellings.
Comparing all Machine Learning models employed in this work, the Random Forest performed best on average
as a predictor of spelling errors. Errors at the syllable- and morpheme-levels were predicted best, and errors at
the basic phoneme-grapheme-level were predicted slightly less accurately. Confusions often concerned cases that
are considered linguistically ambiguous or occurred in complex error entanglements. The implications of these
results are discussed.