Publikation
Challenges of using auto-correction tools for language learning
Leo Sylvio Rüdian; M. Dittmeyer; Niels Pinkwart
In: LAK22: 12th International Learning Analytics and Knowledge Conference. International Conference on Learning Analytics & Knowledge (LAK-2022), March 21-25, Online, Pages 426-431, ISBN 978-1-4503-9573-1, ACM, New York, NY, USA, 4/2022.
Zusammenfassung
In language learning, getting corrective feedback for writing tasks is an essential didactical concept to improve learners’ language skills. Although various tools for automatic correction do exist, open writing texts still need to be corrected manually by teachers to provide helpful feedback to learners. In this paper, we explore the usefulness of an auto-correction tool in the context of language learning. In the first step, we compare the corrections of 100 learner texts suggested by a correction tool with those done by human teachers and examine the differences. In a second step, we do a qualitative analysis, where we investigate the requirements that need to be tackled to make existing proofreading tools useful for language learning. The results reveal that the aim of enhancing texts by proofreading, in general, is quite different from the purpose of providing corrective feedback in language learning. Only one of four relevant errors (recall=.26) marked by human teachers is recorded correctly by the tool, whereas many expressions thought to be faulty by the tool are sometimes no errors at all (precision=.33). We provide and discuss the challenges that need to be addressed to adjust those tools for language learning.