Dissertation: Predicting and Manipulating the Difficulty of Text-Completion Exercises for Language Learning

Abstract

The increasing levels of international communication in all aspects of life lead to a growing demand of language skills. Traditional language courses compete nowadays with a wide range of online offerings that promise higher flexibility. However, most platforms provide rather static educational content and do not yet incorporate the recent progress in educational natural language processing. In the last years, many researchers developed new methods for automatic exercise generation, but the generated output is often either too easy or too difficult to be used with real learners. In this thesis, we address the task of predicting and manipulating the difficulty of text-completion exercises based on measurable linguistic properties to bridge the gap between technical ambition and educational needs. The main contribution consists of a theoretical model and a computational implementation for exercise difficulty prediction on the item level. This is the first automatic approach that reaches human performance levels and is applicable to various languages and exercise types.