Predicting Perceived Text Complexity: The Role of Person-Related Features in Profile-Based Models

Text complexity is inherently subjective, as it is not solely determined by linguistic properties but also shaped by the reader’s perception. Factors such as prior knowledge, language proficiency, and cognitive abilities influence how individuals assess the difficulty of a text. Existing methods for measuring text complexity commonly rely on quantitative linguistic features and ignore differences in the readers' backgrounds. In this paper, we evaluate several machine learning models that determine the complexity of texts as perceived by teenagers in high school prior to deciding on their post-secondary pathways. We collected and publicly released a dataset from German schools, where 193 students with diverse demographic backgrounds, school grades, and language abilities annotated a total of 3,954 German sentences. The text corpus is based on official study guides authored by German governmental authorities. In contrast to existing methods of determining text complexity, we build a model that is specialized to behave like the target audience, thereby accounting for the diverse backgrounds of the readers. The annotations indicate that students generally perceived the texts as significantly simpler than suggested by the Flesch-Reading-Ease score. We show that K-Nearest-Neighbors, Multilayer Perceptron, and ensemble models perform well in predicting the subjectively perceived text complexity. Furthermore, SHapley Additive exPlanation (SHAP) values reveal that these perceptions not only differ by the text's linguistic features but also by the students' mother tongue, gender, and self-estimation of German language skills. We also implement role-play prompting with ChatGPT and Claude and show that state-of-the-art large language models have difficulties in accurately assessing perceived text complexity from a student’s perspective. This work thereby contributes to the growing field of adjusting text complexity to the needs of the target audience by going beyond quantitative linguistic features. We have made the collected dataset publicly available at https://github.com/boshl/studentannotations.