We track the development of writing complexity and accuracy in German students' early academic language development from first to eighth grade. Combining an empirically broad approach to linguistic complexity with the high-quality error annotation included in the Karlsruhe Children's Text corpus (Lavalley et al., 2015) used, we construct models of German academic language development that successfully identify the student's grade level. We show that classifiers for the early years rely more on accuracy development, whereas development in secondary school is better characterized by increasingly complex language in all domains: linguistic system, language use, and human sentence processing characteristics. We demonstrate the generalizability and robustness of models using such a broad complexity feature set across writing topics.

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The purpose of history education in Austria has changed over at least the last decade. While the focus used to be to give students a master narrative of the national past based on positivist knowledge, the current objective of history education is to foster historical thinking processes that enable students to form transferable skills in the self-reflected handling and creation of history. A key factor in fostering historical thinking is the appropriation of learning tasks. This case study measures the complexity of learning tasks in Austrian history textbooks as one important aspect of their quality. It makes use of three different approaches to complexity to triangulate the notion: general task complexity (GTC), general linguistic complexity (GLC), and domain-specific task complexity (DTC). The question is which findings can be offered by the specific strengths and limitations of the different methodological approaches to give new insights into the study of task complexity in the domain of history education research. By pursuing multidisciplinary approaches in a triangulating way, the case study opens up new prospects for this field. Besides offering new insights on measuring the complexity of learning tasks, the study illustrates the need for further research in this fieldnot only related to the development of analytical frameworks, but also regarding the notion of complexity in the context of historical learning itself.

The paper presents a new state-of-the-art sentence-wise readability assessment model for German L2 readers. We build a linguistically broadly informed machine learning model and compare its performance against four commonly used readability formulas. To understand when the linguistic insights used to inform our model make a difference for readability assessment and when simple readability formulas suffice, we compare their performance based on two common automatic readability assessment tasks: predictive regression and sentence pair ranking. We find that leveraging linguistic insights yields top performances across tasks, but that for the identification of simplified sentences also readability formulas -which are easier to compute and more accessible -can be sufficiently precise. Linguistically informed modeling, however, is the only viable option for high quality outcomes in finegrained prediction tasks.We then explore the sentence-wise readability profile of leveled texts written for language learners at a beginning, intermediate, and advanced level of German. Our findings highlight that a texts' readability is driven by the maximum rather than the overall readability of sentences. This has direct implications for the adaptation of learning materials and showcases the importance of studying readability also below the document level.