We present a parametrizable approach to exercise generation from authentic texts that addresses the need for digital materials designed to practice the language means on the curriculum in a real-life school setting. The tool builds on a language-aware search engine that helps identify attractive texts rich in the language means to be practiced. Making use of state-ofthe-art NLP, the relevant learning targets are identified and transformed into exercise items embedded in the original context.While the language-aware search engine ensures that these contexts match the learner's interests based on the search term used, and the linguistic parametrization of the system then reranks the results to prioritize texts that richly represent the learning targets, for the exercise generation to proceed on this basis, an interactive configuration panel allows users to adjust exercise complexity through a range of parameters specifying both properties of the source sentences and of the exercises.An evaluation of exercises generated from web documents for a representative sample of language means selected from the English curriculum of 7th grade in German secondary school showed that the combination of language-aware search and exercise generation successfully facilitates the process of generating exercises from authentic texts that support practice of the pedagogical targets.

Foreign language teaching achieves best learning outcomes when individual differences of learners are taken into account. While it is difficult for teachers to support internal differentiation in the classroom, digital tools can adaptively propose individual learning paths through activities so that students can practice with appropriately challenging exercises. But how can sufficiently varied, systematically parametrized exercises be provided to enable a system to match them to individual learner needs? We present an approach for high-variability exercise generation that transforms a single specification into a multitude of exercises varying in complexity. The approach is currently being evaluated in a randomized controlled study in regular German seventh grade English classes, facilitating a systematic empirical exploration of adaptive exercise complexity in relation to learning outcomes.

Adaptive exercise sequencing in Intelligent Language Tutoring Systems (ILTS) aims to select exercises for individual learners that match their abilities. For exercises practicing forms in isolation, it may be sufficient for sequencing to consider the form being practiced. But when exercises embed the forms in a sentence or bigger language context, little is known about how the nature of this co-text influences learners in completing the exercises.To fill the gap, based on data from two large field studies conducted with an English ILTS in German secondary schools, we analyze the impact of co-text complexity on learner performance for different exercise types and learners at different proficiency levels. The results show that co-text complexity is an important predictor for a learner's performance on practice exercises, especially for gap filling and Jumbled Sentences exercises, and particularly for learners at higher proficiency levels.

Integrating adaptivity into Task-Based Language Teaching requires exercises that transmit a specific content but whose complexity is adjusted to the learner’s level. Thus, exercises of varying complexity based on the same text are needed. Revising generated exercise variants is time consuming and redundant where the same underlying linguistic annotations can be used for exercise generation. We present a fully implemented approach to generate generalized exercise specifications as an interim step before turning them into concrete exercises, as well as an interface for efficient reviewing of the specifications.

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