Rüdian, Leo Silvio2024-12-172024-12-172024https://dl.gi.de/handle/20.500.12116/45448Recently, personalization has become a topic of some interest in the field of education. Learners possess different levels of prior knowledge, along with various learning goals and preferences. This diversity underscores the necessity of adapting online courses to suit learners’ needs, with the ultimate goal of engaging learners for optimal learning outcomes. As a representative domain, the field of language learning was chosen, to which personalization has been applied. An initial analysis of commercial language learning apps revealed the scarce realization of personalization, mainly limited to flashcard learning as an item sequencing strategy. Subsequently, the dissertation focused on the overall research question: How can personalization in auto-generated online language learning courses be utilized and combined with fundamental and experimental item sequencing strategies? Therefore, the dissertation employed a modified version of the “Meshing Hypothesis” as an example, proposing that students learn more effectively when instructional teaching methods align with their preference levels. Its original strategy has been controversially discussed in research for decades. The research unfolded in several stages to utilize personalization, commencing with the development of an instrument to collect preferences linked to instructional teaching methods. An experiment was conducted to discern performance differences among learners with varying preference levels. This involved imitating scenarios for some instructional teaching methods within an online language learning course that follows a fixed learning progression. The subsequent phase concentrated on preparing the technical groundwork for personalized courses. To this end, the Moodle learning platform was functionally adapted to facilitate experiments in personalizing online courses. Simultaneously, a contextual foundation was established, enabling the generation of template-based language learning units through cutting-edge generative models. Applied imitations of a selection of instructional methods enhanced the resulting interactive learning materials. In order to optimize the arrangement of the generated learning materials, a novel approach was introduced, combining multiple item sequencing strategies using generative neural networks. A conceptual framework was introduced, allowing for the amplification of sequencing strategies that positively impact learning outcomes or their removal if the effect is negligible. First, the concept was simulated. Then, the technical foundation, generated learning materials, item sequencing strategies, and learner preferences were combined in a final experiment to employ the modified Meshing Hypothesis and to utilize the idea of “unlearning” item sequencing strategies in a real online language learning course. The experiments conducted in this dissertation allow conclusions for generating and personalizing online language learning courses. Furthermore, several proof-of-concepts provide insights into the applicability of the approaches to be drawn for practice.enPersonalizationPreferencesCourse GenerationItem Sequencing StrategiesMoodleModified Meshing HypothesisLanguage LearningText/Dissertation10.18452/28914