To address the research objectives, one analysis phase and three related empirical studies will be conducted. Together, they contribute to answer the overarching research question: How to foster students writing skills by the use of ML-enabled Learning Support?
Analysis Phase: What are freshmen students learning and support needs for the process of ML-enabled academic writing and how can students data literacy for academic writing as a precondition for evaluating and using feedback be assessed?
Study 1 (Design): What are quality indicators of practical and effective ML-enabled learning support with two components: a) formative evaluation: immediate feedback during writing process, b) summative evaluation: automatic scoring of texts and what are influencing contextual factors in both universities (e.g., European teaching culture in Switzerland, Asian teaching culture in Thailand)?
Study 2 (Outcomes): How and to what extent does the use of ML-enabled learning support contribute to freshmen students learning outcomes and the quality of the generated academic text?
Study 3 (Change in Writing Practice): To what extent is the personalized learning support system accepted by students and contributes to altered writing performance and practices in the long run?
Institute for Educational Management and Technologies, University of St. Gallen
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