{"id":{"repo_id":"cau-kiel","oai_identifier":"oai:macau.uni-kiel.de:macau_mods_00008731"},"canonical_url":"https://search.dev.ndltd.org/etd/cau-kiel/oai:macau.uni-kiel.de:macau_mods_00008731","repository":{"repo_id":"cau-kiel","name":"Christian-Albrechts Universität Kiel","base_url":"https://macau.uni-kiel.de/servlets/OAIDataProvider"},"display":{"title":"Towards a Process-Oriented Approach to Feedback Research","abstract":"Feedback is an essential means of supporting learners in improving their performance. However, there is often a gap between the potential and the actual use of feedback. Adopting a process-oriented perspective, this dissertation investigates task-level learning engagement as a central mechanism of the effectiveness of automated feedback on writing performance. Against the background of increasing digitalization and the growing use of artificial intelligence (AI) in education, it focuses on automated feedback during text revision and contributes to a more comprehensive understanding of the feedback process. This dissertation extends the Student–Feedback Interaction Model by Lipnevich and Smith (2022) by incorporating task-level behavioral learning engagement as a component of the feedback process. Behavioral learning engagement is conceptualized as learners' proactive and observable behavior during a learning activity and is operationalized using process measures derived from revision behavior. Across three empirical studies and two additional analyses, participants completed writing and revision tasks with or without automated feedback provided by automatic writing evaluation systems or generative AI. Process measures obtained from log files and keystroke logging were used to investigate behavioral learning engagement and were subsequently examined with regard to their construct validity. The findings demonstrate that behavioral learning engagement mediates the effect of automated feedback on revision performance. Automated feedback increased behavioral learning engagement during text revision, which in turn improved writing performance. Furthermore, the findings provide empirical support for the construct validity of typing time and the total number of keystrokes as process measures of behavioral learning engagement, whereas writing pause measures were not suitable indicators of cognitive learning engagement. Overall, this dissertation highlights task-level behavioral learning engagement as a crucial mechanism in the feedback process and provides theoretical, methodological, and practical contributions to process-oriented feedback research and the further development of AI-supported feedback systems in educational contexts.","abstract_html":"Feedback is an essential means of supporting learners in improving their performance. However, there is often a gap between the potential and the actual use of feedback. Adopting a process-oriented perspective, this dissertation investigates task-level learning engagement as a central mechanism of the effectiveness of automated feedback on writing performance. Against the background of increasing digitalization and the growing use of artificial intelligence (AI) in education, it focuses on automated feedback during text revision and contributes to a more comprehensive understanding of the feedback process. This dissertation extends the Student–Feedback Interaction Model by Lipnevich and Smith (2022) by incorporating task-level behavioral learning engagement as a component of the feedback process. Behavioral learning engagement is conceptualized as learners' proactive and observable behavior during a learning activity and is operationalized using process measures derived from revision behavior. Across three empirical studies and two additional analyses, participants completed writing and revision tasks with or without automated feedback provided by automatic writing evaluation systems or generative AI. Process measures obtained from log files and keystroke logging were used to investigate behavioral learning engagement and were subsequently examined with regard to their construct validity. The findings demonstrate that behavioral learning engagement mediates the effect of automated feedback on revision performance. Automated feedback increased behavioral learning engagement during text revision, which in turn improved writing performance. Furthermore, the findings provide empirical support for the construct validity of typing time and the total number of keystrokes as process measures of behavioral learning engagement, whereas writing pause measures were not suitable indicators of cognitive learning engagement. Overall, this dissertation highlights task-level behavioral learning engagement as a crucial mechanism in the feedback process and provides theoretical, methodological, and practical contributions to process-oriented feedback research and the further development of AI-supported feedback systems in educational contexts.","abstract_has_math":false,"creators":["Schiller, Ronja"],"institution":"Christian-Albrechts-Universität zu Kiel","degree_name":null,"degree_level":"thesis.doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Köller, Olaf","Meyer, Jennifer","Möller, Jens"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12-19","date_published":"2025-12-19","updated_at":"2026-07-24T01:35:26Z","subjects":["Feedback","Task-Level Learning Engagement","Technology-Enhanced Learning","Process Measures"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://macau.uni-kiel.de/receive/macau_mods_00008731","outbound_label":"Repository record","outbound_source":"source_url"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Köller, Olaf","Meyer, Jennifer","Möller, Jens"]},{"key":"dc:creator","label":"Author","values":["Schiller, Ronja"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:publisher","label":"Institution","values":["Universitätsbibliothek Kiel"]},{"key":"dc:type","label":"Dc Type","values":["PhDThesis"]},{"key":"thesis:degree_level","label":"Degree Level","values":["thesis.doctoral"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Christian-Albrechts-Universität zu Kiel"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Feedback","Task-Level Learning Engagement","Technology-Enhanced Learning","Process Measures"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Feedback is an essential means of supporting learners in improving their performance. 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Across three empirical studies and two additional analyses, participants completed writing and revision tasks with or without automated feedback provided by automatic writing evaluation systems or generative AI. Process measures obtained from log files and keystroke logging were used to investigate behavioral learning engagement and were subsequently examined with regard to their construct validity. The findings demonstrate that behavioral learning engagement mediates the effect of automated feedback on revision performance. Automated feedback increased behavioral learning engagement during text revision, which in turn improved writing performance. Furthermore, the findings provide empirical support for the construct validity of typing time and the total number of keystrokes as process measures of behavioral learning engagement, whereas writing pause measures were not suitable indicators of cognitive learning engagement. Overall, this dissertation highlights task-level behavioral learning engagement as a crucial mechanism in the feedback process and provides theoretical, methodological, and practical contributions to process-oriented feedback research and the further development of AI-supported feedback systems in educational contexts."]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Towards a Process-Oriented Approach to Feedback Research"]}]}],"canonical_facts":{"dc:contributor":["Köller, Olaf","Meyer, Jennifer","Möller, Jens"],"dc:creator":["Schiller, Ronja"],"dc:description.abstract":["Feedback is an essential means of supporting learners in improving their performance. However, there is often a gap between the potential and the actual use of feedback. Adopting a process-oriented perspective, this dissertation investigates task-level learning engagement as a central mechanism of the effectiveness of automated feedback on writing performance. Against the background of increasing digitalization and the growing use of artificial intelligence (AI) in education, it focuses on automated feedback during text revision and contributes to a more comprehensive understanding of the feedback process. This dissertation extends the Student–Feedback Interaction Model by Lipnevich and Smith (2022) by incorporating task-level behavioral learning engagement as a component of the feedback process. Behavioral learning engagement is conceptualized as learners' proactive and observable behavior during a learning activity and is operationalized using process measures derived from revision behavior. Across three empirical studies and two additional analyses, participants completed writing and revision tasks with or without automated feedback provided by automatic writing evaluation systems or generative AI. Process measures obtained from log files and keystroke logging were used to investigate behavioral learning engagement and were subsequently examined with regard to their construct validity. The findings demonstrate that behavioral learning engagement mediates the effect of automated feedback on revision performance. Automated feedback increased behavioral learning engagement during text revision, which in turn improved writing performance. Furthermore, the findings provide empirical support for the construct validity of typing time and the total number of keystrokes as process measures of behavioral learning engagement, whereas writing pause measures were not suitable indicators of cognitive learning engagement. 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