TY - JOUR A1 - Brucksch, Michael A1 - Falter, Thomas T1 - Modelle zur Beschreibung des Transfers im Innovationsgeschehen BT - Entwicklung von Referenz- und Modellsystemen für die Darstellung von Transfer und Transfergeschehen JF - TRANSFER Y1 - 2021 UR - https://www.transferforschung.de/_files/ugd/fdf78e_cbdad77073ca4c12a231be63278418a3.pdf ER - TY - CHAP A1 - Günther, Swen A1 - Falter, Thomas A1 - Brucksch, Michael A1 - Beckmann, Wilhelm A1 - Lang, Gabriele A1 - Findeisen, Vivien A1 - Bormann, Paula-Marie T1 - Relevance of transfer processes and indicators in practice 3 Results of a cross-subsystem expert survey in Germany T2 - Academic and Practitioner Proceedings of the 2023 UIIN Conference Series, June 9-11, 2023, Budapest Y1 - 2023 SP - 104 EP - 119 PB - UIIN ER - TY - JOUR A1 - Broser, Christian A1 - Falter, Thomas A1 - Ławrowski, Robert Damian A1 - Altenbuchner, Amelie A1 - Vögele, Daniel A1 - Koss, Claus A1 - Schlamp, Matthias A1 - Dunnweber, Jan A1 - Steffens, Oliver A1 - Heckner, Markus A1 - Jaritz, Sabine A1 - Schiegl, Thomas A1 - Corsten, Sabine A1 - Lauer, Norina A1 - Guertler, Katherine A1 - Koenig, Eric A1 - Haug, Sonja A1 - Huber, Dominik A1 - Birkenmaier, Clemens A1 - Krenkel, Lars A1 - Wagner, Thomas A1 - Justus, Xenia A1 - Saßmannshausen, Sean Patrick A1 - Kleine, Nadine A1 - Weber, Karsten A1 - Braun, Carina N. A1 - Giacoppo, Giuliano A1 - Heinrich, Michael A1 - Just, Tobias A1 - Schreck, Thomas A1 - Schnabl, Andreas A1 - Gilmore, Amador Téran A1 - Roeslin, Samuel A1 - Schmid, Sandra A1 - Wellnitz, Felix A1 - Malz, Sebastian A1 - Maurial, Andreas A1 - Hauser, Florian A1 - Mottok, Jürgen A1 - Klettke, Meike A1 - Scherzinger, Stefanie A1 - Störl, Uta A1 - Heckner, Markus A1 - Bazo, Alexander A1 - Wolff, Christian A1 - Kopper, Andreas A1 - Westner, Markus A1 - Pongratz, Christian A1 - Ehrlich, Ingo A1 - Briem, Ulrich A1 - Hederer, Sebastian A1 - Wagner, Marcus A1 - Schillinger, Moritz A1 - Görlach, Julien A1 - Hierl, Stefan A1 - Siegl, Marco A1 - Langer, Christoph A1 - Hausladen, Matthias A1 - Schreiner, Rupert A1 - Haslbeck, Matthias A1 - Kreuzer, Reinhard A1 - Brückl, Oliver A1 - Dawoud, Belal A1 - Rabl, Hans-Peter A1 - Gamisch, Bernd A1 - Schmidt, Ottfried A1 - Heberl, Michael A1 - Gänsbauer, Bianca A1 - Bick, Werner A1 - Ellermeier, Andreas A1 - Monkman, Gareth J. A1 - Prem, Nina A1 - Sindersberger, Dirk A1 - Tschurtschenthaler, Karl A1 - Aurbach, Maximilian A1 - Dendorfer, Sebastian A1 - Betz, Michael A. A1 - Szecsey, Tamara A1 - Mauerer, Wolfgang A1 - Murr, Florian ED - Baier, Wolfgang T1 - Forschung 2018 T3 - Forschungsberichte der OTH Regensburg - 2018 KW - Forschung KW - Forschungsbericht Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-13826 SN - 978-3-9818209-5-9 CY - Regensburg ER - TY - CHAP A1 - Hauser, Florian A1 - Staufer, Susanne A1 - Röhrl, Simon A1 - Nadimpalli, Vamsi Krishna A1 - Ezer, Timur A1 - Grabinger, Lisa A1 - Mottok, Jürgen A1 - Falter, Thomas ED - Gómez Chova, Luis ED - González Martínez, Chelo ED - Lees, Joanna T1 - LEVERAGING FIVE QUESTIONNAIRES TO ANALYZE STUDENT LEARNING STRATEGIES AND GENERATE AI-POWERED INDIVIDUALIZED LEARNING PATHS T2 - ICERI2025 Proceedings N2 - Background: The COVID-19 pandemic has significantly accelerated the shift toward online and blended learning in higher education, placing renewed emphasis on the individualization of learning content to meet diverse student needs. Even high-quality learning materials may fail to engage learners if they do not align with students’ personal preferences and learning styles. Identifying these learner preferences, therefore, emerges as a critical challenge. Objectives: This paper presents ongoing work within a larger research project aimed at employing artificial intelligence to recommend optimal learning path for students in specific courses. Beyond mere optimization, the goal is to ensure the best possible fit between learning materials and individual learners. Sample & Methods: A total of 27 students from technical degree programs took part in this survey. All participation was voluntary, and data were handled in full compliance with GDPR regulations. Although our broader project integrates fine-grained learning analytics from Moodle, the present abstract focuses exclusively on the self-report questionnaire results. Participants completed five instruments: 1. Index of Learning Styles (ILS) 2. LIST-K (Learning and Study Strategies Inventory – Short version) 3. BFI-10 (Big Five Inventory – 10 items) 4. Custom Preferences Instrument, capturing preferences for specific learning elements (e.g. instructional videos, lecture notes, summaries) and basic demographic data 5. Motivational Value Systems Questionnaire (MVSQ), piloted last semester to assess value orientations and motivational drivers Results: Preliminary analyses of the questionnaire data reveal: - Learning Styles (ILS): The majority lean toward the visual learning type (M = 5.740, SD = 3.430). - Learning Strategies (LIST-K): High scores on metacognitive strategies (M = 3.000; SD = 0.520) and collaboration with peers (M = 3.190; SD = 0.540). - Preferred Learning Elements: Summaries, overviews, and self-checks are most favored. - Value Orientations (MVSQ): Students are primarily driven by the pursuit of personal achievement (M = 4.400; SD = 11.140). Conclusion & Significance: By integrating these five standardized questionnaires, we gain valuable insights into student learning preferences—insights that complement our Moodle analytics in the broader project. Observed trends suggest that learning materials should be concise and designed to facilitate peer interaction and knowledge deepening. These findings will guide the refinement of our AI-driven recommendation engine, enhancing its ability to deliver personalized learning paths that boost both engagement and effectiveness. KW - AI in higher education KW - learning management system KW - adaptive learning KW - personalized learning paths KW - online and blended learning Y1 - 2025 U6 - https://doi.org/10.21125/iceri.2025.0658 SP - 1775 EP - 1784 PB - IATED ER - TY - INPR A1 - Falter, Thomas T1 - Werkstattbericht: Erfahrungen aus der Zusammenarbeit mit KI-Agenten für individualisiertes Lernen N2 - DDer Artikel reflektiert praxisbasiert und philosophisch die Zusammenarbeit von Menschen mit KI-Agenten – insbesondere im Kontext des Lernens von Skills auf Experten-Level im Hochschulbereich. Am Beispiel der Entwicklung des KI-unterstützten Multiagentensystem LASSI, das Lernen unterstützt, wird eine Lernreise in drei Phasen erzählt – vom produktiven Staunen über die Verschiebung von Autorenschaft bis hin zur Übernahme von Verantwortung. Die Perspektiven von Entwickler, Professor und Studierenden zeigen, wie sich Mensch-Agenten-Systeme durch Zusammenarbeit verändern. Ergänzend werden drei philosophische Dimensionen – Embodied Knowledge, Enhanced Technologies und Embedded Ethics – herangezogen, um Potenziale und Risiken dieser Systeme zu beleuchten: Was passiert, wenn Intelligenz entkörperlicht, menschliche Fähigkeiten an Technologien ausgelagert und Werte kodiert werden? Der Text plädiert dafür, Agenten nicht als neutrale Werkzeuge, sondern als Mitgestalter im Soziotechnischen System Bildung zu begreifen. In der Schlussbetrachtung wird die Hochschule als Denkwerkstatt skizziert, die durch KI ihre Rolle neu definieren muss: weg vom Prüfungsbetrieb hin zu einem Raum für Urteilskraft und Selbstreflexion. Lernen mit und von Agenten heißt: Lernen neu denken – gemeinsam gestalten. Y1 - 2026 U6 - https://doi.org/10.13140/RG.2.2.20204.63360 ER -