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E. Yigitbas, M. Schmidt, A. Bucchiarone, S. Gottschalk, and G. Engels, “GaMoVR: Gamification-Based UML Learning Environment in Virtual Reality,” Science of Computer Programming, 2023.
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C. Soyka, M. Ullrich, M. Striewe, and N. Schaper, “Comparison of Required Competences and Task Material in Modeling Education,”10.18417/EMISA.18.7__Enterprise Modelling and Information Systems Architectures, vol. 18, Art. no. 7, 2023, doi:10.18417/EMISA.18.7 .
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M. Ott, N. Meihöfener, and I. Mozgova, “Methodical Approach to Reducing Design Time by using Neural Networks in Early Stages of Concept Development,” in Proceedings of the 34rd Annual International Solid Freeform Fabrication Symposium 2023, Austin, Texas, United States, 2023.
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L. Margraf, D. Krause, and M. Weigelt, “Frontal theta reveals further information about neural valencedependent processing of augmented feedback in extensive motor practice - A secondary analysis,”10.1111/ejn.15951__European Journal of Neuroscience, vol. 57, no. 8, pp. 1297–1316, 2023, doi:10.1111/ejn.15951 .
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M. Weigelt, D. Krause, and I. Güldenpenning, “Learning and Memory in Sports,” in Sport and Exercise Psychology, J. Schüler, M. Wegner, H. Plessner, and R. C. Eklund, Eds. Champaign: Springer, 2023, pp. 41–70.
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G. Jonas-Ahrend, P. Fraser-Abder, M. Kapanadze, F. Joubran, and A. Mazzolini, “Lessons learned from a global review study of physics textbook evaluation and its implications for teaching and learning in the classroom,” presented at the International Consortium of Research on Science and Mathematics Education, Panama City/Panama, 2023.
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S. Haddadian, J. C. Scheytt, G. von Bögel, and T. Grenter, “A Sub-Threshold Microwave RFID Tag Chip, Compatible With RFID MIMO Reader Technology,”10.1109/JRFID.2023.3308332__10.1109/JRFID.2023.3308332__IEEE Journal of Radio Frequency Identification, 2023, doi:10.1109/JRFID.2023.3308332 .
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F. Machon et al., “Design of individual simulation games in manufacturing companies for game-based learning,”10.1016/j.procir.2023.03.145__Procedia CIRP, vol. 119, pp. 1017–1022, 2023, doi:10.1016/j.procir.2023.03.145 .
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U. Acevedo-Salas et al., “Impact of 3D Curvature on the Polarization Orientation in Non-Ising Domain Walls,”10.1021/acs.nanolett.2c03579__Nano Letters, vol. 23, no. 3, pp. 795–803, 2023, doi:10.1021/acs.nanolett.2c03579 .
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F. Winkel, O. Wallscheid, P. Scholz, and J. Böcker, “Pseudo-Labeling Machine Learning Algorithm for Predictive Maintenance of Relays,”10.1109/ojies.2023.3323870__IEEE Open Journal of the Industrial Electronics Society, pp. 1–14, 2023, doi:10.1109/ojies.2023.3323870 .
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F. Winkel, J. Deuse-Kleinsteuber, and J. Böcker, “Run-to-Failure Relay Dataset for Predictive Maintenance Research With Machine Learning,”10.1109/tr.2023.3255786__IEEE Transactions on Reliability, pp. 1–14, 2023, doi:10.1109/tr.2023.3255786 .
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M. Wilkerson et al., “K-12 Data Science Education: Outcomes of a National Workshop; International Perspectives; and Next Steps for the Learning Sciences,” in General Proceedings of the ISLS Annual Meeting: Building Knowledge and Sustaining our Community, 2023, pp. 76–79.
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K. S. Weber et al., “Plant-based dietary patterns and cardiometabolic risk factors in subgroups of individuals with diabetes,”10.1055/s-0043-1767936__2023, doi:10.1055/s-0043-1767936 .
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L. Grotegut, GEProS - Entwicklung und Evaluation einer digitalen Simulation zur Förderung diagnostischer Kompetenz von Lehramtsstudierenden. Paderborn: Universität Paderborn, 2023.
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L. Margraf, D. Krause, and M. Weigelt, “Frontal theta reveals further information about neural valencedependent processing of augmented feedback in extensive motor practice—A secondary analysis,”10.1111/ejn.15951__European Journal of Neuroscience, vol. 57, no. 8, pp. 1297–1316, 2023, doi:10.1111/ejn.15951 .
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L. Faßbender, D. Krause, and M. Weigelt, “Feedback processing in cognitive and motor tasks: A metaanalysis on the feedbackrelated negativity,”10.1111/psyp.14439__Psychophysiology, vol. 60, no. 12, 2023, doi:10.1111/psyp.14439 .
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S. F. Dahm, H. Hyna, and D. Krause, “Imagine to automatize: automatization of stimulus–response coupling after action imagery practice in implicit sequence learning,”10.1007/s00426-023-01797-w__Psychological Research, vol. 87, no. 7, pp. 2259–2274, 2023, doi:10.1007/s00426-023-01797-w .
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T. Hetkämper, K. Koch, M. Webersen, and L. Claes, “Application-based learning of signal analysis methods with the help of a graphical open-source software,”10.21427/159K-G445__presented at the SEFI 2023 Annual Conference, 2023, doi:10.21427/159K-G445 .
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M. Schenke, B. Haucke-Korber, and O. Wallscheid, “Finite-Set Direct Torque Control via Edge Computing-Assisted Safe Reinforcement Learning for a Permanent Magnet Synchronous Motor,”10.1109/tpel.2023.3303651__IEEE Transactions on Power Electronics, pp. 1–16, 2023, doi:10.1109/tpel.2023.3303651 .
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