TY - CHAP A1 - Kellner, Robert ED - Walter, Claudia ED - Riegler, Peter T1 - Elemente gelungener (Online) Lehrveranstaltungen mit großer Wirkung in der Praxis T2 - Tagungsband zum Forum der Lehre an der OTH Regensburg, 04. Mai 2022 N2 - Neue Erkenntnisse aus der Lehr- und Lernforschung und die Möglichkeiten der Digitalisierung bringen neue und spannende Anregungen für die Lehre. In den vergangenen Semestern mussten pandemiebedingt plötzlich ganze Lehrveranstaltungen in ein digitales Format überführt werden. Hinzu kamen sowohl technische als auch didaktische Herausforderungen. Solche Veränderungen auf einmal umzusetzen, kostet viel Zeit und Energie und ist häufig nicht möglich. Einfacher ist es, in kleinen Schritten eine Lehrveranstaltung zu verändern und so den Lernerfolg der Studierenden zu steigern. Dies kann mithilfe von wenigen Elementen mit teilweise nur geringem Aufwand erreicht werden. In diesem Beitrag beschreibt der Autor mehrere dieser Elemente, die zum Gelingen einer Lehrveranstaltung beitragen können und die er in den vergangenen Semestern in seinen Lehrveranstaltungen zur Physik für Ingenieure erfolgreich umgesetzt hat. KW - aktivierende Lehre, moderne Didaktik Y1 - 2022 SN - 1612-4537 SP - 61 EP - 66 ER - TY - CHAP A1 - Kellner, Robert T1 - Making Effective Videos For (Live) Online Learning Quickly T2 - Proceedings of the IUPAP International Conference on Physics Education N2 - Abstract for the presentation held at the 2020 International Conference on Physics Edcucation KW - video KW - multimedia KW - online lectures Y1 - 2022 SN - 978-1-74210-532-1 SP - 109 EP - 109 CY - Sydney ER - TY - JOUR A1 - Parzinger, Michael A1 - Hanfstaengl, Lucia A1 - Sigg, Ferdinand A1 - Spindler, Uli A1 - Wellisch, Ulrich A1 - Wirnsberger, Markus T1 - Comparison of different training data sets from simulation and experimental measurement with artificial users for occupancy detection — Using machine learning methods Random Forest and LASSO JF - Building and Environment N2 - The applications for occupancy detection range from controlling building automation and systems, determining heat transfer coefficients and even assessing the risk of infection in rooms. Studies in the literature use various statistical models, physical models and machine learning techniques to detect occupancy. All these methods require data for training the occupancy detection models. However, data generation is time-consuming and expensive. This study demonstrates the feasibility of using simulated learning data. Using three different data sources, we tested the suitability of different methods for generating learning data. We conducted two experiments in two office spaces with a real user and an artificial user, and we generated a third data set using a building simulation model. In addition, this study compares two different machine learning approaches (Random Forest and LASSO) using environmental parameters. Both machine learning approaches could develop models with a sensitivity of at least 83 % and a specificity of at least 97 % with both training data sets. This work shows that it is possible to determine the presence in rooms using simulated data. The results compared to measured data were just slightly less accurate, and the added value due to the lower effort was considerable KW - Simulated learning data KW - Occupancy detection KW - Random Forest KW - LASSO Y1 - 2022 U6 - https://doi.org/10.1016/j.buildenv.2022.109313 VL - 223 SP - 109313 ER - TY - CHAP A1 - Scheler, M. A1 - Daume, D. A1 - Sojitra, D. A1 - Neumeyer, T. A1 - Steinbach, S. A1 - Beck, T. A1 - Schulze, A. A1 - Hüttl, B. T1 - Precise On-Site Power Analysis of Photovoltaic Arrays by Self-Reference Algorithm T2 - Proceedings of the 8th World Conference on Photovoltaic Energy Conversion N2 - To detect degradation of photovoltaic systems at an early stage, precise performance determinations are essential. Current-voltage measurements on single modules in indoor labs under well-defined conditions are state-of-the-art. However, this method causes a logistical and economic effort. On-site measurements reduce the effort but are prone to error because of poorly determinable test conditions: The detected temperature on the backside of modules as well as the irradiance detected by pyranometer in tilted module plane usually differs from the actual operating conditions. In consequence, the accuracy of measured current-voltage characteristics is poor. A precise on-site power determination is achieved by a self-reference algorithm. Improvements by self-referencing are not achieved by correcting the raw electrical data, but by replacing the measured test conditions (temperature and irradiance) with effective data. These effective values correspond better to the actual module irradiance and temperature values. Furthermore, precise power determination of photovoltaic arrays requires to identify and exclude emporarily deformed current-voltage curves from consideration, as it can arise from non-uniform irradiation within array like shading. In this paper, the application of a digital curve filter is reported and performance determinations by a self-reference algorithm are demonstrated on a photovoltaic array. We review and discuss the measurement concept in terms of its ease of use. …  KW - Environmental Effect, Evaluation, Experimental Methods, Performance, PV Array Y1 - 2022 UR - https://doi.org/10.4229/WCPEC-82022-4DO.1.4 ER - TY - JOUR A1 - Stanzel, Silke A1 - Kellner, Robert T1 - Vorher lesen statt vorgelesen JF - Physik Journal N2 - Just-in-Time-Teaching ist eine Lehrmethode, bei der ein Teil des Wissenserwerbs in Vorbereitung einer Lehrveranstaltung vorausgeht. Online-Tests offenbaren hierbei den aktuellen Lernstand und regen die kritische Auseinandersetzung mit Fachthemen und dem eigenen Studierverhalten an. Die Ergebnisse dieser Tests erlauben es, den Unterricht auf die Bedürfnisse der Studierenden dynamisch anzupassen und zeitliche Freiräume für weitere aktivierende Lehrmethoden zu eröffnen. KW - aktivierende Lehre KW - Just in Time Teaching KW - Force Concept Inventory KW - Blended Learning Y1 - 2022 UR - https://www.pro-physik.de/physik-journal/juli-2022#section-280422 VL - 21 IS - 7 SP - 35 EP - 38 ER -