@inproceedings{Kellner2022, author = {Kellner, Robert}, title = {Elemente gelungener (Online) Lehrveranstaltungen mit großer Wirkung in der Praxis}, series = {Tagungsband zum Forum der Lehre an der OTH Regensburg, 04. Mai 2022}, booktitle = {Tagungsband zum Forum der Lehre an der OTH Regensburg, 04. Mai 2022}, editor = {Walter, Claudia and Riegler, Peter}, organization = {BayZiel}, issn = {1612-4537}, pages = {61 -- 66}, year = {2022}, abstract = {Neue Erkenntnisse aus der Lehr- und Lernforschung und die M{\"o}glichkeiten der Digitalisierung bringen neue und spannende Anregungen f{\"u}r die Lehre. In den vergangenen Semestern mussten pandemiebedingt pl{\"o}tzlich ganze Lehrveranstaltungen in ein digitales Format {\"u}berf{\"u}hrt werden. Hinzu kamen sowohl technische als auch didaktische Herausforderungen. Solche Ver{\"a}nderungen auf einmal umzusetzen, kostet viel Zeit und Energie und ist h{\"a}ufig nicht m{\"o}glich. Einfacher ist es, in kleinen Schritten eine Lehrveranstaltung zu ver{\"a}ndern 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 k{\"o}nnen und die er in den vergangenen Semestern in seinen Lehrveranstaltungen zur Physik f{\"u}r Ingenieure erfolgreich umgesetzt hat.}, language = {de} } @inproceedings{Kellner2022, author = {Kellner, Robert}, title = {Making Effective Videos For (Live) Online Learning Quickly}, series = {Proceedings of the IUPAP International Conference on Physics Education}, booktitle = {Proceedings of the IUPAP International Conference on Physics Education}, address = {Sydney}, isbn = {978-1-74210-532-1}, pages = {109 -- 109}, year = {2022}, abstract = {Abstract for the presentation held at the 2020 International Conference on Physics Edcucation}, language = {en} } @article{ParzingerHanfstaenglSiggetal.2022, author = {Parzinger, Michael and Hanfstaengl, Lucia and Sigg, Ferdinand and Spindler, Uli and Wellisch, Ulrich and Wirnsberger, Markus}, title = {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}, series = {Building and Environment}, volume = {223}, journal = {Building and Environment}, doi = {10.1016/j.buildenv.2022.109313}, pages = {109313}, year = {2022}, abstract = {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}, language = {en} } @inproceedings{SchelerDaumeSojitraetal.2022, author = {Scheler, M. and Daume, D. and Sojitra, D. and Neumeyer, T. and Steinbach, S. and Beck, T. and Schulze, A. and H{\"u}ttl, B.}, title = {Precise On-Site Power Analysis of Photovoltaic Arrays by Self-Reference Algorithm}, series = {Proceedings of the 8th World Conference on Photovoltaic Energy Conversion}, booktitle = {Proceedings of the 8th World Conference on Photovoltaic Energy Conversion}, pages = {4}, year = {2022}, abstract = {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. … }, language = {en} } @article{StanzelKellner2022, author = {Stanzel, Silke and Kellner, Robert}, title = {Vorher lesen statt vorgelesen}, series = {Physik Journal}, volume = {21}, journal = {Physik Journal}, number = {7}, pages = {35 -- 38}, year = {2022}, abstract = {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 Bed{\"u}rfnisse der Studierenden dynamisch anzupassen und zeitliche Freir{\"a}ume f{\"u}r weitere aktivierende Lehrmethoden zu er{\"o}ffnen.}, language = {de} }