@inbook{63109, abstract = {{Game-based Learning (GBL) und Gamification (GF) gewinnen im schulischen Umfeld zunehmend an Bedeutung. Ihr Einsatz bietet die Möglichkeit, eigenverantwortliches Lernen im Unterricht zu fördern, bringt aber auch Herausforderungen für Lehr- und Lernprozesse mit sich. Daher ist es von großer Bedeutung, angehenden Lehrkräften die notwendigen Kompetenzen zu vermitteln, um GBL und GF effektiv in den Unterricht zu integrieren. Vor diesem Hintergrund wurde ein Lehrkonzept für die Hochschullehre entwickelt und hinsichtlich der Zielerreichung evaluiert. Der vorliegende Beitrag gibt einen Einblick in die Gestaltungsaspekte des Seminars sowie erste Erkenntnisse und Erfahrungen der Studierenden.}}, author = {{Truong, Ha My}}, booktitle = {{Lehrkräftebildung in der digitalen Welt. Zukunftsorientierte Forschungs- und Praxisperspektiven}}, editor = {{Herzig, Bardo and Eickelmann, Birgit and Schwabl, Franziska and Schulze, Johanna and Niemann, Jan}}, isbn = {{9783830948377}}, keywords = {{Game-based learning, Gamification, Hochschullehre, Lehrkräftebildung}}, pages = {{179--189}}, publisher = {{Waxmann Verlag GmbH}}, title = {{{Level Up! Gamification in der Lehrkräfteausbildung - Konzeption und Erfahrung eines gamifizierten Seminars in der Hochschullehre für Lehramtsstudierende}}}, doi = {{10.31244/9783830998372}}, year = {{2024}}, } @article{65163, abstract = {{Dieser Beitrag untersucht aktuelle pädagogische und hermeneutische Ansätze der jüdischen, christlichen und muslimischen Religionspädagogik in Kindertora, Kinderbibel und Kinderkoran. Er betont die Notwendigkeit für Lehrkräfte, sich mit den spezifischen pädagogischen und hermeneutischen Ansätzen der drei monotheistischen Religionen vertraut zu machen, um die didaktischen Heiligen Schriften im Unterricht angemessen nutzen zu können. Beispiele aus dem aktuellen Religionsunterricht zeigen Missverständnisse und Überraschungen auf, die durch unzureichendes Wissen entstehen. Der Artikel hebt die Bedeutung einer jüdischen Identitätsbildung, einer christlichen diversitätssensiblen Perspektive und von muslimischen normativen Diskursen in den verschiedenen Religionspädagogiken hervor und diskutiert die Herausforderungen und Chancen, die mit der Nutzung didaktisierter Heiliger Schriften verbunden sind. }}, author = {{Keuchen, Marion}}, journal = {{TheoWeb. Zeitschrift für Religionspädagogik}}, keywords = {{Heilige Schriften, interreligiöses Lernen, Schrifthermeneutik, Identität, diversitätssensible Religionspädagogik, jüdische Religionspädagogik, muslimische Religionspädagogik, christliche Religionspädagogik, Holy scriptures, interreligious learning, hermeneutics of scripture, identity, diversity-sensitive religious education, Jewish religious education, Muslim religious education, Christian religious education}}, pages = {{224--237}}, title = {{{Aktuelle pädagogische und hermeneutische Ansätze aus Judentum, Christentum und Islam in Kindertora, Kinderbibel und Kinderkoran: Identitätsbildung, diversitätssensible Religionspädagogik und normative Diskurse}}}, doi = {{10.23770/tw0360}}, volume = {{2}}, year = {{2024}}, } @inproceedings{45270, abstract = {{Clinical depression is a serious mental disorder that poses challenges for both personal and public health. Millions of people struggle with depression each year, but for many, the disorder goes undiagnosed or untreated. Over the last decade, early depression detection on social media emerged as an interdisciplinary research field. However, there is still a gap in detecting hesitant, depression-susceptible individuals with minimal direct depressive signals at an early stage. We, therefore, take up this open point and leverage posts from Reddit to fill the addressed gap. Our results demonstrate the potential of contemporary Transformer architectures in yielding promising predictive capabilities for mental health research. Furthermore, we investigate the model’s interpretability using a surrogate and a topic modeling approach. Based on our findings, we consider this work as a further step towards developing a better understanding of mental eHealth and hope that our results can support the development of future technologies.}}, author = {{Halimeh, Haya and Caron, Matthew and Müller, Oliver}}, booktitle = {{Hawaii International Conference on System Sciences}}, keywords = {{Social Media and Healthcare Technology, early depression detection, liwc, mental health, transfer learning, transformer architectures}}, title = {{{Early Depression Detection with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based Features}}}, year = {{2023}}, } @inproceedings{50479, abstract = {{Verifying assertions is an essential part of creating and maintaining knowledge graphs. Most often, this task cannot be carried out manually due to the sheer size of modern knowledge graphs. Hence, automatic fact-checking approaches have been proposed over the last decade. These approaches aim to compute automatically whether a given assertion is correct or incorrect. However, most fact-checking approaches are binary classifiers that fail to consider the volatility of some assertions, i.e., the fact that such assertions are only valid at certain times or for specific time intervals. Moreover, the few approaches able to predict when an assertion was valid (i.e., time-point prediction approaches) rely on manual feature engineering. This paper presents TEMPORALFC, a temporal fact-checking approach that uses multiple sources of background knowledge to assess the veracity and temporal validity of a given assertion. We evaluate TEMPORALFC on two datasets and compare it to the state of the art in fact-checking and time-point prediction. Our results suggest that TEMPORALFC outperforms the state of the art on the fact-checking task by 0.13 to 0.15 in terms of Area Under the Receiver Operating Characteristic curve and on the time-point prediction task by 0.25 to 0.27 in terms of Mean Reciprocal Rank. Our code is open-source and can be found at https://github.com/dice-group/TemporalFC.}}, author = {{Qudus, Umair and Röder, Michael and Kirrane, Sabrina and Ngomo, Axel-Cyrille Ngonga}}, booktitle = {{The Semantic Web – ISWC 2023}}, editor = {{R. Payne, Terry and Presutti, Valentina and Qi, Guilin and Poveda-Villalón, María and Stoilos, Giorgos and Hollink, Laura and Kaoudi, Zoi and Cheng, Gong and Li, Juanzi}}, isbn = {{9783031472398}}, issn = {{0302-9743}}, keywords = {{temporal fact checking · ensemble learning · transfer learning · time-point prediction · temporal knowledge graphs}}, location = {{Athens, Greece}}, pages = {{465–483}}, publisher = {{Springer, Cham}}, title = {{{TemporalFC: A Temporal Fact Checking Approach over Knowledge Graphs}}}, doi = {{10.1007/978-3-031-47240-4_25}}, volume = {{14265}}, year = {{2023}}, } @inproceedings{52816, abstract = {{Manufacturing companies face the challenge of reaching required quality standards. Using optical sensors and deep learning might help. However, training deep learning algorithms require large amounts of visual training data. Using domain randomization to generate synthetic image data can alleviate this bottleneck. This paper presents the application of synthetic image training data for optical quality inspections using visual sensor technology. The results show synthetically generated training data are appropriate for visual quality inspections.}}, author = {{Gräßler, Iris and Hieb, Michael}}, booktitle = {{Lectures}}, keywords = {{synthetic training data, machine vision quality gates, deep learning, automated inspection and quality control, production control}}, location = {{Nuremberg}}, pages = {{253--524}}, publisher = {{AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany}}, title = {{{Creating Synthetic Training Datasets for Inspection in Machine Vision Quality Gates in Manufacturing}}}, doi = {{10.5162/smsi2023/d7.4}}, year = {{2023}}, } @article{49434, abstract = {{Die Rollenspiel-Methode ist ein handlungs- und anwendungsbezogenes Instrument, um Studierende bereits während der universitären Ausbildung für unterschiedliche professionelle Sicht- und Handlungsweisen zu sensibilisieren. In diesem Sinne stellt der folgende Beitrag ein Rollenspiel vor, welches als hochschuldidaktisches Material für die inklusionssensible Lehrer*innenbildung genutzt werden kann und Studierende auf zukünftige multiprofessionelle Kooperationshandlungen in der schulischen Praxis vorbereiten soll. Dieses bietet einen geeigneten Anlass, um die professionsübergreifende Zusammenarbeit „gefahrlos“ im Rahmen einer fiktiven kollegialen Fallkonferenz zu erproben sowie unterschiedliche pädagogische Professionsverständnisse aufzudecken und zu reflektieren. Darüber hinaus werden erste Durchführungserfahrungen und Evaluationsergebnisse diskutiert, die im Zuge der wissenschaftlichen Begleitforschung der Teilmaßnahme „Multiprofessionelle Kooperation in inklusiven Ganztagsschulen“ des Bielefelder QLB-Projekts BiProfessional erhoben wurden.}}, author = {{Schuldt, Alessa and Palm, Manfred and Neumann, Phillip and Böhm-Kasper, Oliver and Demmer, Christine and Lütje-Klose, Birgit}}, journal = {{Zeitschrift für Konzepte Und Arbeitsmaterialien für Lehrer*innenbildung Und Unterricht}}, keywords = {{Rollenspiel, mulitprofessionelle Kooperation, inklusionssensible Lehrerbildung, Hochschuldidaktik, Blended Learning}}, number = {{4}}, title = {{{„Jede*r von uns sieht die Situation eben unterschiedlich – das ist zwar eine Schwierigkeit, aber auch eine Bereicherung“}}}, doi = {{10.11576/DIMAWE-6699}}, volume = {{5}}, year = {{2023}}, } @article{39976, abstract = {{The context of the study is the increasing digitalisation of the living environment of primary school students, which is to be introduced into primary schools according to theoretical and educational policy guidelines. In this regard, further teacher training on digital media in classrooms are particularly relevant, on the one hand to promote teachers’ digital-related pedagogical knowledge and content knowledge (DPaCK). On the other hand, studies also reveal positive correlations among teacher training, teaching activities, and students’ learning outcomes. In-service teacher training courses with adaptive support by a trainer in particular have proven to be effective. Against the background of various research studies on professional development of teachers, a corresponding model of tripartite learning outcomes has been established and serves as a broad theoretical framework. However, the specific relationship between in-service teacher training with adaptive support, DPaCK, and computational thinking of primary school students in the context of the German primary school subject Sachunterricht has not been sufficiently studied. Therefore, the following research questions can be derived: (1) To what extent does training with adaptive support on the topic of learning robots contribute to the development of teachers’ DPaCK? (2) Which effects can be ascertained on the students’ computational thinking in technology-related Sachunterricht? To investigate this relationship, an intervention study in a pre-post design with an experimental group, a control group, and a baseline is appropriate. As results are not yet available at this point, the present paper focuses on the presentation of the theoretical background and empirical approaches.}}, author = {{Janicki, Nicole and Tenberge, Claudia}}, journal = {{Australasian Journal of Technology Education}}, keywords = {{technology education, teacher professionalisation, Computational Thinking, digitalization, learning robots}}, title = {{{Technology education in elementary school using the example of 'learning robots' – development and evaluation of an in-service teacher training concept}}}, doi = {{https://doi.org/10.15663/ajte.v9.i0.103}}, volume = {{9}}, year = {{2023}}, } @inproceedings{31849, author = {{Hoffmann, Max and Biehler, Rolf}}, booktitle = {{Proceedings of the Fourth Conference of the International Network for Didactic Research in University Mathematics (INDRUM 2022, 19-22 October 2022)}}, editor = {{Trigueros, Marı́a and Barquero, Berta and Hochmuth, Reinhard and Peters, Jana}}, keywords = {{Teaching and learning of specific topics in university mathematics, Transition to, across and from university mathematics, Student Teachers, Geometry, Congruence, Double Discontinuity.}}, publisher = {{University of Hannover and INDRUM.}}, title = {{{Student Teachers ’ Knowledge of Congruence before a University Course on Geometry}}}, year = {{2023}}, } @article{45299, abstract = {{Many applications are driven by Machine Learning (ML) today. While complex ML models lead to an accurate prediction, their inner decision-making is obfuscated. However, especially for high-stakes decisions, interpretability and explainability of the model are necessary. Therefore, we develop a holistic interpretability and explainability framework (HIEF) to objectively describe and evaluate an intelligent system’s explainable AI (XAI) capacities. This guides data scientists to create more transparent models. To evaluate our framework, we analyse 50 real estate appraisal papers to ensure the robustness of HIEF. Additionally, we identify six typical types of intelligent systems, so-called archetypes, which range from explanatory to predictive, and demonstrate how researchers can use the framework to identify blind-spot topics in their domain. Finally, regarding comprehensiveness, we used a random sample of six intelligent systems and conducted an applicability check to provide external validity.}}, author = {{Kucklick, Jan-Peter}}, issn = {{1246-0125}}, journal = {{Journal of Decision Systems}}, keywords = {{Explainable AI (XAI), machine learning, interpretability, real estate appraisal, framework, taxonomy}}, pages = {{1--41}}, publisher = {{Taylor & Francis}}, title = {{{HIEF: a holistic interpretability and explainability framework}}}, doi = {{10.1080/12460125.2023.2207268}}, year = {{2023}}, } @inproceedings{33734, abstract = {{Many applications require explainable node classification in knowledge graphs. Towards this end, a popular ``white-box'' approach is class expression learning: Given sets of positive and negative nodes, class expressions in description logics are learned that separate positive from negative nodes. Most existing approaches are search-based approaches generating many candidate class expressions and selecting the best one. However, they often take a long time to find suitable class expressions. In this paper, we cast class expression learning as a translation problem and propose a new family of class expression learning approaches which we dub neural class expression synthesizers. Training examples are ``translated'' into class expressions in a fashion akin to machine translation. Consequently, our synthesizers are not subject to the runtime limitations of search-based approaches. We study three instances of this novel family of approaches based on LSTMs, GRUs, and set transformers, respectively. An evaluation of our approach on four benchmark datasets suggests that it can effectively synthesize high-quality class expressions with respect to the input examples in approximately one second on average. Moreover, a comparison to state-of-the-art approaches suggests that we achieve better F-measures on large datasets. For reproducibility purposes, we provide our implementation as well as pretrained models in our public GitHub repository at https://github.com/dice-group/NeuralClassExpressionSynthesis}}, author = {{KOUAGOU, N'Dah Jean and Heindorf, Stefan and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023)}}, editor = {{Pesquita, Catia and Jimenez-Ruiz, Ernesto and McCusker, Jamie and Faria, Daniel and Dragoni, Mauro and Dimou, Anastasia and Troncy, Raphael and Hertling, Sven}}, keywords = {{Neural network, Concept learning, Description logics}}, location = {{Hersonissos, Crete, Greece}}, pages = {{209 -- 226}}, publisher = {{Springer International Publishing}}, title = {{{Neural Class Expression Synthesis}}}, doi = {{https://doi.org/10.1007/978-3-031-33455-9_13}}, volume = {{13870}}, year = {{2023}}, } @article{29240, abstract = {{The principle of least action is one of the most fundamental physical principle. It says that among all possible motions connecting two points in a phase space, the system will exhibit those motions which extremise an action functional. Many qualitative features of dynamical systems, such as the presence of conservation laws and energy balance equations, are related to the existence of an action functional. Incorporating variational structure into learning algorithms for dynamical systems is, therefore, crucial in order to make sure that the learned model shares important features with the exact physical system. In this paper we show how to incorporate variational principles into trajectory predictions of learned dynamical systems. The novelty of this work is that (1) our technique relies only on discrete position data of observed trajectories. Velocities or conjugate momenta do not need to be observed or approximated and no prior knowledge about the form of the variational principle is assumed. Instead, they are recovered using backward error analysis. (2) Moreover, our technique compensates discretisation errors when trajectories are computed from the learned system. This is important when moderate to large step-sizes are used and high accuracy is required. For this, we introduce and rigorously analyse the concept of inverse modified Lagrangians by developing an inverse version of variational backward error analysis. (3) Finally, we introduce a method to perform system identification from position observations only, based on variational backward error analysis.}}, author = {{Ober-Blöbaum, Sina and Offen, Christian}}, issn = {{0377-0427}}, journal = {{Journal of Computational and Applied Mathematics}}, keywords = {{Lagrangian learning, variational backward error analysis, modified Lagrangian, variational integrators, physics informed learning}}, pages = {{114780}}, publisher = {{Elsevier}}, title = {{{Variational Learning of Euler–Lagrange Dynamics from Data}}}, doi = {{10.1016/j.cam.2022.114780}}, volume = {{421}}, year = {{2023}}, } @inproceedings{60304, abstract = {{The focus towards multi-material and lightweight assemblies, driven by legal requirements on reducing emissions and energy consumptions, reveals important drawbacks and disadvantages of established joining processes, such as welding. In this context, mechanical joining technologies, such as clinching, are becoming more and more relevant especially in the automotive industry. However, the availability of only few standards and almost none systematic design methods causes a still very time- and cost-intensive assembly development process considering mainly expert knowledge and a considerable amount of experimental studies. Motivated by this, the presented work introduces a novel approach for the methodical design and dimensioning of mechanically clinched assemblies. Therefore, the utilization of regression models, such as machine learning algorithms, combined with manufacturing knowledge ensures a reliable estimation of individual clinched joint characteristics. In addition, the implementation of an engineering workbench enables the following data-driven and knowledge-based generation of high-quality initial assembly designs already in early product development phases. In a subsequent analysis and adjustment, these designs are being improved while guaranteeing joining safety and loading conformity. The presented results indicate that the methodological approach can pave the way to a more systematic design process of mechanical joining assemblies, which can significantly shorten the required number of iteration loops and therefore the product development time.}}, author = {{Zirngibl, Christoph and Martin, Sven and Steinfelder, Christian and Schleich, Benjamin and Tröster, Thomas and Brosius, Alexander and Wartzack, Sandro}}, booktitle = {{Materials Research Proceedings}}, issn = {{2474-395X}}, keywords = {{Joining, Structural Analysis, Machine Learning}}, location = {{Erlangen-Nürnberg}}, publisher = {{Materials Research Forum LLC}}, title = {{{Methodical approach for the design and dimensioning of mechanical clinched assemblies}}}, doi = {{10.21741/9781644902417-23}}, volume = {{25}}, year = {{2023}}, } @inproceedings{30236, abstract = {{Recent reinforcement learning approaches for continuous control in wireless mobile networks have shown impressive results. But due to the lack of open and compatible simulators, authors typically create their own simulation environments for training and evaluation. This is cumbersome and time-consuming for authors and limits reproducibility and comparability, ultimately impeding progress in the field. To this end, we propose mobile-env, a simple and open platform for training, evaluating, and comparing reinforcement learning and conventional approaches for continuous control in mobile wireless networks. mobile-env is lightweight and implements the common OpenAI Gym interface and additional wrappers, which allows connecting virtually any single-agent or multi-agent reinforcement learning framework to the environment. While mobile-env provides sensible default values and can be used out of the box, it also has many configuration options and is easy to extend. We therefore believe mobile-env to be a valuable platform for driving meaningful progress in autonomous coordination of wireless mobile networks.}}, author = {{Schneider, Stefan Balthasar and Werner, Stefan and Khalili, Ramin and Hecker, Artur and Karl, Holger}}, booktitle = {{IEEE/IFIP Network Operations and Management Symposium (NOMS)}}, keywords = {{wireless mobile networks, network management, continuous control, cognitive networks, autonomous coordination, reinforcement learning, gym environment, simulation, open source}}, location = {{Budapest}}, publisher = {{IEEE}}, title = {{{mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks}}}, year = {{2022}}, } @book{30291, abstract = {{The volume comprises a variety of research approaches that seek to explore and understand employees’ learning and development through and for work. Working life reveals challenges through technological, economic and societal development that can only rudimentarily be addressed by formal education and training. Workplace learning becomes more and more important for employees and enterprises to successfully cope with these challenges. Workplace learning is a steadily growing field of educational research but it lacks so far a scholastic canon – there is rather a diversity of research approaches. This volume reflects this diversity by bringing together researchers from different countries and different theoretical backgrounds, presenting their current research on topics that all are relevant for understanding presages, processes and outcomes of workplace learning. Hence, this volume is of relevance for researchers as well as practitioners in the field and policy makers.}}, editor = {{Harteis, Christian and Gijbels, David and Kyndt, Eva}}, isbn = {{9783030895815}}, issn = {{2210-5549}}, keywords = {{new generation of researchersthe team level of workplace learningindividual level of workplace learningorganizational level of workplace learningsocietal level of workplace learninginterdependent cross-level research approachesWork AgencyWork-life perspectivesTeam learningTeam climateSocial influences on team learningKnowledge construction in teamsLearning cultureAcknowledgement of competencesTechnology and professional learningCreation of a learning eco-systemDiversity as a challenge for organisationsHigher education as preparation for WPLSocial support in networks and professional learningvocational and professional education}}, publisher = {{Springer International Publishing}}, title = {{{Research Approaches on Workplace Learning}}}, doi = {{10.1007/978-3-030-89582-2}}, year = {{2022}}, } @inbook{30289, abstract = {{This chapter presents a discussion of the concept of agency. Agency is understood as a multifaceted construct describing the idea that human beings make choices, act on these choices, and thereby exercise influence on their own lives as well as their environment. We argue that the concept is discussed from three different perspectives in the literature—transformational, dispositional, and relational—that are each related to learning and development in work contexts. These perspectives do not reflect incompatible positions but rather different aspects of the same phenomena. The chapter also offers an avenue of insight into empirical studies that employ agency as a central concept as well as discussions about concepts that closely overlap with ideas of human beings as agents of power and influence.}}, author = {{Goller, Michael and Paloniemi, Susanna}}, booktitle = {{Research Approaches on Workplace Learning}}, isbn = {{9783030895815}}, issn = {{2210-5549}}, keywords = {{Agency Workplace learning Professional development Proactivity Self-direction}}, publisher = {{Springer International Publishing}}, title = {{{Agency: Taking Stock of Workplace Learning Research}}}, doi = {{10.1007/978-3-030-89582-2_1}}, year = {{2022}}, } @inproceedings{34140, abstract = {{In this paper, machine learning techniques will be used to classify different PCB layouts given their electromagnetic frequency spectra. These spectra result from a simulated near-field measurement of electric field strengths at different locations. Measured values consist of real and imaginary parts (amplitude and phase) in X, Y and Z directions. Training data was obtained in the time domain by varying transmission line geometries (size, distance and signaling). It was then transformed into the frequency domain and used as deep neural network input. Principal component analysis was applied to reduce the sample dimension. The results show that classifying different designs is possible with high accuracy based on synthetic data. Future work comprises measurements of real, custom-made PCB with varying parameters to adapt the simulation model and also test the neural network. Finally, the trained model could be used to give hints about the error’s cause when overshooting EMC limits.}}, author = {{Maalouly, Jad and Hemker, Dennis and Hedayat, Christian and Rückert, Christian and Kaufmann, Ivan and Olbrich, Marcel and Lange, Sven and Mathis, Harald}}, booktitle = {{2022 Kleinheubach Conference}}, keywords = {{emc, pcb, electronic system development, machine learning, neural network}}, location = {{Miltenberg, Germany}}, publisher = {{IEEE}}, title = {{{AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic System Development}}}, year = {{2022}}, } @inproceedings{33510, abstract = {{In the manufacture of real wood products, defects can quickly occur during the production process. To quickly sort out these defects, a system is needed that finds damage in the irregularly structured surfaces of the product. The difficulty in this task is that each surface is visually different and no standard defects can be defined. Thus, damage detection using correlation does not work, so this paper will test different machine learning methods. To evaluate different machine learning methods, a data set is needed. For this reason, the available samples were recorded manually using a static fixed camera. Subsequently, the images were divided into sub-images, which resulted in a relatively small data set. Next, a convolutional neural network (CNN) was constructed to classify the images. However, this approach did not lead to a generalized solution, so the dataset was hashed using the a- and pHash. These hash values were then trained with a fully supervised system that will later serve as a reference model, in the semi-supervised learning procedures. To improve the supervised model and not have to label every data point, semi-supervised learning methods are used in the following. For this purpose, the CEAL method (wrapper method) is considered in the first and then the Π-Model (intrinsically semi-supervised).}}, author = {{Sander, Tom and Lange, Sven and Hilleringmann, Ulrich and Geneiß, Volker and Hedayat, Christian and Kuhn, Harald}}, booktitle = {{2022 Smart Systems Integration (SSI)}}, keywords = {{Machine Learning, CNN, Hashing, semi-supervised learning}}, location = {{Grenoble, France}}, publisher = {{IEEE}}, title = {{{Detection of Defects on Irregularly Structured Surfaces using Supervised and Semi-Supervised Learning Methods}}}, doi = {{10.1109/ssi56489.2022.9901433}}, year = {{2022}}, } @article{44529, abstract = {{According to the German Rectors’ Conference (HRK), German higher education teaching fails to meet the demand to integrate competence-oriented learning objectives. Despite a wide-ranging debate on the use of learning objectives, empirical research on their effectiveness is scarce. The present study uses the features of digital teaching platforms to investigate the perception and effectiveness of learning objectives applying a randomised controlled experiment followed by a survey in a course for undergraduate economics students (N = 30). Controlling group preconditions and the treatment effect allows to draw conclusions about the different learning outcomes of the student groups. The specification of behaviour-oriented learning objectives in the online course system leads to significantly better performance in the treatment group. A stronger perception of the learning objectives in the treatment group supports this effect that remains significant in a regression analysis. Thus, the study provides an empirical justification to integrate learning objectives in university teaching.}}, author = {{Auer, Thorsten Fabian}}, issn = {{2199-8825}}, journal = {{die hochschullehre}}, keywords = {{learning objectives, academic performance, perception, teaching methods, experiment}}, number = {{1}}, pages = {{662--675}}, title = {{{Die Wirksamkeit von Lernzielen für Studienleistungen – eine experimentelle Studie}}}, doi = {{http://doi.org/10.3278/HSL2248W}}, volume = {{8}}, year = {{2022}}, } @article{48878, abstract = {{Due to the rise of continuous data-generating applications, analyzing data streams has gained increasing attention over the past decades. A core research area in stream data is stream classification, which categorizes or detects data points within an evolving stream of observations. Areas of stream classification are diverse\textemdash ranging, e.g., from monitoring sensor data to analyzing a wide range of (social) media applications. Research in stream classification is related to developing methods that adapt to the changing and potentially volatile data stream. It focuses on individual aspects of the stream classification pipeline, e.g., designing suitable algorithm architectures, an efficient train and test procedure, or detecting so-called concept drifts. As a result of the many different research questions and strands, the field is challenging to grasp, especially for beginners. This survey explores, summarizes, and categorizes work within the domain of stream classification and identifies core research threads over the past few years. It is structured based on the stream classification process to facilitate coordination within this complex topic, including common application scenarios and benchmarking data sets. Thus, both newcomers to the field and experts who want to widen their scope can gain (additional) insight into this research area and find starting points and pointers to more in-depth literature on specific issues and research directions in the field.}}, author = {{Clever, Lena and Pohl, Janina Susanne and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}}, issn = {{2076-3417}}, journal = {{Applied Sciences}}, keywords = {{big data, data mining, data stream analysis, machine learning, stream classification, supervised learning}}, number = {{18}}, pages = {{9094}}, publisher = {{{Multidisciplinary Digital Publishing Institute}}}, title = {{{Process-Oriented Stream Classification Pipeline: A Literature Review}}}, doi = {{10.3390/app12189094}}, volume = {{12}}, year = {{2022}}, } @article{35111, abstract = {{Forschendes Lernen in der Lehrer:innenbildung ist seit der Ausweitung schulpraktischer Anteile bzw. der Einführung des sogenannten Praxissemesters eng verwoben mit der Lehrer:innenausbildung. Gleichzeitig wird bisher weitestgehend different beantwortet, was Forschendes Lernen ist und sein kann sowie warum es wie hochschuldidaktisch gerahmt wird und werden sollte. Der Beitrag widmet sich dieser Frage. Dabei zeigt sich einerseits, dass hochschuldidaktische Zugänge zur Realisierung Forschenden Lernens in der Lehrer:innenbildung erst vor dem Hintergrund theoretischer Annahmen zur Entwicklung von Lehrpersonen und deren Professionalität sowie zur Gestalt - gemeint ist hier der Beitrag zu dieser Entwicklung sowie der Anteil an Professionalität - Forschenden Lernens entwickelt werden können, eine solche Fundierung aber oftmals ausbleibt. Andererseits wird herausgearbeitet, inwiefern eine Differenz zwischen wissenschaftlicher Forschung und Forschung im Forschenden Lernen besteht. Daran anschließend wird eine habitustheoretische Fundierung Forschenden Lernens vorgestellt und es werden exemplarisch deren Implikationen für die Gestaltung Forschenden Lernens benannt. Abschließend wird anhand empirischer Rekonstruktionen beispielhaft eine praktische Umsetzung diskutiert.}}, author = {{Bloh, Thiemo and Caruso, Carina}}, issn = {{2199-8825}}, journal = {{die hochschullehre}}, keywords = {{Forschendes Lernen, Lehrer:innenausbildung, Praxissemester, Professionalisierung / Research-based learning, teacher education, internships, professionalization}}, number = {{21}}, pages = {{299–312}}, publisher = {{wbv }}, title = {{{Ein kritisch-multiperspektivischer Blick auf Forschendes Lernen in der Lehrkräftebildung}}}, doi = {{10.3278/HSL2221W}}, volume = {{8}}, year = {{2022}}, }