@inproceedings{52514, author = {{Berndt, Axel}}, booktitle = {{{Digitale Methoden in den Geisteswissenschaften. Herausforderungen und Chancen}}}, title = {{{Mozart-Dekonstruktionen: Genese einer KI-generierten Konzertmusik}}}, year = {{2024}}, } @article{56236, abstract = {{AbstractBackgroundReal‐world problems are important in math instruction, but they do not necessarily trigger students' task motivation. Personalizing real‐world problems by (1) matching problems to students' shared living environment (context personalization) and (2) asking students to pose their own problems (active personalization) might be two interventions to increase students' task motivation.AimIn the current study, we investigated the effects of context personalization and active personalization on students' self‐efficacy expectations, intrinsic value, attainment value, utility value, and cost.SampleThe participants were 28 fifth‐ and sixth‐grade students who voluntarily took part in a six‐month afterschool program in which they posed problems with the aim of creating a math walk in their hometown.MethodUsing a within‐subjects design, at the end of the afterschool program, the students rated their self‐efficacy expectations and task values for four self‐developed problems associated with their hometown, four peer‐developed problems associated with their hometown, and four instructor‐provided problems associated with unfamiliar locations.ResultsStudents reported higher self‐efficacy expectations, intrinsic value, attainment value, and utility value for active‐personalized than non‐personalized problems. To a lesser extent, context personalization promoted intrinsic value and attainment value. No effect was found for cost.ConclusionsActive personalization (i.e. asking students to pose their own real‐world problems) is suited to enhance students' task motivation, specifically their self‐efficacy expectations, intrinsic value, attainment value, and utility value. Context personalization still boosts students' intrinsic value and attainment value. Implementation in classroom instruction is discussed.}}, author = {{Schönherr, Johanna}}, issn = {{0007-0998}}, journal = {{British Journal of Educational Psychology}}, number = {{2}}, pages = {{407--424}}, publisher = {{Wiley}}, title = {{{Personalizing real‐world problems: Posing own problems increases self‐efficacy expectations, intrinsic value, attainment value, and utility value}}}, doi = {{10.1111/bjep.12653}}, volume = {{94}}, year = {{2024}}, } @inproceedings{56159, author = {{Peñaloza, Rafael and Turhan, Anni-Yasmin}}, booktitle = {{Proceedings of the 37th International Workshop on Description Logics (DL 2024), Bergen, Norway, June 18-21, 2024}}, editor = {{Giordano, Laura and Jung, Jean Christoph and Ozaki, Ana}}, publisher = {{CEUR-WS.org}}, title = {{{Rough, Rougher, Roughest: Extending EL with a Hierarchy of Indiscernibility Relations}}}, volume = {{3739}}, year = {{2024}}, } @article{54836, author = {{Schönherr, Johanna and Schukajlow, Stanislaw}}, issn = {{0742-051X}}, journal = {{Teaching and Teacher Education}}, publisher = {{Elsevier BV}}, title = {{{Preservice teachers' judgments of students’ expectations of success and task values: Close relations with their personal task motivation}}}, doi = {{10.1016/j.tate.2024.104659}}, volume = {{148}}, year = {{2024}}, } @article{56235, author = {{Schönherr, Johanna and Strohmaier, Anselm R. and Schukajlow, Stanislaw}}, issn = {{1747-938X}}, journal = {{Educational Research Review}}, publisher = {{Elsevier BV}}, title = {{{Learning with visualizations helps: A meta-analysis of visualization interventions in mathematics education}}}, doi = {{10.1016/j.edurev.2024.100639}}, volume = {{45}}, year = {{2024}}, } @inbook{56237, author = {{Schönherr, Johanna and Mayer, Richard E.}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783031712906}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Anxiety Moderates the Effects of Drawing Support on Drawing Accuracy in Mathematical Modeling}}}, doi = {{10.1007/978-3-031-71291-3_26}}, year = {{2024}}, } @misc{56588, author = {{Breuer, Saskia Rebecca}}, booktitle = {{Encyclopedia of the Bible and its Reception}}, title = {{{Poverty/Poor}}}, year = {{2024}}, } @inbook{60061, author = {{Vukadinovic, Vojin Sasa}}, booktitle = {{Die Wir-gegen-die-Gesellschaft. Warum der von Arthur M. Schlesinger vor 30 Jahren diagnostizierte Samen der identitätspolitischen Spaltung aufgegangen ist}}, editor = {{Kostner, Sandra}}, pages = {{87--94}}, title = {{{Vom Benutzen der Historie für Ideologeme der Gegenwart. Anmerkungen zu Schlesingers Überlegungen zum „Kampf der Schulen“}}}, year = {{2024}}, } @inbook{60067, author = {{Vukadinovic, Vojin Sasa}}, booktitle = {{Siebter Oktober Dreiundzwanzig}}, editor = {{Vukadinović, Vojin Saša}}, isbn = {{978-3-96042-142-3}}, pages = {{11--38}}, publisher = {{Querverlag}}, title = {{{Siebter Oktober Dreiundzwanzig. Einleitung}}}, year = {{2024}}, } @article{60069, author = {{Bartels, Mette and Vukadinovic, Vojin Sasa}}, journal = {{Ariadne. Forum für Frauen- und Geschlechtergeschichte}}, number = {{80}}, pages = {{1--3}}, publisher = {{Stiftung Archiv der deutschen Frauenbewegung}}, title = {{{Editorial}}}, year = {{2024}}, } @article{60071, author = {{Barberi, Alessandro and Serloth, Barbara and Vukadinovic, Vojin Sasa}}, journal = {{ZUKUNFT. Die Diskussionszeitschrift für Politik, Gesellschaft und Kultur}}, number = {{Nr. 10/2024}}, pages = {{2--4}}, title = {{{Editorial. Siebter Oktober Dreiundzwanzig}}}, year = {{2024}}, } @misc{59832, booktitle = {{ZUKUNFT. Die Diskussionszeitschrift für Politik, Gesellschaft und Kultur}}, editor = {{Barberi, Alessandro and Serloth, Barbara and Vukadinovic, Vojin Sasa}}, isbn = {{978-3-89656-344-6}}, number = {{10}}, title = {{{Siebter Oktober Dreiundzwanzig}}}, year = {{2024}}, } @misc{59829, booktitle = {{Medienimpulse. Beiträge zur Medienpädagogik}}, editor = {{Barberi, Alessandro and Serloth, Barbara and Vukadinovic, Vojin Sasa}}, title = {{{Antisemitismus}}}, volume = {{ 3/2024}}, year = {{2024}}, } @book{60078, author = {{Hilgert, Joachim}}, isbn = {{9783662694114}}, issn = {{2731-3824}}, publisher = {{Springer Berlin Heidelberg}}, title = {{{Mathematical Structures}}}, doi = {{10.1007/978-3-662-69412-1}}, year = {{2024}}, } @inbook{60072, author = {{Vukadinovic, Vojin Sasa}}, booktitle = {{Freiheit ist keine Metapher. Antisemitismus, Migration, Rassismus, Religionskritik}}, editor = {{Vukadinović, Vojin Saša}}, isbn = {{978-3-96042-136-2}}, pages = {{9--17}}, publisher = {{Querverlag}}, title = {{{Vorwort zur zweiten Auflage}}}, year = {{2024}}, } @article{60047, abstract = {{Abstract Purpose Cardiopulmonary exercise testing (CPET) is considered the gold standard for assessing cardiorespiratory fitness. To ensure consistent performance of each test, it is necessary to adapt the power increase of the test protocol to the physical characteristics of each individual. This study aimed to use machine learning models to determine individualized ramp protocols based on non-exercise features. We hypothesized that machine learning models will predict peak oxygen uptake ($$\dot{V}$$ V ˙ O2peak) and peak power output (PPO) more accurately than conventional multiple linear regression (MLR). Methods The cross-sectional study was conducted with 274 (♀168, ♂106) participants who performed CPET on a cycle ergometer. Machine learning models and multiple linear regression were used to predict $$\dot{V}$$ V ˙ O2peak and PPO using non-exercise features. The accuracy of the models was compared using criteria such as root mean square error (RMSE). Shapley additive explanation (SHAP) was applied to determine the feature importance. Results The most accurate machine learning model was the random forest (RMSE: 6.52 ml/kg/min [95% CI 5.21–8.17]) for $$\dot{V}$$ V ˙ O2peak prediction and the gradient boosting regression (RMSE: 43watts [95% CI 35–52]) for PPO prediction. Compared to the MLR, the machine learning models reduced the RMSE by up to 28% and 22% for prediction of $$\dot{V}$$ V ˙ O2peak and PPO, respectively. Furthermore, SHAP ranked body composition data such as skeletal muscle mass and extracellular water as the most impactful features. Conclusion Machine learning models predict $$\dot{V}$$ V ˙ O2peak and PPO more accurately than MLR and can be used to individualize CPET protocols. Features that provide information about the participant's body composition contribute most to the improvement of these predictions. Trial registration number DRKS00031401 (6 March 2023, retrospectively registered). }}, author = {{Wenzel, Charlotte and Liebig, Thomas and Swoboda, Adrian and Smolareck, Rika and Schlagheck, Marit Lea and Walzik, David and Groll, Andreas and Goulding, Richie P. and Zimmer, Philipp}}, issn = {{1439-6319}}, journal = {{European Journal of Applied Physiology}}, number = {{11}}, pages = {{3421--3431}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Machine learning predicts peak oxygen uptake and peak power output for customizing cardiopulmonary exercise testing using non-exercise features}}}, doi = {{10.1007/s00421-024-05543-x}}, volume = {{124}}, year = {{2024}}, } @misc{60086, author = {{Divkovic, Denis and Kirschbaum, Julia and Rahlf, Henning Christoph and Knorr, Lukas and Meschede, Henning}}, publisher = {{International Conference on Smart Energy Systems}}, title = {{{Optimising Heat Planning: Cost effective heat planning for low carbon district heating}}}, year = {{2024}}, } @article{60092, abstract = {{Abstract Purpose Research supports physical activity as a method to heighten stress resistance and resilience through positive metabolic alterations mostly affecting the neuroendocrine system. High-intensity interval training (HIIT) has been proposed as a highly effective time-saving method to induce those changes. However, existing literature relies heavily on cross-sectional analyses, with few randomised controlled trials highlighting the necessity for more exercise interventions. Thus, this study aims to investigate the effects of HIIT versus an active control group on the stress response to an acute psychosocial stressor in emotionally impulsive humans (suggested as being strong stress responders). Methods The study protocol was registered online (DRKS00016589) before data collection. Sedentary, emotionally impulsive adults (30.69 ± 8.20 y) were recruited for a supervised intervention of 8 weeks and randomly allocated to either a HIIT (n = 25) or a stretching group (n = 19, acting as active controls). Participants were submitted to a test battery, including saliva samples, questionnaires (self-efficacy- and perceived stress-related), visual analogue scales (physical exercise- and stress-related), and resting electroencephalography and electrocardiography assessing their reaction to an acute psychological stressor (Trier Social Stress Test) before and after the exercise intervention. Results HIIT increased aerobic fitness in all participants, whereas stretching did not. Participants from the HIIT group reported perceiving exercising more intensively than those from the active control group (ƞp2 = 0.108, p = 0.038). No further group differences were detected. Both interventions largely increased levels of joy post-TSST (ƞp2 = 0.209, p = 0.003) whilst decreasing tension (ƞp2 = 0.262, p < 0.001) and worries (ƞp2 = 0.113, p = 0.037). Finally, both interventions largely increased perceived levels of general self-efficacy (ƞp2 = 0.120, p = 0.029). Conclusion This study suggests that 8 weeks of HIIT does not change the psychoneuroendocrine response to an acute psychological stress test compared to an active control group in emotionally impulsive humans. Further replications of supervised exercise studies highly powered with active and passive controls are warranted. }}, author = {{Javelle, F. and Bloch, W. and Borges, U. and Burberg, T. and Collins, B. and Gunasekara, N. and Hosang, T. J. and Jacobsen, T. and Laborde, S. and Löw, A. and Schenk, A. and Schlagheck, Marit Lea and Schoser, D. and Vogel, A. and Walzik, D. and Zimmer, P.}}, issn = {{1439-6319}}, journal = {{European Journal of Applied Physiology}}, number = {{10}}, pages = {{2893--2908}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Eight weeks of high-intensity interval training versus stretching do not change the psychoneuroendocrine response to a social stress test in emotionally impulsive humans}}}, doi = {{10.1007/s00421-024-05471-w}}, volume = {{124}}, year = {{2024}}, } @article{60093, author = {{Proschinger, Sebastian and Belen, Sergen and Adammek, Frederike and Schlagheck, Marit Lea and Rademacher, Annette and Schenk, Alexander and Warnke, Clemens and Bloch, Wilhelm and Zimmer, Philipp}}, issn = {{0889-1591}}, journal = {{Brain, Behavior, and Immunity}}, pages = {{397--408}}, publisher = {{Elsevier BV}}, title = {{{Sportizumab – Multimodal progressive exercise over 10 weeks decreases Th17 frequency and CD49d expression on CD8+ T cells in relapsing-remitting multiple sclerosis: A randomized controlled trial}}}, doi = {{10.1016/j.bbi.2024.12.017}}, volume = {{124}}, year = {{2024}}, } @article{60094, author = {{Patt, Nadine and Kupjetz, Marie and Schlagheck, Marit Lea and Hersche, Ruth and Joisten, Niklas and Kool, Jan and Gonzenbach, Roman and Nigg, Claudio R. and Zimmer, Philipp and Bansi, Jens}}, issn = {{2211-0348}}, journal = {{Multiple Sclerosis and Related Disorders}}, publisher = {{Elsevier BV}}, title = {{{Predictors of six-month change in health-related quality of life in people with multiple sclerosis: A secondary data analysis of a randomized controlled trial}}}, doi = {{10.1016/j.msard.2024.105826}}, volume = {{90}}, year = {{2024}}, }