Response inhibition for the basketball pump fake - Wickemeyer, Carolin; Güldenpenning, Iris; Weigelt, Matthias (Book of Abstracts of the 66th Conference of Experimental Psychologists)

Dielectric metasurface for wave-vector variant and circular polarization dependent transmission - Wetter, Helene; Gao, Wenlong; Rehberg, Falk; Wingenbach, Jan; Schumacher, Stefan; Zentgraf, Thomas (Proceedings of The 14th International Conference on Metamaterials, Photonic Crystals and Plasmonics)

Auditory Pointers: Spatial Navigation with Sounds - Berndt, Axel ({Forschungskolloquium zur Audiokommunikation})

Mozart-Dekonstruktionen: Genese einer KI-generierten Konzertmusik - Berndt, Axel ({Digitale Methoden in den Geisteswissenschaften. Herausforderungen und Chancen})

Personalizing real‐world problems: Posing own problems increases self‐efficacy expectations, intrinsic value, attainment value, and utility value - Schönherr, Johanna (British Journal of Educational Psychology)
Abstract Background Real‐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. Aim In 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. Sample The 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. Method Using 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. Results Students 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. Conclusions Active 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.

Rough, Rougher, Roughest: Extending EL with a Hierarchy of Indiscernibility Relations - Peñaloza, Rafael; Turhan, Anni-Yasmin (Proceedings of the 37th International Workshop on Description Logics (DL 2024), Bergen, Norway, June 18-21, 2024)

Preservice teachers' judgments of students’ expectations of success and task values: Close relations with their personal task motivation - Schönherr, Johanna; Schukajlow, Stanislaw (Teaching and Teacher Education)

Learning with visualizations helps: A meta-analysis of visualization interventions in mathematics education - Schönherr, Johanna; Strohmaier, Anselm R.; Schukajlow, Stanislaw (Educational Research Review)

Anxiety Moderates the Effects of Drawing Support on Drawing Accuracy in Mathematical Modeling - Schönherr, Johanna; Mayer, Richard E. (Lecture Notes in Computer Science)

Poverty/Poor - Breuer, Saskia Rebecca (Encyclopedia of the Bible and its Reception)

Vom Benutzen der Historie für Ideologeme der Gegenwart. Anmerkungen zu Schlesingers Überlegungen zum „Kampf der Schulen“ - Vukadinovic, Vojin Sasa (Die Wir-gegen-die-Gesellschaft. Warum der von Arthur M. Schlesinger vor 30 Jahren diagnostizierte Samen der identitätspolitischen Spaltung aufgegangen ist)

Siebter Oktober Dreiundzwanzig. Einleitung - Vukadinovic, Vojin Sasa (Siebter Oktober Dreiundzwanzig)

Editorial - Bartels, Mette; Vukadinovic, Vojin Sasa (Ariadne. Forum für Frauen- und Geschlechtergeschichte)

Editorial. Siebter Oktober Dreiundzwanzig - Barberi, Alessandro; Serloth, Barbara; Vukadinovic, Vojin Sasa (ZUKUNFT. Die Diskussionszeitschrift für Politik, Gesellschaft und Kultur)

Siebter Oktober Dreiundzwanzig (ZUKUNFT. Die Diskussionszeitschrift für Politik, Gesellschaft und Kultur)

Antisemitismus (Medienimpulse. Beiträge zur Medienpädagogik)

Mathematical Structures - Hilgert, Joachim

Vorwort zur zweiten Auflage - Vukadinovic, Vojin Sasa (Freiheit ist keine Metapher. Antisemitismus, Migration, Rassismus, Religionskritik)

Machine learning predicts peak oxygen uptake and peak power output for customizing cardiopulmonary exercise testing using non-exercise features - Wenzel, Charlotte; Liebig, Thomas; Swoboda, Adrian; Smolareck, Rika; Schlagheck, Marit Lea; Walzik, David; Groll, Andreas; Goulding, Richie P.; Zimmer, Philipp (European Journal of Applied Physiology)
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 ˙ O 2peak ) 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 ˙ O 2peak 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 ˙ O 2peak 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 ˙ O 2peak 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 ˙ O 2peak 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).

Optimising Heat Planning: Cost effective heat planning for low carbon district heating - Divkovic, Denis; Kirschbaum, Julia; Rahlf, Henning Christoph; Knorr, Lukas; Meschede, Henning