A deep understanding of mathematical equations and their physical meaning is an important skill for physicists and therefore a central learning goal in university physics education. However, many students approach mathematical expressions in physics in a purely procedural manner, using them as calculation tools without critically evaluating their physical plausibility. Making sense of equations, that is, being able to interpret and evaluate equations within a physical context, is essential for the development of expertlike reasoning in physics. Frequently employed strategies that assist in the sensemaking process are dimensional, covariational, special, and limiting case analysis. Despite their importance, these strategies are rarely taught or practiced explicitly in university physics courses. The present intervention study systematically implemented sensemaking strategies within a university course for first-year physics students covering topics of electro- and magnetostatics as well as electrodynamics. Traditional exercises were enriched with tasks requiring students to critically evaluate the plausibility of self-derived equations. The tasks were integrated into recitation sessions and homework assignments. A total of N=67 physics students participated in the study. A quasiexperimental pre-post design with two groups was employed to evaluate the effectiveness of the intervention approach. Results show that students highly valued all four strategies equally and reported an increased perceived competence in their application after the intervention. Significant improvements in performance were observed for dimensional (d=0.67) as well as for special case analysis (d=0.50). Students’ performance in analyzing covariations only showed a tendency favoring the intervention group. No intervention-related impact on the performance of limiting case analysis was found, suggesting that this strategy may require more extensive practice. The findings underscore the importance and usefulness of explicitly teaching strategies to support students’ sensemaking of physics equations.

Mit Audio Analyzer lassen sich zu akustischen Phänomenen Spektrogramme, Frequenzspektren und Oszillogramme in hervorragender Qualität erzeugen, was quantitative Analysen von Schallereignissen ermöglicht. Hier betrachten wir jedoch niederfrequente elektromagnetische Wellen von Blitzentladungen in der Atmosphäre und schätzen die Höhe der Ionosphäre ab.

Mit Audio Analyzer lassen sich zu akustischen Phänomenen Spektrogramme, Frequenzspektren und Oszillogramme in hervorragender Qualität erzeugen, was quantitative Analysen von Schallereignissen ermöglicht. Hier betrachten wir jedoch niederfrequente elektromagnetische Wellen von Blitzentladungen in der Atmosphäre und schätzen die Höhe der Ionosphäre ab.

The presented data is based on investigations carried out in the framework of the European RFCS (Research Fund for Coal and Steel) funded project HOLLOSSTAB (2016-2019). The campaign's overall goal is presented in more detail in [1] and [2]. The experiments were performed in the Structural Laboratory at the Bundeswehr University Munich to investigate the cross-section behavior of cold-formed square and rectangular hollow sections (SHS and RHS). Two grades of mild and high-strength steel (S355 and S500) and seven section sizes were examined. The profiles cover all four cross-section classes according to EN1993-1-1 [3]. Monotonic stub column, short beam, and long-beam column tests were performed to investigate the load-bearing capacity. The outputs were load-deformation curves for each specimen. The experimental tests were accomplished by digital image correlation (DIC) to obtain an overview of the full deformation field in the specimens. Recalculations with advanced FE-shell simulations, based on scanned specimen geometries (spatial 3D point clouds) and nonlinear material models obtained from tensile coupon tests, were modeled to reproduce the real behavior obtained during the tests.

This study presents an approach to integrate innovative forms of recitation tasks into first-year introductory mechanics, with a primary focus on smartphone-based experimental tasks and additional programming tasks for comparison. Smartphones enable inexpensive physics experiments with digitized first-hand data collection outside lab settings. Such student experiments can enhance homework assignments, breaking down barriers between lectures, recitation groups, and labs, and thereby linking theoretical and experimental aspects of undergraduate physics education. To explore this potential, we implemented and evaluated a sample set of nine smartphone-based experimental tasks and three programming tasks as weekly exercises in a first-year physics course at RWTH Aachen University. Through twelve short surveys involving up to 188 participants, we investigated students' perceptions of learning with the new tasks, focusing on factors such as goal clarity, difficulty, or feasibility at home. In two additional surveys with 108 and 78 participants, students compared the new experimental and programming tasks to each other and to standard recitation tasks based on affective variables. Our findings indicate that the smartphone-based experimental tasks were generally well-suited to the students, which tended to outperform the programming tasks in terms of perceptions of learning with the tasks and affective responses. Overall, students responded positively to the new experimental tasks, with perceptions comparable to, or only partly below those of established standard recitation tasks. Given that most of the experimental tasks were newly implemented "on-the-fly" within a running course, while the standard recitation tasks have been refined over years, these results are encouraging. They suggest that smartphone-based experimental tasks can be successfully integrated into teaching.

While lab courses are an integral part of studying physics aiming at a huge variety of learning objectives, research has shown that typical lab courses do not reach all the desired goals. While diverse approaches by lab instructors and researchers try to increase the effectiveness of lab courses, experimental tasks remain the core of any lab course. To keep an overview of these developments and to give instructors (and researchers) a guideline for their own professional efforts at hand, we introduce a research-informed framework for designing experimental tasks in contemporary physics lab courses. In addition, we demonstrate within the scope of the EU-co-funded DigiPhysLab-project how the framework can be used to characterize existing or develop new high-quality experimental tasks for physics lab courses.

Understanding size and scale (USS) and order-of-magnitude reasoning (OMR) are critical for scientific literacy. This study examines an educational approach to enhance these skills in high school science, considering the cognitive prerequisites and challenges. It explores worked examples (WEs) as an effective method to teach USS and OMR, comparing their impact on students' knowledge and transfer abilities with conventional tasks in a quasi-experimental study. The results indicate significant improvements in procedural and conceptual knowledge, effective for diverse learners regardless of prior knowledge.

I-beams with corrugated webs have higher torsional stiffness than that of flat web beams. Furthermore, the geometrical dimensions of the beam and the web corrugation heavily influence the precision of the currently used traditional pen-and-paper methods for determining the elastic lateral-torsional buckling moment. This study aims to suggest several machine learning models with the intention of predicting the elastic lateral-torsional buckling moment of corrugated web beams. Multiple machine learning models, including Random Forests, Gradient Boosting, Categorical Boosting, and Deep Neural Networks, were deployed to develop and train models to predict the elastic critical lateral-torsional buckling moments of I-beams with corrugated web. The database used for training the different models was compiled through linear bifurcation analyses conducted on shell finite element models. The study evaluates the precision of the various machine learning models by examining their performance against statistical parameters derived from both predicted and test data. The findings from the parametric evaluation highlight the surprisingly high performance and accuracy of the machine learning models.

Abstract At the University of Göttingen, we implemented undergraduate research projects into a first-year mechanics course for physics majors and teacher-training students. Our primary goal was to foster students’ affective factors and higher-order thinking skills in a self-directed, crosslinking, inquiry-based learning setting. A total of 160 students were organized into 40 small groups which worked on one of six open-ended experimental tasks, utilizing smartphone sensors for flexible first-hand data collection outside laboratories. The tasks originate from the Erasmus+ project DigiPhysLab and were significantly modified and opened to be used for undergraduate research projects. In this manuscript, we present the underlying rationales behind this program, outline the core concepts behind the developed experimental tasks, and explain the actual implementation. Additionally, we offer insights into the assessment process for the project work, including the evaluation of scientific posters and responses to eight reflection questions. To facilitate this, we have employed two rubrics to ensure a comprehensive evaluation process.

Pictures in physics education go beyond instructional functions and serve affective roles, such as attracting attention, creating fascination, and fostering engagement with the depicted content. Recognizing the importance of these affective functions highlights the need to understand and utilize aesthetic pictures in a research-based educational environment. Prior research suggests that aesthetic and affective attractiveness in pictures enhances enjoyment and engagement with the physics content. This paper offers three main contributions: Firstly, it conceptualizes and presents research-based criteria for selecting pictures perceived as aesthetically pleasing, drawing on insights from psychology and physics education research. Following these criteria, aesthetic pictures related to a given curricular content can be selected. Secondly, the paper applies these criteria to selecting pictures showing geometrical optics. It then delves into an evaluation of students' aesthetic and affective perception of the selected pictures. A validated instrument measured these responses, showing strong reliability (aesthetic perception: α_C = 0.87 [0.85, 0.89]; affective perception: α_C = 0.82 [0.80, 0.85]). Thirdly, it combines decorative and instructional functions in tasks and compares students' perceptions of aesthetic pictures (AP) and classroom experiment pictures (CEP) in junior high school (N = 118), using a crossover design. Results indicated significantly better aesthetic and affective evaluations for APs, with large effect sizes (AP vs. CEP, aesthetic and affective perception: d = 1.05 - 1.56 and 0.85 - 1.48, respectively). We conclude that the here developed and investigated criteria are useful for selecting aesthetic and affective pictures. This provides a basis for further leveraging their educational potential to create fascination and engagement in science education.

According to modern understanding, sliding friction is caused by the displacement of the irregularities and individual atoms in the interaction zone of the sliding interfaces, leading to microscopic oscillations around the equilibrium position (the stick/slip model of Tomlinson). This is in contrast to the traditional account of sliding friction (static interlocking asperities), which in fact is insufficient (see online Appendix for more information about the theoretical background and its development).

Smartphones and tablets are an integral part of our daily lives, and their capabilities extend well beyond communication and entertainment. With a broad choice of built-in sensors, using these mobile devices as experimental tools (MDETs) allows for a many different measurements, covering several fields of physics (mechanics, acoustics and waves, magnetism, optics, etc.). Building on this development, the present contribution is about exploring the potential of MDETs in physics undergraduate research. Two examples related to acoustics (bottle Helmholtz resonator, singing glasses) will be discussed in detail, and four further possibilities are referred to as perspectives. Results of these student research projects are encouraging throughout (accuracy, agreement with theory), in many cases providing a basis for further improvements and insight. Additionally, it is argued that these examples provide a "proof of concept" that the use of smartphones for experimental projects can mobilize and stimulate among students the educational potential of "higher order thinking skills" (HOTs) widely discussed in the literature on undergraduate research, such as autonomy, curiosity, creativity, and others.

Biology education research has shown that deeply rooted intuitions can influence students' understanding of biological phenomena. One example is design teleology, the intuition that organisms' traits were designed to fulfill a goal. Another example is psychological essentialism, the intuition that organisms have fixed essences. Past research has found both of these intuitions to be conceptual obstacles for understanding evolution. In our study, we investigated whether conceptions stemming from these two intuitions are also expressed in the context of genetics. So, we used two tests to identify teleology and essentialism conceptions in the context of genetics: a multiple-choice questionnaire (Genetic Essentialism and Teleology Questionnaire) and an implicit test (Genetic Essentialism and Teleology Implicit Association Test [IAT]). The development and validation process of these two tests are presented elsewhere. The present article presents the results of the administration of both tests to 332 secondary school students. It was found that: (1) those students exhibited teleology and essentialism conceptions in the context of genetics; the former, but not the latter, were less frequent the older students were; (2) teleology and essentialism conceptions were not correlated; and (3) measures from the explicit and implicit tests were not correlated with each other. Recommendations to improve teaching about genetics are made; for instance, we suggest that students' genetic essentialism conceptions could be addressed by emphasizing the existence of variation within populations and species, and the impact of mutations on phenotypic outcomes. We also recommend that teachers address design teleology and psychological essentialism conceptions independently from each other.

Physics lab courses permanently undergo transformations, in recent times especially to adapt to the emergence of new digital technologies and the Covid-19 pandemic in which digital technologies facilitated distance learning. Since these transformations often occur within individual institutions, it is useful to get an overview of these developments by capturing the status quo of digital technologies and the related acquisition of digital competencies in physics lab courses. Thus, we conducted a survey among physics lab instructors (N=79) at German, Finnish, and Croatian universities. The findings reveal that lab instructors already use a variety of digital technologies and that the pandemic particularly boosted the use of smartphones/tablets, simulations, and digital tools for communication/collaboration/organization. The participants generally showed a positive attitude toward using digital technologies in physics lab courses, especially due to their potential for experiments and students' competence acquisition, motivational effects, and contemporaneity. Acquiring digital competencies is rated as less important than established learning objectives, however, collecting and processing data with digital tools was rated as an important competency that students should acquire. The instructors perceived open forms of labwork and particular digital technologies for specific learning objectives (e.g., microcontrollers for experimental skills) as useful for reaching their learning objectives. Our survey contributes to the reflection of what impact the emergence of digital technologies in our society and the Covid-19 pandemic had on physics lab courses and reveals first indications for the future transformation of hands-on university physics education.

This paper investigates the suitability and interpretability of a data‐driven deep learning algorithm for multi cross sectional overstrength factor prediction. For this purpose, we first compile datasets consisting of experiments from literature on the overstrength factor of circular, rectangular and square hollow sections as well as I‐ and H‐sections. We then propose a novel multi‐head encoder architecture consisting of three input heads (one head per section type represented by respective features), a shared embedding layer as well as a subsequent regression tail for predicting the overstrength factor. By construction, this multi‐head architecture simultaneously allows for (i) the exploration of the nonlinear embedding of different cross‐sectional profiles towards the overstrength factor within the shared layer, and (ii) a forward prediction of the overstrength factor given profile features. Our framework enables for the first time an exploration of cross‐section similarity w.r.t. the overstrength factor across multiple sections and hence provides new domain insights in bearing capacities of steel cross‐sections, a much wider data exploration, since the encoder‐regressor can serve as meta model predictor. We demonstrate the quality of the predictive capabilities of the model and gain new insights of the latent space of different steel sections w.r.t. the overstrength factor. Our proposed method can easily be transferred to other multi‐input problems of Scientific Machine Learning.

Background: Context Based Science Education (CBSE) has a long-standing tradition and is discussed as a highly promising approach in science education. It is supposed that CBSE can foster pupils engagement and learning. However, classroom implementations of CBSE based on solid empirical evidence are surprisingly scarce. Purpose: The present research-based report of practice seeks to bridge this theory practice gap for some specific forms of CBSE. We examine the use of science problems based on newspaper articles and the real-life contexts they provide (newspaper story problems, NSP). Design and Methods: While the research background has been reported elsewhere, the main objective of the present contribution is to provide a detailed account of the practical aspects of the approach. Two concrete, curriculum-relevant classroom teaching experiments based on newspaper story problems are reported, combined with a quasi-experimental study comparing NSPs against conventional textbook problems. The implementation of the teaching learning sequence in classroom practice is described in detail. Additionally, tasks types similar to NSPs, but using other ways of contextualisation (e.g. advertisements) will be discussed as perspective. Results: A considerable improvement in motivation was found, which proved stable at least in the medium term. Learning turned out to also be fostered to a sizeable extent, including the educationally important issue of transfer. Conclusions: The use of NSPs as a form of CBSE can have large positive, robust, and sustainable effects of both motivation and learning. Being flexible and practical to implement, they appear thus highly suited to classroom application. In perspective, a series of similar forms of tasks implementing CBSE is presented, such as by experimental of aesthetic contexts.

The present text provides a short, non-technical account of some historical and educational background and, based on this, of the rationale of the use of mobile devices in physics education.

Better and simpler possibilities of structural optimization due to increasing computational power but also for reasons of environmental sustainability, the use of materials and their reusability lead to greater acceptance towards more advanced numerically intensive, so‐called “design by analysis” methods like geometrically and materially nonlinear analyses with imperfections (GMNIA). The general choice of imperfections and their combination in such models, especially for slender cross‐sections of intermediate length prone to an interaction between a global and local plate‐buckling, is crucial in terms of the reached load‐bearing capacity. Annex C of EN 1993‐1‐5 make use of the “70%‐rule” for the combination of imperfection modes and amplitudes. This rule postulates that two GMNIA calculations should be carried out when local and global interactive buckling may be dominant; one with 100% + 70% of the maximum specified amplitude in either case. In addition, extended information is provided on the choice and combination of imperfections in the newly introduced and currently available draft of the prEN1993‐1‐14:2020 (Design assisted by finite element analysis). Although information is provided how the local and global imperfections should be combined, it is not stated when it is relevant to consider those. Based on carried out GMNIA simulations on SHS/RHS and I‐shaped sections, this paper presents a general decision support on the choice of equivalent imperfections. Based on numerical analysis, the developed flowchart and design routine allows for the decision whether the consideration of the interaction of local and global imperfections is required or not.