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Science education researchers have developed a refined understanding of the structure of science teachers’ pedagogical content knowledge (PCK), but how to develop applicable and situation-adequate PCK remains largely unclear. A potential problem lies in the diverse conceptualisations of the PCK used in PCK research. This study sought to systematize existing science education research on PCK through the lens of the recently proposed refined consensus model (RCM) of PCK. In this review, the studies’ approaches to investigating PCK and selected findings were characterised and synthesised as an overview comparing research before and after the publication of the RCM. We found that the studies largely employed a qualitative case-study methodology that included specific PCK models and tools. However, in recent years, the studies focused increasingly on quantitative aspects. Furthermore, results of the reviewed studies can mostly be integrated into the RCM. We argue that the RCM can function as a meaningful theoretical lens for conceptualizing links between teaching practice and PCK development by proposing pedagogical reasoning as a mechanism and/or explanation for PCK development in the context of teaching practice.

Deformation of continuous fibre reinforced plastics during thermally-assisted forming or joining processes leads to a change of the initial material structure. The load behaviour of composite parts strongly depends on the resultant material structure. The prediction of this material structure is a challenging task and requires a deep knowledge of the material behaviour above melting temperature and the occurring complex forming phenomena. Through this knowledge, the optimisation of manufacturing parameters for a more efficient and reproducible process can be enabled and are in the focus of many investigations. In the present paper, a simplified pultrusion test rig is developed and presented to investigate the deformation behaviour of a thermoplastic semi-finished fiber product in a forming element. Therefore, different process parameters, like forming element temperature, pulling velocity as well as the forming element geometry, are varied. The deformation behaviour in the forming zone of the thermoplastic preimpregnated continuous glass fibre-reinforced material is investigated by computed tomography and the resultant pulling forces are measured. The results clearly show the correlation between the forming element temperature and the resulting forces due to a change in the viscosity of the thermoplastic matrix and the resulting fiber matrix interaction. In addition, the evaluation of the measurement data shows which forming forces are required to change the shape of the thermoplastic unidirectional material with a rectangular cross-section to a round one.

This international curricular review provides a structured overview of the particle physics content in 27 state, national, and international high-school physics curricula. The review was based on a coding manual that included 60 concepts that were identified as relevant for high-school particle physics education. Two types of curricula were reviewed, namely curricula with a dedicated particle physics chapter and curricula without a dedicated particle physics chapter. The results of the curricular review show that particle physics concepts are explicitly or implicitly present in all reviewed curricula. However, the number of particle physics concepts that are featured in a curriculum varies greatly across the reviewed curricula. We identified core particle physics concepts that can be found in most curricula. Here, elementary particles, fundamental interactions, and charges were identified as explicit particle physics concepts that are featured in more than half of the reviewed curricula either as content or context. Indeed, theoretical particle physics concepts are more prominent in high-school physics curricula than experimental particle physics concepts. Overall, this international curricular review provides the basis for future curricular development with respect to particle physics and suggests an increased inclusion of experimental particle physics concepts in high-school physics curricula.

Objective Chronic totally occluded right coronary artery (CTO-RCA) often poses a problem in decision making for/against bypass grafting due to the lack of standardized indication criteria. The aim of the study was to investigate whether qualitative angiograms can be useful in decision making for/against surgical revascularization of CTO-RCA. Methods A retrospective cohort study was conducted with 69 patients who underwent elective CABG procedure, including single graft to the RCA. The distal run-off of the bypassed RCA was measured intraoperatively using the ultrasonic transit-time method. As a primary endpoint of the study, the flow values were analysed in regard to diameter of the recipient artery. As a secondary endpoint, the correlations between the regional and global LV function, Rentrop grading, type of collateral pathway, number of donor sources, comorbidity, and the graft flow and the diameter of the recipient artery were investigated using uni- and multi-variate regression analyses. Results In general, the flow values correlated significantly with the diameter of the recipient artery. Significantly lower flow ( p  < 0.0001) and diameter values ( p  < 0.05) were found in hypo/akinetic and infarcted area reflecting functionality of the CTO-RCA territory. Conclusions The qualitative angiograms combined with regional wall motion studies can be useful in decision making for revascularization of CTO-RCA. Revascularization of akinetic/infarcted CTO-RCA territory is associated with lower graft flows even in patients presented with high Rentrop class and high degree of collaterality, suggesting necessity of viability tests prior to bypass surgery.

Computer-based analysis of preservice teachers’ written reflections could enable educational scholars to design personalized and scalable intervention measures to support reflective writing. Algorithms and technologies in the domain of research related to artificial intelligence have been found to be useful in many tasks related to reflective writing analytics such as classification of text segments. However, mostly shallow learning algorithms have been employed so far. This study explores to what extent deep learning approaches can improve classification performance for segments of written reflections. To do so, a pretrained language model (BERT) was utilized to classify segments of preservice physics teachers’ written reflections according to elements in a reflection-supporting model. Since BERT has been found to advance performance in many tasks, it was hypothesized to enhance classification performance for written reflections as well. We also compared the performance of BERT with other deep learning architectures and examined conditions for best performance. We found that BERT outperformed the other deep learning architectures and previously reported performances with shallow learning algorithms for classification of segments of reflective writing. BERT starts to outperform the other models when trained on about 20 to 30% of the training data. Furthermore, attribution analyses for inputs yielded insights into important features for BERT’s classification decisions. Our study indicates that pretrained language models such as BERT can boost performance for language-related tasks in educational contexts such as classification.

Science education researchers typically face a trade-off between more quantitatively oriented confirmatory testing of hypotheses, or more qualitatively oriented exploration of novel hypotheses. More recently, open-ended, constructed response items were used to combine both approaches and advance assessment of complex science-related skills and competencies. For example, research in assessing science teachers’ noticing and attention to classroom events benefitted from more open-ended response formats because teachers can present their own accounts. Then, open-ended responses are typically analyzed with some form of content analysis. However, language is noisy, ambiguous, and unsegmented and thus open-ended, constructed responses are complex to analyze. Uncovering patterns in these responses would benefit from more principled and systematic analysis tools. Consequently, computer-based methods with the help of machine learning and natural language processing were argued to be promising means to enhance assessment of noticing skills with constructed response formats. In particular, pretrained language models recently advanced the study of linguistic phenomena and thus could well advance assessment of complex constructs through constructed response items. This study examines potentials and challenges of a pretrained language model-based clustering approach to assess preservice physics teachers’ attention to classroom events as elicited through open-ended written descriptions. It was examined to what extent the clustering approach could identify meaningful patterns in the constructed responses, and in what ways textual organization of the responses could be analyzed with the clusters. Preservice physics teachers (N = 75) were instructed to describe a standardized, video-recorded teaching situation in physics. The clustering approach was used to group related sentences. Results indicate that the pretrained language model-based clustering approach yields well-interpretable, specific, and robust clusters, which could be mapped to physics-specific and more general contents. Furthermore, the clusters facilitate advanced analysis of the textual organization of the constructed responses. Hence, we argue that machine learning and natural language processing provide science education researchers means to combine exploratory capabilities of qualitative research methods with the systematicity of quantitative methods.

Continuous carbon fibre-reinforced thermoplastic composites have convincing anisotropic properties, which can be used to strengthen structural components in a local, variable and efficient way. In this study, an additive manufacturing (AM) process is introduced to fabricate in situ consolidated continuous fibre-reinforced polycarbonate. Specimens with three different nozzle temperatures were in situ consolidated and tested in a three-point bending test. Computed tomography (CT) is used for a detailed analysis of the local material structure and resulting material porosity, thus the results can be put into context with process parameters. In addition, a highly curved test structure was fabricated that demonstrates the limits of the process and dependent fibre strand folding behaviours. These experimental investigations present the potential and the challenges of additive manufacturing-based in situ consolidated continuous fibre-reinforced polycarbonate.

Reflecting in written form on one’s teaching enactments has been considered a facilitator for teachers’ professional growth in university-based preservice teacher education. Writing a structured reflection can be facilitated through external feedback. However, researchers noted that feedback in preservice teacher education often relies on holistic, rather than more content-based, analytic feedback because educators oftentimes lack resources (e.g., time) to provide more analytic feedback. To overcome this impediment to feedback for written reflection, advances in computer technology can be of use. Hence, this study sought to utilize techniques of natural language processing and machine learning to train a computer-based classifier that classifies preservice physics teachers’ written reflections on their teaching enactments in a German university teacher education program. To do so, a reflection model was adapted to physics education. It was then tested to what extent the computer-based classifier could accurately classify the elements of the reflection model in segments of preservice physics teachers’ written reflections. Multinomial logistic regression using word count as a predictor was found to yield acceptable average human-computer agreement (F1-score on held-out test dataset of 0.56) so that it might fuel further development towards an automated feedback tool that supplements existing holistic feedback for written reflections with data-based, analytic feedback.

Gynaecologic neoplasms are reported to have the highest potential for developing of ischemic stroke. The history of a female patient, in whom recurrent cerebral embolism was the first clinical sign of occult ovarian neoplasm is described and the casuistic literature to characterise this clinical phenomenon reviewed. Among a large spectrum of neoplasms complicating with ischemic stroke, ovarian carcinoma is one of the most frequently reported in the casuistic literature. The source of systemic microembolisation is endocardits of non-infectious origin; the characteristic diagnostic findings are thrombocytopenia, elevated D-dimers level, and a specific stroke pattern in magnetic resonance imaging. Meticulous diagnosis in female, otherwise 'healthy' patients with ischemic stroke, to detect the underlying neoplastic disease is of paramount importance, as early surgical intervention on cancer promises successful therapy for both, cancer and thromboembolism.