Jesper Bruun

Science education faces a persistent challenge: Students experience the material world around them through their bodies. Yet, they are asked to describe this world by drawing on scientific concepts...

Models are commonly used in astronomy education to help students understand and discuss difficult or abstract concepts. From a physics education research perspective, metaphors and analogies are often used to support reasoning about abstract phenomena. This study investigates how students draw on classroom experiments as physical analogies (a multimodal instance of conceptual metaphor) when reasoning about exoplanetary atmospheres. We conducted two lessons: one about cloud formation on Earth and exoplanets and another about lightning on Earth and exoplanets, both including a hands-on experiment related to the topic. The students were then split into peer-led focus groups and tasked with discussing the experiment and its relation to weather in planetary atmospheres. We audio-recorded these group discussions and extracted relevant excerpts. We analyzed these excerpts sequentially using thematic analysis, network analysis, and conceptual blend analysis. We identified four input spaces used by students (relevant physics, the experiment, Earth conditions, and exoplanet conditions) and three ways in which students use analogies (mapping between domains, identifying limitations, and extending the analogy). Across these analyses, we found that students integrate these input spaces to construct blend spaces that mediate their reasoning about processes and concepts in Earth and exoplanetary weather phenomena. We also discuss implications for future research and teaching.

Inquiry-based science education (IBSE) is widely recognised as an approach to teaching that emphasises student agency and supports learners in independently exploring questions and constructing exp...

Mathematics plays many roles in physics and physics education. While these roles have previously been extensively discussed in the physics education research community, no systematic picture of the multifaceted considerations has yet been formed. To gain a comprehensive overview of the previous studies on the topic, we conducted a systematic literature review on 122 journal articles published between 2000 and 2023 that examine the role of mathematics in physics and physics learning. In the reviewed articles, we employed qualitative content analysis, coded each article for its characteristics, and used network maps for visualization. We identified eight thematic article categories, highlighting the complex integration of mathematics in, for example, physical reasoning, problem solving, modeling, and experiments. Additionally, the review examines theoretical frameworks and contexts, revealing an overemphasis on problem solving in mechanics and a limited exploration of advanced physics topics like quantum mechanics. A detailed inductive analysis further identified six overarching roles that mathematics plays in physics and physics education: (i) supporting learning and achievement, (ii) enabling mathematical manipulations, (iii) guiding reasoning and sensemaking, (iv) facilitating experiments and modeling, (v) serving as a language, and (vi) providing a structural foundation for physics as a science. Based on the analysis, we discuss how the roles assigned to mathematics have been conceptualized in the reviewed articles and provide an overall picture of the types and features of the reviewed corpus. Our findings suggest opportunities for future research, including deeper explorations of underrepresented physics contexts and targeted investigations into specific roles of mathematics in teaching and learning.

Small-angle X-ray and neutron scattering (SAXS and SANS) are versatile techniques for studying the structure of various materials and particles, enabling investigations of structures from a few to hundreds of nanometres. However, interpreting SAXS or SANS data can be challenging, hence the need for effective training. Here, we present the website SAStutorials.org, which offers tutorials on small-angle scattering (SAS) data analysis. The website serves as a learning platform that supports active learning both in the classroom and through self-study. The tutorials cover basic concepts, advanced modelling and theoretical aspects of SAS. Each tutorial follows a structure based on the principle of SOLO taxonomy, guiding learners from minimal or no knowledge in a given area to the ability to tackle real-world problems. SAStutorials.org has been developed as a community tool, providing tutorials that encompass a broad range of data analysis types, leveraging various programs and investigating different materials. Moreover, the website and all its data are open source, encouraging contributions from the community.

Problem-solving in physics and mathematics has been characterised in terms of five phases by Schoenfeld, and these have previously been used to describe also online and blended behaviour. We argue that expanding the use of server logs to make detailed categorisations of student actions can help increase knowledge about how students solve problems. We present a novel approach for analysing server logs that relies on network analysis and principal component analysis. We used the approach to analyse student interactions with an online textbook that features physics problems. We find five ‘components of behavioural structure’: Complexity, Linear Length, Navigation, Mutuality, and Erraticism. Further, we find that problem-solving sessions can be divided into four overarching groups that differ in their Complexity and further into ten clusters that also differ on the other components. Analysing typical sessions in each cluster, we find ten different behavioural structures which we describe in terms of Schoenfeld's phases. We suggest that further research integrates this approach with other methodological approaches to get a fuller picture of how learning strategies are used by students in settings with online features.

Social semiotic discussions about the role played by representations in effective teaching and learning in areas such as physics have led to theoretical proposals that have a strong common thread: in order to acquire an appropriate understanding of a particular object of learning, access to the disciplinary relevance aspects in the representations used calls for the attainment of representational competence across a particular critical constellation of systematically used semiotic resources (which are referred to as modes, see more on this later). However, an affirming empirical investigation into the relationship between a particular object of learning and different representational formulations, particularly with large numbers of students, is missing in the literature, especially in the context of university-level physics education. To start to address, this research shortfall the positioning for this article is that such studies need to embrace the complexities of student thinking and application of knowledge. To achieve this, both factor and network analyses were used. Even though both approaches are grounded in different frameworks, for the task at hand, both approaches are useful for analyzing clustering dynamics within the responses of a large number of participants. Both also facilitate an exploration of how such clusters may relate to the semiotic resource formulation of a representation. The data were obtained from a questionnaire given to 1368 students drawn from 12 universities across 7 countries. The questionnaire deals with the refraction of light in introductory-level physics and involves asking students to give their best prediction of the relative visual positioning of images and objects in different semiotically constituted situations. The results of both approaches revealed no one-to-one relationship between a particular representational formulation and a particular cluster of student responses. The factor analysis used correct answer responses to reveal clusters that brought to the fore three different complexity levels in relation to representation formulation. The network analysis used all responses (correct and incorrect) to reveal three structural patterns. What is evident from the results of both analyses is that they confirm two broad conclusions that have emerged from social semiotic explorations dealing with representations in relation to attempting to optimize teaching and learning. The first, which is linked to a facilitating-awareness perspective, is that any given disciplinary visual representation can be expected to evoke a dispersed set of knowledge structures, which is referred to as their relevance structure. Thus, the network analysis results can be seen as presenting a unique starting point for studies aiming to identify such relevance structure. The second broad conclusion is that disciplinary visual representation can and often does contain more disciplinary-relevant aspects than what may be directly visible in a given representation. These are referred to as the appresent aspects that need to become part of the total awareness needed by someone to constitute an intended meaning. The results of the factor analysis can then also be seen to be a way of capturing all the disciplinary-relevant aspects (both present and appresent). Educational implications are discussed.

ASSIST-ME var et 4-årigt forskningsprojekt, som involverede 11 forskergrupper fra 8 europæiske lande i perioden 2013-2016. Det undersøgte, hvorledes formative og summative evalueringsmetoder kan støtte og forbedre undersøgelsesbaserede tilgange i europæisk science, teknologi og matematik uddannelser med henblik på at formulere guidelines og anbefalinger rettet mod relevante aktører. Det meget komplekse projekt var rammen om en lang række udviklingselementer, med rum for betydelig national variation, koncentreret om fire fælles forskningsspørgsmål. Artiklen beskriver hovedideerne i projektet og derefter koncentrerer sig om tre aspekter: Relationerne mellem formative og summative formål med evaluering og mulighederne for at kombinere dem, det policy-relaterede arbejde, og herunder udvælgelsen af relevante aktører, samt fremtidige forskningsspørgsmål, som udspringer af projektet.