Recent advancements in educational research have underscored the significance of multiple representations (MR) in teaching practices, supporting student learning outcomes. Despite this progress, to our knowledge, two issues remain open: currently, there is no established tool for evaluating the quality of representations used in learning materials, and there is no comprehensive research on how such representations are integrated into teaching resources, especially in school textbooks. Our study aims to address these gaps by developing an analytical tool for categorizing representations in physics learning materials. We designed a grid for the analysis based on the theoretical underpinnings of MR, cognitive load theory, and modes of reasoning subframeworks. This grid helps identify MR features: building parameters, functionalities, and levels of optimization for learning environment usage, thus establishing the insights necessary to improve and enhance the quality of the representations. We also tested and applied this tool to force diagrams (FDs), which are commonly used in helping students learn mechanics, examining these representations across selected chapters from widely used high school physics textbooks. In this paper, we provide the results of our investigation of the current state of FDs in these textbooks as a compare-and-contrast case study. At the same time, we aim to offer insights into the effectiveness and potential areas for improvement in deploying MR for teaching-learning purposes.

Evidence from physics education research suggests that the introduction of mathematical formalism in quantum physics instruction can support the development of students’ conceptual understanding, particularly by promoting functional ways of reasoning about quantum phenomena. However, empirical findings on the effectiveness of such approaches at the secondary school level remain inconclusive, and little is known about the extent to which learning outcomes are shaped by instructional design as opposed to learners’ affective and cognitive characteristics. This study addresses this gap by investigating the relative influence of instructional approach on students’ understanding of single-photon states, while controlling for prior knowledge, cognitive load, situational interest, and physics self-concept. A quasiexperimental pre-post design was implemented with $N=172$ upper secondary students, comparing a qualitative instructional condition with an approach supplemented by a reduced Dirac formalism. Learning outcomes were assessed using the framework of Functional Fidelity and Fidelity of Gestalt. A multivariate analysis of covariance, followed by univariate analyses of covariance, was conducted to examine the contributions of instructional and learner-related variables. The findings indicate that affective variables emerge as strong predictors of functional understanding and suggest that learning in quantum physics is driven less by instructional format than by learners’ affective and cognitive prerequisites. In particular, fostering interest appears to be a key mechanism for supporting the development of functional understanding. The results highlight the importance of integrating affective dimensions into instructional design and provide a basis for future research on the interaction between instructional approaches and learner characteristics in quantum physics education.

Scientific knowledge does not necessarily replace intuitive misconceptions; successful physics reasoning may therefore require learners to control responses that remain cognitively available. However, inhibitory-control accounts have yet to explain whether the same conceptual conflict elicits a stable amount of control across representational contexts or whether control mobilization varies with contextual typicality and physics training. This study addressed this issue using a negative-priming (NP) paradigm in the domain of static friction. High-school students and undergraduate physics majors made static-friction judgments in a prototypical context and a nonprototypical context. Within the inhibitory account adopted here, NP magnitude was interpreted as a behavioral index of the control mobilized during the preceding incongruent prime. Probe-phase reaction times were analyzed on the log scale using a linear mixed-effects model with Group, Context, and Prime Type and their interactions. The analysis revealed a context-dependent pattern of NP, with the magnitude of this contextual modulation differing between the two groups. Specifically, NP was negligible in the prototypical context for both high-school students and physics majors. By contrast, reliable NP emerged in the nonprototypical context. This increase was substantially larger among high-school students than among physics majors. When translated back to the original reaction-time scale, NP in the nonprototypical context corresponded to an approximately 20.77% increase for high-school students, compared with a 5.65% increase for physics majors. Taken together, these findings show that the inhibitory control mobilized by the same conceptual conflict is not fixed across representations. The nonprototypical representation amplified inhibitory-control demand, whereas physics training attenuated this amplification. The training-related advantage is therefore better characterized as reduced contextual sensitivity of inhibitory mobilization than as a context-invariant absence of conceptual conflict. The results advance process-based accounts of conceptual change by linking problem representation, domain knowledge, and task-evoked inhibitory control.

Students often hold intuitive misconceptions that coexist with scientifically normative understanding. A growing body of research suggests that inhibitory control plays an important role in resolving such interference by suppressing misconception-based responses. In science learning, however, the same underlying concept is often encountered through multiple external representations, which may repeatedly activate similar misconceptions. It therefore remains unclear whether inhibitory control is also involved when the same concept is expressed in different but closely related representation formats. Extending prior findings on inhibitory control in stroboscopic representation involving the position-velocity indiscrimination (PVI) misconception, the present research examined whether similar demands on inhibitory control arise in position-time graph representations. To address this question, we conducted two studies using a negative priming (NP) paradigm, with eye tracking added in Study 2. Study 1 involved 45 high school students and tested whether inhibitory control was involved in overcoming the PVI misconception in position-time graphs. Study 2 involved 37 physics major undergraduates and further examined how attentional allocation unfolded during this process. In both studies, congruent probes following incongruent primes elicited slower responses than congruent probes following neutral primes, indicating a reliable NP effect. In Study 1, probe accuracy was also lower after incongruent primes. Eye tracking in Study 2 further showed that, during incongruent primes, participants oriented earlier to graph regions in which the two objects occupied the same position, whereas access to the graph region indicating equal velocity, defined by equal slope, was delayed relative to neutral primes. In the probe stage, congruent probes following incongruent primes elicited later entry into, and greater attentional investment on, the graph region that contained the scientifically relevant cue for making the correct velocity judgment. Taken together, these findings suggest that the PVI misconception is activated early, competes for attentional resources, and requires inhibitory control together with attentional reallocation toward scientifically relevant information. More broadly, considered alongside prior evidence from stroboscopic representations, the present results provide initial evidence that the inhibitory demands associated with overcoming the PVI misconception may generalize across closely related visual representations of the same kinematic concept. This study advances understanding of the cognitive processes underlying physics learning and offers implications for instruction aimed at mitigating persistent misconceptions.

Physics problem-solving assessments often prioritize accuracy over presentation. However, the presentation quality of a student’s written solution—such as its handwriting clarity and layout—may play an important role both as an indicator and facilitator of student problem-solving and achievement. In this study, we investigated the relationships between show-work presentation (SWP), conscientiousness, and student performance in a large-enrollment introductory calculus-based physics course. Using an emergent coding process, we first iteratively developed a reliable rubric for quantifying several presentation qualities of student solutions to two introductory mechanics final exam show-work questions. We found that the coding produced scores for 2 orthogonal factors, which we label as Form, measuring layout and legibility, and Annotation, measuring student notation of their work. Statistical analyses showed that Form was a moderate-to-weak positive predictor of various course performance measures ($r\sim 0.3$), including show-work score, exam score, nonexam scores, and course grade. Even when controlling for ACT Math and conscientiousness scores, Form still accounted for 4%–5% of the variance of these course performance scores. In contrast, Annotation was at best very weakly and inconsistently (in sign) correlated with these performance scores. Sex differences were also observed, with female students’ mean scores for both Form and Annotation typically about one-half standard deviation higher than male students’ scores. Simultaneous mediation models of the relation between sex and course outcomes included the mediators of ACT score, conscientiousness, and Form. Results indicate that Form partially mediated the relation between sex and course grade, show-work score, multiple-choice score, and nonexam score. Specifically, women tended to score higher on Form, and this effect in turn led to a positive effect on scores on all of these outcomes, typically compensating for other negative effects. The (simultaneous) mediations of ACT score and conscientiousness were much weaker, consistent with being vanishingly small. It is important to note that these are correlational results only, and control-treatment designs are called for to investigate potential causal links between Form and performance. Overall, by developing and implementing a validated rubric measuring show-work presentation quality, we find evidence that presentation quality may play a relatively small but reliable role in student performance on show-work problems and is a weak but reliable predictor of most components of course performance. These results suggest that show-work presentation may be an important factor to consider in physics instruction.

Traditional physics laboratory courses often focus on experiments with well-known results, limiting students’ engagement in authentic scientific practices. Course-based undergraduate research experiences (CUREs), where students engage in real research with unknown outcomes, have been shown to support positive student outcomes, such as increased self-efficacy, persistence, and engagement in scientific practices. However, discipline-specific studies of CUREs in physics remain limited. We describe the development, structure, and initial implementation of a photovoltaics-focused CURE in a second-year undergraduate physics laboratory course at the University of Colorado Boulder. To examine how students experienced the course, we analyzed end-of-semester reflection assignments using the five CURE components (i.e., scientific practices, discovery, relevance, collaboration, and iteration), as well as established dimensions of research authenticity, as analytic frameworks. Students described experiences associated with all five CURE components, with collaboration, relevance, and scientific practices appearing most prominently in their reflections. Students also associated authentic research with meaningful scientific contribution, engagement in authentic scientific practices, and navigating the uncertainty and setbacks inherent in research, although fewer explicitly identified themselves as researchers or scientists. A subset of students additionally connected the course to their immediate thinking about future academic and professional pathways. This work contributes both a discipline-specific model for implementing CUREs in experimental physics laboratory courses and provides insight into how students interpret and experience authentic research within this course context.

Making sense of mathematical expressions is a central component of expertlike reasoning in physics. Previous research has shown that sensemaking strategies can be successfully integrated into university instruction and that students benefit from explicit support in using such strategies. However, it remains largely unexplored to what extent the amount of practice influences students’ learning. In the present study, we investigate dose-effect relationships for sensemaking tasks with limiting case analysis. The study was conducted during a two-week preparatory course for incoming undergraduate physics students at a German university. The course focuses on the repetition of important mathematical concepts and prepares students for the demands of the first semester of physics studies. Limiting case analysis was systematically embedded in daily exercises that students solved in recitation sessions. A total of $N=103$ students participated in the study and were randomly assigned to one of five intervention groups, each with a different number of limiting case tasks (0–8 tasks). The study employed a pre-post design to assess perceived usefulness, instructional integration, competence, and performance regarding the analysis of limiting cases. The results show that students’ perceived competence increased with the number of tasks, with effect sizes up to $d=0.88$. Students’ performance also showed an increase in effect size for a greater number of tasks with effect sizes up to $d=1.07$. The observed patterns in the target variables are compatible with a sigmoidal dose-effect relationship. Overall, this proof-of-concept study provides the first empirical evidence for dose-effect relationships in the context of sensemaking strategies in physics and demonstrates the feasibility of dose-effect study designs in undergraduate physics education research. The study highlights the importance of explicitly integrating sensemaking strategies into undergraduate physics and indicates that increased and targeted practice can foster both students’ confidence and their ability to apply such strategies.

Despite the widespread availability of large language models (LLMs) in higher education, instructors vary substantially in their adoption and use of these tools, and the reasons for this variation remain poorly understood. A mixed-methods survey of 90 STEM faculty in the Research Corporation for Science Advancement (RCSA) Cottrell community examined relationships between AI use, attitudes, institutional context, and instructional practice. Exploratory factor analysis identified a coherent construct, AI pedagogical orientation, that strongly predicted self-reported AI use across research, teaching, and other professional activities. Qualitative analysis indicated that this construct reflected differing views about the role AI should play in disciplinary thinking, learning, and expertise development, rather than simply positive or negative attitudes toward AI. Institutional initiatives, demographic variables, and information sources showed comparatively weak associations with AI use. The results suggest that existing technology-adoption models may not fully explain adoption in contexts where technologies interact directly with disciplinary reasoning and knowledge production.

Career preparation for participation in the quantum industry is often framed in terms of formal educational pipelines, workforce projections, and knowledge and skills needed for various roles. Less is known about how quantum industry professionals themselves characterize their preparation for participation in the field. In this paper, we analyze interviews with quantum industry professionals working across a range of positions and company types to examine the experiences that enable entry into the quantum industry. Using thematic analysis, we identify four trajectories: (1) continuity of research practice from academia to industry, (2) reframing of prior expertise for quantum applications, (3) incremental engagement through various professional opportunities, and (4) network-enabled entry. These trajectories often co-occur within individual narratives, showing that preparation emerges from a combination of educational, professional, and relational experiences. Our findings demonstrate that supporting preparation for quantum industry careers requires more than the design of formal coursework and degree programs. Our results highlight the importance of experiential learning opportunities that allow students to apply their knowledge and skills and develop professional connections that facilitate entry into quantum careers.

This study presents the design and evaluation of a physical-digital learning environment for teaching rotational motion transmission in introductory engineering education. The environment integrates SimulaGear, an interactive digital simulation for analyzing gear systems, with a physical-IoT experimental system composed of a 3D-printed gear mechanism, infrared sensors, ESP32-based data acquisition, Message Queuing Telemetry Transport communication, and real-time visualization through Node-RED dashboards. A quasiexperimental pretest-post-test design was implemented with 37 first-year engineering students enrolled in introductory physics courses. The control group worked exclusively with the SimulaGear digital simulation, whereas the experimental group participated in a physicaldigital experience that combined SimulaGear with the physical-IoT experimental system. Conceptual understanding, student engagement, and technology acceptance were evaluated using a conceptual test, the Utrecht Work Engagement Scale for Students, and a survey based on the technology acceptance model. Results showed that both groups improved their conceptual understanding after the intervention, with the group that used the physical-digital system showing a specific significant improvement in transmission ratios in multiple-gear systems. In addition, the physical-digital experience produced significant gains in vigor and overall engagement, while the digital-only experience did not show significant changes in engagement. Technology acceptance was high in both groups, but significantly higher in the experimental group. These findings suggest that integrating physical-IoT systems with digital simulations can strengthen students’ engagement and technology acceptance while supporting the interpretation of complex mechanical configurations through the comparison of theoretical predictions and real-time experimental data.

In a previous quantitative retrospective study, we showed that different demographic groups of students leave different numbers of problems blank on physics exams, leading to inequities in course outcomes. In that work, we argued that there were good reasons to treat these blanks as missing data, rather than indicators of a lack of understanding. In this paper, we refine this analysis and show more detailed breakdowns of uncollected test item responses by race and ethnicity and first-generation college student status, coming to the same conclusion: test item responses are uncollected for students with different ethnic and racial backgrounds at different rates, and these patterns are not exclusive to low-performing students. We also correct an error from our previous work, finding here that there is no significant gender difference in uncollected test item responses. Finally, we provide a more robust analysis of course level data illustrating that blanks are a variable controlled at the course level rather than the student level, providing more evidence for the use of a course deficit model (rather than a student deficit model) when examining equity disparities, and also suggesting that there are plausible means for instructors to minimize uncollected test item responses, and therefore reduce or even eliminate the bias associated with this missing data. We provide a couple suggestions for faculty who want to minimize the impacts of blanks.

Generative artificial intelligence (GenAI) tools are increasingly used in physics classrooms, yet little is known about why high school students accept them for subject-specific learning. Prior technology-acceptance research, especially the technology acceptance model (TAM), shows that perceived usefulness and perceived ease of use shape attitudes and behavioral intention toward a tool. However, this work has rarely examined whether students’ beliefs about the subject they are learning also shape those technology perceptions. Drawing on TAM and expectancy-value theory (EVT), we surveyed 340 Grade 10 students who had used DeepSeek in physics lessons and tested an extended structural equation model. Results showed that students were more willing to use DeepSeek when they perceived it as easy to use and useful and when they held positive attitudes toward using it for physics learning. Physics-specific beliefs helped explain these technology perceptions: students with stronger expectancy for success in physics perceived DeepSeek as easier to use; students who saw physics as useful for their future perceived DeepSeek as more useful; students who enjoyed physics held more positive attitudes; and perceived cost weakened both ease-of-use and attitude judgments. Students who regarded doing well in physics as personally important also tended to evaluate DeepSeek more favorably, partly because they saw it as useful for achieving physics-learning goals. Nested model comparisons clarified what the EVT extension added beyond the basic TAM framework. Adding physics-task values produced the largest incremental gain, especially for perceived usefulness ($\mathrm{\Delta }{R}^2=0.151$). Supplementary follow-up evidence indicated that the same level of intention could reflect learning-oriented or shortcut-oriented use and that some students linked shortcut-oriented use to low confidence and topic difficulty rather than to disinterest alone. Overall, the findings suggest that GenAI acceptance in physics is shaped by both technology perceptions and students’ motivational relationship with physics, supporting a domain-sensitive extension of TAM for subject-specific GenAI learning.

Text embeddings are increasingly used in physics education research to organize, compare, and cluster large collections of written text. Their appeal is clear: once student responses have been mapped into a vector space, similarity comparisons and clustering become computationally inexpensive. However, in assessment contexts, the relevant question is not merely whether clusters can be produced but whether the geometry of the embedding space preserves grading-relevant distinctions. We tested this premise using 992 handwritten student-problem solutions from a high-stakes engineering thermodynamics exam, transcribed into five textual representations and embedded using nine embedding mechanisms. We compared embedding similarity and embedding-based hierarchical clusters against human-assigned scores. Across models, representations, and clustering choices, embedding similarity showed a consistent but modest relationship to score similarity, and the resulting clusters were score-enriched but not score-equivalent. Experiments with a synthetic dataset suggest that this may be due to embeddings behaving like novices when categorizing physics-problem solutions; that is, their similarity geometry is strongly influenced by surface features rather than conceptual, semantic structure. These findings suggest that rubric-blind transcription and out-of-the-box, state-of-the-art embeddings can support exploratory organization and human-in-the-loop review of physics solutions, but they do not provide an unsupervised basis for grading without external validation against the assessment construct of interest.

Measurement claims carry high stakes, yet students and early-career professionals rarely receive explicit training in the reasoning chain that justifies them. Despite growing emphasis on transferable competence, measurement reasoning remains a blind spot: learners seldom map a measurand (the quantity of interest) to an estimand (the reported quantity inferred from data), anchor inference in an explicit noise or likelihood model, or report results with uncertainty and traceability. This fragmentation weakens rigor and communication across domains. We define cross-disciplinary measurement literacy as the ability to connect a measurand to an estimand, justify inference under explicit noise and likelihood models, and report results with uncertainty and traceability. We operationalized this construct through a unified $\mathrm{Encoding}\to \mathrm{Evolution}\to \mathrm{Readout}\to \mathrm{Estimation}$ schema integrated with metrological closure and implemented it in a $4\times 2\text{−}\mathrm{h}$ module. This multicohort descriptive evaluation includes five cohorts with verified pre- and postadministration records ($N=114$) and two undergraduate team-task cohorts. Cohort-level summaries showed immediate post-test means 34–54 points above pretest means and normalized gains of 0.642–0.782 on an intervention-aligned assessment. Team rubric summaries provided evidence of feasible group-level enactment of the schema, while uncertainty budgeting and traceability remained the principal difficulties. This study is designed as a bounded descriptive evaluation. It provides evidence for the schema’s teachability and its utility as a short-term organizer for measurement reasoning, specifically within the instructional context studied.

The controlled-not (cnot) gate is essential to algorithms in quantum computing for its ability to entangle qubits. As such, it is important to understand how students learning quantum computing reason around the function and use of this critical quantum gate. To investigate this, we conducted think-aloud interviews in which students solved problems involving the cnot gate to understand students’ “cnot toolbox”—the strategies and cognitive resources students use when reasoning about the effect of the cnot gate. We identify three cognitive resources related to the cnot gate: (1) the procedural resource of applying cnot to specific states, (2) a qualitative description of cnot’s effect on the target qubit given the control qubit, and (3) the idea that the control qubit is not changed when cnot is applied to computational basis states. We find that students’ use of the first resource is foundational to their understanding of the second and third, that the second and third resources can sometimes lead students to incorrect conclusions, and that students can use each of these resources separately or in tandem. We also explore how students use these resources in conjunction with Dirac notation, superposition states, and entanglement to reason both productively and unproductively about quantum computing problems.

Despite daily observations of the Sun, many students continue to struggle with the mechanisms explaining its apparent motion. Even after formal instruction, students often develop only fragmentary understandings and construct alternative mental models. This study investigates these models in depth to gain genuine insight into the knowledge structures students develop regarding the Sun’s apparent motion. We conducted individual task-based think-aloud interviews with 16–18-year-olds in which they articulated their mental models verbally, through drawings and by using three-dimensional materials. These explanations were then analyzed to determine whether the mental models included the essential elements, characteristics, and connections consistent with scientific understanding. The analysis showed that students often described individual aspects of the phenomenon correctly, but rarely provided adequate allocentric explanations. Missing components in their models were frequently supplemented with alternative ideas that clustered around five themes: cardinal directions, distance, orientation of Earth’s axis, lines of sight and sunrays, and the orientation of the Sun’s orbital plane. These themes, the variety within each, and the implications of our findings are discussed in detail.

Promoting students’ interest in physics is a key goal of physics education. Describing students’ types of interest provides a great opportunity to offer practical and gender-independent recommendations to teachers for designing interesting learning activities. We investigated students’ types of interest in physics and their association with different student characteristics in a cross-cohort study involving German-speaking students aged 14–16 years ($N=1219$). We assessed the students’ interest in a classical and a modern physics content area, namely, mechanics and particle physics, as well as different student characteristics, such as physics-related self-concept. The collected data on students’ interest was analyzed using mixed Rasch models to unveil qualitative differences in the assessed constructs between different groups of students. The study showed that the vast majority of students (71%) can be categorized into one single type of interest in both content areas. Students of this first interest type were only interested in physics content set in certain contexts, for example, the context “one’s own body.” The second type of interest was specific to the different content areas. Students of the second interest type had relatively high physics-related self-concept and were interested in physics content, even when set in scientific or technical contexts. Whether students belong to one or the other type of interest was best described with a model comprising their degree of physics-related self-concept and their sex. Finally, we introduced the hierarchy of students’ levels of interest in physics (HOLIP) to describe the relative interestingness of different contexts for the first type of interest (i.e., the vast majority of students). We recommend using the HOLIP as a guideline for designing learning activities that promote interest in physics.

Various active learning methods have been developed for introductory physics, and these methods are increasingly being adopted by instructors. However, instructors often do not implement these methods exactly as was originally intended by the developers, as they may face issues related to funding and institutional support for active learning and/or have different instructional contexts (e.g., student populations) and environments (e.g., physical classroom layouts) than the developers. Existing research does not sufficiently capture the range of variation in instructor implementation of established active learning methods, especially in comparison with high-fidelity implementations. In this study, we first identify the critical components (i.e., components without which the active learning method cannot be said to have been implemented) of three named active learning methods: Student-Centered Active Learning Environment with Upside-Down Pedagogies (SCALE-UP), Investigative Science Learning Environment (ISLE), and Tutorials. We then evaluate the fidelity with which 18 different introductory physics instructors implement these methods by analyzing classroom observations and comparing the extent to which these broader implementations use each critical component in their classroom to high-fidelity implementations. We find across all three active learning methods that broader implementations and high-fidelity implementations spend similar amounts of class time on critical components. At the same time, we observe substantial variation in the sequences of activities through which broader implementers operationalize these critical components (e.g., doing a few long activities versus many short activities). Finally, we find no clear relationship between fidelity of implementation (as measured by student and instructor behaviors during class) and student conceptual learning gains for our study’s sample of instructors.

The interplay between mathematics and physics mutually affects both disciplines and is the subject of ongoing research. Here, we study the interplay in the empirical context of an MSc program for practicing high-school physics teachers putting strong emphasis on advanced physics courses taught by active physicists, together with pedagogical content courses taught by physics education researchers. Which aspects of the interplay between mathematics and physics do teacher instructors wish to convey to the teachers? We held semi-structured interviews with the physics instructors of the program, focusing on their goals for their courses. Thematic analysis of the interviews exposed several aspects of the interplay that instructors reported on integrating in their teaching, resulting in a characterization consisting of three families. The first strengthens established aspects in the literature: coherence, a tool for calculation, and mathematical derivation of new physics results (extraction). The second highlights new facets of existing aspects: the universality of geometry in physics, limits of approximations as invitations to explore, and simulations as virtual experiments (representations). The third comprises aspects that are less prominent in the physics education literature: concretization of abstract mathematics in physics contexts, and conflict as a mechanism for discovery (e.g., the Lorentz force’s dependence on velocity conflicting with Galilean invariance). Our findings provide a framework bridging tertiary physics and teaching across all levels—aspects of the interplay between mathematics and physics that are manifest in advanced physics could inform curricular design for high school and beyond, and for teacher education.

Motivated by the question of how the design of online team-working activities affects learning experiences, we present an analysis of three such activities studied by advanced undergraduate and postgraduate distance learning students in astrophysics and space sciences. The activities incorporate periods of team-working ranging from 1 to 9 weeks and have varied assessment strategies. Our study focuses on participation rates, the nature of student interactions, and the reported student experience and is informed by the content of asynchronous discussion forums and interviews with students. We find that variation in student participation differs between the three activities, and we suggest reasons for this related to activity design. We identify major themes of student interaction, these being related to learning and development, group building, and team self-organization. The incidence of communications in these thematic areas appears similar across all three activities. Our analysis framework includes subcategories of these themes, and interactions indicative of peer learning are found to be a major component of student communications. Such interactions are enhanced at the times at which teams are required to make major decisions to progress their joint work. Interviews with students reveal anxieties about participation in team-work and unequal contributions to group outputs. They also present a range of views about the different assessment models used. Overall, interviewees from all three activities report having positive experiences in their teams and that they see the activities as valuable for their individual learning as well as being useful preparation for future employment.