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Despite the popularity of the flipped classroom, its effectiveness in achieving greater engagement and learning outcomes is currently lacking substantial empirical evidence. This study surveyed 563 undergraduate and postgraduate students (61 % female) participating in flipped teaching environments and ten convenors of the flipped courses in which the student sample was enrolled. Results suggest that higher education students can be differentiated based on their preferences for elements of a flipped classroom, resulting in two clusters of students: those who embrace most aspects of a flipped classroom environment as well as prefer it (labelled \"Flip endorsers\") and those who are close to neutral on some elements of a flipped classroom environment but who especially do not endorse the pre-learning aspects (labelled \"Flip resisters\"). Flip endorsers were found to have more positive attitudes towards the course activities (both pre-class and in-class) and to have felt more involved and engaged in the content. These findings shed some light on the types of students who might prefer flipped classrooms, but more importantly identify those who are likely to resist a change to a flipped classroom environment. The findings also suggest that although students may find the flipped classroom more difficult, student outcomes and active participation in class activities do improve when course convenors (a) use a theoretical perspective to inform their flipped teaching strategy, (b) integrate assessment into the design of their flipped classroom, and (c) flip the entire course. (HRK / Abstract übernommen).

Evolving knowledge and professional practice combined with advances in pedagogy and learning technology create challenges for accredited professional programs. Internationally a sparsity of literature exists around curriculum development for professional programs responsive to regulatory and societal drivers. This paper evaluates a participatory curriculum development framework, adapted from the community development sector, to determine its applicability to promote engagement and ownership during the development of a Bachelor of Midwifery curriculum at an Australian University. The structures, processes and resulting curriculum development framework are described. A representative sample of key curriculum development team members were interviewed in relation to their participation. Qualitative analysis of transcribed interviews occurred through inductive, essentialist thematic analysis. Two main themes emerged: (1) ‘it is a transformative journey’ and (2) focused ‘partnership in action’. Results confirmed the participatory curriculum development process provides symbiotic benefits to participants leading to individual and organisational growth and the perception of a shared curriculum. A final operational model using a participatory curriculum development process to guide the development of accredited health programs emerged. The model provides an appropriate structure to create meaningful collaboration with multiple stakeholders to produce a curriculum that is contemporary, underpinned by evidence and reflective of ‘real world’ practice. •Participatory curriculum development framework promotes engagement of interested parties (stakeholders).•Professional programs benefit from stakeholder involvement in maintaining the fit between theory and practice.•Inclusive representation, creates a curriculum that is responsive to changes within knowledge and societal expectations.•A participatory curriculum development process results in a curriculum that is owned by all involved parties.

According to Pamela Cotterill (1992), while the participatory model may be problematic it is better than the male-centred alternative. [...]women who can play the game and use the interview effectively, in this case by wresting control of it from the interviewer, are masculinist. [...]any academic mystique surrounding us as researchers was absent. [...]while she attempted to occupy a position of white, Australian sociologist with respect to her \"subjects,\" she was continually located as white, Singaporean permanent resident, AWARE member by the friends and acquaintances whom she interviewed.

Spatial capture–recapture (SCR) is now routinely used for estimating abundance and density of wildlife populations. A standard SCR model includes sub-models for the distribution of individual activity centers (ACs) and for individual detections conditional on the locations of these ACs. Both sub-models can be expressed as point processes taking place in continuous space, but there is a lack of accessible and efficient tools to fit such models in a Bayesian paradigm. Here, we describe a set of custom functions and distributions to achieve this. Our work allows for more efficient model fitting with spatial covariates on population density, offers the option to fit SCR models using the semi-complete data likelihood (SCDL) approach instead of data augmentation, and better reflects the spatially continuous detection process in SCR studies that use area searches. In addition, the SCDL approach is more efficient than data augmentation for simple SCR models while losing its advantages for more complicated models that account for spatial variation in either population density or detection. We present the model formulation, test it with simulations, quantify computational efficiency gains, and conclude with a real-life example using non-invasive genetic sampling data for an elusive large carnivore, the wolverine (Gulo gulo) in Norway. area search, binomial point process, continuous sampling, NIMBLE, non-invasive genetic sampling, Poisson point process, spatial capture–recapture, wolverine

Spatial capture–recapture (SCR) is now routinely used for estimating abundance and density of wildlife populations. A standard SCR model includes sub‐models for the distribution of individual activity centers (ACs) and for individual detections conditional on the locations of these ACs. Both sub‐models can be expressed as point processes taking place in continuous space, but there is a lack of accessible and efficient tools to fit such models in a Bayesian paradigm. Here, we describe a set of custom functions and distributions to achieve this. Our work allows for more efficient model fitting with spatial covariates on population density, offers the option to fit SCR models using the semi‐complete data likelihood (SCDL) approach instead of data augmentation, and better reflects the spatially continuous detection process in SCR studies that use area searches. In addition, the SCDL approach is more efficient than data augmentation for simple SCR models while losing its advantages for more complicated models that account for spatial variation in either population density or detection. We present the model formulation, test it with simulations, quantify computational efficiency gains, and conclude with a real‐life example using non‐invasive genetic sampling data for an elusive large carnivore, the wolverine ( Gulo gulo ) in Norway.

Abstract 1. Spatial capture-recapture (SCR) is a popular method for estimating the abundance and density of wildlife populations. A standard SCR model consists of two sub-models: one for the activity centers of individuals and the other for the detections of each individual conditional on its activity center. So far, the detection sub-model of most SCR models is designed for sampling situations where fixed trap arrays are used to detect individuals. 2. Non-invasive genetic sampling (NGS) is widely applied in SCR studies. Using NGS methods, one often searches the study area for potential sources of DNA such as hairs and faeces, and records the locations of these samples. To analyse such data with SCR models, investigators usually impose an artificial detector grid and project detections to the nearest detector. However, there is a trade-off between the computational efficiency (fewer detectors) and the spatial accuracy (more detectors) when using this method. 3. Here, we propose a point process model for the detection process of SCR studies using NGS. The model better reflects the spatially continuous detection process and allows all spatial information in the data to be used without approximation error. As in many SCR models, we also use a point process model for the activity centers of individuals. The resulting hierarchical point process model enables estimation of total population size without imputing unobserved individuals via data augmentation, which can be computationally cumbersome. We write custom distributions for those spatial point processes and fit the SCR model in a Bayesian framework using Markov chain Monte Carlo in the R package nimble. 4. Simulations indicate good performance of the proposed model for parameter estimation. We demonstrate the application of the model in a real-life scenario by fitting it to NGS data of female wolverines (Gulo gulo) collected in three counties of Norway during the winter of 2018/19. Our model estimates that the density of female wolverines is 9.53 (95% CI: 8–11) per 10,000km2 in the study area. Competing Interest Statement The authors have declared no competing interest.