This dataset comprises data from one survey undertaken with senior secondary Omani students and a tightly-structured interview undertaken with secondary Omani students and their teachers.

The survey is an adaptation of a WHO instrument and included questions related to gender, age and rurality of the school attended. It was translated into Arabic and back translated into English to ensure validity of the translation. The Arabic version was further trialled using a number of senior secondary classes in Omani schools as well as specialists in the schools of health education in both the Ministries of Education and Health to check the content and translation validity.

Antibiotic resistance is a worldwide problem that is increasing largely due to the misuse of antibiotics in human health and agriculture. This situation is further exacerbated by a dearth of new antibiotic development, the focus of pharmaceutical companies having shifted to more lucrative treatments for chronic conditions such as elevated blood pressure. To conserve the efficacy of the current crop of antibiotics, it is vital that they are used appropriately by individuals. Effective education may be a means to achieve such appropriate use. This paper reports on a large-scale, mixed methods study, which employed a survey and oral questionnaires, undertaken with senior secondary Omani students. The study explored students' understanding of antibiotic resistance as well as their attitudes to the issue of antibiotic resistance. The study findings indicated that, although some students had a reasonably clear understanding of antibiotic resistance, many had serious misconceptions that could result in misuse of antibiotics. The article concludes with suggestions for amending second-ary school pedagogy in Oman to address the misconceptions.

Graph-theoretic approaches are commonly used to map landscape connectivity networks to inform environmental management priorities. We developed the new General Landscape Connectivity Model (GLCM), as a operationally practical way of evaluating and mapping habitat networks to inform conservation priorities and plans. GLCM is built on two complementary metapopulation ecology-based measures: Neighbourhood habitat area (Ni) and habitat link value (Li). Ni is a measure of the amount of connected habitat to each location considering its cross-scale connectivity to neighbouring habitat. The remaining Ni across a region can be reported as an indicator of Ecological Carrying Capacity for wildlife (plants and animals). Li at any location is its contribution to the landscape connectivity of the study region (i.e. which is reported as summed Ni across a region) by virtue of providing the 'least-cost' linkages between concentrations of habitat. Mapped Li provides valuable insights into the pattern of a region's habitat network, highlighting functioning habitat corridors and stepping-stones, and candidate areas for conservation and restoration. Due to its foundations in ecological theory and its parsimonious design, GLCM addresses a number of criteria we list as important, while addressing criticisms often levelled at graph-theoretical approaches. We present results for three south-east Australian casestudies using continuous-value ecological condition surfaces as input. However, a simple habitat/non-habitat binary surface approximating a threshold ecological condition can also be used. GLCM has been designed to specifically address the need for generic landscape connectivity assessment at regional scales, and broader. It incorporates connectivity analyses across a range of spatial scales and granularities relevant to broad ranges of taxa and movement processes (foraging, dispersal and migration). Successively finer spatial scales are more intensively sampled based on a simple scaling-law. This approach allows analysis resolutions to be determined by data-driven ecological relevance rather than by processing limitations. The operational advantages of GLCM means that landscape connectivity assessments can be readily updated with refined or changed inputs including time-series remote sensing of land cover, or applied to alternative scenarios of land use, ecological restoration, climate projections or combinations of these.

Remotely sensed imagery of the Earth’s surface is acquired as multi-scale image data over coarse and fine scales due to variable sensors mounted on spaceborne, airborne or Unmanned Aerial Vehicles (UAV). Measuring and monitoring of the Earth surface requires repeatable and accurate measurements, so the quality and consistency of remotely sensed data must be maximised. For sensors that acquire images in the electrooptical wavelength range (0.4–0.7 μm), the effects of shadow and illumination require compensation to maximise the accuracy and quality of data. These effects contaminate image scenes and are a result of sun-object-sensor geometry, surface morphology and the Earth’s atmosphere. The research in this thesis is method-focussed and consists of four studies to examine shadow and illumination effects offering techniques and directions to compensate for these effects in images of high spatial resolution.

The first study applied an n-dimensional colour space approach to separate shadowed and illuminated pixels from naturally dark or bright objects. An adjustment factor was derived that delineates shadow from directly illuminated areas and separates naturally dark objects from shadow. The factor was applied to the image and accuracy assessment showed a 2.7% classification improvement on the shadow-compensated image. The method required noa priori information and the minimal umbra recovery highlighted the requirement to quantify diffuse skylight in compensation techniques.

The second study presented an alternative technique for shadow detection and abundance for high spatial resolution imagery acquired under clear sky conditions from airborne or spaceborne sensors. The method quantified the proportion of diffuse skylight in each image pixel, termed Scattering Index (SI), thus providing a per pixel measure of shadow extent and abundance. Comparative evaluation was performed against two other methods on high-resolution Worldview-3 (1.2 m) and ADS40 (50 cm) images captured over a common scene. Evaluation showed the method improved the accuracy of classifying shadow pixels and, unlike the other methods, it was invariant to scene and sensor characteristics. The method negated the need for complex sun-object-sensor corrections, was simple to apply, and was invariant to the exponential increase in scene complexity associated with higher-resolution imagery.

The third study was a field-based examination of shadow behaviour at different spectral wavelengths to quantify shadow empirically and accurately. A “FieldSpec® Pro FR” Spectroradiometer and a Canon 450D digital SLR camera were used to measure signatures of cast shadow. The field-based experiment used an occulter to cast shadow onto a Spectralon white plate to produce incrementally adjusted shadow depths. Results showed that shadow depth was darker and more ‘blue’ at the proximal areas and conversely that image brightness values increased towards distal areas. Since image brightness is a result of sun-object-sensor geometry, the conclusion was that a normalised spectral signature is invariant to geometry and can be used to quantify shadow depth.

The last study used all previous results to guide a closer examination of the physics principles behind shadow and illumination effects that resulted in a more concise definition of shadow. The characteristics of illumination, reflectance and Bidirectional Distribution Functions (BRDF) were examined and resulted in a recommendation that ‘atsurface’ reflectance be used as a standard radiometric unit for shadow compensation in remotely sensed imagery. A review of current physics-based approaches for compensation techniques helped define an alternative approach for shadow detection and removal in high-resolution imagery.

These studies demonstrated that an alternative approach to compensation of shadow and illumination effects in high-resolution imagery is required. Current approaches require supporting data such as Digital Surface Models (DSM) or BRDF references and these are rarely available or adequate for the exponential increase of detail in high resolution imagery. The findings provide an alternative approach that uses independent physics-based references to overcome existing limitations and can be applied to any electro-optical sensor. Additionally, this research provides a direction for research into shadow compensation techniques that can overcome the challenges associated with the exponential detail that is inherent in high-resolution imagery.

The development of vaccines against a range of deadly or debilitating diseases represents one of the major medical advances of our time, saving many millions of lives. The COVID-19 pandemic has led to extensive media coverage on vaccination. As a consequence, vaccination views and varied sides of the debate have become a common topic of social conversation and argument worldwide. Despite the undoubted benefits vaccines have provided, there is still skepticism about their safety amongst some sections of society. Anti-vaccine messages are being amplified and disseminated widely by social media, sometimes invoking either pseudo-science or anti-scientific justification. The prevalence and apparent influence of the current anti-vaccine movement suggest that the goals of science education including scientific literacy, critical thinking and argumentation based on reliable evidence, and sound reasoning are not being met for a substantial proportion of the population. This chapter will examine some of the non-scientific arguments currently being communicated about mass vaccination on social media platforms and the implications for science education in engaging with this problematic socio-scientific issue.

The genesis of this chapter is an article in the Journal of Vocational Education and Training (Coll, Taylor, & Nathan, 2003) exploring workplace-based learning as a means of developing Education for Sustainability (EfS). In the 16 years since that publication much has changed, and sadly not for the better. Climate change, which at that time was not perceptibly impacting many people’s lives, is increasingly an immediate reality, with record annual temperatures being recorded almost year on year (Lindsey & Dahlman, 2019), and global climate-related economic losses in 2019 of AU$76 billion (KPMG, 2019, cited in Snape & Ryan, 2019). While individual weather events cannot in themselves be attributed to climate change with any certainty, significant droughts worldwide, an increase in devastating fires (Yu, Xu, Abramson, Li, & Guo, 2020), and an increase in the frequency and severity of hurricanes reflect the trend that the climate is changing, and are consistent with longstanding IPCC predictions (Intergovernmental Panel on Climate Change, 2014). Key messages from the latest Global Environment Outlook (GEO6) (UN Environment Programme, 2019) record a grim picture of the unprecedented rate of deterioration of the global environment. The extensively documented problems in addition to climate change include: worldwide loss of biodiversity, such as the global collapse of insect populations, many of which are vital in providing ecological services such as pollination (Carrington, 2019), increasing air pollution, and degradation of land and freshwater resources including plastic pollution of the oceans (see also, Hayward, 2018).

We present a model for Professional Development (PD) for in-service generalist primary teachers of science. The Remote Classroom Modelling (RCM) model is specifcally designed to address salient challenges in the context of professional isolation. We share the principles that supported the design of this PD prototype, and the insights and lessons learned from a pilot of the model. We employed open-ended questionnaires, individual interviews and focus group interviews using a qualitative exploratory study to capture the experiences of teachers and students involved in this trial. Our findings suggest that the model supports primary teachers in developing: (1) their technological, pedagogical, and content knowledge, (2) their leadership, and (3) their resourcefulness in science. Our analysis reveals two important factors underpinning the successful implementation of this model: participant engagement and expert support. We discuss the applicability of this model in different settings and propose an agenda to progress the development of the RCM.