Why the Constraints-Led Approach is not teaching games for understanding: A clarification

Background There is some apparent confusion regarding similarities and differences between two popular physical education (PE) pedagogical frameworks, that is, the Constraints-Led Approach (CLA) and Teaching Games for Understanding (TGfU). Purpose Our aim in this commentary is to detail important theoretical and pedagogical concepts that distinguish these approaches, as well as to recognise where commonalities exist. Findings In particular, we note that TGfU had its roots in the 1960s in the absence of a substantial theoretical framework, although several attempts to retrospectively scaffold theories around TGfU have subsequently emerged in the literature. TGfU is a learner-centred approach to PE in which teachers are encouraged to design modified games to develop the learner's understanding of tactical concepts. In contrast, the CLA has arisen more recently from the umbrella of Nonlinear Pedagogy (NLP), emerging from the empirically rich theoretical framework of ecological dynamics. The CLA adopts a ‘learner–environment’ scale of analysis in which practitioners are encouraged to identify and modify interacting constraints (of task, environment and learner) to facilitate the coupling of each learner's perceptual and action systems during learning. The CLA is a broader framework which has been adapted for the design of (re)learning environments in PE, sport and movement therapy. Other key distinctions between the approaches include: the overall goals; the way in which the learner and the learning process are modelled; the use of questioning as a pedagogical tool; the focus on individual differences vs. generic concepts; and how progressions and skill interjections are planned and implemented. Conclusions Despite such distinctions, the two approaches are somewhat harmonious and key similarities include: their holistic perspective of the learner; the proposed role of the teacher and the design characteristics of learning tasks in each. Both TGfU and the CLA have a powerful central focus on the nature of learning activities undertaken by each individual learner. This clarification of TGFU and the CLA is intended to act as a catalyst for more empirical work into the complementarity of these juxtaposed pedagogical approaches to learning design.

The White Possessive : Property, Power and Indigenous Sovereignty

The White Possessive explores the links between race, sovereignty, and possession through themes of property: owning property, being property, and becoming propertyless. Focusing on the Australian Aboriginal context, Aileen Moreton-Robinson questions current race theory in the first world and its preoccupation with foregrounding slavery and migration. The nation, she argues, is socially and culturally constructed as a white possession. Moreton-Robinson reveals how the core values of Australian national identity continue to have roots in Britishness and colonization, built on the disavowal of Indigenous sovereignty. Whiteness studies are central to Moreton-Robinson’s reasoning, and she shows how blackness works as a white epistemological tool that bolsters the social production of whiteness—displacing Indigenous sovereignties and rendering them invisible in a civil rights discourse, sidestepping issues of settler colonialism. Throughout this critical examination Moreton-Robinson proposes a bold new agenda for critical Indigenous studies, one that involves deeper analysis of the prerogatives of white possession within the role of disciplines.

Imagination for inclusion: Shared understandings across diverse contexts of educational practice

This chapter explores inclusive education as a social imaginary; that is, a common understanding that has become a global perspective. We trace the roots of inclusive education in early movements for social justice and the development of special education and note that these two domains continue to be seen in the ongoing tensions within the practice of inclusive education. We conclude that although much has been achieved in opening up greater opportunities for all children and young people to participate in and engage with education, there is still much work to be done. Creative imagining, discursive dialogue, and courageous actions in breaking down barriers in schools and communities will strengthen the local and global social imaginary of inclusive education, thus affording even greater opportunities for all children and young people regardless of any categorisation that may have been applied to their differences.

Teaching about deliberative politics: Case studies of classroom–community learning projects in four nations

Theories of deliberative politics position grass-roots community members as more than spectators of politics, and instead recognize their capacity for political engagement by discussing and evaluating options in order to make decisions about issues affecting community life. The processes and products of journalism can assist deliberative politics by providing community members with information resources that are vital for understanding the root causes of problems, weighing up competing claims, forming networks around shared concerns, reaching decisions and undertaking action. This article presents the findings of case studies of four community–classroom projects--one each from Australia, New Zealand, the United States and South Africa--that develop the capacity of journalism students to be effective contributors to deliberative politics. The research points to the importance of learning activities that prepare students to work in diverse communities, map significant community places and structures, identify leaders and stakeholders, engage in respectful dialogue about problems and perspectives, and appreciate community frames and values.

Evidence-based practice

This chapter traces the history of evidence-based practice (EBP) from its roots in evidence-based medicine to contemporary thinking about its usefulness to public health practice. It defines EBP and differentiates it from ‘evidence-based medicine’, ‘evidence-based policy’ and ‘evidence-based healthcare’. As it is important to understand the subjective nature of knowledge and the research process, this chapter describes the nature and production of knowledge. This chapter considers the necessary skills for EBP, and the processes of attaining the necessary evidence. We examine the barriers and facilitators to identifying and implementing ‘best practice’, and when EBP is appropriate to use. There is a discussion about the limitations of EBP and the use of other information sources to guide practice, and concluding information about the application of evidence to guide policy and practice.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.