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Developing a psychological understanding of students’ perceptions of their school environment and the relationship with academic achievement
No full textEducational research has demonstrated the importance of variables such as Socioeconomic Status (SES), Gender and Attendance in relation to academic achievement. In addition, research has also highlighted the importance of the physical learning environment in relation to academic achievement and in particular objective characteristics such as temperature, air quality and noise. The way in which students subjectively perceive their school environment has received less attention. However, one recent study has demonstrated how students’ subjective perceptions of their physical school environment, along with SES, Gender and Attendance, are all significantly related to academic achievement. The current study applies a new and distinct methodological approach to an existing data set to develop a psychological model of the physical environment to uncover latent factors which may be important in describing the relationship between the environment and academic performance. The study was conducted on data from 387, S5 students in five secondary schools in Scotland. Exploratory Factor Analysis was conducted to identify the underlying factor structure of the original 60 item questionnaire that was used to measure students’ perceptions of their school environment. Multiple regression analyses were then conducted to investigate the relationship between SES, Gender, Attendance and the new factors identified in the factor analysis, with academic achievement. The factor analysis identified a nine-factor model that reflects distinct dimensions of the physical and social aspects of the school and provides a comprehensive understanding of how students experience their surroundings. The multiple regression analyses confirmed that SES, Gender, and Attendance were significant predictors of academic achievement and that the inclusion of the nine factors explained an additional 11% of the variance in academic achievement. The implications of these findings are discussed in terms of how this psychological model of the physical school environment could be used to inform future educational design, policy, and interventions.<br/
Problematising scripted curricula:Stenhouse's vision of teacher enquiry in new times
Get PDFThis paper critically examines the proliferation of scripted curricula across Anglophone educational systems. Building on Nikolaidis, Fitz and Warnick’s (2024) prescriptive continuum, we identify three distinct but interconnected script typologies: pedagogical, curricular, and behavioural. Drawing on examples from the United States, England and Australia, we analyse four interrelated problem framings - efficiency, quality assurance, expertise, and consistency - that construct scripted curricula as solutions to perceived educational deficiencies. We interrogate how these framings construct particular conceptions of teaching and teacher development, while marginalising or foreclosing alternatives. Stenhouse's vision of teachers as curriculum researchers serves as a critical counterpoint, highlighting what is at stake across the prescriptive spectrum when professional judgement is subordinated to external direction. This discursive analysis advances understanding of how scripted curricula affect teacher professionalism, contextual responsiveness, and educational equity
An exploration into coaches’ perceptions of the technical, physical and psychological requirements for successful, developing pace bowlers
Get PDFThe purpose of this study was to identify coaches’ perceptions of the technical, physical and psychological requirements for successfully developing pace bowlers. Six international male pace bowling coaches were interviewed with their responses analysed using a reflexive thematic analysis approach. Thematic analysis revealed three key themes: 1) no one size fits all, 2) the catalysts for developing pace bowlers and 3) identifying and developing talent. Additionally, coaches identified bowling action, particularly the balance between control and bowling fast, and the role of the strength and conditioning coach as being important to the progression of developing pace bowlers. The findings highlight the need for a collaborative approach between coaches, strength and conditioning coaches and sports psychologists which is vital when supporting the development of young pace bowlers. Further studies are needed to develop a more standardised approach to the criteria used for identifying and developing pace bowlers
Open-world verification:a grand challenge for autonomous systems
Get PDFAutonomous systems use independent decision-making with only limited human intervention to accomplish goals in complex and unpredictable environments. As the autonomy technologies that underpin them continue to advance, these systems will find their way into an increasing number of applications in an ever wider range of settings. If we are to deploy them to perform safety-critical or mission-critical roles, it is imperative that we have justified confidence in their safe and correct operation. Verification is a key process for establishing such confidence. However, autonomous systems pose challenges to existing verification practices. This paper highlights viewpoints of the Roadmap Working Group of the IEEE Robotics and Automation Society Technical Committee for Verification of Autonomous Systems, identifying these grand challenges, and providing a vision for future research efforts that will be needed to address them
Beyond romantic resistance:anti-capitalist aesthetics in narrative cinema
Get PDFKitchen’s Film, Negation and Freedom offers a compelling analysis of Romanticism’s political legacy in cinema, but its conceptual and historical scope remains limited by the absence of feminist perspectives. Engaging with the historiography of women’s cinema and the gendered aesthetics of film production would not only enrich Kitchen’s framework but also expand the possibilities for understanding cinematic resistance in more inclusive and transformative ways. While Kitchen’s study contributes meaningfully to debates on cinema and critique, it remains incomplete without engaging the gendered politics of film history and theory. A feminist intervention would not only recover marginalized voices but also reframe the very terms of artistic and political negation
Effective energy price prediction using LSTM and ARIMA in the smart grid
Get PDFIn the Smart Grid, demand response plays a fundamental role due to the 70% energy wastage in the current power grid. This paper utilizes Long Short-Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) models to predict electricity prices. The study incorporates seasonal energy variations in both demand and electricity prices. The forecasting models were implemented and evaluated using various performance metrics. The deep learning LSTM model demonstrated lower error rates compared to the traditional statistical ARIMA model. Several challenges were encountered during the research, including unexpected negative Regional Reference Prices (RRP), model limitations, and unpredictable price fluctuations. In terms of accuracy, statistical models like Simultaneous Perturbation Stochastic Approximation (SPSA) were also implemented, revealing significant prediction variations. Both statistical models and machine learning approaches were used in the prediction process. However, there is potential for improved accuracy by employing different techniques, such as alternative models, hybrid approaches, and dynamic hyperparameter tuning. This research has important implications for future work in enhancing model accuracy, demand response modeling, and Smart Grid optimization
Institutionalizing human rights in sports mega events:a case study of the United 2026 FIFA Men’s World Cup
Get PDFThe North American 2026 FIFA Men’s World Cup (FWC) is the first tournament in which human rights policies and plans have been in place for the awarding body and host nation from the bid stage. This paper examines the FWC human rights governance arrangements, through an analysis of strategic documents, observations of three host cities, and semi-structured interviews with key stakeholders. Drawing on institutional theory, we argue that human rights institutional logic formation, via stakeholder engagement and isomorphic pressures, influences FIFA. However, we also demonstrate that this influence is limited due to the primacy of business agendas at the executive level. We conclude that while positive steps have been made, with human rights structures, policies, and processes now in place at FIFA, host nation(s) and host cities; issues of implementation remain throughout each level of the FWC institutional field
A unified machine learning framework for gestational diabetes mellitus diagnosis
Get PDFPregnancy is an extraordinary journey marked by many bodily changes. One notable change is the rise in blood sugar levels, leading to a condition called gestational diabetes mellitus (GDM). It happens when the body struggles to produce or effectively use insulin during pregnancy. Several health risks of GDM highlight the critical need for accurate prediction and timely intervention. To address this, the study presents a predictive framework validated on a small real-world cohort dataset from a Brazilian public health setting. The core of this research is a composite predictive model that integrates a diverse ensemble of machine learning and deep learning algorithms. In order to enrich the training material, a function was created to generate new instances based on initial dataset records. The framework's ability to combine the strengths of various models and leverage a meta-classifier for final predictions was rigorously tested across multiple datasets. The results demonstrate exceptional performance by achieving high AUC scores of 88.91%, 95.55%, and 98.71% on original imbalanced, balanced, and augmented datasets, respectively. Additionally, the model shows strong performance across other metrics, including accuracy, precision, recall, and F1 score. These findings validate the generalizability and robustness of the predictive framework. Furthermore, the paper outlines a practical application of this model within a remote-sensing framework in the management information system (MIS) at basic health units (BHUs). It can facilitate proactive GDM management and improve maternal-fetal health outcomes in low-resource settings. The work showcases the predictive framework’s potential to improve GDM management and maternal-fetal health outcomes
Machine learning for Multi Objective Convex Separable Programming (MOCSP) with aggregation of linear approximations and portfolio optimization
Get PDFA novel technique is developed for nonlinear optimization problem which is convex, separable and having multiple objective functions. In the development of the model all the objectives and the constraints of the multi objective model are linearly approximated over suitable intervals. The linear approximations are then aggregated to account for the original problem. The developed technique has been utilized for portfolio optimization problem. Firstly, the minimum variance model has been formulated and solved with machine leaning techniques. Secondly, the risk aversion model has been formulated and solved. The results obtained are combined into a multi objective framework of convex separable programming problem. All the three problems have been solved with the help of the XGBoost, neural network, and decision forest regression models. The renowned Python machine libraries of scikit-learn and keras have been utilized. The results identified portfolios that can return more financial benefits to the investors while investing in the capital market. The results of the proposed MOCSP approach are 22.5% improved in case of risk aversion model. Additionally, 17% improvement has been recorded in case of the minimum risk model. The MAE and RMSE for both XGBoost and decision forest regression have a frail value 0.0001. MAE and RMSE for the neural network regression have been recorded 1% and 2%, respectively. Both Accuracy and F1 score for XGBoost are 91%, for neural network regression are 98%, and for decision forest are 92%, respectively
Strategic interventions for enhancing destination competitiveness in island- based adventure tourism:insights from a small island destination
Get PDFThis study investigates the factors influencing the competitiveness of small island adventure tourism, with a focus on Qeshm Island, Iran. Drawing on the Resource-Based View (RBV) and the Triple Bottom Line (TBL) framework, this research examines how natural resources, sustainability, and infrastructure contribute to destination competitiveness. Using semi-structured interviews with 16 key stakeholders, the study identifies critical factors that shape tourism development, emphasizing the roles of sustainability, accessibility, and government support. By mapping the interrelationships among these factors, the findings provide a structured framework for enhancing tourism management strategies. The study underscores the necessity of strategic interventions that balance economic growth, environmental conservation, and community engagement, offering valuable insights for policymakers, tourism planners, and industry stakeholders seeking to strengthen the long-term resilience and sustainability of adventure tourism in small island destinations