Flipped learning relocates direct instruction to pre-class activities and uses class time for guided practice, feedback, and collaboration. Its effectiveness in physical education (PE), where learning spans cognitive, psychomotor, and social domains, remains unclear. This systematic review was registered in PROSPERO (CRD42024543676) and reported in accordance with PRISMA 2020. Five bibliographic databases and Google Scholar were searched for peer-reviewed intervention studies published from January 2018 to January 2024. Ten studies met the eligibility criteria, and their findings were synthesised thematically. Across secondary and tertiary PE settings, flipped lessons were generally associated with higher motivation, participation, self-efficacy, collaboration, conceptual understanding, and skill performance. Outcomes varied according to the quality and alignment of pre-class materials, students’ completion of preparatory work, teacher readiness, and contextual factors. Most interventions were short and geographically concentrated, and heterogeneity in study designs and measures precluded strong causal or long-term conclusions. Flipped lessons are a promising approach to active learning in PE, but benefits are not automatic. Effective implementation requires accessible, well-designed pre-class resources, purposeful in-class activities, teacher training, and support for students who struggle with self-directed preparation. Longer, more diverse, and methodologically robust studies are needed.
Ce Ren, Chunmei Li, R. Bailey et al.· International Sports Studies· 0 citations
Artificial intelligence has entered academic publication. It is already present in drafting, editing, translation, formatting, reviewing, and manuscript preparation. It is used by students, early-career researchers, senior scholars, reviewers, editors, and publishers. Some uses are minor. Some are substantial. Some are legitimate. Some are not. The task for journals is therefore not to pretend that AI can be excluded from scholarly writing. The task is to decide how it should be used, disclosed, governed, and judged. Academic writing has always involved tools. Authors use word processors, grammar checkers, reference managers, statistical software, translation programmes, plagiarism-detection systems, and journal submission platforms. These tools have changed the mechanics of writing and publication. Generative AI changes more than mechanics. It can produce fluent prose. It can summarise complex material. It can imitate disciplinary styles. It can create abstracts, titles, responses to reviewers, tables, outlines, and plausible literature overviews. It can also invent references, distort arguments, conceal weak reasoning, and produce text that appears scholarly without being scholarly. This editorial offers a simple position. AI is not an author. AI is not a scholar. AI is not a source of academic responsibility. It is a tool. Used well, it can support clarity, accessibility, and editorial preparation. Used poorly, it can damage quality. Used dishonestly, it can threaten trust in academic publication. The distinction matters. A journal should not treat all AI use as misconduct. Nor should it treat all AI use as harmless assistance. The proper standard is human accountability.
R. Bailey, M. L. Guinto· International Sports Studies· 0 citations
This study aims to respond to the pressing need for leadership frameworks that support education for sustainable development (ESD) in higher education. In Jordanian universities, structural and governance constraints hinder effective ESD integration, necessitating a context-specific leadership model.
A two-round Fuzzy Delphi Method was used to identify and prioritise essential components of the sustainable leadership model. Round 1 established expert consensus on key elements, while Round 2 ranked their relevance to refine the model’s structure.
The final model comprises five dimensions: self, university community, educational integration, sustainability governance and sustainability partnerships. High-priority items highlight the role of ethical leadership, stakeholder empowerment, integrated planning and cross-sector collaboration in driving institutional change.
The study is context-specific, focusing on Jordanian universities, which may limit the direct transferability of the model to other national systems. However, the use of internationally informed expert input and alignment with global ESD frameworks supports broader adaptability. Future studies could apply the model in comparative settings to test its generalisability and refine its components.
The model provides higher education leaders and policymakers with a strategic framework for embedding ESD across governance, curriculum, operations and community engagement, particularly in resource-constrained environments.
By promoting ethical leadership, inclusive decision-making and collaborative partnerships, the model supports higher education’s role in addressing sustainability challenges. It encourages institutions to become catalysts for societal transformation by equipping graduates with the skills and values necessary to contribute to the sustainable development goals (SDGs).
This model offers a practical, adaptable framework for embedding ESD in universities facing systemic limitations. It supports global sustainability efforts by illustrating how leadership can operationalise the SDGs through context-sensitive educational reform.
Ra'ed Ali Mohammaed Al-Khamaiseh, R. Bailey, J. Ibbini et al.· International Journal of Sus...· 0 citations
Researchers using latent profile and latent class analysis (LPA/LCA) commonly assign individuals to their modal class without evaluating whether this simplification distorts reported class sizes, profile means, or high-severity subgroups. Existing classification-quality diagnostics-entropy, average posterior probabilities, and Masyn's odds of correct classification and classification probability-assess how sharply a model separates its classes, not whether hard-assigned summaries diverge from probability-weighted ones. We address this through an empirical benchmark (four-class SCL-90 solution; N = 59,408), a fully crossed simulation (class separation, class balance, and indicator-class discrimination precision; 27 conditions), and a six-index diagnostic framework. Hard-assigned and probability-weighted summaries were interchangeable under favorable conditions but diverged under low class separation, severe imbalance, or low precision-most acutely for the smallest, most extreme class. The framework offers simulation-calibrated thresholds for documenting assignment adequacy in continuous-indicator LPA; extension to categorical LCA requires further validation. Hard assignment is most defensible when its adequacy is documented rather than assumed.
Xiaohui Chen, Siguang Chen, Chenglin Wang et al.· Educational and Psychologica...· 0 citations
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