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ESP in context:a systematic review of practices, challenges and innovations in a middle-income country
No full textEnglish for Specific Purposes (ESP) is an important component in educational systems worldwide, particularly in middle-income countries (MICs) such as Indonesia, where globalisation and internationalisation have driven the demand for English proficiency. This systematic literature review synthesises existing research on ESP practices within the Indonesian context to identify key trends, gaps, and challenges in current practices and inform future curriculum development and policy directions. Sixty studies, screened against predefined inclusion criteria, were analysed to address the overarching question of how ESP is conceptualised and implemented in Indonesia. The synthesis identified three themes concerning students’ language and communication needs across professional fields, the challenges and strategies involved in ESP teaching, and the use of innovations such as technology and alternative pedagogical approaches. Overall, the review synthesises current trends, challenges, and practices in ESP education, while offering insights transferable to other MICs with similar socioeconomic conditions. The findings provide implications for researchers, educators, and policymakers seeking to enhance ESP provision in comparable contexts
Rethinking the gradual release of responsibility in ITE mentoring:an andragogical perspective
No full textConsistent, high-quality mentoring is essential in initial teacher education (ITE), as mentoring effectiveness significantly influences preservice teachers’ learning during classroom professional experience. Yet, ITE mentoring practices are often informal rather than structured or reflective. This qualitative case study, conducted within a government-funded university-school partnership in Victoria, Australia, investigates a targeted intervention aimed at enhancing mentoring. It explores how primary school teachers applied the familiar Gradual Release of Responsibility (GRR) framework to mentor second-year preservice teachers. Data from interviews and focus groups affirm that the GRR framework provides mentors with a structured, systematic, but somewhat linear approach to mentoring. By adopting a mentoring-as-andragogy paradigm, the study provides new insights into how adult learning theory can support mentor development and improve practice. The findings contribute to a deeper understanding of how effective mentoring structures can bridge informal practices with more deliberate, theoretical approaches.</p
Scalable and effective negative sample generation for hyperedge prediction
No full textHypergraphs have demonstrated their superiority in modeling complex systems compared to traditional graphs by directly capturing the interactions among multiple entities. Hyperedge prediction, which aims to predict unobserved potential hyperedges, is a fundamental task in hypergraph analysis. A critical component in hyperedge prediction is the sampling of informative negative hyperedges from significantly larger candidate negative sets, compared to traditional graphs, to enhance model training efficacy. Most existing methods utilize predefined heuristics to sample negative hyperedges, resulting in limited generalizability due to their reliance on these predefined rules. The new state-of-the-art in this field is generation-based methods, which treat negative sampling as a generative task. Nevertheless, current generation-based approaches are not scalable to large hypergraphs. Additionally, diffusion models have demonstrated superior performance in numerous generative tasks, yet their potential application in the generation of negative hyperedges remains unexplored. However, the adaptation of diffusion models to this specific task presents challenges due to: (1) diffusion models are inherently designed to generate high-quality positive samples, which are well-defined, as opposed to negative samples; (2) diffusion models are traditionally employed in continuous space, whereas negative sampling for hyperedge prediction operates in discrete space.To address these complexities, we introduce SEHP (Scalable and Effective Negative Sample Generation for Hyperedge Prediction), which employs a conditional diffusion model to iteratively generate and refine negative hyperedges, thereby advancing them towards the decision boundary to improve model performance. SEHP further enhances scalability by effectively sampling sub-hypergraphs, integrating global structural information into the diffusion model for batch training. Extensive experiments conducted on real-world datasets demonstrate that SEHP surpasses existing state-of-the-art methods in both prediction accuracy and scalability. The code of our paper is available at https://github.com/SLQu/SEHP</p
