Search Results (85)

This study examines the relationship between national open educational resource (OER) policies and Sustainable Development Goal 4 (SDG4) outcomes across 187 countries between 2015 and 2024, with particular attention to the moderating role of artificial intelligence (AI) readiness. Despite widespread optimism about digital technologies as catalysts for universal education, systematic evidence linking formal OER policy frameworks to measurable improvements in educational access and completion remains limited. The analysis employs fixed effects and difference-in-differences estimation strategies using an unbalanced panel dataset comprising 435 country-year observations. The research investigates how OER policies associate with primary completion rates and out-of-school rates while testing whether these relationships depend on countries’ technological and institutional capacity for advanced technology deployment. The findings reveal that AI readiness demonstrates consistent positive associations with educational outcomes, with a ten-point increase in the readiness index corresponding to approximately 0.46 percentage point improvements in primary completion rates and 0.31 percentage point reductions in out-of-school rates across fixed effects specifications. The difference-in-differences analysis indicates that OER-adopting countries experienced completion rate increases averaging 0.52 percentage points relative to non-adopting countries in the post-2020 period, though this estimate remains statistically imprecise (p equals 0.440), preventing definitive causal conclusions. Interaction effects between policies and readiness yield consistently positive coefficients across specifications, but these associations similarly fail to achieve conventional significance thresholds given sample size constraints and limited within-country variation. While the directional patterns align with theoretical expectations that policy effectiveness depends on digital capacity, the evidence should be characterized as suggestive rather than conclusive. These findings represent preliminary assessment of policies in early implementation stages. Most frameworks were adopted between 2019 and 2022, providing observation windows of two to five years before data collection ended in 2024. This timeline proves insufficient for educational system transformations to fully materialize in aggregate indicators, as primary education cycles span six to eight years and implementation processes operate gradually through sequential stages of content development, teacher training, and institutional adaptation. The analysis captures policy impacts during formation rather than at equilibrium, establishing baseline patterns that require extended longitudinal observation for definitive evaluation. High-income countries demonstrate interaction coefficients between policies and readiness that approach marginal statistical significance (p less than 0.10), while low-income subsamples show coefficients near zero with wide confidence intervals. These patterns suggest that OER frameworks function as complementary interventions whose effectiveness depends critically on enabling infrastructure including digital connectivity, governance quality, technical workforce capacity, and innovation ecosystems. The results carry important implications for how countries sequence educational technology reforms and how international development organizations design technical assistance programs. The evidence cautions against uniform policy recommendations across diverse contexts, indicating that countries at different stages of digital development require fundamentally different strategies that coordinate policy adoption with foundational capacity building. However, the modest short-term effects and statistical imprecision observed here should not be interpreted as evidence of policy ineffectiveness, but rather as confirmation that immediate transformation is unlikely given implementation complexities and temporal constraints. The study contributes systematic cross-national evidence on aggregate policy associations while highlighting the conditional nature of educational technology effectiveness and establishing the need for continued longitudinal research as policies mature beyond the early implementation phase captured in this analysis. Full article

This study examines how climate change and vegetation are integrated into teacher education curricula across 26 countries, addressing a critical gap in understanding how future teachers are prepared to respond to the climate and biodiversity crises. To evaluate curricular integration systematically, we developed and validated the Climate and Vegetation Curriculum Integration Index (CCVI), which measures four dimensions: climate change, vegetation, links between the two, and pedagogical strategies. Content analysis of 70 official curriculum documents was conducted, with high inter-rater reliability (κ = 0.72–0.85) and internal consistency (Cronbach’s α = 0.89) confirming the robustness of the instrument. Results show that integration remains partial and uneven: climate change content is more common than biodiversity, while vegetation is often marginalized, perpetuating the phenomenon of “plant blindness.” Exemplary cases in Finland, Germany, Mexico, Norway, and Switzerland demonstrate that high levels of integration are achievable, but intra-country variability often exceeds cross-country differences, highlighting the influence of institutional design. The study concludes that teacher education worldwide is not yet aligned with the urgency of global sustainability challenges. The CCVI provides a practical tool for benchmarking progress and guiding reforms, underscoring the need to embed sustainability as a core element of teacher preparation to foster ecological literacy, resilience, and civic engagement. Full article

Retrieval-augmented generation (RAG) has emerged as an effective approach for analyzing massive and diverse data. It offers promising avenues for energy management and intelligent decision support amid the accelerating digital transformation of the power industry. However, when applied to this specialized domain, traditional RAG systems face two key challenges: (1) poor comprehension of domain-specific terminology, leading to irrelevant retrieval, and (2) limited precision in re-ranking the retrieved results. To address these limitations, this paper presents an innovative integrated optimization framework. The framework enhances RAG performance in the electric power domain through two key strategies. First, we adapt a base embedding model to the domain using contrastive learning and iteratively refine hard negative samples to improve retrieval quality. Second, we employ a large language model (LLM) as a teacher to distill re-ranking knowledge into a lightweight bidirectional encoder representations from transformers (BERT) model, using a hybrid loss function that combines mean squared error (MSE) loss and margin ranking loss. The framework aims to simultaneously improve the model’s understanding of domain-specific terminology and the re-ranking accuracy of critical information. Experimental results on both a private power-domain dataset and the public DuReader_robust benchmark demonstrate that the proposed framework achieves significant performance gains. Comprehensive ablation studies confirm the necessity of each component and reveal their synergistic effects within the framework. Furthermore, sensitivity analyses of key hyperparameters confirm the effectiveness of our hybrid loss and identify optimal configurations that enhance both retrieval and generation performance. This work not only introduces an effective optimization framework tailored for domain-specific RAG applications but also advances industrial intelligence by enhancing the accuracy and reliability of information services. Full article

Learning the host language is a crucial factor in the settlement of migrants and refugees in a new country. It offers opportunities, but can also generate exclusion, marginalization, and isolation, hindering the possibilities of participation and the creation of social networks. Host language classes, therefore, play a crucial role, fostering critical awareness that enables learners to act within their social and cultural context. This promotes agency, autonomy, and empowerment, transforming differences into productivity and fostering social justice. This article focuses on classes of Portuguese as a host language (HL) in northern Portugal, examining how these contexts shape learners’ relationship with the language beyond depoliticized or subaltern approaches. It discusses the main obstacles and difficulties in these educational settings, as well as mechanisms that could contribute to more democratic and effective practices. Drawing on interviews with teachers (n = 10), trainers (n = 4), volunteers (n = 8), and students (n = 20) involved in the HL learning process, the content analysis highlights how policies and pedagogical practices impact students and how they are interpreted by these actors, revealing their impact on processes of participation, belonging, and citizenship. The results indicate an emergent form of collective autonomy in the relationship among students, the host society, and teachers, which means that teaching practices encompass not only the development of communication skills but also the civic and political awareness of learners. Lastly, while the language teachers identified more practical barriers in these teaching and learning contexts, the students described emotional and sociocultural obstacles. Full article

Background/Objectives: This study examined how maker-based physical education (PE) lessons, co-designed within a Professional Learning Community (PLC), expanded student participation and supported teacher professional growth. Focus was placed on engaging sport-marginalized students, often excluded due to ability, motivation, or social background. Methods: This qualitative single-case study examined a PE-focused professional learning community (PLC) that collaboratively designed maker-based PE lesson prototypes and partially implemented them in regular PE classes. Data included PLC documents, lesson plans, classroom observations, student work, and semi-structured teacher interviews, and were analyzed using inductive category analysis. Results: Three lesson types emerged: (1) physical data measurement and analysis, (2) performance feedback, and (3) play- and game-based formats. These diversified participation by promoting student roles beyond performers, such as creators and analysts. Sport-marginalized students took on new roles as creators and analysts and, at the same time, showed increased engagement in physical activities and more active participation in lessons as performers. Teachers shifted from skill-focused instruction to reflective, practice-based teaching. The PLC enabled sustained innovation and collective growth. Conclusions: Maker-based PE offers a low-cost, adaptable model for inclusive curriculum reform that promotes creativity, wellbeing, and participation. Future studies should explore its long-term impact, broader implementation, and strategies to support ongoing PLC-based innovation. Full article

This study reconceptualizes resilience by centering marginalized teachers’ agentic resistance as a critical pathway to wellbeing. Using critical qualitative inquiry, we conducted in-depth interviews with 17 U.S. teachers who identified with marginalized racial, gender, and/or sexual identities to explore how they resist structural oppression and sustain their professional and personal identities. Findings revealed that resistance emerged in various ways, including inclusive curriculum design, confrontation with colleagues or administrators, embodied identity expression, and support drawn from contexts and resources for resistance. These resources included social movements and ancestors’ legacies, demonstrating that resistance is not merely individual coping but a collective, identity-affirming practice. We argue that resilience must be reframed to include intentional and proactive resistance, which enables marginalized teachers to challenge oppressive school structures, promote educational equity, and sustain their own flourishing. This study offers a critical framework that shifts resilience from passive endurance to politically engaged practice, emphasizing the need for institutional supports that empower marginalized teachers to resist, persist, and thrive in inequitable educational systems. Full article

This qualitative study examines the impact of societal violence on the school climate in Arab society in Israel, focusing on teachers’ perspectives. Violence is conceptualized as an extreme, intentional form of aggression aimed at causing physical, psychological, or emotional harm. In the Israeli context, Arab society, constituting about 21% of the population, experiences disproportionately high rates of violent crime, reflecting historical marginalization, structural inequality, under-policing, and sociocultural transformations. Within schools, these societal dynamics are reported to negatively affect the learning environment, including diminished teacher motivation, concerns about teaching quality, heightened perceptions of unsafety, strained parent–school relationships, and increased parental aggression. Sixteen teachers participated in semi-structured interviews. Thematic analysis of the data revealed that financial pressures, emphasis on personal honor, and erosion of family values are perceived as key drivers of violence in the community. Teachers also reported adverse effects on students’ emotional, social, and behavioral functioning, as well as academic performance. These findings underscore the urgent need for interventions that enhance school safety, provide trauma-informed teacher training, expand psychological services, and strengthen parental collaboration. Future research should include students’ and parents’ perspectives, examine geographically diverse schools, and explore cross-cultural comparisons to better understand the educational consequences of societal violence. Full article

Schoolyards are increasingly recognized as critical spaces for inclusion, yet research on their role in addressing gender and disability remains limited. This study examines Greek teachers’ perceptions of schoolyard inclusivity, analyzing how views differ across teaching levels, professional experience, and institutional contexts. A quantitative survey design was employed, applying an intersectional framework seldom used in schoolyard research to capture both structural and cultural dimensions of exclusion. Teachers identified barriers such as uneven surfaces, limited adaptive equipment, and the absence of sensory-friendly areas, alongside cultural dynamics, particularly the dominance of competitive sports in central spaces, that marginalize girls and students with disabilities. Findings indicate that educators with longer service, advanced academic qualifications, and training in Special Education were more sensitive to issues of equity and accessibility, while secondary-level teachers were more critical than primary colleagues, reflecting adolescence as a period of intensified gendered exclusion. Situating these results within international debates on playground design, hidden curriculum, and Universal Design for Learning, the article concludes that inclusive schoolyards require not only physical redesign, but also cultural transformation, participatory co-design, and teacher-led practices aligned with global sustainability agendas. Full article

Israeli higher education institutes are challenged by the growing number of ultra-orthodox students. This requires coping with novel aspects unfamiliar to participants, as students and as teachers in the education system, utilizing online learning as a lever for empowering this marginalized population. The aim of the proposed research was to explore perceptions of ultra-orthodox students studying in B.Ed. programs within a secular college of education towards online courses. Data included transcriptions from 68 narratives of interviews, which were analyzed using a mixed-methods approach, which helped us achieve an in-depth understanding of the difficulties and challenges of these higher education students. Altogether, five themes were identified, namely: technical challenges, ethical/religious challenges, academic challenges, engagement challenges, and aspects of availability. Statements referring to academic challenges and engagement challenges were the most frequent. The number of positive and negative statements was balanced. Also, distinct patterns of responses were identified for married vs. single ultra-orthodox women. Findings demonstrate the complexity of utilizing online learning among ultra-orthodox B.Ed. students, in a twofold manner: personally and community-wise. The study may shed light on online learning in additional marginal communities worldwide that are traditional in nature, and that may benefit from online courses. Full article

Addressing Artificial Intelligence (AI) faculty deficiencies in higher education, this paper develops the AI+ Dual-qualified Teacher Capability Ecology Model (AI-DTCEM) based on Capability Ecology Theory. The model is developed after a thorough analysis of the current state of new engineering talent cultivation in universities and the innovative practical abilities required in the AI+ environment. This paper proposes an implementation framework characterized by “three-dimensional collaboration, four-tier progression, and five-element drive.” Additionally, it uses the collaborative education project involving Hangzhou Normal University, Zhejiang University, and Hangzhou Ruishu Technology Co., Ltd. as a backdrop to introduce a deep collaborative education model, showcasing the theoretical and practical achievements of this project. Using NetLogo as the simulation platform, this paper designs a 96-month system dynamics experiment to compare and analyze the outcomes of four scenarios: the baseline experiment, the AI-enhanced experiment, the policy-driven experiment, and the comprehensive optimization experiment. This study reveals the following findings: (1) Policy-driven initiatives are crucial for the successful construction of dual-qualified teacher teams, with the policy-driven scenario achieving the highest overall skill level (9.332). (2) The application of AI technology significantly enhances teacher skill development, resulting in AI skill improvements ranging from 116.6% to 163.4%. (3) The comprehensive optimization scenario (utilizing a collaborative mechanism) achieves systemic advantages, realizing a 100% dual-qualified teacher ratio. However, this comes with diminishing marginal returns on investment. This research provides a theoretical foundation, quantitative analysis, and practical pathways for developing dual-qualified teacher teams in the AI+ era. Full article

Increasingly, scholars are attending to questions of identity and power in French as a second language (FSL) education. An underdeveloped area of research is the experience of queer, trans, and nonbinary FSL teachers in Canada. Understanding how marginalized teachers navigate building inclusive and equitable learning spaces is the focus of this study. To this end, this study used narrative inquiry and photo elicitation methods to understand how—if at all—participants embody inclusivity in their classroom practices. Four themes emerged from this study: (1) (in)visibility of queerness, (2) performing a balancing act, (3) urgency to disrupt, and (4) navigating the teaching of a gendered language. These findings suggest that while participants in this study strive to build inclusive spaces for themselves and their students, external factors, such as fear of opposition and being reprimanded, abound. These findings offer insights into discursive moves to facilitate a meaningfully queered and inclusive FSL learning space, and contributes to the growing body of queer applied linguistics by revealing how queer teachers’ embodied practices can reshape inclusivity in FSL education. Full article

Reinforcement Learning from Human Feedback (RLHF) produces powerful instruction-following models but relies on a preference-labeling process that is both costly and slow. An effective alternative, Reinforcement Learning from AI Feedback (RLAIF), uses large language models as teachers for relabeling; however, this introduces substantial label noise. In our setting, we found that AI teachers flipped approximately 50% of the original human preferences on the dataset, a condition that degrades the performance of standard direct preference optimization (DPO). We propose noise-robust DPO (nrDPO) and nrDPO-gated, two drop-in variants that make DPO resilient to noisy preferences. nrDPO reweights each pair by (i) a margin-confidence term from a frozen reference policy (base or SFT), (ii) a context-stability term that penalizes preferences that change under truncated histories, and (iii) a length correction to curb verbosity bias. nrDPO-gated further filters low-confidence pairs via a simple threshold on the reference margin. On a dataset with heavy synthetic noise (30% flips), nrDPO-gated improves the preference accuracy by +3.8% over vanilla DPO; in a realistic RLAIF setting, nrDPO-gated is the only configuration that recovers competitive alignment, reaching ≈60% on a 5k relabeled set (vs. ≈49–50% for vanilla DPO) and approaching RLHF baselines. Full article

The social integration of artificial intelligence (AI) poses fundamental challenges to value-driven domains such as education, where the adoption of new technologies raises not merely technical but also deeply rooted ethical and identity-related questions. While dominant technology acceptance models (e.g., TAM and UTAUT) primarily focus on cognitive-rational factors (e.g., perceived usefulness), they often overlook the cultural and value-based elements that fundamentally shape adaptation processes. Addressing this research gap, the present study examines how two hitherto under-researched factors—religiosity and teacher’s sense of calling—influence teachers’ attitudes toward AI and, ultimately, its adoption. The research is based on a survey of 680 Catholic secondary school teachers in Hungary. To analyse the data, we employed structural equation modelling (PLS-SEM) to examine the mechanisms of influence among religiosity, sense of calling, and AI attitudes. The results indicate that neither religiosity nor a sense of calling exerts a significant direct effect on AI adoption, and their indirect effects are also marginal. Although statistically significant relationships were found—stronger religiosity reduces a supportive evaluation of AI, while a higher sense of calling increases AI-related concerns—their practical significance is negligible. The study’s main conclusion is that successful AI integration, building on teachers’ pragmatic attitudes, is achieved not by neglecting value-based factors, but by developing critical AI literacy that treats technology as a responsible amplifier of pedagogical work. This finding suggests that value-based extensions of technology acceptance models should be approached with caution, as the role of these factors may be more limited than theoretical considerations imply. Full article

Over the past two decades, rising concerns about teacher stress and professional sustainability have fueled the development of instruments assessing teacher resilience. This review aims to map the existing resilience assessment tools specifically designed for educators, evaluating their theoretical frameworks, psychometric soundness, and contextual relevance. Twelve instruments were analyzed through an extensive literature review of peer-reviewed studies published over the past twenty years, including general, preservice, EFL, and teacher-specific scales for special education. Findings reveal a progression from early instruments emphasizing intrapersonal traits to current tools incorporating ecological and contextual dimensions. While several scales demonstrate satisfactory reliability and cross-cultural applicability, many still suffer from conceptual limitations, insufficient cultural adaptation, or marginal psychometric robustness. This review concludes that despite significant advances, future research must prioritize culturally grounded frameworks, broader subgroup validation, and advanced psychometric methodologies to ensure accurate, inclusive, and practical assessments of teacher resilience across diverse educational settings. Full article

In this paper, we argue that what is sometimes at fault for the poor attendance and lack of engagement in schools observed from historically marginalized families is a missed opportunity to increase understanding or cultural relevance on the part of schools. In this paper, we use the construct of “counter stories” which has the potential to change the script on the instrumentalist demands of quantity versus quality in parent engagement. By providing examples of what we consider “quality” engagement techniques via the staff’s interpretation of their roles within one demographically rich early learning center, the strategies used to engage parents are documented. Counter-stories of practice show family-centrism as interpreted by school leaders. By describing one community context and its practices of building relationships with newcomer families, relationally driven parent engagement techniques are revealed. The authors highlight how inquiry-based methods surpass the generic approaches described in policy. When parent engagement “arises” from within parents’ motivations and informs authentic knowing (by teachers and school leaders), community systems are elevated. Professionals’ decisions about children and community groups that are informed by families’ knowledge are consequently meaningful and authentic. Full article