Large Language Models (LLMs) are complicated artificial intelligence that are being used more in educational settings. Applying them into colleges and universities, particularly in mathematics-related fields, seems to be a good way to help students learn and get engaged. The objective of this study is to investigate undergraduate students' perceptions of the efficacy of LLMs in facilitating the understanding of mathematical concepts at selected universities in Malaysia. A quantitative survey design was used, involving undergraduate students enrolling in mathematics courses at various Malaysian higher education institutions. Data were gathered using structured questionnaires and processed employing descriptive and inferential statistical techniques. The results show that using LLMs has a big positive impact on how confident, motivated, and knowledgeable students are in math's. Respondents said that LLMs help them grasp abstract math issues better by giving them simple explanations, detailed problem-solving techniques, and specific feedback. Also, the fact that AI-generated help is there and easy to get to encourage students to study independently and makes them feel more comfortable in solving arithmetic problems. The study finds that LLMs are beneficial digital learning tools that help students learn more about math ideas at Malaysian institutions by centred on the students themselves.

With the increasing complexity and quantity of experimental and observational data, life scientists rely on programming to automate analyses, enhance reproducibility, and facilitate collaboration. Scripting languages like Python are often favored for their simplicity and flexibility, enabling researchers to focus primarily on high-level tasks. Compiled languages such as C++ and Rust offer greater efficiency, making them preferable for intensive or repeated computations. In educational settings, instructors may wish to teach both types of languages and thus may wish to translate content from one programming language to another. In research contexts, researchers may wish to implement their ideas in one language before translating the code to another. However, translating between programming languages requires significant effort, prompting our interest in using large language models (LLMs) for semi-automated code translation. This study explores the use of an LLM (GPT-4) to translate 559 short-form programming exercises from Python into C++, Rust, Julia, and JavaScript. We used three prompting strategies-instructions only, code only, or both combined-and compared the translated code's output against the Python code's output. Translation success differed considerably by prompting strategy, and at least one of the strategies tested was effective for nearly every exercise. The highest overall success rate occurred for Rust (99.5%), followed by JavaScript (98.9%), C++ (97.9%), and Julia (95.0%). Our findings demonstrate that LLMs can effectively translate small-scale programming exercises between languages, reducing the need for manual rewriting. To support education and research, we have manually translated all exercises that were not translated successfully through automation, and we have made our translations freely available.

This study conducts a meta-analysis to examine the effects of organizational culture and transformational leadership on teacher performance across multiple educational contexts. A systematic search was performed through ScienceDirect, Eric, Taylor & Francis, and Google Scholar databases, applying rigorous inclusion criteria for quantitative studies published between 2015 and 2025. From 1250 identified articles, 22 met the eligibility requirements, consisting of 11 studies on organizational culture and 11 on transformational leadership. The random-effects model for organizational culture revealed a significant positive effect on teacher performance (r = 0.448, p < .001), categorized as moderate, though high heterogeneity (I² = 80.18%) indicated contextual variation across studies. In contrast, transformational leadership showed a consistent and significant positive effect (r = 0.441, p < .001) under the fixed-effects model, with low heterogeneity (I² = 4.05%), reflecting stable results across different educational settings. These findings affirm that both organizational culture and transformational leadership are critical determinants of teacher performance, but the influence of organizational culture is more context-dependent. Theoretically, this study enriches the literature on educational management by synthesizing empirical evidence, while practically, it offers insights for policymakers and school leaders in designing strategies to enhance teacher performance through organizational culture development and transformational leadership practices.

Health and hygiene are crucial for maintaining overall well-being, especially for students who spend long hours interacting with peers in shared environments. Poor hygiene practices continue to contribute to the spread of various communicable diseases. This review-based study summarizes existing research on the awareness and importance of hygiene among students. The findings show that although many students understand basic hygienic practices, regular and consistent implementation is still limited. Factors such as school support, parental influence, availability of facilities, and digital learning tools significantly shape students’ hygiene behaviours. The study concludes that continuous reinforcement and proper facilities are essential to improve hygiene habits and reduce disease transmission in educational settings.

English learning for visually impaired students still faces various challenges, particularly due to the limited availability of learning media suited to their needs. This study aims to analyze the challenges and perceptions of teachers and visually impaired students toward the use of audiobooks in English learning at SLB Negeri 1 Tabanan. This research was designed as a qualitative study, with data collected through interviews. Then, Miles and Huberman’s theory is used in analysing data where data analysis consists of four parts: data collection, data reduction, data presentation, and conclusion drawing/verification. Researchers used interviews to collect data with one English teacher and four visually impaired students to obtain deeper insights into their learning challenges, experiences with existing media, and expectations of audiobook-based English learning. The study results are. First, teachers faced limitations in accessing and developing learning media, so they relied mainly on braille, WhatsApp voice notes, and the Merdeka Mengajar modules. Second, students experienced difficulties in using braille, felt bored with monotonous media, and required more varied and engaging audio-based resources. Third, both teachers and students expressed positive perceptions of audiobooks, considering them practical, flexible, and capable of enhancing learning motivation. They expected audiobooks with short duration, clear and expressive narration, bilingual content, and replayable features. The findings reinforce the importance of adopting inclusive, flexible, and learner-centered approaches in special education settings, particularly through the use of audiobooks.

During the pandemic, masks were one of the main protective ways to prevent the spread of infection. Everyone was required to wear masks in academic institutions, however the types of masks used varied based on the setting. This has a lot of impact on learners during the pandemic, and this study aimed to find the problems faced by students. The study was a longitudinal observational study using a predesigned, prevalidated questionnaire. A total of 140 students responded to the questionnaire. The majority used disposable masks, followed by other types or combinations. We found that more than 54% wore masks for a period of 1-6 h in a day. In terms of problems, about 16.8%, 15%, and 14.5% experienced breathlessness, mask-induced acne, and fogging of glasses. Majority agreed that 70.7% had an opinion that wearing masks affects their performance in examinations due to various reasons. 53.3% agreed that mask wearing affects their listening in classes along with verbal and nonverbal communication with friends and teachers. After the COVID-19 onset, face masks became mandatory across Asia, especially China. Surveyed individuals (98%) agreed that proper mask use is crucial for prevention. Mask wearing is now a societal norm. While masks reduce COVID-19 transmission risks, they also present drawbacks. Addressing these challenges, particularly in educational settings, is essential for comprehensive public health education and adaptation to new health practices.

PurposeThis study analyses the sociomotor dynamics of the traditional sporting game (TSG) Bear, Guardian and Hunters from the perspective of motor praxeology, exploring how players adjust their strategies according to the internal logic of the game and their socio-affective status.MethodThis is an observational study involving 10 university students, recording a total of 1,810 observational sequences during the game’s development. Variables such as type of striking and distance from the Bear were assessed, along with sociometric indices of rejection and their influence on motor decisions.ResultsThe phases of the game condition players’ behaviour. In the initial phase, motor aggressiveness predominates; in the intermediate phase, a defensive strategy is adopted; and in the final phase, extreme prudence prevails. Players with higher levels of social rejection exhibit more aggressive behaviour, and the Hunters tend to distance themselves more from the Bear when the latter is marginalised.ConclusionThis TSG provides a valuable educational setting for developing socio-affective relationships, managing motor aggressiveness, and fostering interpersonal relationships.

Collaborative problem solving (CPS) is a critical competency in the Artificial intelligence (AI) era, requiring the integration of cognitive and social skills through real-time dialogue and coordination. While prior studies have explored CPS behaviours using human-coded text from online platforms, limited research has examined how machine learning (ML) and deep learning (DL) models perform on spoken peer dialogue in face-to-face (F2F) classroom settings. This study investigates the automatic classification of CPS phases using a validated coding framework applied to two classroom tasks—one supported by a GenAI assistant and one not. A total of 7,744 utterances were manually labelled across nine CPS subskills and three broader facets. Six ML and five DL models were evaluated, including lightweight BERT variants combined with various classifiers. Results show that BERT-based models significantly outperform traditional ML approaches. Specifically, BERT+ANN achieved better overall performance in smaller, imbalanced datasets, while BERT+CNN performed better in larger datasets. Reducing label granularity from nine subskills to three facets consistently improved classification accuracy and F1 scores. Both models achieved AUROC scores around 0.90, indicating strong discriminative capability. Several key insights emerged from the findings: Model architecture matters: Simpler classifiers like ANN preserve BERT’s semantic representations and offer stable performance, especially in smaller or imbalanced datasets. Task context influences CPS behaviour: Different tasks elicit distinct CPS skill distributions, with task regulation dominating in technical tasks and communicative participation more prevalent in reflective tasks. Label granularity affects performance: Reducing the number of classification labels (e.g., from 9 subskills to 3 facets) significantly improves model accuracy and generalizability. Lightweight models are viable: Even with a reduced-capacity BERT model, competitive performance was achieved, suggesting potential for real-time, resource-efficient deployment in educational settings. This study contributes to educational AI by introducing a novel oral CPS dataset, benchmarking multiple models, and demonstrating the feasibility of lightweight architectures for real-time deployment. Limitations include the small sample size and single-modality input. Future work should explore multimodal features, larger and more diverse classrooms, and teacher-facing dashboards for actionable feedback. The findings support the development of scalable, ethical, and human-centered learning analytics tools that enhance collaborative learning in AI-enhanced education.

Background/Objectives: School violence represents a significant concern for educational communities worldwide, affecting student well-being and academic development. While prior research has documented prevalence rates and risk factors, limited studies have examined social–cognitive factors associated with antisocial behavior specifically within vocational education contexts using integrated analytical approaches. This exploratory cross-sectional study examined social–cognitive factors—specifically self-reported attitudes about aggression norms, prosocial attitudes, and school climate perceptions—associated with violence-supportive attitudes among Greek vocational students. Methods: A cross-sectional design employed validated self-report instruments and traditional statistical methods. The sample comprised 76 vocational high school students (38.2% male; ages 14–18; response rate 75.2%) from one school in Patras, Greece. Validated instruments assessed attitudes toward interpersonal peer violence (α = 0.87), peer aggression norms across four subscales (α = 0.83–0.90), and school climate dimensions (α = 0.70–0.75). Analyses included descriptive statistics, Pearson correlations with bootstrapped confidence intervals, MANOVA for multivariate group comparisons, independent samples t-tests, propensity score matching for urban–rural comparisons, polynomial regression for developmental patterns, and path analysis for theoretical model testing. Results: Strong associations emerged between perceived school-level and individual-level aggression norms (r = 0.80, p < 0.001, 95% CI [0.71, 0.87]), representing one of the strongest relationships documented in school violence research. Violence-supportive attitudes demonstrated inverse associations with prosocial alternative norms (r = −0.37, p < 0.001, 95% CI [−0.55, −0.16]). Significant gender differences emerged for teacher–student relationships (d = −0.78, p = 0.002), with females reporting substantially more positive perceptions. Propensity-matched urban students demonstrated higher aggression norm endorsement compared to rural students across multiple indicators (d = 0.61–0.78, all p < 0.020). Polynomial regression revealed curvilinear developmental patterns with optimal teacher relationship quality during mid-adolescence (ages 15–16). Path analysis supported a sequential association model wherein school-level norms related to individual attitudes through prosocial alternative beliefs (indirect effect β = −0.22, p = 0.002, 95% CI [−0.34, −0.11]). Conclusions: This preliminary investigation identified social–cognitive factors—particularly normative beliefs about aggression at both individual and environmental levels—as strongly associated with violence-supportive attitudes in Greek vocational education. The exceptionally strong alignment between school-level and individual-level aggression norms (r = 0.80) suggests that environmental normative contexts may play a more substantial role in attitude formation than previously recognized in this educational setting. Gender and urban–rural differences indicate meaningful heterogeneity requiring differentiated approaches. Future research should employ longitudinal designs with multi-informant assessment and larger multi-site samples to establish temporal precedence, reduce method variance, and test causal hypotheses regarding relationships between normative beliefs and behavioral outcomes.

Abstract In this paper the design and implementation of a school apparatus for measuring Young's modulus using two methods: the sonic resonance method (SRM) and the impulse excitation technique (IET) is presented. The SRM apparatus includes a tunable oscillator, a driver (speaker or gramophone cartridge), and a movable sensor connected to a preamplifier and oscilloscope. The IET apparatus involves inducing vibrations in the sample using a small hammer, with the resulting damped vibrations recorded by a microphone and analyzed via Fourier transform to identify the resonant frequency. Methods for identifying nodal points using Lissajous figures and a two-channel oscilloscope were also explored. Experimental examples with steel cylindrical and prismatic samples demonstrated the apparatus's effectiveness, achieving a measurement accuracy with a relative error of less than 1.5%. The study confirms the suitability of both methods for accurately determining Young's modulus in an educational setting.

This study explores the use of art and cultural practices as a means to instill Islamic values in the learning process of Islamic Religious Education. The background of this research arises from the challenges faced by Islamic education in the era of globalization, particularly the declining interest of students in Islamic-oriented activities and the strong influence of popular culture. Using a descriptive qualitative approach by reviewing relevant literature, the study finds that various forms of Islamic art such as nasheed, hadrah, calligraphy, and Islamic-themed drama can help students understand and appreciate religious values in a more engaging and meaningful way. These artistic activities serve as a bridge between Islamic teachings and everyday life, allowing values such as good character, piety, and love for the Qur’an to be internalized more effectively. However, the implementation still encounters several obstacles, including limited teacher competence and insufficient curriculum support. The study concludes that strengthening collaboration with the community, utilizing digital technology, and developing extracurricular programs are important steps to reinforce the application of Islamic values through art and cultural approaches within educational settings.

Lifelong learning plays a crucial role in both personal development and societal advancement. By continually enhancing their skills, individuals can better adapt to change and contribute to progress. In this regard, artificial intelligence (AI) supports lifelong learning by enabling personalised learning experiences, increasing accessibility, and fostering continuous education. This study examines existing research on AI’s role in supporting lifelong learning, with a focus on personalised education, skill development, and the reduction of learning gaps across educational stages. A systematic literature review was carried out following the guidelines established to examine the evolving contributions of AI to the development and support of lifelong learning practices. This study follows the standards outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Articles relevant to the study’s objectives were identified through a systematic search of the Scopus database, limited to English-language publications in the field of social sciences over the past ten years. The search strategy employed the following string: (“Artificial Intelligence” OR “AI”) AND (“Lifelong learning” OR “continu* education”) AND (“personali* learning” OR “skill* development”). This process yielded a total of 14 selected studies, from which three themes were identified through thematic analysis: 1) perspectives shaping AI in lifelong learning, 2) benefits of AI tools in education, and 3) AI’s potential for optimising and transforming learning. Findings show that diverse perspectives, along with social and cultural factors, shape the design and effectiveness of AI in lifelong learning. Various AI tools, such as adaptive learning platforms, provide personalised content and immediate feedback, enabling learners to progress at their own pace and promote skill acquisition. These tools offer clear benefits: they foster personalised learning experiences that go beyond mere productivity gains to truly enhance learners’ capabilities. Personalised education models also optimise resource allocation by tailoring content to individual needs, improving outcomes in higher education settings. Looking forward, AI presents significant opportunities to transform education through tailored learning and teacher support. AI must balance technology with human interaction to foster critical thinking, creativity, and problem-solving essential to lifelong learning.

ABSTRACT While the benefits of Continuing Professional Development (CPD) for enhancing pedagogical skills are well documented, its role in shaping teachers' agency and reflective teaching (RT) as cognitive‐affective constructs remains underexplored. Existing studies primarily focus on CPD's impact on instructional effectiveness, leaving a significant gap in understanding how it influences teachers' self‐regulation, professional autonomy, and reflective engagement. Moreover, the extent to which CPD is associated with variations in agency and RT, particularly in EFL contexts, has not been adequately investigated. To address these gaps, this study examined the relationships among CPD, agency, and RT using a survey‐based approach. A total of 384 Chinese EFL teachers participated in the study, completing three validated instruments: the Continuing Professional Development Questionnaire (CPDQ), the Teacher Agency Scale (TAS), and the Reflective Teaching Inventory (RTI). Correlational analyses revealed significant positive associations among CPD, agency, and RT. Furthermore, multiple regression analysis indicated that CPD accounted for 51% of the variance in teacher agency and 44% in RT, suggesting that greater engagement in CPD is linked to higher levels of agency and reflective teaching. This study contributes to the field by reframing CPD as more than a means of pedagogical improvement, emphasising its role in fostering teacher cognition, autonomy, and reflective practice. The findings challenge conventional CPD frameworks that prioritise technical training over cognitive‐affective development, advocating for professional development programs that actively cultivate teacher agency and reflection. For policymakers and teacher educators, the results underscore the need for CPD models that integrate structured opportunities for reflective inquiry, collaborative learning, and decision‐making autonomy. Future research should employ longitudinal and mixed‐methods designs to further explore the mechanisms through which CPD shapes teacher agency and reflection over time and across diverse educational settings.

This study examines the role of Organisational Citizenship Behaviour (OCB) in resolving conflicts within an Islamic educational setting. Conflicts are an inevitable aspect of collective life; yet, their management reflects the maturity and solidarity of an organisation. The research aims to identify and describe how OCB contributes to effective conflict management in maintaining organisational harmony. Using a qualitative case study design, data were collected through purposive and snowball sampling, with observations, interviews, and document analysis. The data were analysed using an inductive conceptualisation approach to capture patterns emerging from participants’ experiences. The findings reveal that conflicts within the institution are generally situational and temporary, and do not escalate into latent disputes. The existence of organisational forums, a tradition of deliberation, and the strong leadership role of spiritual figures ensure that conflict management operates effectively. Furthermore, collective solidarity is reinforced through cooperative problem-solving and mutual agreements. OCB—manifested through sincerity, humility, loyalty, and faith in spiritual rewards—serves as social capital in preserving social equilibrium. The integration of spirituality-based OCB and deliberation-oriented conflict resolution fosters a resilient and adaptive organisational culture. These findings suggest that cultivating spirituality-driven OCB can transform conflict from a disruptive force into a constructive process of social learning and institutional strengthening.

Introduction The integration of advanced digital applications within the educational sector remains a significant challenge. It has been determined that extracurricular educational settings possess a considerable capacity to enhance and differentiate digital instruction. However, educators frequently require assistance to transition from the use of existing digital resources to the creation of their own, personalized, customized learning materials. Professional learning communities (PLCs) have been identified as a promising framework in which teachers can collaborate to develop innovative and practical concepts. While the potential of PLCs is widely recognized, research on this topic is fragmented. A multitude of definitions and operationalizations have been proposed regarding the structure and functioning of PLCs. This complicates a systematic investigation of their effectiveness and makes it difficult to distinguish empirically based findings from purely anecdotal reports. Consequently, a definitive evidence base for the design of effective PLCs remains elusive. Methods The present study posits that the afore mentioned gap is addressed by means of a scoping review. Utilizing the PRISMA-ScR framework, a systematic analysis of German and English-language literature is conducted with the objective of mapping and synthesizing research on the composition, collaboration, and effectiveness of PLCs in the STEM field or in relation to digitalization. Results The results of the study provide a structured overview of the key characteristics, structure, and success factors of PLCs. The present article elucidates the findings that emerged from a comparative and harmonising analysis of diverging PLCs. Discussion The ensuing discussion pertains to the configuration, efficacious design, and execution of professional learning communities, in conjunction with their operational methodologies and investigative endeavours. This synthesis provides a foundation for the conceptualisation of future cooperative initiatives aimed at the development of STEM- or digital educational resources.

The rapid integration of large language models (LLMs) into educational, professional, and public discourse has prompted increasing scrutiny of their multilingual capabilities. While English dominates as a testing and training language, understanding LLM performance in less-resourced languages—such as Czech—is critical for equitable AI deployment. This study investigates a subtle but systematic bias in LLM behaviour: the relative verbosity of their responses in Czech versus English within the domain of elementary geometry. We compiled a dataset of 48 paired mathematical prompts, posed in both Czech and English to six prominent LLMs (ChatGPT, Claude, Gemini, Mistral Large, Copilot Quick-Nuance, and Copilot Deep-Thinker), yielding 576 total responses. Each model was accessed in a controlled language-specific context to ensure fair comparison. Using surface-level metrics—word count and character count—we observed a consistent pattern: English responses were significantly longer than Czech ones across all models. Statistical analysis confirmed the robustness of these differences, with medium to large effect sizes (Cohen’s d) in both metrics. Notably, even morphologically richer Czech did not yield longer outputs in character count, contradicting initial assumptions. Beyond confirming a consistent verbosity gap, our analysis employed rigorous statistical testing, including paired t-tests and Wilcoxon signed-rank tests, as well as effect size estimation to quantify the magnitude of the disparity. We interpret these findings in the context of known architectural and training imbalances in LLM development—particularly differences in how text is segmented and processed, alongside the relative abundance of English-language data. While stylistic conventions and user context may also influence response length, our results consistently indicate that LLMs, even those marketed as multilingual, tend to produce more verbose output in English. This raises concerns about potential discrepancies in explanation quality across languages, which may have implications for fairness and pedagogical effectiveness in multilingual educational settings. The study lays the groundwork for follow-up research that will move beyond surface metrics toward semantic content analysis of mathematical reasoning across languages. Future work will assess whether English verbosity corresponds to greater mathematical depth, or if Czech responses deliver equivalent content more concisely. This line of inquiry is vital for ensuring fairness, clarity, and effectiveness in multilingual AI deployment—especially in contexts such as mathematics education, where explanation quality directly impacts learning outcomes.

This study examines Indonesian students’ perceptions of their Indonesian-accented English (IAE) and how this accent reflects their linguistic identity while studying in Malaysia. Although English is widely used as a second language in Malaysia, Indonesian students often navigate a multilingual environment where accents serve as visible markers of identity and a potential source of judgment. The study aims to explore (1) students' attitudes towards their Indonesian accent when speaking English and (2) the extent to which they associate the accents with their cultural identity. A descriptive qualitative design was employed, using a Likert-scale questionnaire distributed to 36 Indonesian students at University Sains Malaysia and supported by semi-structured online interviews with four participants. Questionnaire data were analyzed using descriptive statistics, and interview responses were analyzed using thematic analysis. Results show that most students view their Indonesian accent positively and do not consider it a marker of poor English. Many reject the idea that IAE diminishes their credibility and instead consider it an authentic part of their cultural background. A smaller group, however, expressed discomfort or a desire to adopt more internationally dominant accents. Overall, the results highlight a growing acceptance of linguistic diversity aligned with English as a Lingua Franca principles. The study suggested that accent awareness, identity-affirming pedagogies, and inclusive language policies are essential in international education settings. These insights contribute to ongoing discussions about accent perception, linguistic identity, and native-speaker ideology in Southeast Asian Contexts.

This study aims to examine the teaching and learning process through the implementation of differentiated learning strategy for teaching speaking in the Merdeka Curriculum at a remote island school. Employing a case study design, data were collected through direct classroom observation. Participants were selected using purposive sampling, involving one English teacher and five eighth-grade students. Data were analyzed using the Miles and Huberman model, including data reduction, data display, and conclusion drawing. The findings revealed that the teacher applied differentiation based on students’ readiness, interests, and learning profiles by using varied speaking tasks, contextual materials, and flexible grouping. Despite constraints such as limited technology, restricted instructional time, and lack of resources, the differentiated learning strategy fostered student engagement and improved speaking confidence. The results indicate that differentiated learning, when implemented responsively, can enhance inclusive and effective speaking instruction in remote educational settings.

ABSTRACT This article examines a teaching intervention in an Argentinean public primary school designed to improve sixth graders’ reading comprehension in a socio-educationally vulnerable context. The intervention used read-aloud work with commentary on Martín Fierro within a sociocultural framework that conceives reading as dialogic and culturally mediated. An exploratory phase showed teachers value literature yet face structural barriers to implementing it. A mixed-methods design compared experimental and control groups, revealing gains in synthesis, inference, and interpretation. Findings highlight canonical literature’s potential to enhance comprehension and inform culturally responsive literacy practices in vulnerable educational settings across diverse Latin American contexts.

ABSTRACT This study investigates the research landscape of social studies in early childhood education through a combined bibliometric analysis and systematic review. Drawing on 119 studies retrieved from the Web of Science database, the bibliometric analysis explores global publication trends, co-authorship networks, co-citation patterns, and keyword co-occurrence. Additionally, 23 studies that met the inclusion criteria were systematically reviewed. These studies employed qualitative, quantitative, and mixed methods, focusing on children, families, and both pre-service and in-service teachers. Research Findings: The findings underscore the contribution of social studies education to the development of executive functions, early literacy, social and emotional learning in young children. Moreover, the review highlights the critical role of family engagement and emphasizes that learning should extend beyond formal educational settings. Practice or Policy: Studies emphasize that teachers’ beliefs, curriculum choices, and sociocultural perspectives are crucial for implementing inclusive, anti-bias pedagogy, with justice-oriented approaches effectively enhancing teachers’ competencies and confidence.