The rapid expansion of research on academic resilience in Indonesia has been driven by digitalization, hybrid learning, and demands for equitable quality. However, a systematic synthesis of intellectual structure, thematic evolution, collaboration networks, and scholarly impact remains absent. In this study, a comprehensive bibliometric and science mapping analysis of academic resilience research in the Indonesian context from 2000 to 2025 was conducted. Performance analysis and science mapping techniques—including co-word analysis, co-citation analysis, bibliographic coupling, thematic evolution mapping, and burst keyword detection—were integrated and visualized using VOSviewer. Records were retrieved through Publish or Perish based on Google Scholar. The findings reveal three major patterns. First, publication trends indicate a shift from predominantly psychological and pandemic-related online learning themes toward institutional and systemic concerns and culturally embedded educational practices. Second, five dominant thematic clusters were identified: individual capacities, social support, academic outcomes, institutional and learning environments, and Indonesian cultural–linguistic contexts. Third, scholarly influence is concentrated in review articles and pandemic-era empirical studies employing validated measurement scales and mechanism-based structural models. Cross-national comparative studies were found to enhance citation reach. Overall, the intellectual trajectory of academic resilience research in Indonesia is structured around a dominant explanatory pathway linking self-efficacy and support to resilience and subsequent outcomes. Substantive research gaps remain at the lecturer and organizational levels, as well as in the systematic integration of Islamic and Indonesian linguistic–cultural frameworks into resilience theory. These findings provide a systematic intellectual mapping of the field and offer a foundation for advancing contextually grounded and policy-relevant research agendas.
Proficiency in academic writing is widely recognized as a foundational competence for students enrolled in English-medium instruction programs; however, systematic evidence regarding the nature and distribution of writing difficulties across socio-academic groups remains limited. In the present study, linguistic error patterns in undergraduate academic writing were examined within an English-medium instruction context, with particular attention given to disciplinary background and gender as socio-educational variables. A total of 49 undergraduate students from the University of Dhaka participated in the study, and academic essays were collected as writing samples. A multi-stage analytical framework was employed, combining manual linguistic error analysis with quantitative statistical procedures conducted using SPSS (Version 25). Errors were categorized into four principal domains: grammatical errors, lexical errors, mechanical errors, and discourse-level errors. The distribution of errors revealed that grammatical errors constituted the most frequent category, whereas discourse-level errors occurred least frequently. Independent-samples t-tests were performed to examine differences across gender and disciplinary affiliation. No statistically significant differences were identified between male and female students in overall linguistic error production. In contrast, statistically significant differences were observed between disciplinary groups, with students from social science disciplines producing fewer linguistic errors than their counterparts from science disciplines. The results underscore the importance of discipline-sensitive writing instruction and targeted pedagogical interventions aimed at strengthening grammatical accuracy and genre-specific discourse competence. This study contributes to a more nuanced understanding of academic writing difficulties in English-medium instruction in higher education and provides an empirical foundation for curriculum design and academic writing support programs.
With digital-intelligence technologies becoming increasingly embedded in educational practice, limitations in traditional teacher education have gradually emerged, particularly in terms of competency alignment, curriculum organization, and training models. In response to these challenges, this study addresses the professional development requirements of teachers in the digital-intelligence era by proposing a competency framework comprising Digital Literacy, Innovation Literacy, Subject Literacy, Humanistic Literacy, Educational Literacy, and Critical Thinking (DISHEL-CT), and by constructing an integrated training model grounded in technology empowerment, competency foundation, and practice-oriented innovation. Taking the geography teacher education program at Beibu Gulf University as an empirical case, the curriculum system was systematically reorganized into a structured model characterized by dual foundations, three extensions, four modules, and a central practice axis with two supporting wings. During instructional implementation, a double-helix nested case-based approach and a theme-based situational digital teaching model were adopted to integrate digital technologies across the entire teaching, learning, assessment, and instructional management process. Additionally, an evaluation scale grounded in the DISHEL-CT competency structure was developed to examine the outcomes of the training reform. Empirical analysis indicates that the reform measures were associated with observable progress in pre-service teachers’ disciplinary understanding, educational practice awareness, humanistic orientation, and digital-instructional capability. The findings offer a practice-oriented reference for the reform of teacher education programs in the digital-intelligence era.
Scientific literacy is a fundamental twenty-first-century competency, yet Indonesian students’ scientific literacy remains low based on the 2022 PISA results. This study aims to analyze the pedagogical needs of elementary school teachers in developing electronic modules to improve students’ scientific literacy in Grade 5 science learning. This study employed a qualitative approach with a descriptive qualitative design involving eight Grade-5 elementary school teachers in cluster M, Madiun, East Java, Indonesia. Data were collected through semi-structured in-depth interviews and analyzed using thematic analysis to explore in depth the teachers’ pedagogical needs in developing scientific literacy through the use of electronic modules. Results: The findings reveal dimensions of teachers’ pedagogical needs including (1) the need to strengthen teachers’ understanding of scientific literacy concepts, (2) the need for curriculum-aligned and in-depth materials supported by structured scientific readings to facilitate conceptual understanding, (3) the need for methods and learning strategies providing systematic scenarios, supporting differentiation, and improving teachers’ pedagogical competence in teaching scientific literacy, (4) a gap between positive perceptions of electronic modules and the absence of implementation experience due to limited access and digital competence, (5) the need for technical features including contextual multimedia, interactive activities, higher-order thinking skills-based assessments, and lightweight electronic module designs compatible with infrastructure limitations. The findings indicate that the development of electronic modules to improve students’ scientific literacy must be responsive to teachers’ pedagogical needs by integrating conceptual, methodological, and technical aspects.
Artificial intelligence (AI) changes the way university students learn and improve skills. A total of 476 valid responses were received. This study examined AI anxiety’s dual effect on university students’ proactive skill development and the related mechanisms. The results show that AI anxiety promotes proactive skill development through challenge appraisal and inhibits it through threat appraisal. AI literacy strengthens the positive challenge appraisal and weakens the negative threat appraisal. A high self-driven profile and other profiles driven by resource management, crisis response, and competence are conducive to developing skills proactively. AI anxiety has a complex influence on students’ proactive skill development. At the same time, this provides guidance for universities’ digital transformation and talent cultivation, underscoring the importance of improving AI literacy and fostering constructive mindsets.
Significant regional imbalances have long been observed in China’s development, with the level of educational development in the western region consistently lagging behind the national average. Structural disparities are also evident among the provinces within the region. To systematically identify the determinants of these disparities and to characterize the spatial development patterns, a multidimensional evaluation framework was constructed using six indicators: number of schools, number of teachers, average years of schooling, public library collection size, governmental fiscal education expenditure, and number of internet users. Panel data from 12 western provinces (including municipalities and autonomous regions) for 2008 and 2017 were employed. Indicator weights were determined using the entropy method, followed by cluster analysis to classify the levels of educational development across the region. The findings indicate a steady overall improvement in educational development in western China, although substantial interprovincial disparities persist. Based on these results, policy recommendations are presented, including the optimization of the education policy system, improvement of resource allocation structures, strengthening of high-quality talent recruitment and incentive mechanisms, and coordinated planning of educational resources. The conclusions provide empirical support and policy guidance for enhancing educational equity and promoting balanced development in western China.
In the context of the digital era and ongoing reforms in higher education, how to cultivate and enhance university teachers’ digital teaching competence has become a prominent topic among scholars. Accordingly, this study examined the relationships between university assessment mechanisms and teachers’ digital teaching competence based on 422 valid questionnaires. The results indicate that instructional assessment, research assessment, administrative assessment, and qualification assessment all exert positive effects on teachers’ digital teaching competence. Social support moderates the negative relationship between work stress and digital teaching competence, and further moderates the mediating role of work stress in the relationships between the four dimensions of assessment mechanism rationality and digital teaching competence. The findings provide insights and recommendations for optimizing assessment mechanisms and promoting the modern professional development of university teachers.
The reduction of excessive academic burden in China’s basic education system has been established as a central objective of national education reform and has become a subject of intense policy debate. To elucidate the complex strategic interactions that shape the implementation of the “Double Reduction” policy, a multi-agent evolutionary game model was constructed incorporating three principal stakeholder groups: government authorities, schools and teachers, and students and parents. Replicator dynamic equations were employed to examine the evolutionary stability of stakeholder strategies and the conditions under which equilibrium outcomes emerge. Through numerical simulations, the influence of regulatory enforcement intensity on behavioral trajectories and convergence patterns was evaluated. The results reveal that asymptotically stable equilibria exist, with optimal system performance achieved when government bodies maintain active and credible regulatory oversight, educational institutions engage in substantive and sustained burden-reduction efforts, and families adopt cooperative and adaptive responses. By clarifying the mechanisms through which stakeholder interactions determine collective outcomes, this study provides theoretical support for the refinement of policy coordination and the long-term enhancement of education governance capacity. These findings contribute not only to the understanding of the “Double Reduction” policy’s systemic impact but also to broader discussions on the role of evolutionary game theory in evaluating multi-agent policy interventions in education systems.
The psychometric validity of multiple-choice questions (MCQs) generated by an advanced Artificial Intelligence (AI) language model (ChatGPT) was evaluated in comparison with those developed by experienced human instructors, with a focus on mathematics teacher education. Two parallel 30-item MCQ tests—one human-designed and one AI-generated—were administered to 30 mathematics teacher trainees. A comprehensive psychometric analysis was conducted using six metrics: item difficulty index (Pi), discrimination index (D), point-biserial correlation, item-test correlation (Rit), Cronbach’s alpha (α) for internal consistency, and score variance. The analysis was facilitated by the Analysis of Didactic Items with Excel (AnDIE) tool. Results indicated that the human-authored MCQs exhibited acceptable difficulty (mean Pi = 0.55), moderate discrimination power (mean D = 0.31), and strong internal consistency (Cronbach’s α = 0.752). In contrast, the AI-generated MCQs were found to be substantially more difficult (mean Pi = 0.22), demonstrated weak discrimination (mean D = 0.16), and yielded negative internal consistency reliability (Cronbach’s α = −0.1), raising concerns about their psychometric quality. While AI-generated assessments offer advantages in terms of scalability and speed, the findings underscore the necessity of expert human review to ensure content validity, construct alignment, and pedagogical appropriateness. These results suggest that AI, in its current form, is not yet equipped to autonomously generate assessment instruments of sufficient quality for high-stakes educational settings. A hybrid test design model is therefore advocated, wherein AI is leveraged for initial item drafting, followed by rigorous human refinement. This approach may enhance both efficiency and quality in the development of educational assessments. The implications extend to educators, assessment designers, and developers of educational AI systems, highlighting the need for collaborative human-AI frameworks to achieve reliable, valid, and pedagogically sound testing instruments.
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