Journal of International Education and Practice

Bridging the Connectivity Gap: A Multi-Year Study of Regional Inequities and Academic Outcomes in Brazil’s ENEM (2015–2023)

2026-02-24T15:44:24+08:00

The National High School Exam (ENEM) is central to social mobility in Brazil, yet it functions within a landscape of deep-seated territorial imbalances. This study shifts the focus toward "digital capital" to evaluate how disparate levels of technological access across Brazil’s five geographic regions correlate with candidate success. Through a longitudinal analysis of INEP microdata covering 33 million participants from 2015 to 2023, the research identifies critical shifts in the exam's demographic and performance profile. The data highlights a dramatic 51% contraction in total registrants over the nine-year period, alongside a pivot toward a younger candidate base predominantly aged 17–25. While the South and Southeast reached a saturation point in internet connectivity (≥95%), the North region struggled with a slower evolution, trailing at 81% by 2023. This digital divide was further exacerbated by the 2020 pandemic, which widened the participation gap between regions to 8%. Performance metrics across all five areas of knowledge exhibited systemic volatility between 2017 and 2019, with regional asymmetries remaining most stubborn in cognitively cumulative fields like Mathematics and Natural Sciences. Ultimately, the study concludes that digital capital is a structural determinant of educational attainment, suggesting that achieving equity in Brazilian higher education requires comprehensive policies that integrate connectivity with robust socioeconomic support.

Conversation Breakdown and Institutional Discourse in Ghanaian ESL Classrooms: A Conversation Analytic Investigation

2025-12-19T09:49:29+08:00

This study responds to the growing pedagogical interest in optimizing communicative competence within English as a Second Language (ESL) classroom interaction by investigating a critical, yet under-explored, domain, the interactional trouble sources that initiate conversation breakdown. Grounded in a Conversation Analytical (CA) framework, the research methodology utilizes a hybrid approach: CA modeling for the micro-analysis of recorded data and content analysis for the qualitative interview data. The empirical base consists of 52 h of recorded ESL classroom discourse extracted from the Ghana Senior High School corpus of academic spoken English database collected by the researchers and research assistants, and augmented by interviews with practicing ESL teachers. A systematic analysis of the interactional sequences showed a pronounced presence of both etic (analyst-defined) and emic (participant-oriented) conversational trouble sources. The findings delineate six salient categories of trouble sources, namely, mishearing/non-hearing, vagueness, topic transition, information deficit, and lexical inappropriacy. These trouble sources demonstrably impeded interactional flow. Notably, the research establishes that the origins of these trouble sources are multi-layered, transcending mere surface-level linguistic (phonology, syntax, lexis) deficiencies to include institutional factors such as instructional ambiguity, procedural misalignments, disciplinary actions, and culturally situated vocabulary choices. This evidence mandates that future ESL research accord greater significance to the impact of institutional discourse (especially, classroom discourse) features as a primary generator of interactional trouble.

From Assistance to Academic Dependency: An Analysis of Students’ Use of ChatGPT in Homework Completion

2026-03-27T14:43:52+08:00

The rapid expansion of generative artificial intelligence has significantly transformed students' learning practices, particularly in the completion of homework assignments. Since the public release of ChatGPT, concerns have emerged regarding its pedagogical value, potential academic dependency, and implications for academic integrity. This study investigates the extent and forms of ChatGPT use among lower and upper secondary school students, focusing on the boundary between academic assistance and dependency. The study also considers the concept of cognitive offloading, referring to students’ reliance on AI tools to reduce cognitive effort during task completion. A mixed-methods explanatory sequential design was employed, combining quantitative data from 132 students (grades VI–XII) with qualitative interview insights. Statistical analyses included descriptive statistics, ANOVA, regression analysis, exploratory factor analysis (EFA), and mediation analysis (model with bootstrapping). Results indicate that ChatGPT is widely used primarily as a supportive tool rather than a full replacement for independent work. Academic dependency levels were generally low; however, regression and mediation analyses revealed that dependency partially mediates the negative relationship between ChatGPT use and independent learning engagement (44% of the total effect). Effect sizes ranged from small to moderate, suggesting a practical but not dominant influence. Findings suggest that ChatGPT itself is not inherently detrimental; rather, its impact depends on the intensity and manner of use. The study highlights the need for structured pedagogical guidance and clear ethical frameworks to ensure responsible integration of generative AI in secondary education.

The Effect of Coaching vs. Training in the Practice of Early Literacy Instruction in Elementary Classrooms

2026-04-17T12:06:51+08:00

Teachers' instructional practices, preparation, and literacy-based professional development (PD) support are crucial in developing the skills and practices necessary to facilitate children's successful literacy development. Traditionally, English Language Arts (ELA) teachers receive literacy-based PD through training techniques, but research has shown that instructional coaching is more effective as PD for teachers. In developing countries, teacher training often relies on outdated methods and covers topics such as classroom behaviour. Additionally, the literature has suffered from a lack of comparative studies that examine teachers’ training models across both training modes, traditional training and coaching and highlight their strengths and weaknesses in this context. Therefore, this study was designed to train teachers in effective literacy instruction in a developing country using a comparative approach, making it the first of its kind. Participants were randomly assigned to either a traditional training group or a coaching group. In total, 80 ELA teachers were observed using the English Language and Literacy Classroom Observations (ELLCO) tool before and after PD training, which served as an assessment of teachers’ in-class practices and competencies. The type of teacher intervention, coaching vs. training, moderated the relationship between PD workshops and teachers’ practices, with this relationship being significant only in the instructional coaching group, R2 = 0.73, F(2, 76) = 37.49, p < 0.001. This recommends developing an effective, consistent coaching model by linking teachers’ skills to classroom practices, predicting teachers’ performance, and boosting young children's early literacy for future success.

Detecting Student Inattention Using Deep Learning and Behavioral Analysis

2025-12-12T14:54:07+08:00

Student inattention in classrooms negatively impacts learning outcomes and academic performance, posing a significant challenge for educators. Traditional methods of monitoring engagement rely on subjective teacher observations, which can be inconsistent, labor-intensive, and prone to bias. To address these limitations, this paper presents an AI-driven framework that uses deep learning and behavioral analysis to detect student inattention in real time. The proposed system integrates computer vision techniques including facial expression recognition, posture analysis, head pose estimation, and eye-gaze analysis, employing convolutional neural networks (CNNs) to extract spatial features and recurrent neural networks (RNNs) to model temporal patterns. The framework was evaluated using annotated classroom video data collected from real teaching sessions, capturing natural student behavior under typical classroom conditions. Experimental results demonstrate that the proposed approach achieves high accuracy in distinguishing attentive from inattentive states, outperforming traditional machine learning baselines while maintaining real-time performance. Beyond detection, the system provides actionable insights for educators by highlighting patterns of disengagement across time and students. By combining CNN-based spatial analysis with RNN-based temporal modeling, the framework offers an objective, scalable, and practical solution for monitoring classroom engagement, enabling timely interventions, personalized instruction, and improved learning outcomes.

Community Engagement in Management of Public Secondary Schools: A Systematic Comparative Review of India and Tanzania

2026-01-06T13:45:39+08:00

This systematic review examines the role of community engagement as a governance mechanism in the management of government secondary schools in developing countries, with a comparative focus on India and Tanzania. The review synthesized evidence from 61 current and relevant peer‑reviewed literature sources published between 2015 and 2025, identified through systematic searches of ERIC, Scopus, Web of Science, UNESCO, and government repositories. The main focus was on exploring the contributions of community engagement in the management of government secondary schools in India and Tanzania. This helped recommend further studies on challenges and ways of community engagement in schools managed by the government. Using a thematic synthesis approach aligned with PRISMA principles, the review analyses patterns across governance structures, accountability mechanisms, and school‑level outcomes. Findings indicate that community engagement contributes to improved resource mobilization, enhanced transparency, better school facilities, and stronger school-community trust, while challenges include political interference, elite capture, and uneven stakeholder capacity. The key contribution of this review lies in its comparative governance of education in elementary government institutions, which demonstrates how differing decentralization architectures in India and Tanzania shape the effectiveness of community engagement. The study proposes a Community–State Partnership Framework to guide policy and practice, emphasizing that community engagement should complement rather than substitute for state responsibility in education governance.