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31,042 Article Results

Bridging the transition gap: the role of practical career education in enhancing independent living skills for adolescents with developmental disabilities in Vietnam

10.11591/ijere.v15i4.38936
Dao Thi Thu Thuy , Nguyen Thi Quynh Hoa , Nguyen Thi Kim Hoa , Nguyen Thi Huyen , Nguyen Thi Phuong Hoa
The transition to adulthood for adolescents with developmental disabilities (DD) in Vietnam is fraught with challenges, marked by a gap between rights-based policies and practical support. This study addresses the critical need to identify effective interventions for enhancing their independent living skills (ILS). Adopting the social cognitive career theory (SCCT) framework, we conducted a quantitative survey with 458 special education teachers and parents to evaluate the impact of three career education (CE) dimensions: practical teaching methods, teacher competence, and experiential environment. Multiple regression analysis revealed that practical teaching methods were the most significant predictor of socio-adaptive competence (β=.48). A key finding was the “will-skill” discrepancy, where students’ high motivation (M=3.35) contrasted with their limited functional skills (M=3.18). We conclude that in resource-constrained contexts like Vietnam, hands-on, task-based instruction is paramount. The study provides empirical evidence for policymakers to prioritize practical vocational training over theoretical approaches to effectively bridge the gap between students’ aspirations and their actual capabilities, fostering genuine autonomy.
Volume: 15
Issue: 4
Page: 2993-3003
Publish at: 2026-08-01

Mathematical mindset shift: reducing math anxiety and enhancing attitudes in secondary school students through growth mindset intervention

10.11591/ijere.v15i4.32397
Noor Atiqah Mohd Noh , Aini Marina Ma'rof , Yusni Mohamad Yusop
This study examines the effectiveness of a growth mindset intervention (GMI) in improving mathematical mindset, reducing math anxiety, and enhancing attitudes toward mathematics among secondary school students. A true-experimental design was employed, involving 69 Form Two students from secondary schools in Kedah, Malaysia. Participants were randomly assigned to a treatment group (n=35) or a control group (n=34). Data were analyzed using multivariate analysis of covariance (MANCOVA). The results revealed significant differences in math anxiety (p=0.000) and attitudes toward math (p=0.000) between the treatment and control groups after controlling for covariates (math scores and gender). However, no significant difference was observed in mathematical mindset between groups (p=0.292). Within the treatment group, significant improvements were noted in mathematical mindset (p=0.001), math anxiety (p=0.000), and attitudes toward math (p=0.000), while the control group showed no significant changes across these variables. Students in the treatment group experienced reduced math anxiety and improved attitudes toward math following the intervention. These findings suggest that GMI is an effective strategy for mitigating math anxiety and fostering positive attitudes toward mathematics, providing valuable insights for educators seeking to improve students' learning experiences and outcomes.
Volume: 15
Issue: 4
Page: 3182-3192
Publish at: 2026-08-01

Writing a research paper with artificial intelligence: a step-by-step guide for junior researchers

10.11591/ijere.v15i4.38930
Emad Al-Mahdawi , Nkaepe Olaniyi
Early-career researchers often have a sound idea yet struggle to turn it into a publishable manuscript that reviewers can trace and evaluate. This paper synthesizes practical guidance on structuring and drafting research articles using the introduction-methods-results-and-discussion (IMRaD) convention, while addressing emerging concerns about the responsible use of generative artificial intelligence (AI) in academic writing. A documentary narrative synthesis was conducted using 36 high-authority sources, including writing guides, guidance from journal editors, publisher and ethics policies, and recent empirical studies on AI-assisted writing. Recommendations were coded using an explicit IMRaD-aligned codebook and then consolidated into a step-by-step workflow from question formulation to submission checks. The synthesis indicates that treating IMRaD as a traceability checklist improves alignment between research questions, methods, results, and claims, and that iterative revision is more effective than one-pass drafting. AI support is most defensible when limited to language and process assistance, combined with disclosure, reference verification, and full human accountability for all content. The paper concludes with an actionable checklist and a visual ‘traceability map’ that can be adapted for research training and supervision.
Volume: 15
Issue: 4
Page: 3583-3590
Publish at: 2026-08-01

Soft skills development in future foreign language teachers: evidence from digital educational artifacts

10.11591/ijere.v15i4.38727
Akbike Boranbayeva , Gulnur Yerik , Svetlana Minasyan
In the context of digital transformation in teacher education, the development of soft skills among future foreign language teachers has become an important dimension of professional preparation. This qualitative case study aimed to identify the factors shaping soft skills development through the analysis of digital educational artifacts created in a technology-enhanced learning environment. The study involved 48 undergraduate students enrolled in a foreign language teacher education program during one academic semester. The data corpus included reflective essays, discussion posts, collaborative project outputs, multimedia assignments, and digital portfolios produced within a learning management system (LMS)-based course. Content analysis and thematic coding were used to identify recurring patterns in the artifacts. The findings revealed five interrelated groups of factors influencing soft skills development: pedagogical design and teaching methods, pedagogical strategies and learning activities, communication and collaboration in digital environments, organization and management of learning activities, and professional and personal development. The results show that digital educational artifacts provide rich evidence of authentic soft skills manifestation beyond traditional self-report methods. The proposed five-factor model may serve as a practical framework for teacher educators and curriculum designers in digitally mediated teacher education.
Volume: 15
Issue: 4
Page: 3518-3528
Publish at: 2026-08-01

Exploring the relationship between teacher work engagement and organizational citizenship behavior in early childhood education

10.11591/ijere.v15i4.39029
Yixuan Zhao , Connie Shin
The role of teacher work engagement (TWE) is very important in creating positive organizational citizenship behavior (OCB) within the early childhood education (ECE) context. This study hypothesizes the use of TWE structural equation modeling (SEM) with advanced logistic regression (ALR) (TWE-SEM-ALR) model and predict the occurrence of OCB among teachers. When 250 early childhood educators in both public and privates collected a teacher-based questionnaire data, it was observed that the information was gathered in questionnaires. Evaluation was done through experimental means, where an 80:20 train-test splitting was used. The model with the suggested accuracy of 92.4 and precision of 0.91, recall as 0.93, F1-score as 0.92, and area under the curve (AUC) of 0.95 was better than models based on logistic regression, support vectors machines, and SEM-only models. The error analysis and the ablation studies also served to validate the role of every model component, as it was shown that the combination of SEM and ALR greatly minimizes the error in predictions and enhances classification stability. The findings of the effectiveness of the proposed framework in explaining complex engagements-behavior relationships and it has rich implications to educational administrators who wish to improve teacher performance and organizational performance.
Volume: 15
Issue: 4
Page: 3310-3321
Publish at: 2026-08-01

Artificial intelligence literacy and foreign language confidence among university second language learners

10.11591/ijere.v15i4.38707
Ashraf Ragab Ibrahim , Hesham Ramadan Abdo , Mohammad Cherif Boraai , Mohamed Ebrahim Khalf , Mohamed Ali Nemt-allah , Hamed Samy Ghareib
This quantitative correlational-predictive study investigated the relationship between artificial intelligence (AI) literacy and foreign-language confidence among university second-language learners. Two samples were recruited: a psychometric sample (N=341) for scale validation and a main sample (N=541) for hypothesis testing. Participants completed the artificial intelligence literacy scale (AILS), measuring awareness, usage, evaluation, and ethics dimensions, and the foreign language confidence scale (FLCS), assessing foreign language competence, sense of linguistic security, and sense of linguistic ownership. Pearson correlation analyses revealed statistically significant positive associations between all AI literacy and foreign language confidence dimensions (r=.398–.574). Simple linear regression demonstrated that AI literacy significantly predicted foreign language confidence (F(1, 539)=270.742, p
Volume: 15
Issue: 4
Page: 2764-2774
Publish at: 2026-08-01

Developing a transdisciplinary design-based in-service science teacher training framework

10.11591/ijere.v15i4.38783
Joelash R. Honra , Ma. Kristina B. B Dela Cruz , Jermae B. Dizon-Yi , Raianne Joy V. Maulion , Sean Derrick M. Oliquiano , James C. Ollero , John Lorence A. Villamin
Contemporary science education requires teachers to facilitate learning that addresses complex, real-world problems beyond disciplinary boundaries. Yet, many in-service science teachers lack professional development that supports transdisciplinary problem-solving and innovative pedagogy. This qualitative study used a grounded theory (GT) approach to examine teachers’ experiences in a transdisciplinary, design-based training program and to develop a framework for effective professional learning. Participants engaged in sustained training grounded in design thinking and authentic problem contexts. Data were collected through semi-structured interviews, focus groups, reflective journals, training artifacts, and observations, and analyzed using constant comparative methods. Findings indicated shifts in teachers’ conceptions of problem-solving, enhanced capacity to integrate disciplinary and non-disciplinary perspectives, and changes in instructional planning and classroom practice. Design thinking functioned as a mediating process that helped teachers navigate ambiguity, collaboration, and iterative reflection. The resulting transdisciplinary design-based in-service science teacher training framework highlights key principles: authentic problem contexts, structured yet flexible design processes, collaborative inquiry, and iterative reflection. The study offers an empirically grounded framework with implications for teacher professional development, curriculum design, and policy.
Volume: 15
Issue: 4
Page: 2814-2822
Publish at: 2026-08-01

Modernizing geography teacher education through STEAM integration to enhance geoecological competence

10.11591/ijere.v15i4.39056
Roza Mukhitdinova , Kuat Baymyrzayev , Murat Auyelbek
This study examines the effectiveness of integrating science, technology, engineering, arts, and mathematics (STEAM) into geography teacher education to enhance geoecological competence among prospective teachers. A mixed-methods pedagogical experiment was conducted with 178 undergraduate students from two pedagogical universities in Kazakhstan, including an experimental group (n=90) and a control group (n=88). The intervention involved the integration of interdisciplinary STEAM projects, digital modeling, geographic information systems (GIS), and practice-oriented tasks focused on regional geoecological problems. Data were collected through curriculum analysis, tests, questionnaires, project evaluation, reflective reports, and expert assessment. The results revealed statistically significant improvement in the experimental group across the cognitive, activity-based, value-motivational, and reflective components of geoecological competence compared with the control group (p
Volume: 15
Issue: 4
Page: 2983-2992
Publish at: 2026-08-01

A pathway model for physics teachers’ pedagogical philosophy core competency development

10.11591/ijere.v15i4.39090
Ze He , Lumeng Chao , Xiaofei Xue
The implementation of the National Education Development Plan (2024–2035) has propelled Chinese physics education from traditional knowledge transmission toward a core competency-oriented teaching model. This transformation need to impose new demands on educationally underserved central and western regions, how teachers adapt to this shift presents an urgent challenge. This study aims to systematically analyze secondary physics teachers’ cognitive levels regarding core competencies, examine the consistency between teaching philosophies, practices, and focus on how individual teacher characteristics and external support conditions influence professional development. The sample comprised 320 secondary physics teachers from Ulanqab City, selected via stratified random sampling to ensure representativeness, with 48 teachers participated in a quasi-experimental intervention study. Findings indicate that teachers’ overall core literacy cognition levels were moderately high (M=3.86, SD=0.42), though 35.0% reached high levels. Significant differences existed across teaching experience and professional titles (p
Volume: 15
Issue: 4
Page: 2904-2918
Publish at: 2026-08-01

Tutor feedback, simulation-based learning, and AI-aware practice in MSc electrical engineering module: a case study

10.11591/ijere.v15i4.38929
Emad Al-Mahdawi , Nkaepe Olaniyi
Engineering programs routinely collect tutor evaluation data for quality assurance, yet the evidence is often not translated into a transparent, reproducible module enhancement plan that can be audited, monitored, and reported as scholarly work. This paper proposes a tutor feedback-to-enhancement (TFE) framework that transforms a standard tutor feedback worksheet into: i) a coded evidence base; ii) a descriptive closed-item profile; and iii) an evidence-to-action matrix (EAM) that links observed strengths and gaps to targeted interventions and measurable indicators. The framework is demonstrated through a single-module case study (an introductory MSc electrical power engineering systems (EPES) module) using one completed tutor feedback sheet containing closed ratings and open comments. The closed items show uniformly positive evaluations (7/12 items rated excellent and 5/12 rated good; no satisfactory/unsatisfactory responses; one item not applicable). The open-text evidence highlights simulation as a core learning scaffold, the importance of equitable access to laptops and e-learning resources, and a specific curriculum enhancement need for a dedicated lecture on photovoltaic (PV) design and battery energy storage systems (BESS). The main contribution is a practical, low-cost method that operationalizes routine tutor feedback into an auditable enhancement pathway, including an explicit artificial intelligence-aware (AI-aware) practice component that emphasizes verification and engineering judgment.
Volume: 15
Issue: 4
Page: 3567-3575
Publish at: 2026-08-01

Bridging ethics and performance in engineering education through predictive learning analytics

10.11591/ijere.v15i4.38767
Hamza Abu Owida , Areen Arabiat
This literature review examines the opportunities, implementation challenges, ethical implications, and emerging directions of predictive learning analytics (PLA) in engineering education. Using a structured review of the literature, the study synthesizes evidence from several publications with emphasis on studies examining risk prediction, personalized support, curricular improvement, interpretability, fairness, and intervention design. The review shows that PLA can improve early identification of at-risk students, support adaptive learning pathways, and inform data-driven refinements in engineering curricula; however, its impact depends on data quality, model transparency, institutional capacity, and the availability of timely human support. The analysis further indicates that the most consequential barriers are fragmented data ecosystems, the difficulty of translating predictions into effective interventions, and unresolved ethical concerns related to privacy, bias, consent, and student agency. The article contributes to educational research by offering an integrated synthesis that connects technical development with pedagogical evaluation and ethical governance in engineering education. It concludes by proposing that future PLA adoption should align predictive modeling with explainable artificial intelligence, learning-theory-informed intervention design, and institution-level implementation strategies. Publications were selected for relevance to PLA in engineering education and then synthesized narratively across opportunities, challenges, ethics, and future directions.
Volume: 15
Issue: 4
Page: 2973-2982
Publish at: 2026-08-01

Developing primary student innovators through local wisdom and integrated learning

10.11591/ijere.v15i4.39994
Suwicha Wansudon , Trai Unyapoti , Phatcharida Inthama
Elementary science, technology, engineering, arts, and mathematics (STEAM) education often struggles with abstract, disconnected lessons, leading to students engaging in “making without learning”. Addressing this gap, this study evaluated a novel STEAM learning package that uniquely integrates regional local wisdom with educational board games to enhance the innovator competencies and STEAM process knowledge of fifth-grade students. Employing a rigorous pretest-posttest quasi-experimental design, the study involved 240 students across four diverse school jurisdictions in Bangkok. To address common methodological limitations in educational research, a linear mixed model (LMM) was utilized to isolate the intervention’s true effect from nested school contexts. The results demonstrated that the experimental group achieved significantly higher innovator competencies and STEAM knowledge compared to the control group. Crucially, the variance analysis revealed that school administrative structure and resources had a negligible impact on student outcomes. This underscores the study’s strongest novelty: combining cultural heritage with gamification not only provides a highly effective pedagogical scaffold but also serves as an equitable instructional tool, successfully bridging the gap between abstract science and meaningful application across diverse educational environments.
Volume: 15
Issue: 4
Page: 3479-3488
Publish at: 2026-08-01

The relationship between arithmetic proficiency and artificial intelligence-assisted learning

10.11591/ijere.v15i4.39461
Khalid Marnoufi , Imane Ghazlane , Fatima Zahra Soubhi , Bouzekri Touri
Amidst the rapid developments witnessed in educational environments, this study aims to investigate the dynamic relationship between the desire for artificial intelligence (AI) supported learning and proficiency in mental arithmetic, considering the latter a decisive factor in enhancing cognitive acquisition. The study focused specifically on the academic elite, represented by students in the mathematical sciences track at the qualifying secondary level. To ensure the accuracy of the results, the methodology relied focusing particularly on the arithmetic subtest within the Wechsler intelligence scale for children as an effective tool for measuring logical reasoning and working memory. The target sample consisted solely of adolescents, who were characterized by a similarity and a homogeneity in their developmental stages and ages. Selection and analysis criteria were based on two pillars, the general scores obtained in the arithmetic subtest, and a systematic evaluation of the students’ aptitude and inclination toward using AI tools. The results concluded that there is a close correlation between arithmetic ability and the quality of logical reasoning in AI contexts. Furthermore, statistically significant homogeneity confirmed that students proficient in AI skills demonstrate higher levels of creative thinking and the ability to apply logic in learning.
Volume: 15
Issue: 4
Page: 3292-3300
Publish at: 2026-08-01

Artificial intelligence usage in the teaching-learning process: perception and challenges

10.11591/ijere.v15i4.38964
Ishani Basak , Benny Thomas , Shinto Thomas
Artificial intelligence (AI) has advanced in the post-pandemic era and is unavoidable in teaching and learning. Teachers’ perceptions, as the primary gatekeepers, are essential for ensuring quality education and inclusive classrooms, with AI as a collaborator. While some teachers resist these technological shifts, others are actively adapting an AI-assisted teaching approach. We conducted this study to understand the reasons for teachers’ resistance (challenges and difficulties) and how they perceive the use of AI in the teaching, learning, and assessment process, because the first step in effective incorporation is having a favorable attitude towards it. Hence, this study explored the perceptions of 15 secondary private school teachers, selected through purposive sampling, regarding the incorporation of AI into teaching, learning, and assessment processes, as well as the challenges they faced. The researchers developed an in-depth interview schedule and conducted interviews to understand participants’ perceptions and challenges. The data is analyzed following the thematic analysis steps by Braun and Clarke. Thematic analysis revealed that teachers demonstrated a positive understanding towards the pedagogical relevance of AI, rather than merely having a favorable perception. Furthermore, teachers predominantly viewed AI as an additional tool to enhance the effectiveness of knowledge transactions and instructional design. The challenges include infrastructure accessibility and professional training; time management for preparation, skill updating, and fulfilling varied teaching and other responsibilities; the inability to verify the accuracy of information; and parental mindset. This study offers insights for developing AI-aided teacher training and relevant curricula for schools.
Volume: 15
Issue: 4
Page: 3124-3140
Publish at: 2026-08-01

Phono-syntactic error pattern analysis in impromptu speaking among English learners: a content analysis

10.11591/ijere.v15i4.39044
Angelie V. Temario , Harold John U. Mamites , April Jane G. Sales , Marcelina S. Deiparine
Second language learners, particularly Filipino learners of English, often experience linguistic difficulties in producing accurate phonological and syntactic forms, especially during spontaneous speaking tasks. This study examines the dominant phono-syntactic error patterns found in impromptu speaking performances and explores their pedagogical implications using Corder’s error analysis framework. Employing a qualitative descriptive research design, the study analyzed recorded impromptu speaking performances of 36 Bachelor of Arts in English Language (BAEL) students. The analysis revealed that in the phonological aspect, segmental errors were more prevalent than suprasegmental errors, with vowel substitution emerging as the most frequent error type, accounting for 22 errors (57.1%). In the syntactic aspect, misformation was identified as the most dominant error category, with 164 errors (54.88%). These findings suggest that while learners demonstrate emerging grammatical awareness, they still encounter difficulties in maintaining phonological and syntactic accuracy during spontaneous speech production. The study highlights the importance of integrating pronunciation and grammar instruction within communicative speaking activities. Furthermore, impromptu speaking tasks serve as an effective diagnostic tool for identifying learners’ linguistic challenges and guiding instructional strategies aimed at improving spoken language proficiency.
Volume: 15
Issue: 4
Page: 3646-3657
Publish at: 2026-08-01
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