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30,938 Article Results

Examination of social studies teacher candidates’ views on digital citizenship

10.11591/ijere.v15i3.38957
Ayşegül Çelik Geldi , Ebru Kamiş
This research aims to reveal prospective teachers’ understanding of the concept of digital citizenship. This study utilized a qualitative research model, employing phenomenological design to identify pre-service teachers’ understanding of digital citizenship. The study group consisted of 100 prospective teachers enrolled in the Social Studies Education Department of the Faculty of Education at a university in Türkiye during the 2025-2026 academic year, selected according to convenient sampling. A semi-structured interview form consisting of five questions was prepared. Content analysis was used in the analysis of the study’s data. Based on the research findings, it was determined that prospective teachers defined digital citizenship, digital ethics, digital security, digital bullying, and digital literacy. Finding reveal that participants defined digital citizenship across dimensions such as digital literacy, ethics, security, bullying and identity, though often superficially. Results indicate partial awareness but limited competencies, highlighting the need to strengthen teacher education programs in digital citizenship.
Volume: 15
Issue: 3
Page: 2041-2050
Publish at: 2026-06-01

Student perspectives on designing generative AI-based learning companions for higher education in Oman

10.11591/ijere.v15i3.38991
Saleem Raja Abdul Samad , Pradeepa Ganesan , Shanmuga Pria , Madhubala Radhakrishnan , Khadija Ahmed Al Isaei
Artificial intelligence (AI) has rapidly permeated nearly every sector, from healthcare and finance to manufacturing, transportation, and communication. The emergence of generative AI (GenAI) applications, including intelligent assistants, recommendation engines, and predictive analytics, has further accelerated this transformation. Within education, these advances are reshaping teaching and learning, moving away from traditional instructor-centered models toward learner-centered ecosystems that emphasize adaptability, inclusivity, and self-directed growth. Despite the growing interest in AI-supported learning, existing literature reveals several important gaps that limit the effective integration of AI in educational contexts especially, students’ learning behaviors, and preferred learning resources, have received limited attention. This study explores student’s perceptions of AI-based educational tools to inform the design of an effective AI assistive learning companion for higher education. A structured survey was administered to 135 students across IT and business programs, examining demographics, language skills, learning habits, AI tool exposure, perceptions, concerns, and self-assessed learning confidence. The result of study highlights the need for AI learning companions that are adaptive, language-aware, discipline-specific, and ethically responsible, providing personalized scaffolding, practical skill reinforcement, and support for self-directed learning. These insights inform the design of AI tools that enhance learning confidence, inclusivity, and effectiveness in higher education.
Volume: 15
Issue: 3
Page: 2366-2378
Publish at: 2026-06-01

Quality of life and well-being in university students with intellectual disabilities

10.11591/ijere.v15i3.38715
Nerea Felgueras Custodio , José María López-Díaz , José David Carnicero Pérez
This study examined the impact of an intervention applied through the module ‘emotional competencies and personal development’, aimed at university students with intellectual disabilities and autism, with the goal of improving their well-being and self-perceived quality of life. To this end, the INICO-FEAPS scale was used, designed for the multidimensional assessment of quality of life in people with intellectual disabilities. The results showed an improvement in perceived quality of life after the intervention, regardless of the degree of disability, the presence of autism, age or gender. This shows that individualized interventions focused on personal development can promote the well-being of participants, highlighting the importance of programs that strengthen socio-emotional skills and pre-employment preparation. A quasi-experimental design of repeated measures without a control group was used with 14 participants. Pre-post differences were analyzed using the Wilcoxon signed-rank test, estimating effect sizes. Significant improvements were observed in social inclusion, interpersonal relationships, and physical well-being, with high-magnitude effects on overall quality of life indices. Although the findings are preliminary, they provide evidence on the feasibility and potential impact of social-emotional interventions in understudied university contexts.
Volume: 15
Issue: 3
Page: 1930-1940
Publish at: 2026-06-01

Assessing integrated social-emotional and cognitive competencies in pre-service teachers: scale development

10.11591/ijere.v15i3.38305
Joy D. Talens
Developing integrated social-emotional and cognitive (ISEC) competencies is essential for pre-service teachers (PST), yet these competencies are often underemphasized in teacher education programs. This study aimed to develop and provide preliminary evidence for a reliable, structurally sound instrument to measure ISEC competencies among PST. Using a design and development research approach, PST-ISEC learning competency scale was developed and examined for internal structure and reliability. An initial pool of 74 items, refined through literature review, and expert validation, was administered to purposively selected PST from higher education institutions in one Philippine region (n=370 for exploratory factor analysis (EFA); n=405 for confirmatory factor analysis (CFA)). EFA with Varimax rotation reduced the scale to 22 items across five dimensions: collaborative spirit (CS), hopeful mindset (HM), mindful confidence (MC), emotional resilience (ER), and responsible decision-making and accountability (RD). CFA confirmed five-factor structure with acceptable fit, and internal consistency indices indicated adequate reliability. Convergent and discriminant validity analyses supported construct distinctiveness. The PST-ISEC scale provides a theoretically grounded tool for formative assessment, program evaluation, and targeted interventions. Future studies should examine criterion-related and predictive validity, measurement invariance, and cross-context applicability to strengthen its utility.
Volume: 15
Issue: 3
Page: 2588-2596
Publish at: 2026-06-01

Talent identification and development of youth fencers: coaches’ perspectives

10.11591/ijere.v15i3.39062
Hayder N. Jawoosh , Lim Hooi Lian , Rahimi Che Aman
Talent identification (TID) in sports has been found to be heavily influenced by the expertise and judgment of the coach. However, the factors that inform this judgment have been found to be complex and understudied, especially in the sport of fencing. Inconsistencies have also been found in the concept and construct of TID in the development programs of young athletes. Therefore, the purpose of this study was to identify the essential criteria that inform TID in young male fencers aged between 12 and 15 years and to describe the criteria in the selection of young athletes to a fencing club. A qualitative research methodology was employed in this study. In this research, six male coaches from professional clubs affiliated with the Iraqi Fencing Federation were interviewed. The results of this study revealed that technical, tactical, and mental factors, especially fencing ability, decision-making capacity, and intrinsic motivational factors, were found to be essential in TID. Physiological, physical, and anthropometric factors were also found to be of little importance in TID. In conclusion, TID in young fencers needs to be informed by a holistic approach that considers different dimensions of development. Further research in this area needs to be conducted to refine the criteria in TID.
Volume: 15
Issue: 3
Page: 2577-2587
Publish at: 2026-06-01

Evaluating socioemotional skill interventions for preschool children with autism spectrum disorder: a systematic review

10.11591/ijere.v15i3.38875
Richard Rickie Akat , Suziyani Mohamed , Nurul Khairani Ismail
This study aims to systematically review and synthesize recent empirical evidence on socioemotional skill interventions for preschool children with autism spectrum disorder (ASD) from an educational evaluation perspective. Guided by the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, a systematic search of Scopus and Web of Science (WoS) identified 15 peer-reviewed studies published between 2019 and 2025. The included studies were thematically analyzed and appraised using the mixed methods appraisal tool (MMAT). The findings revealed three dominant intervention themes: i) relationship-based and developmental interventions; ii) structured and skill-focused interventions; and iii) creative, expressive, and technology-supported approaches, demonstrating overall positive effects on socioemotional outcomes. However, the strength of evidence remains moderate due to methodological heterogeneity, small sample sizes, and limited longitudinal designs. These findings highlight the importance of developmentally appropriate and flexible intervention designs in inclusive early childhood education. The review offers practical implications for educators, curriculum developers, and policymakers. Specifically, it emphasizes the need for evidence-based socioemotional programs to strengthen inclusive preschool practices for children with ASD.
Volume: 15
Issue: 3
Page: 2021-2032
Publish at: 2026-06-01

Illuminative evaluation of mathematics curriculum implementation in improving students’ numeracy achievement

10.11591/ijere.v15i3.39078
Jose Bonatua Hasibuan , Deni Darmawan , Suhendra Suhendra , Deni Kurniawan
Persistent evidence from national and international assessments indicates that students’ numeracy achievement remains low, suggesting a gap between intended curriculum goals and classroom implementation. This study conducts an illuminative evaluation of secondary mathematics curriculum implementation as an instructional system, examining how curriculum enactment relates to students’ numeracy achievement in the Indonesian secondary context. Employing a sequential explanatory mixed-methods design, the quantitative phase assessed the numeracy performance of 288 secondary students across content domains, cognitive levels, and item formats, while the qualitative phase investigated instructional practices, assessment culture, and the school learning milieu to explain the observed achievement patterns. The findings indicate uneven numeracy achievement, with performance concentrated at procedural levels and declining from knowing to applying and reasoning. Students perform relatively better on objective formats but demonstrate a limited ability to justify solutions in open-ended tasks. Qualitative evidence further indicates a misalignment between reasoning-oriented curriculum intentions and efficiency-driven classroom practices that emphasize procedural accuracy. These findings provide evidence-based insights into aligning curriculum design, classroom instruction, and assessment practices to strengthen reasoning-oriented numeracy learning.
Volume: 15
Issue: 3
Page: 2608-2617
Publish at: 2026-06-01

Beyond diagnosis: using PNImodified and composite priority indices to orchestrate meta-skills-driven academic management innovation

10.11591/ijere.v15i3.38478
Chi Che , Sukanya Chaemchoy , Pruet Siribanpitak
This study translates an academic management–meta-skills integration framework into a data-driven innovation roadmap for private higher education institutions (HEIs) in Sichuan, China. Using an explanatory sequential mixed-methods design, Phase 1 surveyed 400 undergraduates who provided dual ratings of current performance (degree of success, D) and desired priority (importance, I), enabling computation of the modified priority needs index (PNImodified=(I−D)/D) across meta-skills domains and academic management subcomponents. In Phase 2, institutional leaders and senior academics rated the feasibility and impact of aligned innovations; these ratings were integrated with PNImodified to calculate a composite priority index (CPI) and propose phased implementation sequencing. Results indicated the largest perceived meta-skills development needs in adaptive expertise (PNImodified=0.48) and relational dynamics (0.34). At the academic management level, curriculum development (0.56) and evaluation and assessment (0.45) emerged as the most critical domains. Curriculum structuring (0.65), instructional design (0.53), meta-skills evaluation modules (0.59), and learning engagement (0.59) consistently ranked as top subcomponent priorities and were positioned as Phase 1 actions in CPI-based sequencing. Experts rated the overall innovation as highly suitable (M=4.55) and feasible (M=4.53). The combined indices provide a practical decision tool for sequencing meta-skills-oriented academic management innovations in Sichuan private HEIs.
Volume: 15
Issue: 3
Page: 1862-1875
Publish at: 2026-06-01

The digital shift in parental strategies for heritage language maintenance among expatriate families in Saudi Arabia

10.11591/ijere.v15i3.37729
Musa Alghamdi , Said Muhammad Khan , Shazia Hamid , Saira Abbas
Around two-fifths of the population living in Saudi Arabia consists of expatriates. However, there is limited research on how these families maintain their heritage languages (HLs) in a digital world with limited institutional support. Maintaining HLs is important for identity, cultural continuity, and a sense of belonging across generations, especially for families living far from home. This qualitative study explores how expatriate parents in Saudi Arabia use digital tools to help their children maintain HLs, using Fishman’s reversing language shift (RLS) framework and family language policy (FLP) theory. Researchers interviewed 36 expatriate parents from 15 different national and linguistic backgrounds and analyzed the data with reflexive thematic analysis in NVivo. The results show that families are moving from exclusively home-based language practices to a mix of digital strategies, such as apps, video calls, and online learning spaces, which help strengthen cross-border connections and increase language exposure. However, these new practices also increase mothers’ workload, as they take on most of the planning, mediation, and emotional support. The study suggests policy and practical steps that fit with Saudi Arabia’s Vision 2030, such as providing subsidized multilingual digital resources and family-focused support programs. The clear research design makes the study easy to replicate, and future research should include lower-income families, children’s views, and long-term studies of digital family language practices.
Volume: 15
Issue: 3
Page: 2716-2728
Publish at: 2026-06-01

Cognitive and metacognitive learning strategies as correlates of university students’ mathematics proficiency

10.11591/ijere.v15i3.36934
Polemer M. Cuarto , Enya Marie D. Apostol
Mathematics remains one of the most difficult disciplines in the school curriculum. As such, strategies to address these difficulties are being implemented by educators over the years. This study aimed to determine the influence of the cognitive and metacognitive learning strategies on the mathematics proficiency of university freshmen. Specifically, it sought to assess how students’ use of various cognitive and metacognitive strategies relates to their performance in mathematics. A descriptive-correlational research design was employed to describe the prevailing levels of these learning strategies and examine their association with mathematics proficiency. Data were gathered from 80 randomly selected freshmen students through a validated researcher-made questionnaire and record analysis of their mathematics grades. Results revealed significant positive correlations between control, elaboration, rehearsal, planning, monitoring, and evaluation strategies with mathematics proficiency. The study recommends providing additional mathematics support to struggling students such as remedial and tutorial classes and integrating cognitive and metacognitive learning strategies into the mathematics syllabi. These findings imply that strengthening students’ cognitive and metacognitive awareness can significantly improve their ability to learn and perform in mathematics. Furthermore, integrating these strategies into instructional design may help develop more independent, reflective, and effective learners, leading to higher mathematics achievement.
Volume: 15
Issue: 3
Page: 2338-2347
Publish at: 2026-06-01

Validating the factor structure of primary school teaching quality using the PDCA cycle

10.11591/ijere.v15i3.37326
Nhat Thong Du , Van Dat Tran
Improving teaching quality is a critical goal of educational reform, particularly in the context of Vietnam’s 2018 General Education Program (GEP). However, there is still a lack of valid instruments for assessing instructional practices based on continuous improvement models. This research fills this void by creating and validating the primary school teaching quality scale (PSTQS), based on the Plan-Do-Check-Act (PDCA) cycle, a well-known quality management framework. A cross-sectional survey design was used to collect data from 528 primary school teachers in Ho Chi Minh City. The 20-item PSTQS was constructed to align with the PDCA model and the competencies outlined in the 2018 GEP. The sample was randomly split into two groups for a two-phase validation: exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). EFA found a four-factor structure that matched the PDCA cycle and accounted for 45.3% of the variance. CFA confirmed the model’s fit (comparative fit index (CFI)=0.95, Tucker-Lewis index (TLI)=0.94, root mean square error of approximation (RMSEA)=0.04, standardized root mean square residual (SRMR)=0.05). The scale demonstrated strong internal consistency (composite reliability (CR)>0.79), with robust convergent and discriminant validity. The PSTQS is a reliable, valid tool for evaluating teaching practices through a quality improvement lens. While results are specific to Ho Chi Minh City, the scale offers a foundation for broader application and supports continuous professional development and policy implementation.
Volume: 15
Issue: 3
Page: 1971-1985
Publish at: 2026-06-01

Transparent insights: explainable AI with machine learning classifiers for early stage of depression classification

10.12928/telkomnika.v24i3.27651
S. M. Rakibul; University of Frontier Technology Islam , Shaykh; University of Frontier Technology Yunus , Rashiduzzaman; Daffodil International University Shakil , Fatema Tuz; University of Frontier Technology Johora , Aditya; University of Frontier Technology Rajbongshi , Sujon Chandra; University of Frontier Technology Sutradhar
Depression is a widespread mental health condition characterized by enduring feelings of persistent sadness, loss of interest, and impaired daily functioning. Untreated depression can result in significant implications, such as academic failure, social isolation, and even suicide. This study presents a machine learning (ML)–based framework for classifying depression severity among university students using the Zahir depression scale dataset, comprising 478 responses categorized into mild, moderate, severe, and profound depression. In order to address the issue of class imbalance, we utilized the synthetic minority over sampling technique (SMOTE) on the dataset. In addition, seven different ML algorithms are employed to classify the severity of depression, and each algorithm’s efficiency is determined by four performance evaluation metrics. Among the applied ML classifiers, extra tree classifier outperformed with an average accuracy of 97.85% and 95.75% precision, 95.76% recall, and 95.75% F1-score. To enhance interpretability, the shapley additive explanations (SHAP) method was integrated to identify influential features, providing transparency and insight into the model’s decision process. The proposed framework demonstrates that combining explainable artificial intelligence (XAI) with traditional ML can support healthcare professionals in early depression screening and data driven mental health interventions.
Volume: 24
Issue: 3
Page: 915-925
Publish at: 2026-06-01

Academic engagement and artificial intelligence platform behaviors in grammar achievement

10.11591/ijere.v15i3.37822
Wang Yadan , Soon Singh Bikar Singh , Connie Shin , Zheng Juncai , Zhang Qianqian
This study is among the first to use archival institutional records to test the incremental validity of artificial intelligence platform behaviors (AI_index) in predicting grammar achievement (GA). Using data from 405 non–English-major freshmen enrolled in a compulsory grammar course at a private Chinese university, we examined whether AI_index predicts end-of-semester grammar exam performance beyond course-embedded behavioral academic engagement (AE_index). AE_index was derived from grade-book quizzes and class interactions, whereas AI_index was constructed from institutional platform logs capturing coursework completion and assigned video viewing. Indices were scaled to a 0–100 range, and GA was measured by a unified final exam. Descriptive statistics, correlations, and hierarchical regression analyses showed that AE_index was a small but significant predictor of exam performance, whereas AI_index was weak and non-significant and added no incremental predictive value beyond AE_index. Together, the two indices explained a modest proportion of variance in GA. These findings suggest that completion-based platform metrics are unlikely to reflect effortful learning unless platform tasks align with summative assessment demands (e.g., translation and proofreading). The findings caution against using completion-based AI metrics as high-stakes indicators without demonstrated task–assessment alignment.
Volume: 15
Issue: 3
Page: 2690-2699
Publish at: 2026-06-01

Development of a recommendation system for selecting a formula in cataract surgery

10.12928/telkomnika.v24i3.27580
Arseniy; Volgograd State Medical University Lomakin , Anastasiya; Volgograd State Technical University Donskaya , Alexander; Volgograd State Medical University Zubkov
Accurate intraocular lens (IOL) power calculation remains a critical factor for achieving optimal refractive outcomes in cataract surgery. This study analyzes existing methods and software solutions for selecting formulas used to calculate IOL power. To solve this problem, a support medical decision-making recommendation system (SMDRS) was developed to analyze patient biometric data and predict the most suitable calculation formula. Among the evaluated machine learning approaches, the random forest (RF) algorithm demonstrated the highest stability and classification accuracy, leading to its selection as the core predictive engine. The system was validated using retrospective clinical data and evaluated in a functioning ophthalmology clinic. Performance evaluation demonstrated that the system increased the success rate of surgical outcomes in complex cases from 73.5% to 90.5%, thereby confirming its impact on improving the efficiency of optical calculations in clinical practice. By minimizing human error and standardizing decision-making, the proposed solution offers a robust tool for ensuring consistently superior surgical results.
Volume: 24
Issue: 3
Page: 904-914
Publish at: 2026-06-01

Lightweight SDN/NFV-based framework for dynamic data-flow and network slice adaptation

10.12928/telkomnika.v24i3.27810
Sumbal; Wigan and Leigh College and University Centre Zahoor , Ali; Calrom Ltd. Mamoon
The increasing demand for responsive and reliable network services in next-generation communication systems has intensified the need for dynamic resource management and quality of service (QoS) assurance. Software-defined networking (SDN) and network function virtualization (NFV) provide programmability and flexibility for modern networks. However, practical platforms that demonstrate real-time adaptive behavior remain limited. This study differs from prior simulation-focused work by demonstrating real-time adaptive slice control in a reproducible container-based SDN/NFV emulation environment. A bottleneck-aware slice controller is developed to classify degradations as network-limited, server-limited, or service failure using joint indicators, and to select rerouting or service migration using stability constraints and a lightweight action-cost model. Experimental results show that throughput is restored to above 90% of nominal capacity. Recovery typically occurs within two to three control iterations. Service continuity is maintained with low control-plane overhead. The work provides a reproducible experimental baseline and a decision mechanism that reduces incorrect reroutes/migrations under ambiguous key performance indicator (KPI) drops.
Volume: 24
Issue: 3
Page: 779-785
Publish at: 2026-06-01
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