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31,456 Article Results

Educational responsiveness to industrial upgrading: a longitudinal evaluation of higher vocational education in Shandong

10.11591/ijere.v15i5.40038
Qiushi Wang , Norazlinda Saad
Shandong provides a concrete case for examining whether higher vocational education (HVE) can keep pace with industrial upgrading. Since the new and old kinetic energy conversion (NOKEC) strategy began in 2018, provincial policy has prioritized high-tech manufacturing and the “ten strong industries,” while HVE program structures have adjusted more slowly. This study evaluates the coordination between industrial upgrading and HVE program structure across 16 prefecture-level cities from 2016 to 2023. A dual-system indicator framework was built from official statistical yearbooks, education quality reports, and provincial bulletins. After min-max standardization, the entropy weight method (EWM), coupling coordination degree (CCD) model, and obstacle degree model were used to measure temporal evolution, spatial variation, and main constraints. The results show that coordination improved from marginal to good, but the HVE subsystem remained below the industrial subsystem throughout the period. Qingdao, Jinan, and Yantai reached high-quality coordination, while Heze remained relatively constrained. Graduate employment in strategic industries was the leading obstacle factor. The findings suggest that curriculum renewal, teacher development, enterprise co-designed modules, and graduate tracking should be strengthened to improve HVE responsiveness to regional industrial change.
Volume: 15
Issue: 5
Page: 4166-4178
Publish at: 2026-10-01

TPACK-based AI integration in Kazakh language teacher education: evidence from a controlled pedagogical experiment

10.11591/ijere.v15i5.39655
Baimukhambetova Kuralay , Kalibek Ybyraimzhanov , Bakirova Sholpan
Pre-service teacher education in Kazakhstan faces a persistent gap between digital infrastructure investment and teachers’ practical ability to integrate artificial intelligence (AI) and digital tools into Kazakh language instruction. This study examined whether a technological, pedagogical, and content knowledge (TPACK)-aligned specialist course could simultaneously improve TPACK among pre-service primary school teachers. A quasi-experimental pretest–posttest control-group design was employed with 123 third-year pre-service teachers (experimental: n=62; control: n=61) at two universities in Shymkent, Kazakhstan. The Dubovitskaya diagnostics of educational motivation orientation scale and a researcher-developed digital competence survey were used to assess outcomes. Between-group differences were analyzed using the independent-samples student’s t-test (α=0.05), with effect sizes reported via Cohen’s d. The intervention produced statistically significant, large-effect improvements in professional motivation (t=6.84, p
Volume: 15
Issue: 5
Page: 4412-4421
Publish at: 2026-10-01

Interprofessional attitudes, empathy, communication skills, and patient safety competency in traditional Korean medicine and nursing students

10.11591/ijere.v15i5.39879
Mikyoung Lee , Hunpyeong Park
Patient safety competency is an essential part of health professions education and may be influenced by learners’ interprofessional attitudes, empathy, and communication skills. This study investigated the relationships among these variables and identified factors influencing patient safety competency among traditional Korean medicine and nursing students. A cross-sectional correlational study was conducted among 238 students at a university in South Korea, consisting of 150 nursing students and 88 traditional Korean medicine students. Data were collected using questionnaires assessing interprofessional attitudes, empathy, communication skills, and patient safety competency. Data were analyzed using descriptive statistics, t-tests, analysis of variance (ANOVA), Pearson’s correlation coefficients, and multiple regression analysis. Patient safety competency varied by academic year, patient safety education (PSE) experience, college life satisfaction, and major satisfaction. Students with clinical practicum (CP) experience demonstrated significantly higher interprofessional attitudes and patient safety competency than those without practicum experience. Patient safety competency was positively associated with interprofessional attitudes, empathy, and communication skills. Furthermore, PSE experience, major satisfaction, interprofessional attitudes, empathy, and communication skills were significant predictors of patient safety competency, with interprofessional attitudes identified as the strongest predictor. Patient safety competency among traditional Korean medicine and nursing students was influenced more by modifiable educational and interpersonal factors than by fixed background characteristics. These findings support the development of interprofessional, communication-centered, and empathy-informed patient safety curricula to enhance collaborative readiness and safe practice prior to entry into clinical settings.
Volume: 15
Issue: 5
Page: 4220-4231
Publish at: 2026-10-01

Evaluating the effectiveness, structure, and influencing factors of legal awareness education activities in higher education institutions

10.11591/ijere.v15i5.39776
Hiep Ho Duc , Long Nguyen Trong , Van Vu Hong
This study evaluates the effectiveness, structure, and influencing factors of legal awareness education activities in higher education institutions in Vietnam. Using a convergent mixed-methods design, the research surveyed 375 administrators, lecturers, and students and conducted 15 semi-structured interviews to integrate statistical evidence with contextual interpretation. The results show that structural components were rated fairly high, but experiential methods and updated legal content remained weaker than lecturer competence and content relevance. Legal attitude received the highest outcome score, whereas lawful behavior, particularly in cyberspace, was less consistent. Reliability, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA) results confirmed acceptable measurement quality, and regression analysis explained 46.2% of the variance in legal awareness outcomes. Educational content and methods were the strongest predictors, followed by environment and educational actors. The study contributes a systemic evaluation framework and highlights the awareness-attitude-behavior gap in digital legal education.
Volume: 15
Issue: 5
Page: 3667-3676
Publish at: 2026-10-01

Demographic determinants and interaction effects on parental involvement: a case study from Kosovo

10.11591/ijere.v15i5.39611
Serdan Kervan , Soner Yıldırım , Erdoğan Tezci , Taleh Khalilov
This study examined parental involvement in education among 1,228 parents in Kosovo whose children attend preschool and primary school, with respect to native language, gender, education level, number of children, and the child’s grade level. Findings indicated that the highest parental involvement was in home organization and academic monitoring (HOAM) and the lowest in school-based active participation (SBAP). Multivariate analysis of variance (MANOVA) by native language revealed significant differences across all four dimensions. Discriminant analysis (DA) results showed that SBAP was the strongest discriminator; Albanian-speaking parents scored higher in SBAP and teacher–parent communication (TPC), whereas Turkish-speaking parents scored higher in home-based learning support (HBLS) and HOAM. Interaction analyses showed that language was a stronger discriminator particularly for fathers. The interactions of native language×education level, native language×number of children, and education level×gender were not significant. Overall, the findings demonstrate that the multidimensional structure of parental involvement varies systematically across demographic factors.
Volume: 15
Issue: 5
Page: 4435-4455
Publish at: 2026-10-01

Language anxiety, speaking proficiency, and language learning beliefs among English major students: a mediation analysis

10.11591/ijere.v15i5.38887
Charmaine Kaye Puno Balboa , Gideon Sindad Sumayo
This study examined the relationship between English language anxiety and speaking proficiency among Bachelor of Secondary Education (BSEd) major in English students at the University of Southern Mindanao (USM)–Libungan Campus, with language learning beliefs as a mediating variable. Many tertiary learners experience anxiety in speaking English, yet the mechanisms through which this affects oral performance remain unclear. Using a quantitative descriptive–correlational design, data were collected from 150 students through standardized questionnaires and a speaking proficiency assessment. Descriptive statistics, regression analysis, and mediation analysis were employed to analyze the data. Results showed that students experienced English language anxiety at an “agree” level, with fear of negative evaluation as the most prominent factor. Their speaking proficiency was generally at a modest level, while language learning beliefs were positive, particularly in terms of motivation and expectations. Regression results indicated that language anxiety did not directly predict speaking proficiency. However, mediation analysis revealed that language learning beliefs significantly mediated the relationship between anxiety and speaking performance. The findings suggest that anxiety influences speaking proficiency primarily through learners’ beliefs about language learning. Addressing both emotional and cognitive factors is therefore essential in improving students’ oral communication skills.
Volume: 15
Issue: 5
Page: 4625-4633
Publish at: 2026-10-01

The impact of vocational training on female labor productivity an evaluation in Vietnam’s construction sector

10.11591/ijere.v15i5.39427
Truc Thai Phuong , Lam Nguyen Son
Vietnam’s construction sector has experienced strong growth through rapid urbanization and infrastructure investment. However, female workers continue to face significant barriers, including high informal employment rates (60–65%), 15–20% lower labor productivity than males, and limited access to vocational training (only 19–25% hold certificates). This study investigates the impact of the share of females aged 15+ with vocational training certificates (STFW15+) on female construction labor productivity (FCLP). Using a short time-series dataset (2010–2024, n=15) from General Statistics Office (GSO) of Vietnam, International Labor Organization (ILO), and World Bank, the study applied ForestDiffusion synthetic data pipeline-time-series to generate 75 high-quality synthetic samples (total 90 observations). An artificial neural network (ANN) model integrated with SHapley Additive exPlanations (SHAP) was employed to analyze feature importance and nonlinear interactions. Results show that STFW15+ is the strongest predictor of FCLP (mean |SHAP|=0.514). It exerts a strong positive effect and acts as a powerful moderator, amplifying benefits from income and economic growth while mitigating the negative impact of informal employment. The findings highlight the critical role of gender-responsive technical and vocational education and training (TVET). This study recommends targeted vocational training programs, gender integration in public investment, and public-private partnerships to advance gender equality and support sustainable development goals (SDGs) 5 and 8.
Volume: 15
Issue: 5
Page: 4207-4219
Publish at: 2026-10-01

Mechanism-based digital history instruction and student learning processes in Vietnam

10.11591/ijere.v15i5.40117
Vuong Thi Hai Yen , Tran Thi Thu , Nguyen Thi Van Anh
This study examines how digitally integrated instructional designs in history education co-occur with changes in students’ learning processes through underlying cognitive mechanisms. Rather than comparing the effectiveness of instructional models, the study focuses on identifying the conditions under which such designs operate in classroom contexts. An exploratory mixed-method case study design was employed, drawing on data from a survey of 120 students, classroom observations, and analysis of student-generated learning products. The internal consistency of the survey instrument reached a Cronbach’s alpha of 0.82. The findings indicate that higher levels of student engagement and improved information structuring are associated with tasks that require learners to make decisions about learning approaches, process multiple sources of information, and verify knowledge. These patterns are interpreted as manifestations of activated cognitive mechanisms under specific instructional conditions, rather than direct effects of digital tools. The results suggest that such task designs may be associated with indicators of creative competence, particularly when learners are required to organize, evaluate, and reinterpret historical information. The study proposes a mechanism-based approach to analyzing digital history instruction and contributes to understanding the relationship between instructional design and student learning processes in technology-rich environments.
Volume: 15
Issue: 5
Page: 4267-4280
Publish at: 2026-10-01

Uncovering hidden predictors of teacher knowledge and attitudes in child sexual abuse prevention

10.11591/ijere.v15i5.38744
Tetti Solehati , Helmy Hazmi , Rachelya Nurfirdausi Islamah , Yanti Hermayanti , Cecep Eli Kosasih , Mira Trisyani Koeryaman , Muhammad Yusuf
Teachers play a critical role in preventing child sexual abuse (CSA) in school settings. However, empirical evidence on how sociodemographic characteristics and information exposure shape teachers’ knowledge and attitudes remains limited, particularly in low- and middle-income contexts. This study aimed to examine the associations between teachers’ sociodemographic characteristics and exposure to CSA-related information with their knowledge and attitudes toward prevention. A quantitative cross-sectional study was conducted from March to December 2024 involving 56 teachers from two districts in West Java, Indonesia, using total population sampling. Data were collected using structured questionnaires and analyzed using descriptive statistics and chi-square tests, with Phi (φ) and Cramér’s V used to assess the strength of associations. Teachers’ knowledge was significantly associated with gender and information sources, including newspapers, Instagram, and headmasters (p
Volume: 15
Issue: 5
Page: 4026-4037
Publish at: 2026-10-01

Perceived competence and faculty needs in the key result areas for academic promotions in a Philippine state university

10.11591/ijere.v15i5.39595
Rose Mary L. Almacen , Adrian P. Ybañez , Ryan O. Tayco , Benjamin S. Villagonzalo Jr. , Dawn Iris Calibo-Senit
Faculty promotion in Philippine state universities depends on performance in instruction, research, extension, and professional development, yet institutions often lack evidence on whether faculty are equally prepared across these domains. This study examined perceived competence and developmental needs among regular faculty members at a large, multi-campus Philippine state university. Using a descriptive-analytical cross-sectional design, all 824 regular faculty members were invited to an online survey; 346 responded, and 300 valid responses were analyzed. Descriptive statistics, association tests with false discovery rate adjustment, and thematic analysis of open-ended responses were used. Not applicable responses were excluded from domain score denominators to avoid penalizing faculty for non-applicable promotion indicators. Findings showed uneven competence across the four domains. Instruction remained the strongest area, while research was the most constrained domain, with low levels of indexed publications, scientific presentations, and intellectual property outputs. Qualitative themes highlighted unequal access to opportunities, research support needs, workload pressures, funding constraints, and field-alignment concerns. Promotion-oriented faculty development should therefore prioritize structured research mentoring, protected time, support for converting outputs, and equitable access mechanisms across campuses.
Volume: 15
Issue: 5
Page: 4321-4330
Publish at: 2026-10-01

Work readiness, internship quality, and employability in logistics education: a PLS-SEM study

10.11591/ijere.v15i5.39982
Anh Lan Nguyen , Chung Xuan Le
This study examines how logistics and supply chain management education supports students’ transition from university to employment. It addresses a key issue in application-oriented programs: coursework and internships do not automatically translate into work readiness, perceived employability, or transition confidence. Drawing on employability and career development research, the study tests an integrated model linking career adaptability, career decision clarity, career support perception, work readiness, internship effectiveness, perceived employability, and transition outcome perception. Data were collected from 1,080 students through an anonymous online survey conducted from 23 March to 10 April 2026. Partial least squares structural equation modeling was applied. Results show that career support perception, career adaptability, and career decision clarity enhance work readiness, while work readiness and internship effectiveness improve perceived employability. Perceived employability is the strongest predictor of transition outcome perception, whereas internship effectiveness contributes mainly through employability beliefs rather than direct effects.
Volume: 15
Issue: 5
Page: 3800-3807
Publish at: 2026-10-01

Constrained finite-control-set predictive control of an H-bridge direct current motor drive

10.12928/telkomnika.v24i5.27909
Bouiri; University of Tindouf Abdesselam , Kendzi; University Center Salhi Ahmed Naama Mohammed , Boughazi; University of Bechar Othmane , Drauoi; University of Tindouf Abdelghani
This paper presents a constrained finite-control-set model predictive control (FCS-MPC) scheme for an H-bridge converter-fed direct current (DC) motor drive, aimed at fast dynamic response and low steady-state armature-current ripple. By selecting the converter switching state directly at each sampling instant, the method removes the separate pulse-width modulation (PWM) stage used in conventional cascaded proportional-integral (PI) control and embeds the armature-current limit directly in the cost function. When the predicted armature current exceeds the predefined limit, a penalty term drives the optimizer to choose the sw itching state that returns the current within safe bounds while preserving speed tracking. The controller is evaluated in simulation under fixed-speed reference tracking, variable-speed reference tracking, and a step load-torque disturbance. The results show fast settling, low steady-state speed error, and bounded current during transients; under the idealized simulation conditions the steady-state armature-current total harmonic distortion (THD) is 0.21 %, indicating low current ripple for the studied operating points. The present study is limited to simulation; experimental validation and a quantitative comparison against conventional PI-PWM control are identified as future work.
Volume: 24
Issue: 5
Page: 1816-1826
Publish at: 2026-10-01

Futures thinking framework: relevance to veterinary students

10.11591/ijere.v15i5.39434
Adrian P. Ybañez , Naomi A. Bajao , Siegfried D. Mendoza , Marlou C. Godinez , Dawn I. Calibo-Senit , Ryan O. Tayco , Vernadyn A. Morillo , Eloiza May S. Galon
The futures thinking framework (FTF) is increasingly recognized as an educational approach that helps learners anticipate change and prepare for long-term challenges, but evidence from Philippine veterinary education remains limited. This cross-sectional study examined how selected Filipino veterinary students perceived the relevance of FTF to career preparedness, organizational learning, and professional adaptability. Following an introductory lecture at the 2024 National Congress of Veterinary Medicine Students, an online survey was administered that included demographic items, 18 five-point perception statements (Cronbach’s α: 0.95), and three open-ended questions. Of 204 responses from 12 veterinary schools, 195 were retained after data cleaning. Data were analyzed using descriptive statistics, Kruskal-Wallis tests, exploratory factor analysis (EFA), internal consistency analysis, and thematic analysis. Most respondents were female (61.5%) and in the 5th–6th year (51.8%). The mean perception score was 4.04/5.00, indicating generally favorable views, particularly regarding eagerness to learn more about FTF and the relevance of student organizations to future professional life. After Bonferroni adjustment, only school differences in perceived administrative support for national organization activities remained significant (p=0.0012). The EFA identified three related domains with good-to-excellent reliability (α=0.833–0.939). Qualitative findings revealed concerns about educator/workforce and resource gaps, and organizational suggestions emphasizing planning and mentorship.
Volume: 15
Issue: 5
Page: 4122-4135
Publish at: 2026-10-01

A comparative assessment of machine learning, statistical, and hybrid approaches in amending ECMWF rainfall bias in the Batanghari river basin

10.12928/telkomnika.v24i5.27848
Arif; IPB University Ma'rufi , Tania; IPB University June , I Putu; IPB University Santikayasa , Apip; National Research and Innovation Agency (BRIN) Apip , Siswanto; Agency for Meteorology, Climatology, and Geophysics (BMKG) Siswanto
In tropical basins, rainfall predictions using international numerical weather prediction (NWP) models, like the European Centre for Medium-Range Weather Forecast (ECMWF), exhibit significant persistent biases and insufficient ensemble dispersion. This study evaluates statistical, machine learning (ML)-based, and hybrid bias correction methods to improve daily rainfall estimates for Indonesia’s Batanghari River Basin. Findings indicate that the ECMWF model significantly overestimates wet-day frequency (WDF) (81.5% versus 43.4% observed) and underestimates extreme maximum magnitudes, exhibiting an annual maximum rainfall bias of -15.6 mm/day. Standalone ML-based methods, such as random forest (RF) and extreme gradient boosting (XGBoost), experience significant variance deflation, inadequately addressing these substantial underestimations. In contrast, quantile mapping (QM) significantly reduces the intrinsic drizzle bias and achieves the highest monthly temporal stability among the tested frameworks (Kling-Gupta efficiency (KGE) = 0.341) with low processing requirements. Moreover, hybrid methodologies, including RF-QM and XGBoost-QM, successfully restore the natural ensemble spread and robustly reconstruct the upper tails of extreme rainfall, albeit at the expense of long term volume conservation (resulting in negative monthly KGE). Ultimately, QM is favored as an effective baseline for continuous hydrological modeling, whereas hybrid methodologies are highly suited for evaluating localized flash flood hazards and short-term severe intensities.
Volume: 24
Issue: 5
Page: 1611-1631
Publish at: 2026-10-01

Forecasting particulate matter concentration using deep learning and weather data

10.12928/telkomnika.v24i5.27967
Ahmad; Universitas Indonesia Furqon , Santoso; Universitas Indonesia Soekirno , Naufal; Region II of Meteorological Climatological and Geophysical Agency Ananda , Haryas; Agency for Meteorology Climatology and Geophysics Subyantara Wicaksana , Ahmad; State College of Meteorology Climatology and Geophysics Meijlan Yasir , Tonny; State College of Meteorology Climatology and Geophysics Wahyu Aji , Aviv; State College of Meteorology Climatology and Geophysics Maghridlo , Fadhli; State College of Meteorology Climatology and Geophysics Luthfiansyah , Marzuki; State School of Meteorology Climatology and Geophysics Sinambela , Suko; State School of Meteorology Climatology and Geophysics Prayitno Adi
Particulate matter (PM2.5) concentration is a critical air quality indicator with severe health impacts. Predicting its dynamics is challenging due to complex interactions with meteorological variables. This study evaluates long short-term memory (LSTM) and multilayer perceptron (MLP) networks to identify the most accurate forecasting architecture. We integrated continuous data from automatic weather stations (AWS) and air quality sensors. Data preprocessing involved temporal synchronization, outlier removal, and multivariate imputation by chained equations (MICE). Key input features past particulate matter, temperature, and relative humidity were selected via Pearson correlation and trained using a 24-hour sliding window. Results demonstrate that the single LSTM model outperforms both the MLP and hybrid architectures. The LSTM achieved the lowest root mean square error (RMSE) of 6.862 µg/m³ and a mean absolute percentage error (MAPE) of 37.38%. Although the MLP yielded a slightly higher coefficient of determination (0.810), it exhibited significant magnitude bias by treating sequential data as static features. Ultimately, this research provides a robust, data-driven framework for real-time air quality early warning systems in tropical regions.
Volume: 24
Issue: 5
Page: 1644-1655
Publish at: 2026-10-01
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