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31,456 Article Results

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

The impact of professional autonomy on the teaching effectiveness of geography teachers: a case study in Vietnam

10.11591/ijere.v15i5.39409
Bui Thi Nhiem , Nguyen Thi Nham , Tran Thi Ha Giang
Professional autonomy is a crucial factor influencing teacher motivation, satisfaction, and performance. However, much of the existing research focuses on developed countries, while evidence from developing countries, particularly Vietnam, remains limited. This study aims to identify teachers’ professional autonomy as a crucial mediating mechanism linking contextual and organizational conditions with teaching effectiveness in schools. A cross-sectional survey was conducted with 368 geography teachers in high schools in Hanoi. The proposed research model was tested using partial least squares structural equation modeling (PLS-SEM). The results show that leadership and organizational culture have the strongest impact on teachers’ professional autonomy, while the indirect influence of educational policy is relatively modest. These findings suggest that contextual, leadership, and organizational conditions contribute to teaching effectiveness by expanding teachers’ professional autonomy in classroom practice. This study expands on existing models of teaching effectiveness and leadership by positioning teacher autonomy as a crucial mediating mechanism linking policy and the organizational environment to subject-level teaching outcomes. Empirically, it provides concrete evidence for the subject of geography education, an area where large-scale quantitative studies on teacher autonomy and teaching effectiveness remain limited, particularly in developing and transitioning education systems.
Volume: 15
Issue: 5
Page: 3764-3776
Publish at: 2026-10-01

Constructivist-based teacher education and pre-service teachers’ instructional competence for fostering cognitive activity

10.11591/ijere.v15i5.40296
Bakytkul Yessentayeva , Sazhila Nurzhanova , Askarbek Kussainov , Assel Tanatova , Bakyt Aidarkhan , Iroda Kholdorova
This study examines the effect of a constructivist-based teacher education (CBTE) program on pre-service primary teachers’ (PST) instructional competence for fostering cognitive activity (IC-CA). A quasi-experimental pretest–posttest control group (CG) design was employed with 180 pre-service teachers assigned to experimental (n=90) and control (n=90) groups using stratified allocation within intact cohorts. The intervention consisted of a 12-week program incorporating problem-based learning, collaborative tasks, microteaching, and reflective activities. Results indicated a statistically significant difference in adjusted posttest instructional competence scores in favor of the experimental group (EG) after controlling for baseline performance. Although both groups showed improvement over time, gains were greater in the experimental condition. The findings suggest that structured constructivist teacher education is associated with enhanced development of multidimensional instructional competence for fostering cognitively engaging teaching practices, including instructional design, learner interaction, pedagogical questioning, reflection, and instructional decision-making. The study provides empirical evidence that practice-oriented constructivist learning environments can support the development of such competence in pre-service teachers.
Volume: 15
Issue: 5
Page: 4256-4266
Publish at: 2026-10-01

Virtual laboratories and online learning in engineering education: a review

10.11591/ijere.v15i5.39538
Hamza Abu Owida , Areen Arabiat
The rapid expansion of virtual laboratories and online learning in engineering education, accelerated by the COVID-19 pandemic, has created an urgent need for clearer pedagogical guidance on how digital laboratory modalities should be used. This review addresses the unresolved educational problem of ensuring that virtual and remote laboratory environments support not only conceptual understanding but also practical laboratory skills and broader professional competencies. Using a structured review of recent empirical studies, systematic reviews, and meta-analyses, the paper synthesizes evidence on simulation-based laboratories, remote laboratories, immersive environments, and the wider online learning conditions that shape their effectiveness. The review finds that virtual laboratories are consistently effective for conceptual learning, visualization, flexibility, and access, while remote and immersive environments can strengthen authenticity and practical skill development when combined with active learning, multimodal assessment, and strong instructional support. However, persistent limitations remain in psychomotor skill development, assessment validity, accessibility, and institutional readiness. The review concludes that no single modality is sufficient on its own; rather, a hybrid model that pedagogically integrates virtual, remote, immersive, and physical laboratory experiences offers the most defensible pathway for meaningful and sustainable engineering laboratory education.
Volume: 15
Issue: 5
Page: 3857-3869
Publish at: 2026-10-01

The STUDIES model: validating a human-centric framework for Education 5.0

10.11591/ijere.v15i5.39645
Phichittra Thongpanit , Pratya Thongpanit , Sutep Uamcharoen
This study addresses the gap in teacher education frameworks that integrate Education 5.0 principles with empirically validated metacognitive development. The purpose was to develop and validate a seven-stage human-centric instructional framework, the setting goals, task analysis, universal design, digital learning, integrated knowledge, evaluation, standards-based assessment (STUDIES) model, grounded in constructivism, self-determination theory, and digital humanism, and to evaluate its effectiveness across three sequential phases with pre-service teachers. Three published phases employed one-group pretest–posttest designs. Phase I (N=24, English education) achieved efficiency ratios E1/E2 of 80.56/80.68 (t(23)=26.17, p
Volume: 15
Issue: 5
Page: 3677-3689
Publish at: 2026-10-01

Evaluating integrity and governance efficiency in blockchain-supported online assessment

10.11591/ijere.v15i5.39221
Aivar Sakhipov , Laura Smagulova , Aigul Yelepbergenova , Alexey Fedenko
With the increasing deployment of remote proctoring systems, there is a noticeable gap in online testing governance due to the lack of credible evidence, which might have adverse implications in terms of trust between the testing platform and its participants. This study, therefore, examines the potential impact of blockchain-powered evidence management on auditability and audit process efficiency in the context of online examinations. In our study, we used an experimental research method and examined the impact of tamper-proof distributed ledger technology on auditability and audit time using a sample of 160 exam sessions divided equally into two groups: a control group (CG, n=80) and an experimental group (EG, n=80). The findings suggest a positive and statistically significant influence of blockchain adoption on auditability (12.5% higher), reaching 96.3% compared to 83.8% in the CG, as well as a 37% reduction in audit time. Furthermore, the inter-rater agreement level reached 0.846. Notably, based on our surveys, test-takers find the process more credible and legitimate without any increased privacy concerns.
Volume: 15
Issue: 5
Page: 4068-4079
Publish at: 2026-10-01

Enhancing educators’ digital literacy through digital leadership and organizational culture

10.11591/ijere.v15i5.39153
Sugiarto Sutomo , Heni Rochimah
Despite rapid advances in digital technology and online learning, educators at the education and training center face limited digital access, low information and communication technology (ICT) proficiency, and difficulty integrating technology, reflecting the slow national rise in digital literacy. This study tests whether strengthening digital leadership and aligning organizational culture can improve educators’ digital competencies. A cross-sectional survey of 120 educators was analyzed using path analysis in JASP. Findings indicate that digital leadership strongly predicts organizational culture (β=0.869, z=28.834, p
Volume: 15
Issue: 5
Page: 3777-3789
Publish at: 2026-10-01

The impact of digital comics as a teaching and learning tool in economics education on future-ready talent development: the mediating role of attitude

10.11591/ijere.v15i5.37622
Hainnuraqma Rahim , Abd Hadi Mustaffa , Norashida Othman , Putri Aliah Mohd Hidzir
Economics education at the tertiary level dominated by text-heavy materials that often disengage students. At the same time, the potential of digital comics as an innovative pedagogical alternative are insufficiently examine. Hence, this study examines the impact of digital comics (IMP) on students’ learning in economic subjects at Universiti Teknologi MARA (UiTM) Melaka, with particular focus on the mediating role of attitude (ATT). A quantitative design was employed using structured questionnaires distributed to 146 diploma and degree students to measure the determinants of perceived IMP. Data were analyzed using the partial least squares– structural equation modeling (PLS-SEM). The findings show that ATT and subjective norms (SN) significantly influence the IMP, while digital literacy (DL) and perceived behavioral control (PBC) exhibit no direct effects. In addition, ATT significantly mediates the relationships with SN, PBC and the IMP. Practically, the findings suggest that lecturers can embed digital comics into case-based class activities to enhance engagement and conceptual understanding. The novelty of the study indicated that this is the first studies applying theory of planned behavior (TPB) to digital comics in economic education. This study also offers future research which can be extend the studies into experimental, longitudinal and cross-sectional.
Volume: 15
Issue: 5
Page: 4192-4206
Publish at: 2026-10-01

Gamification and MOOC persistence: an integrated UTAUT2–SDT–engagement model

10.11591/ijere.v15i5.37547
Waraporn Jirapanthong , Siwakorn Banluesapy
Massive open online courses (MOOC) completion rates remain low despite the continued growth of online learning platforms. This study examined the factors influencing learning persistence (PERS) in gamified MOOCs using an integrated unified theory of acceptance and use of technology 2-self-determination theory (UTAUT2–SDT)–engagement (ENG) model. A cross-sectional survey was conducted with 200 MOOC learners in Thailand, and the data were analyzed using partial least squares structural equation modeling (PLS-SEM). The results showed that student ENG significantly predicted behavioral intention (BI) (β=0.352,p
Volume: 15
Issue: 5
Page: 4657-4666
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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