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31,456 Article Results

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

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

Reshaping the doctoral journey in the AI era

10.11591/ijere.v15i5.40091
Chung Xuan Le , Wurong Shih
This article systematically reviews how artificial intelligence (AI) systems, particularly generative AI (GenAI), are reshaping the doctoral journey. Unlike existing reviews that examine AI in higher education broadly, this review positions doctoral candidates as emerging scholars whose AI use intersects with learning, research, academic writing, authorship, and early knowledge production. Drawing on Web of Science Core Collection records from 1998 to 2026, the study followed preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 guidelines and retained 28 studies from an initial pool of 185 records. VOSviewer-based co-occurrence and co-citation analyses were integrated with full-text reading to identify five synthesized themes: AI as academic infrastructure, AI-mediated restructuring of doctoral practices, technology acceptance and negotiation, ethical risks and academic integrity, and wellbeing, adaptation, and support. The findings suggest that AI is intertwined with doctoral education not only as a tool for efficiency but also as part of a broader socio-technical and identity-forming process. The review contributes a doctoral identity-centered synthesis of how AI intersects with research practices, authorship, academic voice, supervision, academic integrity, wellbeing, and doctoral support.
Volume: 15
Issue: 5
Page: 3704-3715
Publish at: 2026-10-01

Web traffic optimization and institutional branding in higher education

10.11591/ijere.v15i5.40342
Mohammad Syahidul Haq , Ahmad Abdullah Zawawi , Muhamad Sholeh , Mufarrihul Hazin , Muhammad Ivan Dwi Saputra
Digital visibility has become an important component of institutional branding in higher education, yet limited empirical evidence explains how faculty-level website optimization contributes to brand image formation. This study evaluates web traffic optimization as a strategic capability using the context, input, process, output, and outcome (CIPOO) framework. An evaluative quantitative design was applied at Universitas Negeri Surabaya, involving 341 respondents consisting of 11 faculty website administrators and 330 website users. Data were collected through a five-point Likert-scale questionnaire and analyzed using descriptive evaluation and exploratory Pearson correlation based on aggregated faculty-level scores. The results show that context and outcome achieved the highest evaluation scores, indicating strong institutional alignment and positive branding outcomes, while process showed greater variability across faculties. Correlation analysis revealed a very strong significant positive association between output and outcome, whereas the input–process and process–output relationships were positive but not statistically significant. The novelty of this study lies in integrating faculty-level web traffic optimization, digital governance, and institutional branding within a single CIPOO-based evaluation model. These findings suggest that universities should strengthen website governance, digital capacity, and traffic monitoring to support institutional brand image.
Volume: 15
Issue: 5
Page: 3882-3891
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

Scaling of sustainable development goals in science education using Thurstone’s pairwise comparison–based scaling technique

10.11591/ijere.v15i5.39770
Ahmet Volkan Yüzüak , Zekiye Merve Öcal , Emrah Hiğde
A lack of evidence-based guidelines on which sustainable development goals (SDGs) need to be focused on in educational settings can be regarded as the key deficiency that leads to an inefficient sustainability learning process. As sustainability-related issues become ever more acute, there is a necessity to determine which SDGs should be prioritized pedagogically. Thus, the current study aims at examining how future teachers evaluate the significance of selected SDGs in relation to environmental learning. Applying the paired comparison methodology, the authors gathered data from 129 third- and fourth-year prospective teachers concerning their perceptions of 11 SDGs (SDG 1, 2, 3, 4, 5, 6, 7, 12, 13, 14, and 15). According to the results, SDG 3, SDG 6, and SDG 2 seem to be the most important goals, while SDG 15, SDG 5, and SDG 14 appear less critical. Thus, one may hypothesize about hierarchy within SDGs based on their importance as prerequisites for environmental problems solving. In terms of practical application, this paper offers a definite methodology that instructors can apply to rank and arrange SDGs for teaching purposes, as well as useful tips for curriculum designers and policymakers who seek to develop sustainability education programs.
Volume: 15
Issue: 5
Page: 3716-3725
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

Teacher readiness and blended learning implementation in Chinese vocational colleges: a quantitative survey study

10.11591/ijere.v15i5.38761
Ding Ge , Mohamad Zuber Abd Majid
Teacher readiness remains a key constraint on blended learning adoption in vocational education, yet limited research has examined how multiple readiness dimensions jointly shape implementation among vocational economics educators in China. This study investigated how technological literacy (TL), attitudes toward technology (AT), educational service quality (ES), and knowledge to create content (KC) predict teacher readiness for blended learning (RL), and how readiness influences actual practice among 390 economics teachers in vocational colleges in Hunan Province, China. A quantitative cross-sectional survey design was employed. Path analysis showed that all four antecedent variables significantly predicted readiness (R²=37.0%), while readiness significantly predicted blended learning practice (β=0.322, p
Volume: 15
Issue: 5
Page: 4080-4092
Publish at: 2026-10-01

Need-supportive instructional leadership, basic psychological needs and teacher professional development in higher education: a multilevel study from Kazakhstan

10.11591/ijere.v15i5.39804
Anara Zhakisheva , Murshida Nurgaliyeva , Bakhyt Galiyeva , Maiya Kayumova , Samal Antikeyeva
Quality of teaching in higher education depends on faculty development and well-being. Drawing on self-determination theory (SDT), this study examined how academic managers’ instructional leadership, operationalized as a management-cycle quality (MCQ) index, relates to faculty outcomes via basic psychological need satisfaction, with organizational culture as context. A cross-sectional study was conducted at Toraighyrov University with 100 staff across 18 departments (50% female; M_age=39.8). Measures included MCQ coding, basic psychological need satisfaction and frustration scale (BPNSFS), Utrecht Work Engagement Scale (UWES-9), Copenhagen Burnout Inventory (CBI), Teachers’ Sense of Efficacy Scale (TSES-12), and trait emotional intelligence (EI). Analyses involved reliability (ω≈.78–.92), confirmatory factor analysis (CFA), inter-rater intraclass correlation coefficient (ICC) (.76–.84), multilevel regression, and bootstrap mediation. Need satisfaction predicted higher engagement (β=.44) and lower burnout (β=−.22), while need frustration predicted higher burnout (β=.39). MCQ independently related to engagement (β=.21) and burnout (β=−.18). Teacher self-efficacy was associated with need satisfaction (β=.31) and trait EI (β=.24). Models explained substantial variance (R² up to .41). Mediation analyses showed that need satisfaction partially mediated MCQ effects on engagement, burnout, and self-efficacy. Findings indicate that need-supportive managerial behavior is a practical lever for improving motivation and well-being, with organizational culture shaping implementation. Limitations include cross-sectional design and modest sample size.
Volume: 15
Issue: 5
Page: 4400-4411
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

Development model of remote sensing data management for long-term storage using big data

10.12928/telkomnika.v24i5.27639
Riyan; National Research and Innovation Agency Mahendra Saputra , Agnes; National Research and Innovation Agency Sondita Payani , Ogi; National Research and Innovation Agency Gumelar
The growing volume of remote sensing data requires efficient approaches for long-term storage and data management. The National Remote Sensing Data Bank (BDPJN) stores multi-resolution satellite imagery acquired since 1993 to support disaster monitoring, climate studies, natural resource management, and other Earth observation applications. However, managing continuously growing archives remains challenging due to manual migration processes, inefficient storage utilization, and limited scalability. This study proposes an integrated remote sensing data management model that combines hierarchical storage management (HSM) with Apache Spark based parallel processing to support policy-driven data migration and long term storage optimization. The proposed model enables data migration across multiple storage tiers based on data characteristics and storage policies while improving migration efficiency through distributed processing. Experimental evaluation was conducted using Landsat-8, SPOT 6, SPOT-7, and Pleiades datasets under both 1 Gbps and 10 Gbps network environments. The results demonstrate migration performance improvements ranging from 80.14% to 91.04% compared with the existing manual migration system. These findings indicate that integrating HSM with Spark-based parallel processing can reduce migration bottlenecks, improve storage utilization, and provide a scalable and cost-effective approach for managing large-scale remote sensing archives while supporting long-term data governance and accessibility.
Volume: 24
Issue: 5
Page: 1535-1549
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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