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31,291 Article Results

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

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

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

BDLock: A blockchain-enabled two tier privacy-aware federated idam service platform using RBAC

10.11591/ijece.v16i5.pp2537-2548
Muhammad Shakil Pervez , Md. Nasim Adnan , Sarker Tanveer Ahmed Rumee , Moinul Islam Zaber
Centralized identity and access management (IDAM) systems suffer from sin-gle points of failure, lack of authorization transparency, and susceptibility to in-sider threats and privilege abuse. While role-based access control (RBAC) of-fers structured permission management, its enforcement through centralized pol-icy engines introduces auditability gaps unacceptable in modern distributed service delivery environments. This paper presents BDLock, a blockchain-enabled two-tier privacy-aware federated IDAM platform integrating OAuth 2.0, OpenID Connect (OIDC), and Hyperledger Fabric 2.4. The first tier validates JSON Web Tokens (JWT) issued by Keycloak against a Spring Boot resource server, the second tier enforces immutable scope-based RBAC rights on the Hyperledger Fabric ledger, ensuring every access decision is tamper-proof and auditable. Unlike prior approaches, BDLock uniquely bridges OAuth-authenticated off-chain identities to cryptographic on-chain Fabric wallet identities, satisfying all six STRIDE-modelled threats categories across both Web2 and Web3 identity models. Validated with up to 1,800 concurrent users, BDLock achieves a peak throughput of approximately 200 transactions per second using round-robin load balancing. At high concurrency, it outperforms single-peer fallback by up to 25%. Furthermore, it maintains uninterrupted access control during peer failures, eliminating the single point of failure found in all nine compared state-of-the-art systems.
Volume: 16
Issue: 5
Page: 2537-2548
Publish at: 2026-10-01

Design science research in developing a religious chatbot based on Bulugh al-Maram

10.11591/ijece.v16i5.pp2782-2794
Aris Tjahyanto , Irmasari Hafidz , Faizal Johan Atletiko
Chatbots have recently gained significant popularity. For instance, ChatGPT has become a preferred tool for many individuals seeking instant answers without relying on human responses. This immediacy sets chatbots apart from books, which require users to search for information manually. This time-consuming process does not align with millennials' preference for convenience and efficiency. Studying hadith independently using the Bulugh al-Maram book demands considerable time and effort. The limited use of natural language processing technologies in religious chatbots restricts their ability to handle complex inquiries effectively. A chatbot capable of answering hadith-related questions could greatly assist the public in studying hadith texts by providing direct responses without extensive searching. This chatbot was designed for web browsers, utilizing deep learning as its core technology. This research led to the development of a chatbot prototype for learning hadith from Bulugh al-Maram. Built using the design science research (DSR) methodology, the prototype achieves an intent recognition rate (IRR) of 86.82%. However, its capabilities are below the BERT model, demonstrating a strong ability to accurately interpret user questions and statements.
Volume: 16
Issue: 5
Page: 2782-2794
Publish at: 2026-10-01

Calibration-guided score fusion for robust multimodal traffic anomaly detection

10.11591/ijece.v16i5.pp2516-2525
Quang Hiep Do , Thien Tan Nguyen
Multimodal traffic anomaly detection is affected by differences in visual and audio score ranges, temporal fluctuations, and unstable decision thresholds. This paper proposes a calibration-guided score fusion (CGSF) framework that processes video frames and audio spectrograms through separate reconstruction-based models. The resulting anomaly scores are temporally smoothed, normalized using validation data, and combined at the score level. A percentile estimated from normal validation samples is then used as the decision threshold. The framework was evaluated on the MAVD and DADA2000 datasets. On MAVD, CGSF achiev,,,,,,ed a ROC-AUC of 0.553, a PR-AUC of 0.082, and an F1-score of 0.129. It outperformed direct fusion in precision, recall, and F1-score, although the gain in ROC-AUC was small. Analysis on DADA2000 showed smoother temporal score behaviour after calibration and smoothing. The results indicate that CGSF mainly improves score comparability and threshold consistency rather than producing a large increase in detection accuracy. Its modular design also allows the visual and audio branches to be trained and updated independently.
Volume: 16
Issue: 5
Page: 2516-2525
Publish at: 2026-10-01

From theory to practice: examining legal literacy among pre-service teachers in Kazakhstan

10.11591/ijere.v15i5.39508
Raushan Kanagatova , Nazigul Gazizova , Aray Amanova , Perizat Seiitkazy , Sayan Zhangazin
Pre-service teachers in Kazakhstan are expected to operate within a complex legal framework that governs student rights, inclusion, and teacher accountability, yet legal education in teacher training programs remains critically underdeveloped. This study investigated the current state of legal literacy among pre-service teachers in Kazakhstan, examining knowledge levels, pedagogical approaches, and students’ perceptions of relevance. Using a convergent mixed-methods design, data were collected from 189 pre-service teachers via survey and 20 semi-structured interviews across five public universities. Findings revealed severely limited factual legal knowledge, low situational confidence, and strong dissatisfaction with the existing “fundamental law” course. Qualitatively, participants described legal training as exclusively lecture-based and disconnected from classroom realities. Applying Nonaka’s knowledge management framework, the study identifies a systemic failure in converting explicit legal knowledge into tacit professional competence. Despite these gaps, students expressed strong motivation for practical legal training (92% rated legal knowledge as essential). The study concludes with recommendations for curriculum reform, including case-based modules, practicum integration, and interdisciplinary collaboration between law and education faculties.
Volume: 15
Issue: 5
Page: 4602-4613
Publish at: 2026-10-01

Supporting learner autonomy through interactive H5P content: evidence from an EMI vocational higher education context

10.11591/ijere.v15i5.39732
Hilda Cahyani , Nadia Hanayeen , Alvin Nouval , Achmad Suyono , Vhirell Bextor Mahtya Setiawan
The integration of interactive digital content has become increasingly important in supporting autonomous learning in higher education, particularly in vocational contexts where practical language competence is essential. This study aims to examine the role of interactive H5P (HTML5 package)-based content embedded in a learning management system (LMS) in supporting autonomous English learning among students enrolled in an international class at a vocational higher education institution in Indonesia. Employing a qualitative-dominant mixed-method approach, data were collected through student questionnaires, reflective feedback, and observations of learning activities within the online learning environment. The findings indicate that H5P interactive content effectively supports learner autonomy by enabling self-paced learning, providing immediate feedback, and increasing student engagement in independent English practice. Quantitative results show a high level of student satisfaction, with a mean score of 4.23, indicating that learners perceived the materials as accessible, motivating, and aligned with their individual learning needs. However, challenges related to technical familiarity and varying levels of learner readiness were also identified. This study provides empirical evidence that H5P-based interactive content enhances learner autonomy through measurable engagement and repeated task interaction.
Volume: 15
Issue: 5
Page: 3736-3749
Publish at: 2026-10-01

Improving PaddleOCR optical character recognition for food composition labels using hybrid SymSpell and LayoutLM correction

10.12928/telkomnika.v24i5.27607
Ahmad; Universitas Qomaruddin Wahyu Rosyadi , Siti; Universitas Qomaruddin Ma’shumah , Muhammad; Institut Teknologi Sepuluh Nopember Qomaruz Zaman
A high level of text recognition accuracy is crucial for optical character recognition (OCR) applications, particularly those designed to extract information from food composition labels. However, packaging materials often introduce distortions, reflections, or irregular printing, which lead to recognition errors and word concatenation. To mitigate this issue, this study proposes a novel post-processing system that integrates PaddleOCR with the spelling correction (SymSpell) algorithm and layout language model (LayoutLM)-guided bigram-based word segmentation to enhance OCR accuracy. The implementation begins with capturing a composition label image using a smartphone camera, followed by initial text extraction performed by PaddleOCR. Consequently, SymSpell corrects misspelled tokens through a domain-specific food and beverage lexicon, while LayoutLM leverages spatial and contextual information to separate concatenated words. The performance was evaluated using the character error rate (CER) metric across four packaging surface categories: flat, curved, reflective, and textured. The results demonstrate consistent improvements across all categories, achieving an average CER of 0.0991, with the greatest reduction on flat surfaces (from 0.0885 to 0.0659). The experiments show that the system is robust across label conditions, lightweight for real-time use, and domain-aware. Its model-agnostic design adds flexibility, enabling effective post-OCR correction across different engines and improving readability for real-world food label applications.
Volume: 24
Issue: 5
Page: 1668-1679
Publish at: 2026-10-01

Real-time multimodal fatigue detection using facial vision and alert integration via ESP32 for occupational health applications

10.11591/ijece.v16i5.pp2750-2768
Andrés Enrique Rojas Primo , Alfredo Lazaro Gutierrez , Felix Pucuhuayla-Revatta
Early detection of work fatigue is a major challenge in industrial settings due to the lack of non-invasive, accessible, and low-cost systems capable of operating in real time. In this context, this research proposes a multimodal real-time fatigue detection system using facial vision and artificial intelligence, aimed at risk prevention and promoting occupational health. The system integrates geometric and behavioral parameters, such as eye aspect ratio (EAR), head tilt, and mouth opening, processed on a Raspberry Pi 5 using MediaPipe and a hybrid convolutional neural network (CNN) MobileViT model. Visual and audible alerts are managed by an ESP32 microcontroller using the message queuing telemetry transport (MQTT) protocol, while a graphical interface developed in Tkinter allows real-time monitoring of operator status. Experimental results, evaluated in a simulated work environment using AI-generated synthetic videos, show an accuracy greater than 97% and a latency of less than 250 ms, confirming the system's effectiveness in the early detection of signs of drowsiness and attention deficit. In conclusion, the proposal represents a non-invasive, scalable, and efficient solution that combines computer vision, deep learning, and the Internet of Things (IoT) to strengthen workplace safety and well-being.
Volume: 16
Issue: 5
Page: 2750-2768
Publish at: 2026-10-01

Robust resource allocation in multi-cell UE-specific RIS-assisted D2D relay networks under imperfect CSI

10.11591/ijece.v16i5.pp2575-2594
Kayode Popoola , Ayodeji Ajani , Stuart Nicholson , Muheeb Ahmed , Srilatha Narayangari Pamuri , Ibrahim Bala Alhassan
Device-to-device (D2D) communication enhances spectral efficiency but remains constrained by limited transmission range, underlay interference, and the half-duplex overhead of conventional relays. User equipment-specific reconfigurable intelligent surfaces (UE-RIS) offer a promising alternative by enabling passive beamforming to strengthen D2D links without additional spectrum consumption. However, existing studies typically assume perfect channel state information (CSI) and single-cell operation, limiting their applicability to practical deployments. This paper proposes a robust multi-cell resource allocation (RMRA) framework for UE-RIS-assisted D2D relay networks under imperfect CSI. A hybrid uncertainty model is adopted, combining statistical Gauss-Markov CSI errors for intra-cell links with bounded norm-ball uncertainty for inter-cell links. The joint optimisation of resource reuse, transmit power allocation, and RIS phase configuration is formulated as a stochastic mixed-integer nonlinear program that maximises network spectral efficiency while satisfying outage and quality-of-service constraints. To efficiently solve the problem, a three-stage algorithm is proposed comprising distance-pruned Hungarian assignment, robust power control using Bernstein-type inequality and S-procedure based semidefinite programming, and soft actor-critic (SAC) based passive beamforming. Simulation results show that RMRA achieves a 94% D2D access rate at light load and over 75% at full load, improves sum spectral efficiency by 34.7% and 70.2% over AF relaying and direct D2D, respectively, attains 118.5 bits/s/Hz/W energy efficiency, and maintains 30.2 bits/s/Hz under severe CSI uncertainty.
Volume: 16
Issue: 5
Page: 2575-2594
Publish at: 2026-10-01
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