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31,291 Article Results

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

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

Humanizing online pedagogy with stream: the mediating role of students’ sense of agency within the community of inquiry

10.11591/ijere.v15i5.37611
Nor Asiah Razak , Che Zalina Zulkifli , Yusri Abdullah , Muhammad Akmal Hakim Azammullah , Puteri Zarina Megat Khalid , Harmeet Singh , Sarfraz Aslam , Ng Soo Boon , Umarkhon Mukhamedov
This study explores whether students’ sense of agency mediates the relationship between stream integration and the three presences of the community of inquiry (CoI) framework, namely, cognitive, social, and teaching presence in online higher education. A cross-sectional survey design was employed to undergraduates (n=107) from four Malaysian universities by validated instruments for data collection, measured stream integration, sense of agency, and CoI presence. PLS-SEM was employed for data analysis using SmartPLS 4 software, adhering to a two-step process. Stream integration significantly predicted cognitive (β=0.573), social (β=0.405), and teaching presence (β=0.433), as well as sense of agency (β=0.497), all p
Volume: 15
Issue: 5
Page: 4289-4305
Publish at: 2026-10-01

AI-supported pedagogical pipeline for computing students’ research competence

10.11591/ijere.v15i5.39313
Kazimova Dinara , Aliyeva Dinara , Kultan Jaroslav , Janabayev Dauren , Kopbalina Saltanat
This study evaluates an artificial intelligence (AI)-enabled co-curricular student club model implemented at two regional universities, organized as a staged four-part pipeline comprising competitive programming, hackathon prototyping, startup/project development, and final project defense. A matched pre/post design was used to assess changes in students’ objective computing knowledge, measured through a supervised computing skills test, and self-reported constructs, measured through an AI literacy and learning disposition survey. Outputs from club activities were also documented and combined into an OutputsIndex. The analyses showed statistically significant improvements in both computing knowledge and survey-based constructs, indicating that the intervention strengthened both foundational technical competence and students’ readiness for applied, performance-based work. AI literacy was also significantly associated with both knowledge scores and real-life outcomes, even after controlling for site and baseline differences, indicating that students with stronger AI-related competencies applied their learning more effectively to performance-based outputs. The findings support the potential of scalable, evidence-based IT student pipelines in Kazakhstan’s higher education context, and suggest that structured, AI-supported pathways can align skill development with responsible AI use, stronger learning outcomes, and measurable real-world performance.
Volume: 15
Issue: 5
Page: 3947-3961
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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