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29,325 Article Results

Development of the Arabic family relation test among school students

10.11591/ijere.v15i1.33469
Marwa Nasser Alrajhi , Kamisah Osman , Hussain Ali Alkharusi
Family relationships are highly valued; therefore, their assessment is essential. Documented literature has emphasized the significance of family relationships in students’ different outcomes. However, there is a lack of psychometrically robust measures of family relationships in Arabic-speaking environments. This study aimed to develop the Arabic family relation test (AFRT) and examine its psychometric properties. The test explores six main dimensions of parent-child relationships: restrictiveness, affection, vulnerability, justice, acknowledgment, and trust. The cross-sectional quantitative design was utilized. The study sample included 601 school students (47.6% males) from grades 5, 7, 9, and 11, selected through a cluster-random sampling approach. The findings revealed a valid 60-item AFRT with a two-second-order-factor, six-first-order-factor factorial structure. Measurement invariance of the AFRT was achieved in the father-child model across groups of age and gender. However, measurement invariance was only supported across age groups in the mother-child model. The AFRT demonstrated good reliability, as indicated by Cronbach’s alpha, test-retest, split-half, and composite reliability. The researchers concluded that the AFRT is an appropriate tool for family assessment; however, further psychometric investigation is recommended. In addition, recommendations for implications and suggestions for future research were provided.
Volume: 15
Issue: 1
Page: 915-932
Publish at: 2026-02-01

Validation of a culturally adapted SACQ in Vietnamese higher education: evidence from a technological university

10.11591/ijere.v15i1.35878
Lan Hoang Thi Quynh , Hanh Pham Hong , Ha Tran Thi Thanh
Grounded in Tinto’s theory of student integration, this study examines the structure of the student adaptation to college questionnaire (SACQ) in Vietnam higher education context, employed a cross-sectional quantitative approach. Data from 363 students at a technological university in Northern Vietnam were analyzed using exploratory and confirmatory factor analyses. Results yielded a refined 29-item scale (SACQ-29) with a five-factor structure, diverging from the original four-factor model. Most notably, social adjustment (SA) is separated into two distinct factors: one comprising positively worded items and another consisting predominantly of reverse-coded items, suggesting distinct adaptation mechanisms in digitally enhanced learning environments. Results revealed that students adapted most effectively to academic demands (M=5.77), but showed weak institutional attachment (IA) (M=3.14). Male students demonstrating significantly better overall adaptation than female students, and living arrangements also influenced adaptation levels, with students living with family showing poorer adaptation compared to others. Comparative analysis revealed high consistency between the SACQ-29 and the original SACQ-67 (r=0.925**). This study provides first validated Vietnamese SACQ, offering a practical tool for early identification of at-risk students and intervention design in contemporary higher education contexts.
Volume: 15
Issue: 1
Page: 112-125
Publish at: 2026-02-01

From algorithms to classrooms: a decade of artificial intelligence in education research

10.11591/ijere.v15i1.34427
Lim Seong Pek , Nahdatul Akma Ahmad , Faiz Zulkifli , Rabindra Dev Prasad , Ari Muzakir , Jun S. Camara
The education industry has seen a substantial transformation thanks to artificial intelligence (AI), which has improved administrative effectiveness, accessibility, and individualized learning. However, issues like moral dilemmas, digital justice, and policy inconsistencies still exist. From 2015 to 2024, this bibliometric research explores how AI is revolutionizing education. Personalized learning, improved accessibility, and expedited administrative procedures have all been made possible by AI; yet, issues with cost, digital equity, and ethics still exist. We used the Web of Science (WoS) database to conduct a comprehensive bibliometric analysis of 291 peer-reviewed articles that were indexed in the Social Sciences Citation Index (SSCI). The PRISMA methodology was used in the study to find and filter pertinent material. Thematic trends, citation patterns, and co-authorship networks were examined using bibliometric tools like VOSviewer. The progress of generative AI tools like ChatGPT, the importance of AI in democratizing education, and the integration of AI into curriculum building are some of the key discoveries. The report identifies significant nations, organizations, and researchers in AI education and emphasizes global research relationships. Our research raises ethical governance issues while shedding light on AI’s potential to promote individualized learning and increase student engagement. These findings support sustainable development goal (SDG) 4 on quality education by highlighting the need for responsible AI use to address the digital divide. This paper offers useful suggestions for academics, educators, and legislators to maximize AI’s promise while tackling its drawbacks.
Volume: 15
Issue: 1
Page: 500-510
Publish at: 2026-02-01

Predictors of teachers’ readiness for inclusive education in Kazakhstan

10.11591/ijere.v15i1.36260
Dinara Ospankulova , Akbota Autayeva , Zhanna Paylozyan , Akmaral Rsaldinova , Aigul Baitursynova
Inclusive education (IE) is increasingly recognized as a key priority in modern educational systems; however, in Kazakhstan, there is limited evidence on the factors influencing teachers’ attitudes and readiness to implement it. This study explores public school teachers’ attitudes toward inclusive education (TATIE) and examines how personal, professional, and institutional factors affect these attitudes. A survey of 638 teachers from Almaty schools was conducted using a validated instrument, and correlation and regression analyses were employed to identify significant predictors. The results indicate that gender, teaching experience (TE), frequency of contact with students with disabilities (SWD), perceived school support, and participation in specialized training significantly influence teachers’ attitudes. Positive attitudes were particularly associated with direct professional experience and strong institutional support, highlighting the importance of targeted professional development and school-level measures. This study contributes to the literature by providing a comprehensive quantitative analysis specific to the Kazakhstani context and offers practical insights to guide policy and enhance the effective implementation of inclusive practices, ultimately improving the quality of education for students with special educational needs.
Volume: 15
Issue: 1
Page: 587-596
Publish at: 2026-02-01

Enhancing Autonomous GIS with DeepSeek-Coder: an open-source large language model approach

10.11591/ijece.v16i1.pp423-436
Kim-Son Nguyen , The-Vinh Nguyen , Van-Viet Nguyen , Minh-Hue Luong Thi , Huu-Khanh Nguyen , Duc-Binh Nguyen
Large language models (LLMs) have paved a way for geographic information system (GIS) that can solve spatial problems with minimal human intervention. However, current commercial LLM-based GIS solutions pose many limitations for researchers, such as proprietary APIs, high operational costs, and internet connectivity requirements, making them inaccessible in resource-constrained environments. To overcome this, this paper introduced the LLM-Geo framework with the DS-GeoAI platform, integrating the DeepSeek-Coder model (the open-source, lightweight version deepseek-coder-1.3b-base) running directly on Google Colab. This approach eliminates API dependence, thus reducing deployment costs, and ensures data independence and sovereignty. Despite having only 1.3 billion parameters, DeepSeek-Coder proved to be highly effective: generating accurate Python code for complex spatial analysis, achieving a success rate comparable to commercial solutions. After an automated debugging step, the system achieved 90% accuracy across three case studies. With its strong error- handling capabilities and intelligent sample data generation, DS-GeoAI proves highly adaptable to real-world challenges. Quantitative results showed a cost reduction of up to 99% compared to API-based solutions, while expanding access to advanced geo-AI technology for organizations with limited resources.
Volume: 16
Issue: 1
Page: 423-436
Publish at: 2026-02-01

Expert evaluation of a web-based grammatical competence module: Fuzzy Delphi method

10.11591/ijere.v15i1.35355
Nur Hidayah Md Yazid , Nur Ainil Sulaiman , Harwati Hashim
Web-based learning modules have been considered indispensable for English as a second language (ESL) learners to utilize autonomously. However, there are still not many reputable grammatical competence modules designed for the transition between secondary school and undergraduate levels. Thus, this study aimed to ascertain expert consensus on developing a web-based grammatical competence module for pre-university ESL learners. The Fuzzy Delphi method (FDM) was employed in this study to create the module. Four broad constructs, which are the design, technical aspects, content, pedagogy of the website were used as references in developing a survey as the instrument for the study. The features in the survey were evaluated by six selected experts based on established criteria for high-quality language learning websites. Data analysis was undertaken using a 5-point fuzzy scale and the Fuzzy Delphi approach Logic Software (FUDELO 1.0). Supported by the findings and a consensus rate of over 75%, a cut-off value (d) of ≤0.2, and a fuzzy score (A) of ≥α-cut value=0.5, expert consensus was reached for the four constructs. The findings support that the module is fitting for pre-university ESL learners and can be used as a supplementary grammar learning module. Empirical studies related to learner performance and engagement outcomes in the future must continue assessing the long-term effectiveness of the module and ensure its long-term efficacy in ESL learning.
Volume: 15
Issue: 1
Page: 784-794
Publish at: 2026-02-01

Analysis of factors in integrated internship models for preservice Islamic education teachers using exploratory factor analysis

10.11591/ijere.v15i1.35767
Karwadi Karwadi , Abd Razak Zakaria , Adhi Setiawan , Moh. Ferdi Hasan
This research identifies key success factors of integrated internship models for prospective Islamic Religious Education (PAI) teachers using exploratory factor analysis (EFA), addressing critical gaps where empirical evidence in religious teacher preparation remains limited. Analyzing 218 PAI students across four Yogyakarta universities through mixed-methods design, EFA revealed a four-factor structure explaining 63.4% variance: observation competence (28.7%), microteaching (13.8%), teaching practice (11.2%), and spiritual reflection (9.7%). The identification of spiritual reflection as an independent factor represents a novel contribution not documented in international teacher education literature, empirically validating integration of spiritual competencies within professional preparation frameworks. The internship component assessment scale (ICAS) demonstrates strong psychometric properties (CVI=0.87, α=0.84), providing the first culturally responsive instrument for Islamic education contexts. This study proposes the integrated internship spiral model (IISM) emphasizing cyclical reinforcement rather than linear progression, challenging conventional designs. Educational implications include redesigning PAI teacher professional education curriculum with proportional resource allocation, implementing mentor training for assessing spiritual-pedagogical dimensions, and embedding technology integration across internship phases. Future research should pursue longitudinal validation, cross-contextual studies in other religious education settings, instrument development strengthening spiritual factor reliability, and comparative effectiveness studies. This research demonstrates that culturally responsive teacher preparation can honor religious authenticity while advancing professional excellence, contributing to holistic transformation of PAI internship programs with potential global application.
Volume: 15
Issue: 1
Page: 342-359
Publish at: 2026-02-01

An enhanced improved adaptive backstepping–second-order sliding mode hybrid control strategy for high-performance electric vehicle drives

10.11591/ijece.v16i1.pp121-134
Huu Dat Tran , Ngoc Thuy Pham
This paper proposes an enhanced hybrid speed control strategy, termed improved adaptive backstepping–second-order sliding mode (IABSSOSM), for six-phase induction motor (SPIM) drives in electric vehicle (EV) propulsion systems. The proposed method combines the systematic design framework of Backstepping in the outer speed and flux loops with a second-order sliding mode controller in the inner current loop. An innovation of the approach is the integration of a variable-gain super-twisting algorithm (VGSTA), which dynamically adjusts the control effort based on disturbance levels, thereby minimizing chattering and enhancing robustness against system uncertainties. To further improve disturbance rejection, a predictive torque estimator is incorporated using a forward Euler discretization, enabling accurate torque prediction and proactive compensation. This hybrid structure significantly improves convergence speed, enhances reference speed tracking accuracy, and ensures fast and precise torque response, and its strong resilience to external load disturbances, system parameter variations enable stable and reliable operation under challenging conditions. The effectiveness of the proposed approach is validated through comprehensive simulations using the MATLAB/Simulink.
Volume: 16
Issue: 1
Page: 121-134
Publish at: 2026-02-01

Credit card fraud data analysis using proposed sampling algorithm and deep ensemble learning

10.11591/ijece.v16i1.pp311-320
Aye Aye Khine , Zin Thu Thu Myint
Credit card fraud detection is challenging due to the severe imbalance between legitimate and fraudulent transactions, which hinders accurate fraud identification. To address this, we propose a deep learning-based ensemble model integrated with a proposed sampling algorithm based on random oversampling. Unlike traditional methods, the proposed sampling algorithm addresses the oversight of parameter selection and manages class imbalance without eliminating any legitimate samples. The ensemble framework combines the strengths of convolutional neural networks (CNN) for spatial feature extraction, long short-term memory (LSTM) networks for capturing sequential patterns, and multilayer perceptrons (MLP) for efficient classification. Three ensemble strategies—Weighted average, unweighted average, and unweighted majority voting—are employed to aggregate predictions. Experimental results show that all ensemble methods achieve perfect scores (1.00) in precision, recall, and F1-score for both fraud and non-fraud classes. This study demonstrates the effectiveness of ensemble model with optimized sampling approach for robust and accurate fraud detection.
Volume: 16
Issue: 1
Page: 311-320
Publish at: 2026-02-01

Evaluating plant growth performance in a greenhouse hydroponic salad system using the internet of things

10.11591/ijece.v16i1.pp505-517
Chonthisa Rattanachu , Wiyuda Phetjirachotkul , Isara Chaopisit , Kronsirinut Rothjanawan
Hydroponic salad cultivation is becoming increasingly popular. However, a common challenge is the lack of time to maintain hydroponic vegetables due to other responsibilities. This study presents a hydroponic system based on the internet of things (IoT) technique, designed to save time by enabling remote control through a mobile application connected to a NodeMCU microcontroller. Various sensors are integrated with the NodeMCU for real-time monitoring and automation. The study also explores the use of RGB LEDs, which significantly accelerated plant growth and reduced cultivation time. A comparative experimental design was employed to evaluate the growth rate of green oak salad vegetables under two different greenhouse systems. The primary factor compared was the greenhouse system type, with plant growth rate as the outcome variable. Each treatment was replicated 10 times. F-tests were used to statistically determine significant differences in growth rates between the two systems across measured intervals. Results showed that the automated greenhouse system produced the highest leaf width and plant weight values. The use of RGB LEDs reduced the cultivation period from 45 days to 30 days, enabling more planting cycles and ultimately increasing overall yield.
Volume: 16
Issue: 1
Page: 505-517
Publish at: 2026-02-01

Accessibility in e-government portals: a systematic mapping study

10.11591/ijece.v16i1.pp357-372
Mohammed Rida Ouaziz , Laila Cheikhi , Ali Idri , Alain Abran
In recent years, several researchers have investigated the challenges of accessibility in e-government portals and have contributed to many proposals for improvements. However, no comprehensive review has been conducted on this topic. This study aimed to survey and synthesize the published work on the accessibility of e-government portals for people with disabilities. We carried out a review using a systematic mapping study (SMS) to compile previous findings and provide comprehensive state-of-the-art. The SMS collected studies published between January 2000 and March 2025 were identified using an automated search in five known databases. In total, 112 primary studies were selected. The results showed a notable increase in interest and research activities related to accessibility in e-government portals. Journals are the most widely used publication channel; studies have mainly focused on evaluation research and show a commitment to inclusivity. “AChecker” and “Wave validator” are the most used accessibility evaluation tools. The findings also identified various accessibility guidelines, with the most frequently referenced being the web content accessibility guidelines (WCAG). Based on this study, several key implications emerge for researchers, and addressing them would be beneficial for researchers to advance e-government website accessibility in a meaningful way.
Volume: 16
Issue: 1
Page: 357-372
Publish at: 2026-02-01

Autonomous mobile robot implementation for final assembly material delivery system

10.11591/ijece.v16i1.pp158-173
Ahmad Riyad Firdaus , Imam Sholihuddin , Fania Putri Hutasoit , Agus Naba , Ika Karlina Laila Nur Suciningtyas
This study presents the development and implementation of an autonomous mobile robot (AMR) system for material delivery in a final assembly environment. The AMR replaces conventional transport methods by autonomously moving trolleys between the warehouse, production stations, and recycling areas, thereby reducing human intervention in repetitive logistics tasks. The proposed system integrates a laser-SLAM navigation approach, customized trolley design, RoboShop programming, and robot dispatch system coordination, enabling real-time route planning, obstacle detection, and material scheduling. Experimental validation demonstrated high accuracy in path following, with root mean square error values ranging between 0.001 to 0.020 meters. The AMR achieved an average travel distance of 118.81 meters and a cycle time of 566.90 seconds across three final assembly stations. The overall efficiency reached 57%, primarily due to reduced idle time and optimized material replenishment. These results confirm the feasibility of AMR deployment as a scalable and flexible intralogistics solution, supporting the transition toward Industry 4.0 smart manufacturing systems.
Volume: 16
Issue: 1
Page: 158-173
Publish at: 2026-02-01

Artificial intelligence of things solution for Spirulina cultivation control

10.11591/ijece.v16i1.pp488-504
Abdelkarim Elbaati , Mariem Kobbi , Jihene Afli , Abdelrahim Chiha , Riadh Haj Amor , Bilel Neji , Taha Beyrouthy , Youssef Krichen , Adel M. Alimi
In the evolving field of Spirulina cultivation, the integration of the internet of things (IoT) has facilitated the optimization of spirulina growth and significantly enhanced biomass yield in the culture medium. This study outlines a control open-pond system for Spirulina cultivation that employs generative artificial intelligence (AI) and edge computing within an IoT framework. This transformative approach maintains optimal conditions and automates tasks traditionally managed through labor-intensive manual processes. The system is designed to detect, acquire, and monitor basin data via electronic devices, which is then analyzed by a large language model (LLM) to generate precise, context-aware recommendations based on domain-specific knowledge. The final output comprises SMS notifications sent to the farm manager, containing the generated recommendations, which keep them informed and enable timely intervention when necessary. To ensure continued autonomous operation in case of connectivity loss, pre-trained TinyML models were integrated into the Raspberry Pi. These models display alarm signals to alert the farm owner to any irregularities, thereby maintaining system stability and performance. This system has substantially improved the growth rate, biomass yield, and nutrient content of Spirulina. The results highlight the potential of this system to transform Spirulina cultivation by offering an adaptable, autonomous solution.
Volume: 16
Issue: 1
Page: 488-504
Publish at: 2026-02-01

Students performance clustering for future personalized in learning virtual reality

10.11591/ijece.v16i1.pp297-310
Ghalia Mdaghri Alaoui , Abdelhamid Zouhair , Ilhame Khabbachi
This study investigates five clustering algorithms—K-Means, Gaussian mixture model (GMM), hierarchical clustering (HC), k-medoids, and spectral clustering—applied to student performance in mathematics, reading, and writing to support the development of virtual reality (VR)-based adaptive learning systems. Cluster quality was assessed using Davies-Bouldin and Calinski-Harabasz indices. Spectral clustering achieved the best results (DBI = 0.75, CHI = 1322), followed by K-Means (DBI = 0.79, CHI = 1398), while HC demonstrated superior robustness to outliers. Three distinct student profiles—beginner, intermediate, and advanced—emerged, enabling targeted adaptive interventions. Supervised classifiers trained on these clusters reached up to 99% accuracy (logistic regression) and 97.5% (support vector machine (SVM)), validating the discovered groupings. This work introduces a novel, data-driven methodology integrating unsupervised clustering with supervised prediction, providing a practical framework for designing immersive VR learning environments.
Volume: 16
Issue: 1
Page: 297-310
Publish at: 2026-02-01

Application of deep learning and machine learning techniques for the detection of misleading health reports

10.11591/ijece.v16i1.pp373-382
Ravindra Babu Jaladanki , Garapati Satyanarayana Murthy , Venu Gopal Gaddam , Chippada Nagamani , Janjhyam Venkata Naga Ramesh , Ramesh Eluri
In the current era of vast information availability, the dissemination of misleading health information poses a considerable obstacle, jeopardizing public health and overall well-being. To tackle this challenge, experts have utilized artificial intelligence methods, especially machine learning (ML) and deep learning (DL), to create automated systems that can identify misleading health-related information. This study thoroughly investigates ML and DL techniques for detecting fraudulent health news. The analysis delves into distinct methodologies, exploring their unique approaches, metrics, and challenges. This study explores various techniques utilized in feature engineering, model architecture, and evaluation metrics within the realms of machine learning and deep learning methodologies. Additionally, we analyze the consequences of our results on enhancing the efficacy of systems designed to detect counterfeit health news and propose possible avenues for future investigation in this vital area.
Volume: 16
Issue: 1
Page: 373-382
Publish at: 2026-02-01
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