Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

31,042 Article Results

Artificial intelligence technologies in teaching Russian as a foreign language

10.11591/ijere.v15i4.38077
Larissa Krymova , Navruz Khasanov , Zhamiila Arstanbekova , Ariya Azamatova , Nuraisha Bekeyeva
The present study aims to investigate the impact of integrating artificial intelligence (AI) technologies into teaching Russian as a foreign language (RFL) from the perspective of educators. Employing a mixed-methods research design, the study utilized several methodologies, including a teacher survey, an analytical-descriptive approach to data interpretation, and the development and evaluation of AI-based interventions. The study sample comprised 120 RFL instructors from three public universities in Kazakhstan. Preliminary findings revealed a considerable awareness among teachers regarding various AI technologies, such as chatbots, voice assistants, the ChatGPT neural network, educational platforms, gaming applications, and task design tools. Nonetheless, the practical utilization of these technologies varied significantly, with only a subset of teachers incorporating them into their regular teaching practices. The study culminated in the development of a conceptual framework for AI-driven educational interventions, incorporating platforms such as Coursera, Moodle, Open EdX, and eFront; game-based applications including Duolingo, Talk2Russia, and Russian Verbs Pro; and task creation tools such as Kahoot! and Quizlet. Following the integration of these interventions into the curriculum, post-implementation evaluations indicated that teachers generally perceived the tools as effective, with the average effectiveness rating surpassing 4.0 out of 5.0 across all assessed categories. The findings of this study have practical applicability; they can be used to enhance professional development programs for teachers of the Russian language and to formulate strategies for the integration of AI technologies into language education within the Central Asian region.
Volume: 15
Issue: 4
Page: 3422-3438
Publish at: 2026-08-01

Teachers’ perspectives on pedagogical challenges of AI integration in English language teaching

10.11591/ijere.v15i4.39226
Parthiban Ganesan , Karthikeyan Padmanathan , Thiyagu Kaliappan , Raja Kumar Subburaj , Durgaprasad Sahoo
Artificial intelligence (AI) tools are rapidly being integrated into English language teaching (ELT). However, limited empirical evidence exists regarding the pedagogical challenges teachers encounter during classroom implementation. This study investigates whether demographic variables influence ELT teachers’ perceptions of AI effectiveness and related pedagogical challenges. A quantitative survey design was employed, involving 200 ELT teachers with prior AI exposure. Data were collected using a validated Likert-scale instrument (α=0.81) and analyzed using independent samples t-tests, one-way ANOVA with Tukey post hoc analysis, and Chi-square (χ²) tests. Results revealed significant gender differences in perceived effectiveness (t=2.504, p=0.044) and pedagogical challenges (t=2.627, p=0.018), with female teachers reporting higher mean scores. Locality differences were significant only for perceived effectiveness (F=4.610, p0.05) or teaching experience (χ²=4.266, p>0.05). Educational level taught was significantly associated with perceived effectiveness (χ²=13.816, p
Volume: 15
Issue: 4
Page: 3439-3450
Publish at: 2026-08-01

Development of ergonomic skills of future teachers of preschool organizations based on klax pedagogy

10.11591/ijere.v15i4.39441
Feruza Abdrimova , Sholpan Kolumbayeva , Aliya Kosshygulova , Gulnur Amirzhanova , Saltanat Khassanova
Training future preschool teachers requires the systematic development of ergonomic skills due to the high physical and emotional demands of their professional activity. However, existing teacher education programs often emphasize theoretical knowledge while providing limited opportunities for the development of practical ergonomic competencies. This study addresses this gap by investigating the effectiveness of klax pedagogy, a movement-oriented and experiential approach, in dev eloping ergonomic competence among future preschool teachers. A quasi-experimental pre-test/post-test control group design was employed with 84 students (experimental group: n=42; control group: n=42). The experimental group participated in klax-based activities focused on posture control, movement coordination, spatial organization, and ergonomic awareness, while the control group received traditional instruction. The results revealed a statistically significant improvement in ergonomic skills in the experimental group (t=9.84, p
Volume: 15
Issue: 4
Page: 3489-3496
Publish at: 2026-08-01

Psychosocial factors of cyberbullying experienced by Malaysian public university undergraduates

10.11591/ijere.v15i4.39582
Siti Nurfathin Idris , Mastura Mahfar , Faizah Mohd Fakhruddin , Azlina Mohd Kosnin , Aslan Amat Senin , Halimah Mohd Yusof , Nur Azmina Paslan
Cyberbullying has emerged as a critical issue among university students in Malaysia, driven by the widespread use of digital technologies and associated with serious psychological consequences. Despite its increasing prevalence, limited research has explored how psychosocial factors shape cyberbullying victimization within the Malaysian higher education context. This study addresses this gap by exploring the psychosocial factors influencing cyberbullying victimization among university students in Malaysia. A qualitative case study approach was employed, involving five university students who had experienced cyberbullying. Data was collected through semi-structured, in-depth interviews and analyzed using thematic analysis. The findings suggest that cyberbullying victimization is influenced by an interplay of psychological and social factors. Psychological factors include passive behavior, low self-esteem, irrational beliefs and personality traits, while social factors encompass peer relationships, parenting styles, social media engagement, and online gaming environments. This study suggests that cyberbullying is a multifaceted phenomenon shaped by both individual vulnerabilities and environmental influences. These findings highlight the need for comprehensive, psychosocial-based interventions to support students’ wellbeing and promote safer digital environments in higher education institutions.
Volume: 15
Issue: 4
Page: 2795-2805
Publish at: 2026-08-01

Competency-based evaluation of scientific-pedagogical master’s programs

10.11591/ijere.v15i4.37191
Nurzada Kozhamberdiyeva , Aliya Kudaibergenova , Bakytgul Imanbekova , Sarash Konyrbayeva , Zhanar Bekturganova , Yücel Gelişli
The goal of this study is to conduct a comprehensive evaluation of the master’s degree programs at the Faculty of Philosophy and Political Science of Al-Farabi Kazakh National University from the perspective of the competency-based approach. The study involves a targeted sample of 120 participants directly involved in the implementation and evaluation of scientific and pedagogical master’s programs: 70 master’s students, 20 teachers, 20 graduates, and 10 employers. The study employs a mixed-method approach, including surveys of master’s degree students, faculty members, graduates, and employers, as well as qualitative analysis of interviews with participants. Course relevance received the highest rating. Faculty members positively evaluate the alignment of the programs with the competency-based approach. Graduates emphasize the applicability of their knowledge, while employers express satisfaction with the graduates’ level of preparation. Kruskal–Wallis test analysis does not reveal statistically significant differences between groups for most indicators, confirming a consistent perception of educational quality. The interviews reveal a generally positive attitude toward the programs but highlighted several shortcomings, such as uneven competency development, insufficient practical focus, and limited engagement with the professional environment. The novelty of the work lies in the comprehensive empirical evaluation of master’s programs based on a competency-based approach from the perspectives of students, teachers, graduates, and employers. The practical significance of the findings lies in justifying the need for stronger integration of theory and practice, greater transparency in competency assessment, and expanded collaboration with employers to enhance the quality of master’s degree training.
Volume: 15
Issue: 4
Page: 3375-3391
Publish at: 2026-08-01

AI literacy and academic engagement in higher education: the mediating role of foreign language anxiety

10.11591/ijere.v15i4.38082
Ashraf Ragab Ibrahim , Mohamed Megahed Nasreldeen , Hesham Hussein Yakout , Hanan Mohamed Elsied , Mohammed Saad Eltawela , Mohamed Ali Nemt-allah
The rapid integration of artificial intelligence (AI) in higher education has raised questions about how technological competencies influence student outcomes, particularly in foreign language learning where anxiety significantly affects performance. This study investigated whether foreign language anxiety (FLA) mediates the relationship between AI literacy (AIL) and academic engagement (AE) among 1,052 English as a foreign language (EFL) learners enrolled in foreign language programs at Al-Azhar University, Egypt. Participants completed three validated instruments: the artificial intelligence literacy scale (AILS), the short-form foreign language classroom anxiety scale (S-FLCAS), and the academic engagement scale (AES). Mediation analysis was conducted using PROCESS Model 4 with 5,000 bootstrap iterations to generate bias-corrected confidence intervals (CI). Correlation analyses revealed that AIL positively correlated with AE (r=.31, p
Volume: 15
Issue: 4
Page: 3613-3622
Publish at: 2026-08-01

From climate time series to planting windows in chili (Capsicum frutescens): a SARIMA–SVM–XGBoost framework with balanced-accuracy thresholding

10.11591/ijece.v16i4.pp2210-2219
Efrans Christian , Nova Noor Kamala Sari , Ressa Priskila , Septian Geges
This study proposes a spatio-temporal decision-support framework that integrates Seasonal Autoregressive Integrated Moving Average (SARIMA), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) to derive adaptive planting windows for chili (Capsicum frutescens) at the sub-district level. The framework addresses key challenges in climate-sensitive agriculture, including spatial data leakage and class imbalance, by employing Leave-One-Group-Out (LOGO) cross-validation and Balanced Accuracy–based threshold optimization. The proposed system transforms heterogeneous environmental data into actionable recommendations by combining climate forecasting, land suitability assessment, and yield prediction within a unified pipeline. Experimental results indicate that the framework effectively captures seasonal climate dynamics and produces consistent planting recommendations aligned with agronomic conditions, enabling multiple planting cycles per year. The primary contribution of this work lies in a transparent and generalizable integration of statistical and machine learning models into a practical decision-support framework. The proposed approach bridges predictive modeling and real-world agricultural decision-making and can be extended to other crops and regions for climate-adaptive agricultural planning.
Volume: 16
Issue: 4
Page: 2210-2219
Publish at: 2026-08-01

Enhancement of YOLOv8 for object detection in adverse weather conditions using generative adversarial network

10.11591/ijece.v16i4.pp2230-2246
Talifhani Calvin Tshipota , Chunling Tu , Mukatshung Claude Nawej , Sempe Thom Leholo
Detecting objects in bad weather like rain, fog, snow, or low light is still difficult because visibility drops, noise increases, and contrast gets worse, all of which hurt detection accuracy. Most current methods either improve detector designs or use image preprocessing on their own. They usually focus on just one type of weather and do not use a common way to evaluate results. This paper introduces a YOLOv8 framework improved with a generative adversarial network (GAN) for image enhancement before detection. Instead of just making images look better, the GAN is trained to help the object detector work better in tough conditions, so it can extract features more effectively when images are degraded. The model was tested on datasets with different weather conditions using standard metrics like Precision, Recall, F1-score, and mean average precision (mAP). Results show that this method consistently improves performance, with up to a 6.5% increase in mAP@0.5 over YOLOv8-STE and 9.2% over IA-YOLO, especially in foggy and low-light situations. These results show that adding GAN-based preprocessing to YOLOv8 makes detection more reliable and still keeps the process fast. This framework offers a practical and scalable solution for real-world uses like self-driving cars, smart transportation, and surveillance.
Volume: 16
Issue: 4
Page: 2230-2246
Publish at: 2026-08-01

Design and characterization of a flexible ultra-low-power analog front-end circuit using organic thin-film transistors for wearable electrocardiogram monitoring

10.11591/ijece.v16i4.pp1767-1777
Suhad Qasim G. Haddad , Ali Falih Chlloob
The shift from reactive to proactive healthcare has accelerated the development of flexible, comfortable, and energy-efficient wearable health monitoring systems. This study addresses a critical technological gap: the inherent trade-off between the mechanical flexibility of organic thin-film transistors (OTFTs) and their limited electrical performance (low charge carrier mobility) compared to rigid silicon-based technologies. To overcome this, we outline the system-level design of a fully configurable analog front-end (AFE) utilizing OTFTs for precise electrocardiogram (ECG) measurement. A theoretical transfer-function-based co-design approach is used to balance gain, noise rejection, and power efficiency via high-fidelity MATLAB/Simulink continuous-time simulations. Simulation results demonstrate that the proposed AFE achieves a 40 dB gain, a precise diagnostic bandwidth of 0.5–150 Hz, a common-mode rejection ratio (CMRR) of 65 dB, and an ultra-low power consumption of 33 µW. These metrics strictly align with standard clinical ECG requirements, outperforming current state-of-the-art all-organic architectures primarily in power efficiency. Consequently, the input signal-to-noise ratio (SNR) is significantly enhanced by 24.53 dB (from 8.01 to 32.54 dB). The novelty of this work lies in achieving silicon-like clinical functionality within an all-organic structure at minimal power. This establishes a new benchmark for flexible electronics and paves the way for invisible, skin-like devices for continuous cardiovascular monitoring.
Volume: 16
Issue: 4
Page: 1767-1777
Publish at: 2026-08-01

Multiobjective framework for congestion management through coordinated scheduling of generation and demand

10.11591/ijece.v16i4.pp1688-1703
Jayesh Priolkar , Govind Kunkolienkar
The safe and secure operation of power system networks remains a significant challenge due to the ever-increasing demand for electrical energy. In deregulated environments, there is a strong emphasis on the optimal and efficient utilization of existing resources. This work aims to address line congestion by optimally re-dispatching generation resources and proactively managing demand through advanced demand response (DR) programs. An elasticity based, multi-period load model is employed to enhance the realism and effectiveness of DR strategies. The novelty of the proposed work is the holistic approach that simultaneously addresses economic, environmental, and technical objectives, incorporating realistic DR behavior and the advanced modified elephant herding optimization (MEHO) technique. This work proposes a MEHO algorithm for multi-objective congestion management with coordinated generation and DR programs, with comparative analysis against MPSO on both IEEE 30-bus and IEEE 118-bus systems. The MEHO algorithm generates seven unique Pareto-optimal solutions that represent various trade-offs between the conflicting objectives, demonstrating the implementation's remarkable performance on the IEEE 30-bus and IEEE 118-bus test system. MEHO achieves 3.5 to 6.2% better cost solutions, 2.3 to 5.2% lower emissions, and 60 to 62.5% better congestion indices across both test systems.
Volume: 16
Issue: 4
Page: 1688-1703
Publish at: 2026-08-01

Remaining useful life estimation for predictive battery maintenance with improved recurrent singular spectrum analysis algorithm

10.11591/ijece.v16i4.pp1817-1831
Chutipongse Boonyakitmaitree , Suchada Sitjongsataporn
As the global electric vehicle (EV) battery market is projected to reach a valuation of over USD 100 billion by the end of 2026, the demand for sophisticated battery management systems (BMS) has become more critical than ever. Accurate remaining useful life (RUL) prediction is essential for ensuring vehicle safety, optimizing maintenance, and evaluating retired batteries for second-life applications. However, existing prognostic methods often struggle to balance computational efficiency with predictive accuracy, especially during the early stages of battery usage. This research proposes combined weighted similarity-based and recurrent singular spectrum analysis (CWS-RSSA), a hybrid forecasting framework that integrates RSSA with a similarity-based approach through a weighted logistic switching mechanism. The algorithm is designed to be computationally lightweight, making it suitable for resource-constrained BMS hardware. The proposed method was validated using NASA and a large-scale dataset from MIT-Stanford consisting of 124 lithium-ion cells. Experimental results demonstrate that CWS-RSSA is capable of early-stage prediction with a relative error of 19.8%, whereas existing methods are unable to provide predictions. In later stages, once sufficient data becomes available, the algorithm achieves near-perfect accuracy with a negligible relative error on the NASA dataset and an average relative error of only 0.14% across the 124 MIT-Stanford batteries. Furthermore, the algorithm demonstrates robust performance in handling capacity regeneration phenomena. These findings suggest that CWS-RSSA represents a scalable and practical advancement for battery health management, supporting the transition toward a sustainable circular energy economy and providing a reliable foundation for second-life battery certification.
Volume: 16
Issue: 4
Page: 1817-1831
Publish at: 2026-08-01

Scenario-driven fault injection for realistic bugs in web application testing

10.11591/ijece.v16i4.pp2014-2030
Asri Maspupah , Joe Lian Min , Yadhi Aditya
Conventional mutation-based fault injection techniques generally produce single-line syntactic faults, which often fail to represent realistic errors at the functional requirement level because requirement context, execution paths, and functional dependencies are not considered. To address this limitation, this study proposes scenario-driven fault injection (SDFI), a scenario-based fault insertion approach that derives faults from functional requirements and test cases. SDFI integrates operational fault localization, web fault taxonomy, fault injection patterns, and functional scenario mapping to produce targeted fault injections at relevant code locations, resulting in a realistic bug dataset with multi-line faults. An experimental evaluation on a real web application produced 29 mutants, achieving a fault detection rate of 89.29% based on the RIP model. Further analysis shows that the generated mutants replicate common real-world bug characteristics, including logic errors, validation anomalies, inter-function data propagation, and multi-line faults affecting client–server application behavior. These results demonstrate that SDFI is effective in producing realistic bug datasets for evaluating software testing quality, improving test case effectiveness, and supporting further research on requirement-based fault realism.
Volume: 16
Issue: 4
Page: 2014-2030
Publish at: 2026-08-01

The adoption of artificial intelligence in government technology in developing countries: a systematic literature review

10.11591/ijece.v16i4.pp2192-2209
Maria Florentina Rumba , Flourensia Sapty Rahayu
This study systematically reviews empirical evidence on Artificial Intelligence adoption in Government Technology across developing countries, addressing three research questions regarding determinant factors for success, strategies to overcome absorptive capacity constraints, and governance frameworks for responsible implementation. To answer these questions, a systematic literature review was carried out using a structured methodology: The Scopus database was searched with a comprehensive keyword strategy, which initially yielded ninety-three publications. Through rigorous screening and eligibility assessment, fourteen articles meeting all inclusion criteria were analyzed thematically. The findings demonstrate that successful AI adoption requires holistic alignment among institutional factors including policy frameworks and public trust, organizational elements such as human resource capacity and bureaucratic culture, and environmental conditions encompassing infrastructure and societal demands. Effective strategies identified include strengthening digital infrastructure, developing human capital, implementing adaptive governance, fostering multi-stakeholder collaboration, and ensuring contextual adaptation. The study further reveals that legitimate and sustainable implementation necessitates integrating collaborative governance as a legitimacy foundation, Governance, Risk, and Compliance (GRC) frameworks as ethical control systems, and Explainable AI as a transparency mechanism. This integrated approach enables AI to generate both operational efficiency and strategic public value, including social inclusion and progress toward sustainable development objectives.
Volume: 16
Issue: 4
Page: 2192-2209
Publish at: 2026-08-01

Social support for hearing parents raising deaf children in Kazakhstan: challenges and solutions

10.11591/ijere.v15i4.38135
Zhansaya Janpeisova , Assel Sarsenova , Karlygash Yessenamanova , Kulmarash Kashkinbayeva
In Kazakhstan, social support for families with hearing-impaired children remains a pressing issue requiring further research and improvement. The aim of this study is to assess the social support provided to families of hearing parents with children who have hearing impairments in Kazakhstan. The study involved 176 families from Kazakhstan, in which the parents are hearing, and the children have hearing loss. This study employed a convergent mixed-methods approach, combining the simultaneous collection and analysis of qualitative (interviews) and quantitative (surveys) data with the subsequent integration of the findings to gain a more comprehensive understanding of the situation. The results showed that 89% of respondents were aware of existing social assistance programs; financial and medical support were considered particularly important, while psychological support was deemed insufficient. Socioeconomic and geographic factors significantly influenced access to services. The data also showed that income level and type of settlement significantly influence families’ access to social services and awareness of support programs, while the age of parents and children has less influence, although the needs of children of different ages vary. The practical significance of the study lies in identifying problems within the social assistance system, such as low information accessibility, long waiting times, inadequate support, and service quality fluctuations. The obtained data can be used to improve social support mechanisms and raise awareness among families with children who have hearing impairments. These findings can be used to improve social support mechanisms and raise awareness of this issue among families with hearing-impaired children.
Volume: 15
Issue: 4
Page: 2931-2945
Publish at: 2026-08-01

Information and communication technology in higher music education: systematic review

10.11591/ijere.v15i4.38319
Yangyang Wang , Aidah Abdul Karim , Intan Farahana Kamsin
Technological advances have facilitated the integration of information and communication technology (ICT) into higher music education over the past decade. However, existing literature remains fragmented and lacks an integrated understandingof this field. This study addresses four research questions concerning developmental trends, types of ICT use and their characteristics, advantages, and challenges of ICT integration in higher music education. Based on 40 articles retrieved from EBSCO, Scopus, and Web of Science (WoS), this review analyses ICT use in higher music education. The findings show that the reviewed articles are unevenly distributed geographically, with a substantial proportion originating from China. Quantitative research designs dominated the reviewed articles. Thematic analysis identified four primary categories of ICT use: network-based and distance learning technologies, digital platforms and tools, artificial intelligence (AI)-powered educational technologies, and immersive and sensor-based ICT applications. ICT can enhance teaching quality, student motivation, personalized learning, flexibility, instructional innovation, and interdisciplinary integration, while challenges include limited emotional interaction, technical barriers, unequal resource allocation, privacy concerns, and ethical risks. Drawing on the technology acceptance model (TAM) and technological pedagogical content knowledge (TPACK) framework, this study proposes an integrative framework to guide teachers and institutions in designing pedagogically meaningful, technically feasible, and context-sensitive ICT-enhanced music education practices.
Volume: 15
Issue: 4
Page: 3151-3171
Publish at: 2026-08-01
1 2 3
...
2069 2070
Show 19 of 2070

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration