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30,895 Article Results

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

Classification of P300 event-related potentials using SNN, CNN and LSTM deep learning models

10.12928/telkomnika.v24i4.27659
Ahlaam; Bright Star University M. Saed , Ibtihal; College of Electrical and Electronics Technology Fawzi Elshami , Ali; University of Benghazi I. Elgayar
Accurate classification of P300 event-related potentials remains challenging due to the complex, non-stationary, and low signal-to-noise characteristics of electroencephalography (EEG) signals in brain-computer interface (BCI) systems. P300-based devices, such as the P300 speller, enable communication for patients with severe motor impairments, including those with locked-in syndrome; however, reliable brain signal classification is still a critical limitation. This study presents a comparative evaluation of deep learning models, including convolutional neural networks (CNN), long short-term memory (LSTM) networks, and spiking neural networks (SNN), for P300 signal classification. SNNs represent a biologically inspired paradigm that models the discrete, time-dependent behavior of neural spiking activity and offers advantages in terms of energy efficiency and hardware implementability. Experimental results demonstrate that CNN achieved the highest average classification accuracy (81.04%), followed closely by SNN (80.94%) and LSTM (80.60%). Although CNN slightly outperformed the other models, SNNs showed comparable accuracy while requiring fewer training samples and offering potential benefits for low power and real-time BCI systems. These findings highlight the trade-offs between classification performance and computational efficiency and underline the promise of SNNs as an efficient alternative for P300-based BCI applications.
Volume: 24
Issue: 4
Page: 1294-1306
Publish at: 2026-08-01

Autonomous trenching robot with intelligent obstacle detection and path optimization for precision cable installation

10.11591/ijeecs.v43.i2.pp425-438
Muhammad Omar , Hamza Ali Nisar , Muhammad Usman , Husnain Siddique , Suffian Zaman , Saad Saleem Khan , Justyna Robinson
Trenching for underground cable and pipeline installation is typically labor intensive, time-consuming, and potentially hazardous, particularly in environments with buried obstacles. This paper presents a low-cost autonomous trenching robot with intelligent obstacle detection and path optimization to improve excavation efficiency, safety, and accuracy. The proposed system integrates ultrasonic and infrared sensors with an embedded controller for real-time obstacle detection and autonomous navigation. A path optimization algorithm automatically adjusts the trenching route whenever an obstacle is detected, allowing continuous operation while reducing unnecessary movement and energy consumption. The robot employs a tracked mobile platform and an automated trenching mechanism capable of maintaining consistent trench depth and width under different terrain conditions. Experimental results demonstrate that the proposed system accurately detects obstacles, successfully replans its path in real time, and performs reliable autonomous trenching with minimal human intervention. Compared with conventional manual trenching methods, the developed robot improves operational efficiency, enhances excavation accuracy, and reduces safety risks for workers. The proposed system provides a practical and scalable solution for underground cable and pipeline installation and has strong potential for future applications in intelligent construction, infrastructure development, and autonomous civil engineering.
Volume: 43
Issue: 2
Page: 425-438
Publish at: 2026-08-01

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

Immersive technology in English language learning: a bibliometric analysis

10.11591/ijere.v15i4.37947
Zhou Bo , Lim Seong Pek , Nahdia Kabir , Mohamed Bouteraa , Asna Asna
This bibliometric analysis, drawing on data from the Web of Science (WoS) core collection, explores the expanding role of immersive technologies in English language education. Virtual reality (VR) and augmented reality (AR) have shown strong potential to improve learner motivation, engagement, and communicative competence, yet their integration into formal English language settings remains uneven. By analyzing 248 peer-reviewed articles published between 2021 and 2025, this study finds significant trends, influential contributors, and emerging areas of interest within the field. The findings show a steady increase in publications and citations, reflecting growing recognition of the educational value of immersive environments. Prominent themes include emotional engagement, lowered language anxiety, and improved performance in vocabulary, speaking, listening, and cultural understanding. Much of the literature underlines authentic and situated learning, VR-based interactive environments, VR-supported problem-based learning, and AR-assisted vocabulary development. The analysis also identifies leading countries, with China and the United States producing the largest share of research, a pattern supported by strong institutional participation worldwide. These insights help guide educators and policymakers as they consider how to bring immersive technologies into English instruction. The study also establishes a foundation for future research on effective, engaging, and sustainable immersive language learning practices. Overall, these findings clarify how research on immersive technologies in English language education has evolved between 2021 and 2025 and identify influential studies and key contributors. They also point to persisting gaps, such as equity, teacher readiness, and cognitive-load–informed design, that warrant further investigation.
Volume: 15
Issue: 4
Page: 3623-3635
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

Decision-tree-based machine learning for detecting coffee agroforestry using SPOT-7

10.12928/telkomnika.v24i4.27747
I Made; IPB University Khrisna Yoga Devandra , I Nengah; IPB University Surati Jaya , Tatang; IPB University Tiryana
This study develops a decision-tree-based machine-learning (ML) approach to identify coffee agroforestry plants using SPOT-7 satellite imagery. The algorithm was developed by examining the combination of image indices derived from SPOT-7 and biophysical variables. Detection using spectral variables is often hampered by spectral similarity between vegetation cover classes. This study found that a ML method that combines spectral and biophysical variables can significantly improve overall accuracy, from 60.4% (using conventional spectral variables alone) to 94% (using integrated spectral-biophysical variables). For detecting and identifying agroforestry coffee classes typically found under tree canopies, the addition of the “land cover” variable published by the Ministry of Environment and Forestry contributes significantly to the classification of agroforestry coffee. Important variables identified in this model are normalized difference vegetation index (NDVI), visible difference vegetation index (VDVI), normalized red-green vegetation index (NRGI), elevation, and land cover.
Volume: 24
Issue: 4
Page: 1307-1319
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

Establishing quality indicators for higher-order thinking skills: development and validation using factor analysis

10.11591/ijere.v15i4.38884
Nia Kania , Florence Kyaruzi
Assessing higher-order thinking skills (HOTS) in mathematics education remains challenging because available instruments often measure critical thinking (CT), creative thinking (CrT), and problem-solving (PS) separately, limiting a coherent evaluation of their interrelated nature. This study developed and empirically validated an integrated set of quality indicators for HOTS assessment using a mixed-methods design. In the qualitative phase, focus group discussions (FGDs) with five experts in mathematics education and educational evaluation generated theory-driven indicators grounded in instructional practice. In the quantitative phase, 70 participants (university lecturers, secondary school teachers, and undergraduate students) were selected through stratified purposive sampling to evaluate the indicators. An exploratory factor analysis (EFA) was performed using principal axis factoring with varimax rotation, and factorability was confirmed using Kaiser-Meyer-Olkin (KMO) and Bartlett’s test of sphericity. The results showed satisfactory construct validity (KMO=0.75; Bartlett’s p
Volume: 15
Issue: 4
Page: 3346-3361
Publish at: 2026-08-01

Assessing English writing needs for civil engineering students: evidence from a Philippine college

10.11591/ijere.v15i4.38708
Sittie Aina T. Pandapatan , Abdullah H. Edris , Johara D. Alangca-Azis , Stephen A. Fadare , Al-nor D. Ansao , Basmah S. Hussain
This study examined the perceived English writing needs of civil engineering students at a private higher education institution in the Philippines. Using a quantitative, descriptive, cross-sectional survey design, data were collected from 216 second- to fourth-year students through purposive sampling and a validated 15-item writing needs questionnaire. The instrument demonstrated strong internal consistency, and the data were analyzed using descriptive statistics. The findings showed that students reported substantial needs in both foundational writing skills and field-related written outputs. The most strongly perceived needs were related to grammar, sentence clarity, idea development, and the writing of engineering reports, executive summaries, formal letters, and memoranda. These results suggest that general English instruction alone may be insufficient to address the disciplinary communication demands of civil engineering. The study highlights the need to integrate discipline-specific writing support into the curriculum to strengthen students’ academic writing, professional communication, and workplace readiness.
Volume: 15
Issue: 4
Page: 3604-3612
Publish at: 2026-08-01

Affine-invariant feature learning for accurate ulcer detection in wireless capsule endoscopy images

10.11591/ijeecs.v43.i2.pp595-606
S. Bhuvaneswari , M. Sulthan Ibrahim
Ulcers are lesions that develop in the lining of the gastrointestinal (GI) tract, particularly in the stomach and small intestine, and may lead to severe complications such as Crohn’s disease and ulcerative colitis if not detected at an early stage. Conventional endoscopic procedures are often uncomfortable for patients and may provide limited visualization of the entire small intestine. Wireless capsule endoscopy (WCE) has emerged as a non-invasive alternative for comprehensive GI tract examination; however, automated ulcer detection from WCE images remains challenging due to image noise, complex tissue structures, and computational requirements. To address these issues, this paper proposes a Camargo’s Indexive Kuwahara filtering-based affine-invariant sliced regression (CIKF-AISR) framework for accurate and efficient ulcer detection. The proposed framework consists of image acquisition, preprocessing, segmentation, and feature extraction stages. Adaptive CIKF is employed to suppress noise while preserving edge information. Subsequently, Von Neumann locality segmentation combined with the Canberra distance measure is utilized to identify regions of interest (ROIs). Finally, affine-invariant saliency sliced regression extracts discriminative shape, color, and texture features for ulcer detection. Experimental evaluation on the Hyper-Kvasir dataset demonstrates that the proposed method achieves higher ulcer detection accuracy, improved precision, enhanced peak signal-to-noise ratio (PSNR), and lower detection time compared with existing deep CNN and VAE-GAN approaches. These results confirm the effectiveness of the proposed framework for computer-aided GI diagnosis.
Volume: 43
Issue: 2
Page: 595-606
Publish at: 2026-08-01

OCTModNet: a deep learning-based framework for optical coherence tomography image multi-class classification

10.11591/ijeecs.v43.i2.pp495-506
Saja Ataallah Muhammed , Abbas M. Ali , Dler Salih Hasan
Sight is one of the most significant senses in human beings. Losing sight can change a human’s life dramatically. Early diagnosis and detection of retinal diseases will prevent sight loss. Optical coherence tomography (OCT) imaging is helpful in ocular imaging with its high resolution and non invasive imaging for diagnosing retinal diseases. This study proposes OCTModNet, a novel multi-class hybrid classification model that is capable of classifying seven different retinal diseases using a combination of publicly available datasets, Kaggle OCT-C8, and OCTDL datasets. For effective extraction of the significant features, the collected data is resized and normalized. Augmentation techniques are used to improve the training and learning process. For model development, we applied transfer learning and fine-tuned several state-of-the-art deep learning models, ResNet50, InceptionV3, DenseNet121, VGG16, and EfficientNetV2S. Two models that performed best, VGG16 and EfficientNetV2S, were selected as the model backbones with integration of a squeeze and excitation block. The results showed that the OCTModNet achieved an outstanding performance with 99% test accuracy, 99% precision, 99% recall, 99% F1-score, and an area under the curve (AUC) as 99% across the unseen data. These results point out the robustness and reliability of the proposed model to classify OCT images and have the potential to enhance clinical decision-making and assist ophthalmologists in the early detection of diseases.
Volume: 43
Issue: 2
Page: 495-506
Publish at: 2026-08-01

Evaluation of hybrid parallelism for scalable training of DenseNet-121 in diabetic retinopathy classification

10.11591/ijeecs.v43.i2.pp662-671
Indar Sugiarto , Djoni Haryadi Setiabudi , Darrell Cornelius Rivaldo , Taweesak Kijkanjanarat , Resmana Lim
Training large and complex deep learning models is often constrained by GPU memory limitations and prolonged training times. While several parallelism strategies have been proposed, this study specifically evaluates hybrid parallelism—a combination of data parallelism and pipeline parallelism—to address both challenges simultaneously. Using a case study on diabetic retinopathy (DR) classification with the DenseNet-121 architecture, we analyze the trade-off between computational efficiency and memory scalability. Results show that although hybrid parallelism does not yet provide speedup compared to a single-GPU setup—due to communication overhead and pipeline fragmentation—it enables training of large models that exceed the memory capacity of a single GPU. The trained model achieved a validation accuracy of 0.737, a quadratic weighted kappa (QWK) of 0.861, and a weighted F1-score of 0.749. In contrast, pure data parallelism showed a potential speedup of up to 1.9× in scenarios where the model still fits within a single GPU. These findings highlight the critical role of hybrid parallelism in overcoming the memory wall in large-scale model training, though optimization to reduce overhead remains a key challenge.
Volume: 43
Issue: 2
Page: 662-671
Publish at: 2026-08-01

Rethinking computer-based examinations in higher education: psychological, technical, and pedagogical challenges from students’ learning experience and future directions

10.11591/ijere.v15i4.39228
Ahmad Adnan AlZyoud , Eman Mohammad Qudah
The swift adoption of computer-based examinations (CBEs) in higher education has revolutionized assessment methods; nonetheless, there is a lack of thorough research investigating the psychological, technical, and pedagogical experiences of students using these systems. This research explores the various challenges associated with CBEs at Yarmouk University and assesses their influence on students’ learning experiences. A cross-sectional quantitative survey was conducted among 545 undergraduate students from various academic fields during the first semester of the 2025–2026 academic year. Both descriptive and inferential analyses were performed to evaluate students’ perceptions. Research shows a moderate level of acceptance for CBEs, but notable worries remain. The main source of stress was found to be technical reliability, often overshadowing worries about educational content. The one-way navigation aspect was recognized as a significant obstacle, which restricted students’ ability to review answers and added to cognitive strain and hasty decision-making. Furthermore, a significant gap in feedback was noted, as students mostly viewed the system as a tool for grading rather than a resource for ongoing learning. The research finds that successful digital assessment necessitates not just operational effectiveness but also adaptable design, alignment with pedagogical goals, and valuable feedback systems. Practical implications involve rethinking navigation elements, improving technical support, and offering focused training for faculty.
Volume: 15
Issue: 4
Page: 2861-2873
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
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