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30,907 Article Results

A model for enhancing teacher training by integrating internet of things, cloud computing, and robotics in a blended learning

10.11591/ijere.v15i4.39473
Meiramgul Mukhambetova , Salamat Idrissov , Nurgul Baitemirova
Despite educational digitalization, a divide persists in integrating robotics and the internet of things (IoT) into classroom practice. Both pre-service and in-service teachers struggle to translate theory into application due to the lack of unified methodological frameworks. This gap demonstrably undermines student academic performance. The present study introduces the teachers-oriented, learning content, methodological approaches, digital support, expected outcomes (T-LMDE) model to bridge this divide through the synergistic integration of key dimensions. The model integrates robotics and IoT with cloud computing, utilizing the Arduino IoT cloud for data analytics and remote control. Delivered via a blended-learning portal, the curriculum provides didactic materials and feedback mechanisms, enabling educators to master complex engineering solutions. The model’s efficacy was empirically validated with 31 participants across two modalities: a 15-week course for pre-service teachers and an 80-hour intensive program for in-service educators. Using a quasi-experimental, mixed-methods design, data were analyzed via paired t-tests and qualitative thematic scrutiny. Findings confirm the model’s positive impact on professional transformation and competency advancement, shifting educators from content consumers to “teachers-as-designers”.
Volume: 15
Issue: 4
Page: 3322-3333
Publish at: 2026-08-01

Blended-language instructional-approach as a determinant of science learning in rural classrooms

10.11591/ijere.v15i4.39263
Atomatofa Rachel Ovuezirie , Sekegor Crescentia Ojenikoh , Avbenagha Andrew , Ewesor Stella
Scientific concepts such as gravity continue to pose challenges for students in rural and under-resourced classrooms, where reliance on a single language of instruction often restricts access to meaning and limits conceptual understanding. This study investigated the impact of a blended English and Urhobo (BEAU) instructional approach on junior secondary one students’ learning and retention of gravity concepts in rural Nigeria. A quasi-experimental pre-test–post-test non-equivalent control group design was employed with 243 students assigned to English-only, Urhobo-only, or BEAU instructional conditions. A validated 30-item multiple-choice gravity test was administered, and analysis of covariance (ANCOVA) was used to examine differences while controlling for pre-test scores. The findings show that students taught using the BEAU language approach achieved better significantly in both post-test and retention tests compared to those taught using English-only or Urhobo-only instructional approaches. The findings provide empirical evidence that the blended language instructional approach enhances science learning outcomes in rural Nigerian contexts. Linguistically responsive instructional approach improves conceptual understanding and supports long-term retention of abstract scientific ideas, underscoring the importance of leveraging students’ linguistic resources to strengthen science education in rural and under-resourced classrooms.
Volume: 15
Issue: 4
Page: 3636-3645
Publish at: 2026-08-01

Evaluating the methodological admissibility of generative AI tools for linear regression in graduate-level research

10.11591/ijere.v15i4.38496
Valery Okulich-Kazarin , Kanat Kozhakhmet
With the growing use of generative artificial intelligence (AI) in academia, a key methodological question concerns the statistical correctness of AI-assisted quantitative analysis. This study empirically evaluates the use of generative AI tools for linear regression in graduate-level research. The authors used a methodological approach in which estimates from four AI systems (ChatGPT 4.0, DeepSeek v3.2, Gemini 3 Pro, and Grok 4.1) were compared with estimates obtained using Microsoft Excel (Windows 10). The analysis was performed on five time series using a fixed prompt structure. Comparability was assessed using thresholds for regression coefficients, the coefficient of determination (R²), and predicted results for 2030. The results show that under controlled conditions and within the ordinary least squares (OLS) method, the AI tools generate statistical results with varying degrees of accuracy. However, deviations in coefficients and predictions highlight the need for systematic validation. The study concludes that AI tools can serve as auxiliary methodological support, provided transparency, reproducibility, and threshold-based verification are ensured in graduate research practice.
Volume: 15
Issue: 4
Page: 2874-2882
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

Procrastination trap: how personality traits fuel generative artificial intelligence over-reliance and erode academic performance

10.11591/ijere.v15i4.39382
Mohamad Rizal Abdul Hamid , Chen Jung Ku , Anath Rau Krishnan , Imran Mehboob Shaikh , Yoke Lian Lau , Mohd Zulkifli Muhammad , Saiful Bahri
This study investigates how long-term personality traits contribute to generative artificial intelligence (GenAI) use and whether these traits have an impact on academic procrastination and performance. The Big five personality model was used for this investigation, and a quantitative methodology was employed to analyze data from 200 undergraduate students in East Malaysia via partial least squares structural equation modeling (PLS-SEM). Findings indicated that while neuroticism and openness were associated with increased levels of GenAI use, conscientiousness was found to be a protective factor against GenAI dependency. In addition, results showed that when students excessively utilize GenAI, they experience increased levels of procrastination which leads to longer procrastination periods and decreased levels of deep learning engagement. Finally, procrastination was identified as a partial mediator between GenAI use and poor academic performance. Thus, GenAI dependency produces a ‘competence illusion’, causing a decline in students’ academic abilities over time. Ultimately, this study supports the need for interventions designed to help students learn self-regulation and develop critical AI literacy skills to enable technology to serve as a cognitive scaffold as opposed to a replacement for students’ own cognitive efforts.
Volume: 15
Issue: 4
Page: 3362-3374
Publish at: 2026-08-01

Learning across ages: the role of intergenerational engagement in elderly education

10.11591/ijere.v15i4.40178
Rita Wong Mee Mee , Muhammad Fairuz Abd Rauf , Rasithra Ravichandran , Mohd Fahmi Mohamad Amran , Khairul Annuar Abdullah , Zuraidy Adnan , Muhammad Farhan Nordin
Intergenerational learning (IGL) has gained increasing attention as a strategy to address the limited effectiveness of elderly education, particularly in the context of digital literacy and lifelong learning. Despite growing research, the field remains fragmented, with limited synthesis translating evidence into actionable educational strategies. This study aims to systematically examine how IGL enhances elderly learning ability and to identify strategic directions for its implementation. A bibliometric-driven analytical approach was employed using Scopus-indexed publications from 2021 to 2025. The top ten most-cited articles were identified through performance analysis and subsequently analyzed using a strengths, weaknesses, opportunities, and threats (SWOT) framework. The findings reveal that IGL significantly improves digital literacy, cognitive engagement, and social well-being among elderly learners. However, challenges such as internalized ageism, low learning confidence, and the absence of structured and scalable frameworks remain critical barriers. The study also highlights opportunities for integrating IGL into formal education and community-based programs, while identifying threats related to technological change and policy limitations. This study contributes a bibliometric-informed strategic synthesis that advances understanding of IGL and provides actionable insights for educators, program designers, and policymakers in elderly education.
Volume: 15
Issue: 4
Page: 3086-3098
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

Video summarization using deep image captioning models

10.11591/ijeecs.v43.i2.pp547-554
Qudes M. B. Aljelawy , Sarah S. Mohammed , Entessar K. Hanoun
This research presents a novel approach for video summarization by leveraging deep image captioning models. A pretrained image captioning model, namely Salesforce's bootstrapped language image pretraining (BLIP), is used to extract keyframes from a video at regular intervals and produce natural language descriptions. These textual descriptions are then filtered for non-repetition and concatenated into a coherent summary, allowing users to understand the video’s content without viewing it in full. The proposed framework aims to improve video browsing, indexing, and retrieval efficiency, particularly for big datasets. The proposed method achieves a significant reduction in redundancy by 40% compared to raw captioning sequences. Evaluation using semantic consistency checks demonstrates that the BLIP-based framework maintains high descriptive accuracy even in complex scenes, providing a scalable solution for large-scale video indexing.
Volume: 43
Issue: 2
Page: 547-554
Publish at: 2026-08-01

Evaluating AI-powered ChatGPT for intelligent virtual learning environments through conversational intelligence

10.11591/ijeecs.v43.i2.pp568-575
Isaac Asampana , Henry Akwetey Matey , Ben Ocra , Jones Yeboah Nyame
The rapid advancement of conversational artificial intelligence has transformed virtual learning by enabling intelligent, adaptive, and interactive educational support through natural language communication. Among recent AI technologies, chat generative pre-trained transformer (ChatGPT) has emerged as a powerful conversational intelligence system capable of providing real-time explanations, personalized learning assistance, and contextual academic interactions. This study evaluates the effectiveness of AI-powered ChatGPT in intelligent virtual learning environments through the lens of conversational intelligence. A quantitative survey instrument, developed based on established technology acceptance constructs, was employed to assess users' perceptions of ChatGPT's usefulness, ease of interaction, and educational effectiveness. Using a simple random sampling technique, data were collected from 1,104 participants and analyzed using descriptive statistical methods in SPSS version 25. The results indicate that ChatGPT's conversational capabilities significantly enhance learner engagement, support personalized learning experiences, and improve perceived academic performance through intuitive human-AI interactions. The findings demonstrate that conversational intelligence plays a critical role in improving the effectiveness of intelligent virtual learning environments by facilitating adaptive knowledge delivery and interactive learning support. This study provides valuable insights into the integration of conversational AI technologies for developing next-generation intelligent digital education systems.
Volume: 43
Issue: 2
Page: 568-575
Publish at: 2026-08-01

Intelligent land use and land cover classification using Sentinel-2 multispectral imagery

10.11591/ijeecs.v43.i2.pp586-594
Neha Vyas , Koushik Sundar , Narayan Vyas
Accurate land use and land cover (LULC) classification is essential for environmental monitoring, agricultural planning, and sustainable resource management. This study investigates the effectiveness of Sentinel-2 multispectral satellite imagery for LULC classification by comparing the performance of three supervised classification algorithms: maximum likelihood classifier (MLC), minimum distance classifier (MDC), and neural networks (NN). Before classification, Sentinel-2 imagery underwent comprehensive preprocessing, including atmospheric correction, radiometric calibration, and cloud masking using ERDAS software to improve image quality and classification reliability. The Villupuram district of Tamil Nadu, India, was selected as the study area due to its diverse land cover characteristics. Classification performance was evaluated using overall accuracy (OA), producer’s accuracy (PA), user’s accuracy (UA), and the Kappa coefficient. Experimental results demonstrate that the MLC achieved the highest OA of 94.81% with a Kappa coefficient of 0.9308, outperforming MDC (90.65%, 0.8753) and NN (84.38%, 0.7917). These findings confirm that supervised classification of Sentinel-2 multispectral imagery provides reliable and accurate LULC mapping, offering valuable geospatial information to support precision agriculture, environmental monitoring, land resource management, and sustainable regional planning.
Volume: 43
Issue: 2
Page: 586-594
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

An artificial neural network-based decision support model for early prediction of mathematics learning challenges using the CRISP DM framework

10.11591/ijeecs.v43.i2.pp618-627
Harry Dhika , Surajiyo Surajiyo , Lasia Agustina , Abdul Muchlis
Identifying mathematics learning difficulties remains a challenge for educators due to the subjectivity and inefficiency of conventional methods in capturing psychological factors. To address this limitation, this study proposes an artificial neural network (ANN)-based decision support model developed within the cross-industry standard process for data mining (CRISP-DM) framework. The model integrates 16 academic indicators (quizzes, exams, remedial frequency, online activity) and psychological factors (motivation, anxiety, self-confidence, interest) from 163 student records at SMA Muhammadiyah 16 Jakarta. Synthetic minority over-sampling technique (SMOTE) and focal loss are applied to handle class imbalance and improve reliability. The proposed model achieves 98% accuracy and a 0.97 F1-score in classifying students into three difficulty levels: Easy, moderate, and difficult. These findings demonstrate the model’s effectiveness in capturing complex relationships between cognitive and affective features. Unlike prior studies that rely solely on academic performance, this work contributes a robust, comprehensive data-driven framework that enhances multiclass classification for early educational intervention within the Indonesian context.
Volume: 43
Issue: 2
Page: 618-627
Publish at: 2026-08-01

Probability density-based quantized spiking neural network for efficient intrusion detection in mobile ad hoc networks

10.11591/ijeecs.v43.i2.pp460-471
Amruth Veerabhadraiah , Devaraj Verma Chitragar
Mobile ad hoc networks (MANETs) enable infrastructure-free wireless communication through decentralized and self-organizing network architectures, making them well suited for dynamic environments such as disaster recovery, military operations, and intelligent transportation systems. However, their distributed topology and continuously changing network structure expose them to sophisticated routing attacks, particularly blackhole attacks (BHA) and wormhole attacks (WHA), which significantly degrade network reliability and challenge conventional intrusion detection techniques. Existing deep learning approaches often struggle to accurately distinguish malicious from legitimate traffic because of high-dimensional feature spaces and limited computational resources in MANET environments. To address these challenges, this paper proposes a probability density-based quantized spiking neural network (PDF-QSNN) framework for efficient intrusion detection in MANETs. The proposed framework first employs adaptive moth flame optimization (AMFO) to identify the most informative features, thereby reducing data redundancy and computational complexity. Subsequently, probability density-based feature encoding generates a discriminative probabilistic representation of network behavior, while the quantized spiking neural network performs lightweight and energy-efficient intrusion classification. Experimental evaluations using simulated BHA and WHA datasets demonstrate that the proposed framework achieves classification accuracies of 92.86% and 91.56%, respectively, outperforming conventional convolutional neural networks (CNN) and stacked recurrent long short-term memory (SRLSTM) models. These results demonstrate the effectiveness of the proposed framework for accurate and computationally efficient intrusion detection in resource constrained MANET environments.
Volume: 43
Issue: 2
Page: 460-471
Publish at: 2026-08-01

FireDetXplainer: an explainable artificial intelligence framework for wildfire detection

10.11591/ijeecs.v43.i2.pp576-585
Janjhyam Venkata Naga Ramesh , Bhargavi Peddi Reddy , Jillellamoodi Naga Madhuri Rajyalakshmi , Rajyalakshmi Uppada , Gaddam Venu Gopal , Rajesh Tulasi
Wildfires pose significant environmental, ecological, and socioeconomic threats, necessitating rapid and reliable detection systems for timely emergency response and disaster mitigation. Recent advances in deep learning have substantially improved wildfire detection accuracy; however, most existing models operate as black-box systems, limiting transparency and reducing user trust in safety-critical applications. This study proposes FireDetXplainer (FDX), an explainable artificial intelligence (XAI) framework designed to enhance the interpretability of deep learning-based wildfire detection while maintaining high predictive performance. The proposed framework integrates convolutional neural network (CNN)-based image classification with explainability techniques to identify the visual regions that contribute most to wildfire detection decisions. By generating intuitive visual explanations, FDX enables users to understand, validate, and trust the model's predictions, thereby supporting transparent and accountable decision-making. Experimental evaluation demonstrates that the proposed framework effectively distinguishes wildfire images from non-fire scenes while providing meaningful visual interpretations that improve model transparency without compromising detection performance. The findings highlight the potential of explainable AI to strengthen the reliability, usability, and practical deployment of intelligent wildfire monitoring systems for environmental surveillance, disaster management, and early warning applications.
Volume: 43
Issue: 2
Page: 576-585
Publish at: 2026-08-01

Low-cost real-time campus assistive navigation device for the visually impaired: The University of Ilorin case study

10.11591/ijeecs.v43.i2.pp555-567
Mahmud Hafeez Owolabi , Idajili John Ojochegbe , Ayinla Shehu Lukman , Jimoh-Mahmud Aishat Oladayo , Yusuf Abdulrahman Olalekan , Olaogun Jerry Oluwajomiloju
Navigating dynamic environments remains a significant challenge for visually impaired individuals due to limited spatial awareness, which restricts their mobility, independence, and safety. Traditional aids such as white canes and guide dogs provide physical support but lack contextual feedback. While recent advances in artificial intelligence (AI) and computer vision (CV) have enabled real-time sensing, many assistive systems remain costly, complex, and dependent on continuous network connectivity. This study introduces a low‑cost, portable campus assistive navigation (CAN) device that delivers real‑time perception and offline guidance. The system employs a custom dataset, an optimized YOLOv11 model, and a Pi Camera for continuous object detection, supported by an HC‑SR04 ultrasonic sensor for obstacle avoidance and auditory alerts. Training and validation confirmed robust convergence, with precision ~0.95, recall near 1.0, and mAP@0.5 exceeding 0.9, while mAP@0.5:0.95 remained above 0.8, demonstrating reliable detection and generalization under strict thresholds. Field tests further reported confidence scores of 0.74–0.84, 98% accuracy in distance measurement, and GPS localization within ±1.5 m. Real‑time auditory and haptic feedback via Bluetooth headphones enhanced mobility and safety. The CAN device offers a scalable, affordable solution for autonomous navigation in campus environments.
Volume: 43
Issue: 2
Page: 555-567
Publish at: 2026-08-01
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