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27,404 Article Results

Pioneering educational frontiers: South Korea-ASEAN synergy in big data integration and future innovations

10.11591/ijere.v14i3.31828
Catherine Joy T. Escuadra , Ella Joy Avellanoza Ponce
This study examines the evolving trends in publication collaboration and research topics related to big data and education in South Korea and the Association of Southeast Asian Nations (ASEAN) region, analyzed through the lens of international relations (IR). Using scientometric methods, the study analyzed 2,427 publications from Web of Science (WoS) through R Studio and VOSViewer, highlighting a marked increase in publication volume, citation, and collaboration in recent years. The research focuses on key areas such as the integration of big data in teaching and performance assessment, the intersection of big data with artificial intelligence (AI), and the varying implementation frameworks across different countries. The findings reveal that while significant progress has been made, there is a need for more structured collaborative efforts. To enhance future research output and collaboration, the study recommends establishing international research networks, organizing joint projects, facilitating exchange programs, and investing in necessary infrastructure. Additionally, it suggests developing policy frameworks and securing funding to support these initiatives. Engaging industry partners and expanding collaborative networks are crucial for advancing the field and optimizing the application of big data in education.
Volume: 14
Issue: 3
Page: 2007-2017
Publish at: 2025-06-01

The effectiveness of edutainment in teaching cell cycle and transport mechanisms

10.11591/ijere.v14i3.30810
Emman A. Litera , Minie L. Bulay
Traditional lecture-based teaching methods prevalent in Philippine schools often lead to passive learning. By contrast, this study investigates the active engagement and enhanced conceptual comprehension facilitated by edutainment through Classcraft v.4.2.6, focusing on the least learned competencies of cell cycle and transport mechanisms. Expert evaluation of edutainment content affirmed its potential as a valuable educational tool. Students exposed to the edutainment method showed significantly improved learning outcomes compared to those taught via traditional lecture method, as validated by statistical analyses. However, challenges such as technological barriers and distractions were acknowledged. To optimize edutainment’s benefits, strategic design considerations and support mechanisms are recommended, including purposeful design, progressive complexity, and educator training. This study significantly updates knowledge in educational practices by highlighting edutainment’s efficacy. By challenging lecture-based teaching, it advocates for more engaging instructional approaches in Philippine secondary education, promising enhanced learning experiences and outcomes.
Volume: 14
Issue: 3
Page: 2400-2408
Publish at: 2025-06-01

Challenges of educational leaders’ utilization of educational portal information systems

10.11591/ijere.v14i3.31984
Hamed Hilal Nasser Al Yahmadi , Yousuf Nasser Said Al Husaini
The study aims to determine the challenges that hinder the adoption of educational portal information systems by Omani educational leaders, in order to explore the manner through which their capabilities can be improved. Moreover, the study uses quantitative research through the questionnaire as the main research instrument. The research population consisted of all educational leaders of the educational portal information systems in the Sultanate of Oman. The research sample included 96 individuals from the study population, selected using a convenience sampling method. Moreover, the study findings concluded that the challenges hinder the adoption of educational portal information systems obtained a moderate response degree, whereas the requirements for developing educational portal information systems obtained a very high response degree. Moreover, there were no statistically significant differences in the challenges hinder the adoption of educational portal information systems and the requirements for their development attributed to the variable of gender, years of experience, technological competency, and job position. Lastly, the study recommends the necessity to encourage leaders to participate in workshops and to keep educational leaders continuously updated on the latest improvements of the educational portal is necessary.
Volume: 14
Issue: 3
Page: 1961-1971
Publish at: 2025-06-01

Unraveling the predictors of research utilization among Thai educators: evidence from PLS-SEM analysis

10.11591/ijere.v14i3.31468
Phuchit Laowang , Suntonrapot Damrongpanit
This groundbreaking study unveils critical factors driving research utilization (RU) among Thai educators, offering vital insights for educational policymakers and administrators. Employing an advanced partial least squares structural equation modeling (PLS-SEM) approach, we examined data from 688 teachers under the office of the basic education commission. Our findings reveal a complex interplay of factors influencing RU, with organizational support (SUPP) emerging as the most potent driver (beta=0.570), followed by knowledge and research skills (KNOWS) (beta=0.539), organizational leadership (LEAD) (beta=0.472), and attributes of research (ATTR) (beta=0.391). Interestingly, ATTR showed the highest direct effect (DE) (beta=0.391), while LEAD had the strongest indirect impact (beta=0.429). Surprisingly, organizational climate (ORGA) showed no significant effect, challenging conventional wisdom. The study explains 52.5% of the variance in RU, providing a robust foundation for evidence-based educational reforms. Delve into our analysis to discover how these relationships between knowledge, leadership, and organizational dynamics shape educational RU in Thailand, and explore our recommendations for enhancing research integration in educational practices.
Volume: 14
Issue: 3
Page: 1684-1694
Publish at: 2025-06-01

The impact of innovative technology on shaping digital design skills in primary school students: a case study of Kazakhstan

10.11591/ijere.v14i3.32777
Saltanat Anapiyayeva , Gulnar Uaisova , Almash Turalbayeva , Sabira Nishanbayeva
The trajectory of digital progress in Kazakhstan has highlighted several challenges within the primary education system. A critical component of developing digital design skills (DDS) lies in the impact of innovative educational technologies on these skills. Despite the potential of such technologies to enhance DDS and engage students in digital literacy, the existing literature falls short in exploring this area comprehensively. The study aims to examine students’ DDS and examines how innovative educational learning technology affects these skills. This study used a quantitative research approach to measure innovative educational learning technology's impact on primary school students’ DDS. The experiment involved 120 participants and uncovered several key insights. The deficiency in DDS and lack of motivation revealed by the study called for systematic changes in how digital literacy is taught. These changes included restructuring curricula, enhanced teacher training, access to digital resources, and more engaging, practical learning environments. The study demonstrated substantial improvements in students’ DDS following the introduction and testing of the author's academic program with the experimental group (EG) participants. The findings from this study can serve as a foundation for developing strategies to enhance DDS in primary school and provide a methodological basis for adapting educational programs to support DDS development.
Volume: 14
Issue: 3
Page: 2389-2399
Publish at: 2025-06-01

Enhancing educational outcomes using AlAfnan taxonomy: integrating cognitive, affective, and psychomotor domains

10.11591/ijere.v14i3.33147
Mohammad Awad AlAfnan
Following the introduction of AlAfnan’s taxonomy of educational objectives, this study offers a framework for educational development encompassing cognitive, affective, and psychomotor domains essential for nurturing well-rounded learners. The cognitive domain emphasizes knowledge acquisition, critical thinking, ethical reasoning, practical application, creativity, and lifelong learning skills. It prepares students to analyze, synthesize, and evaluate information effectively, fostering intellectual depth and adaptability in navigating complex challenges. The affective domain focuses on emotional intelligence, creativity, resilience, collaboration, and visionary thinking. By cultivating these attributes, educators create a supportive environment that encourages self-awareness, empathy, and ethical decision-making. This domain prepares students to excel academically and contribute meaningfully to society, emphasizing holistic personal development alongside academic achievement. The psychomotor domain enhances sensory perception, cognitive-motor integration, feedback responsiveness, creative motor expression, precision, and leadership through physical action. It equips learners with practical skills and dexterity, enabling them to effectively apply theoretical knowledge in real-world contexts. This domain emphasizes hands-on learning experiences that promote mastery, innovation, and leadership in various fields. The study emphasizes that integrating AlAfnan’s taxonomy into educational practices requires strategic alignment of instructional methods and assessment approaches tailored to each domain’s objectives. Educators are encouraged to utilize inquiry-based learning, collaborative projects, experiential activities, and reflective practices to foster comprehensive skill development across all learning styles. This shall foster students’ intellectual curiosity, emotional resilience, and practical competence. This framework promotes a balanced educational approach that prepares learners to thrive in diverse professional settings and contribute actively to global challenges.
Volume: 14
Issue: 3
Page: 2419-2437
Publish at: 2025-06-01

Application of the adaptive neuro-fuzzy inference system for prediction of the electrical energy production in Jakarta

10.11591/ijai.v14.i3.pp1790-1798
Yoga Tri Nugraha , Catra Indra Cahyadi , Rizkha Rida , Margie Subahagia Ningsih , Dewi Sholeha , Indra Roza
Jakarta, as a rapidly growing urban area, faces challenges in balancing energy demand with supply while addressing environmental concerns associated with traditional energy sources. Electrical energy production prediction in urban environments like Jakarta is crucial for effective energy management, ensuring stable supply, and promoting sustainable development. The prediction of electrical energy production in Jakarta is critical for ensuring stable and sustainable energy supply. This research proposed the application of the adaptive neuro-fuzzy inference system (ANFIS) as a predictive tool specifically tailored for Jakarta's energy production prediction context. The research methodology used in this study is the ANFIS. Five levels make up the architecture of the ANFIS model: output, normalization, defuzzification, rule evaluation, and fuzzification. The fuzzification layer converts input variables into linguistic terms using membership functions, while the rule evaluation layer calculates the activation strength of each rule based on the input values. The predicted results of Jakarta electrical energy production from 2023 to 2028 are 65,288 GWh and there is an annual increase of 5.25%. The error contained in ANFIS is with a root mean square error (RMSE) value of 0.0001058% and a mean absolute percentage error (MAPE) value of 0.00875%.
Volume: 14
Issue: 3
Page: 1790-1798
Publish at: 2025-06-01

Evaluating search key distribution impact on searching performance in large data streams

10.11591/ijai.v14.i3.pp2537-2546
Bowonsak Srisungsittisunti , Jirawat Duangkaew , Nakarin Chaikaew
The distribution pattern of search keys is assessed in this study by contrasting four methods of index searching on large-scale JSON files with data streams. The Adelson-Velskii and Landis (AVL) tree, binary search tree (BST), linear search (LS), and binary search (BS) are among the search strategies. We look at the normal distribution, left-skewed distribution, and right-skewed distribution of search-key distributions. According to the results, LS performs the slowest, averaging 653.166 milliseconds, whereas AVL tree performs better than the others in dense index, with an average search time of 0.005 milliseconds. With 0.011 milliseconds per keyword for sparse index, BS outperforms LS, which averages 1007.848 milliseconds. For dense indexing, an AVL tree works best; for sparse indexing, BS is recommended.
Volume: 14
Issue: 3
Page: 2537-2546
Publish at: 2025-06-01

Multilayer stacking for polycystic ovary syndrome diagnosis

10.11591/ijai.v14.i3.pp1968-1975
Kazi Abu Taher , Samia Ahmed , Jannatul Ferdous Esha , Md. Sazzadur Rahman , A. S. M. Sanwar Hosen
Polycystic ovary syndrome (PCOS) is a complicated hormonal condition that is experienced by women. Despite extensive research, the precise reason be hind PCOS remains unknown, and effective treatments are still lacking. Thus, early diagnosis and treatment have a significant positive impact on the health of women. Recently, there has been remarkable performance demonstrated by machine learning (ML)-based detection models for PCOS identification. They are fast and low cost compared to the traditional processes. In this work, a multi stacking PCOS detection model is proposed using K-fold cross validation. The model uses three different ML algorithms namely: na¨ıve Bayes (NB), ran dom forest (RF), and logistic regression (LR) as base classifiers and a neural network, multi-layer perception (MLP) as meta model. This approach utilizes two feature selection techniques and compares the performances on the stack ing methods. Among the two feature selection techniques, Pearson correlation approach performed better with average 98.79% accuracy, 99.17% sensitivity, 98.40% specificity, and 98.79% f1-score.
Volume: 14
Issue: 3
Page: 1968-1975
Publish at: 2025-06-01

Ledger on internet of things: a blockchain framework for resource-constrained devices

10.11591/ijai.v14.i3.pp2506-2518
Suresh Jaganathan , Karthika Veeramani
The increasing use of resource-constrained devices such as the internet of things (IoT) in various applications has led to the need for an optimized blockchain framework for these devices. Blockchain-based IoT networks allow businesses to access and share IoT data within their organization without centralized authority. However, existing frameworks are not designed for IoT applications and lack features like decentralization, scalability, and network overhead. To overcome these limitations, a new blockchain framework is proposed: ledger on internet of things (LIoT), which has a new consensus-based leader election algorithm to address the challenges of existing algorithms with high block creation time and communication overhead. Moreover, a novel data structure has been developed to reduce the storage size of the ledger effectively. The proposed framework also employs a docker for deployment, which provides an efficient and easy setup of blockchain nodes without requiring the individual configuration of each machine, increases the efficiency of the consensus process, and enables convenient deployment and management of the blockchain framework on resource-constrained devices. Furthermore, the performance of the proposed consensus method is analyzed using various performance parameters, including CPU usage, memory usage, transaction execution time, and block generation time.
Volume: 14
Issue: 3
Page: 2506-2518
Publish at: 2025-06-01

Techniques of Quran reciters recognition: a review

10.11591/ijai.v14.i3.pp1683-1695
Ibrahim Alomari , Asma Alshargabi , Mohammed Hadwan
The Quran is the holy book of the Islam. Reading and listening to the Quran is an important part of the daily life of Muslims. Muslims are keen to listen to recitations of Quran by skilled reciters to learn the correct recitation for the purpose of understanding and contemplating. Therefore, there are large variety of audio recitations for many skilled reciters. With the availability of this huge amount of recitations and also with the great progress in voice recognition technologies, many research efforts have been devoted to contribute making recitation better using artificial intelligence. One useful application in this area is identifying the reciters of the Quran. There are various solutions introduced by researchers; however, these solutions vary significantly in terms of accuracy, and efficiency. This research seeks to provide a review of these solutions. It also reviews available datasets using different criteria. Finally, some open issues and challenges were addressed.
Volume: 14
Issue: 3
Page: 1683-1695
Publish at: 2025-06-01

Semantic based medical visual question answering with explainable artificial intelligence

10.11591/ijai.v14.i3.pp2169-2177
Sheerin Sitara Noor Mohamed , Kavitha Srinivasan , Raghuraman Gopalsamy
The medical visual question answering (MVQA) system takes the advantage of both computer vision (CV) and natural language processing (NLP) to accept the medical image and corresponding question as input and generates the respective answer as output. One step further, the MVQA system capable of generating the answer based on the semantics has a distinct place and hence semantic based medical visual question answering (SMVQA) system is proposed in this research. In SMVQA, the semantics for input image and question are generated using layerwise relevance propagation explainable artificial intelligence (LRP XAI) technique and the answer is derived using deductive reasoning method. For this, seven MVQA datasets are used for model creation, testing and validation. The training phase of the SMVQA system is implemented using VGGNet, long short-term memory (LSTM), LRP XAI, ResNet and bidirectional encoder representations from transformers (BERT) to generate a model file. Then the inference is derived in the testing phase based on the generated model file for the test set. Finally, the answer is derived from the inference using natural language toolkit (NLTK) library, term frequency-inverse document frequency (TF-IDF), cosine similarity, best match25 (BM25) techniques along with deductive reasoning. As a result, the proposed SMVQA system gives improved performance then the existing MVQA system especially for abnormality type samples.
Volume: 14
Issue: 3
Page: 2169-2177
Publish at: 2025-06-01

A review of recent deep learning applications in wood surface defect identification

10.11591/ijai.v14.i3.pp1696-1707
Martina Ali , Ummi Raba’ah Hashim , Kasturi Kanchymalay , Aji Prasetya Wibawa , Lizawati Salahuddin , Rahillda Nadhirah Norizzaty Rahiddin
Wood is widely used in construction, art, and home applications due to its aesthetic appeal and favorable mechanical properties. However, environmental factors significantly affect the growth and preservation of wood, often leading to defects that can reduce its performance and ornamental value. Researchers have introduced machine vision and deep learning methods to address the challenges of high labor costs and inefficiencies in identifying wood defects. Deep learning has shown great success in image recognition tasks, yielding impressive results. This paper reviews previous work on deep-learning strategies for identifying wood surface defects. It also discusses data augmentation techniques to address limited defect data and explores transfer learning to enhance classification accuracy on small datasets. Finally, the paper examines the potential limitations of deep learning for defect identification and suggests future research directions.
Volume: 14
Issue: 3
Page: 1696-1707
Publish at: 2025-06-01

Optimizing bioinformatics applications: a novel approach with human protein data and data mining techniques

10.11591/ijai.v14.i3.pp2328-2337
Preeti Thareja , Rajender Singh Chhillar
Biomedicine plays a crucial role in medical research, particularly in optimizing techniques for disease prediction. However, selecting effective optimization methods and managing vast amounts of medical data pose significant challenges. This study introduces a novel optimization technique, integrated bioinformatics optimization model (IBOM) for disease diagnosis, incorporating data mining to efficiently store large datasets for future analysis. Various optimization algorithms, such as whale optimization algorithm (WOA), multi-verse optimization (MVO), genetic algorithm (GA), and ant colony optimization (ACO), were compared with the proposed method. The evaluation focused on metrics like accuracy, specificity, sensitivity, precision, F-score, error, receiver operating characteristic (ROC), and false positive rate (FPR) using 5-fold cross-validation. Results indicated that the 5-fold cross-validation method achieved superior performance with metrics: 98.61% accuracy, 96.59% specificity, 88.63% sensitivity, 99.30% precision, 92.31% F-score, 10.80% error, 92.61% ROC, and a 3.00% FPR. This method was found to be the most effective, achieving an accuracy of 0.92 in disease diagnosis compared to other optimization techniques.
Volume: 14
Issue: 3
Page: 2328-2337
Publish at: 2025-06-01

Artificial intelligence multilingual image-to-speech for accessibility and text recognition

10.11591/ijai.v14.i3.pp1743-1751
Rosalina Rosalina , Hasanul Fahmi , Genta Sahuri
The primary challenge for visually impaired and illiterate individuals is accessing and understanding visual content, which hinders their ability to navigate environments and engage with text-based information. This research addresses this problem by implementing an artificial intelligence (AI)-powered multilingual image-to-speech technology that converts text from images into audio descriptions. The system combines optical character recognition (OCR) and text-to-speech (TTS) synthesis, using natural language processing (NLP) and digital signal processing (DSP) to generate spoken outputs in various languages. Tested for accuracy, the system demonstrated high precision, recall, and an average accuracy rate of 0.976, proving its effectiveness in real-world applications. This technology enhances accessibility, significantly improving the quality of life for visually impaired individuals and offering scalable solutions for illiterate populations. The results also provide insights for refining OCR accuracy and expanding multilingual support.
Volume: 14
Issue: 3
Page: 1743-1751
Publish at: 2025-06-01
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