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29,922 Article Results

Educational innovation in elementary school: a bibliometric analysis

10.11591/ijere.v14i5.33271
Johanna Gallego Alvarez , Lina Rosa Parra Bernal , Vladimir Henao Cespedes
This article presents the results of a bibliometric analysis to answer the research question: What educational innovations have been applied worldwide and published in articles in indexed journals? There were 83 Scopus records obtained, of which, after applying the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, 6 were excluded, obtaining a total of 77 articles for analysis. This analysis focused on trends such as annual productivity, geographic distribution, most relevant journals, thematic areas, and co-occurrence of author keywords. Subsequently, a review of the 10 most relevant articles on the subject was carried out. A decrease in productivity was observed during 2021, the year after the COVID-19 pandemic. Additionally, educational innovation has been articulated with different areas of knowledge. The analysis of the documents highlights the need to train teachers in using tools that facilitate the integration of educational innovation in the classroom. It is recommended that future studies based on the geographic distribution of academic productivity explore the different experiences these countries have had in incorporating educational innovation in the classroom and analyze the public policies that have promoted it.
Volume: 14
Issue: 5
Page: 3418-3427
Publish at: 2025-10-01

Boosting algebra mastery through activity-based learning in an indigenous peoples education secondary school

10.11591/ijere.v14i5.33969
Rolly Najial Apdo , Rachel Basañez Apdo
Algebra is a fundamental area of mathematics, yet many students, particularly indigenous learners, struggle with its concepts and procedures. This study examines the impact of activity-based learning on the conceptual understanding and procedural skills of junior high school students in an indigenous peoples education (IPEd) school. Using a mixed-methods approach, 105 indigenous students from grades 7 to 9 at Daan Taligaman Integrated Secondary School (DTISS), Philippines, participated. Pre-test and post-test scores were analyzed using a paired-samples t-test, while thematic analysis explored students’ learning experiences. The results revealed significant improvements in both conceptual understanding and procedural skills, with grade 7 scores increasing from 41.08% to 80.38% (conceptual) and 34.83% to 74.13% (procedural). A similar trend was apparent for the grades 8 and 9 students. Key themes identified were engagement and enjoyment, increased confidence, and improved understanding. The study highlights the effectiveness of interactive, culturally responsive learning strategies in enhancing algebra mastery among indigenous students and calls for their integration into mathematics education.
Volume: 14
Issue: 5
Page: 4029-4039
Publish at: 2025-10-01

Lecturer’ and students’ perspectives on digital technology use in organizing and writing projects

10.11591/ijere.v14i5.33898
Lyaila Iskakova , Sandugash Turikpenova , Tursynai Abdykadyrova , Yelena Agranovich , Raissa Karsybayeva , Dastan Kultanov
This research aims to determine the problems experienced by lecturers and students in writing projects in order to determine the importance of technology in the organization of project activities. In this context, 24 students and 24 lecturers were included in the study. Six main themes were determined using a semi-structured interview form (general information, project preparation, project writing, cooperation and support, evaluation and feedback, result and presentation). It was concluded that 60% of the students and 20% of the faculty members had no previous experience in project writing. Lecturers and students (30%) had difficulty in finding a project title during the project preparation phase. A total 30% of the students did not know project writing, 50% of the lecturers had lack of knowledge and had problems in accessing the literature. Lecturers (70%) and students (60%) had no problems in finding a project team. Lecturers and students had some problems in sharing the project results, each project increased the knowledge skills of lecturers and students and created new project ideas. It is thought that training activities should be organized for lecturers and students on project writing and what kind of digital technologies they can benefit from in order to create new project ideas.
Volume: 14
Issue: 5
Page: 3631-3641
Publish at: 2025-10-01

Deep transfer learning based disease detection and classification of tomato leaves - a comparative analysis

10.12928/telkomnika.v23i5.26887
Munira Akter; University of Frontier Technology Lata , Marjia; Begum Rokeya University Sultana , Iffat Ara; Begum Rokeya University Badhan , Mastura Jahan; University of Frontier Technology Maria , Fariha Tasnim; University of Frontier Technology Nuha
A wide variety of diseases have a significant impact on tomato plants. To avoid crop quality issues, a prompt and precise diagnosis is crucial. Classifying plant diseases is one of the numerous applications where deep transfer learning models have recently produced remarkable results. This study dealt with fine-tuning by contrasting the most advanced architectures, including Inception V3, ResNet-18, ResNet-50, VGG-16, VGG-19, GoogLeNet, and AlexNet. In the end, a comparison evaluation is conducted. Nine distinct tomato disease classes and one healthy class from PlantVillage make up the dataset used in this study. Precision, recall, F1-score, and accuracy were the basis for a multiclass statistical analysis that assessed the models. The ResNet-50 approach yielded significant results with precision: 82%, recall: 81%, F1-score: 81%, and accuracy: 85%. With this high success rate, it is reasonable to say that mobile applications or IoT-compatible gadgets implemented with the ResNet-50 model can assist farmers in identifying and safeguarding tomatoes against the aforementioned diseases.
Volume: 23
Issue: 5
Page: 1353-1362
Publish at: 2025-10-01

Reflective writing skills among pre service teachers: a scoping review

10.11591/ijere.v14i5.28620
J. A. Mary Kumari , G. S. Prakasha
Reflection is a soul-searching process. It is an innate ability to delve down the memory lane to judge a reaction to a particular situation as right or wrong as a response. The positive reactions are reinforced and the ineffective negative ones are relinquished. Developing reflective skills among preservice teachers include regular reflective practice sessions. They have to painstakingly record all their reflections after the delivery of each lesson as part of their curriculum along with other reflective practice opportunities. This effort should lead to evolution of professional practitioner in the long run. Although, there are factors affecting its development, preservice teachers seem to do it more monotonously without much reflective learning. Their reflective writing skills are way behind the expected level. This study adopts the research design outline advocated by Arksey and O’Malley. The study appraised the research studies conducted from 2015 to 2024 as a part of scoping review. The study throws light on the various aspects related to the teacher-trainees’ reflective writing skills. Future studies may focus on empirical validation of the reflective writing skills among preservice teachers.
Volume: 14
Issue: 5
Page: 3801-3811
Publish at: 2025-10-01

Enhancing network resilience and energy efficiency in the El Abiodh Sidi Cheikh grid through load flow analysis

10.11591/ijece.v15i5.pp4774-4784
Ali Abderrazak Tadjeddine , Soumia Djelaila , Ridha Ilyas Bendjillali , Sofiane Mohammed Bendelhoum , Abdelyamine Boukhobza , Salih Lachache
This study investigates the performance of the El Abiodh Sidi Cheikh (ESC) electrical grid, focusing on energy efficiency, system stability, and the integration of renewable energy sources. Numerical methods, including the Newton-Raphson (NR), accelerated Newton-Raphson (ANR), and fast decoupled (FD) load flow methods, were employed to evaluate power flow, voltage stability, and active power losses. Key results reveal that the NR method achieves the lowest power loss, with a minimal value of 2.32 MW, while voltage violations at specific nodes, such as buses 4 and 13, emphasize the necessity for voltage regulation. Analysis of the sun trajectory and temperature profiles highlights correlations between climatic conditions and energy demand, aiding renewable energy optimization. Additionally, photovoltaic (PV) measurements demonstrate diurnal variations in energy output, critical for enhancing renewable energy integration. These findings underscore the importance of advanced power flow analysis and strategic planning to ensure network resilience, energy efficiency, and reliability in the ESC region.
Volume: 15
Issue: 5
Page: 4774-4784
Publish at: 2025-10-01

A computational study of passive cooling of photovoltaic panels using hybrid material heat sink

10.11591/ijece.v15i5.pp4487-4499
Dang Van Binh , Pham Quang Vu , Pham Manh-Hai
Photovoltaic panels generate electricity from solar energy based on the photovoltaic effect. The conversion efficiency of photovoltaic panels depends on many factors such as solar radiation, wind speed, dust, orientation, tilt angle, and operating temperature. When the operating temperature increases by 1 C, the conversion efficiency of photovoltaic panels decreases by 0.4% - 0.5%. Heat sink is a device used to cool electrical and electronic equipment, including photovoltaic panels. This paper presents calculating the cooling capability of hybrid heat sink made from two materials in steady state using heat transfer theory. Heat sink base is constructed from aluminum and copper layers, with copper layer thickness is 1 and 2 mm. Under different conditions of radiation intensity, wind speed, and tilt angle of photovoltaic panel, results show that heat sink added copper layers of 1 and 2 mm, the operating temperature decreases by about 0.6 K and 1.2 K compared to the aluminum base. Accordingly, the conversion efficiency of photovoltaic panel increased by 0.1% and 0.2%.
Volume: 15
Issue: 5
Page: 4487-4499
Publish at: 2025-10-01

Exploring error patterns in English writing: a pathway to innovative multimodal instructional material

10.11591/ijere.v14i5.33677
Joshua B. Tupas , Jr., Salvador P. Bacio
Considered as a crucial element that leads to better academic performance, Filipino learners always aim to master English language skills. Among various factors that affect the learning of English language skills, the availability of resources that cater to a diverse set of learners is important. Using semiotic or multimodal resources may help teachers assist students in enhancing their macro skills in the English language. This developmental research aimed to design, develop and evaluate a multimodal instructional material (IM) based on students identified common errors in writing. English major education students were selected as participants as they are important role-players in enhancing the future generation of learners in the English language. There were 39 freshman bachelor of secondary education (BSEd) English major students, three English teachers, two curriculum experts, and one information technology expert participated in the study. A panel of experts validated the instruments, which included the questionnaire to gauge the respondents’ writing skills, the adapted rubric for writing proficiency, and the adapted evaluation form for printed IM exclusively used by the university. Results of the study revealed that the respondents’ writing skills were poor. The evaluation conclusively showed that the IM was very acceptable for classroom use and teaching. It was recommended that the developed multimodal IM be used as a supplementary workbook to facilitate the need for primary English textbooks for the freshman BSEd English major students.
Volume: 14
Issue: 5
Page: 3367-3378
Publish at: 2025-10-01

Fuzzy clustering optimization based artificial bee colony algorithm for brain magnetic resonance imaging image segmentation

10.11591/ijece.v15i5.pp4916-4932
Chakir Mokhtari , Mohammed Debakla , Boudjelal Meftah
In brain magnetic resonance imaging (MRI) analysis, image clustering is regarded as one of the most crucial tasks. It is frequently employed to estimate and visualize brain anatomical structures, identify pathological regions, and assist in guiding surgical procedures. Fuzzy c-means algorithm (FCM) is widely used in the MRI image segmentation process. However, it has been several weaknesses such as noise sensitivity, stuck in local optimum and issues with parameters initialization. To address these FCM problems, this paper presents a novel fuzzy optimization method that enhances brain MRI image segmentation by integrating the artificial bee colony (ABC) algorithm with FCM clustering techniques. The proposed method seeks to optimize multiple FCM parameters simultaneously, including the objective function, number of clusters, and cluster center values. The method was evaluated on both simulated and clinical brain MR images, with an emphasis on segmenting white matter, grey matter, and cerebrospinal fluid regions. Experimental results demonstrate significant improvements in segmentation accuracy, achieving a Jaccard similarity (JS) of nearly 1, a partition coefficient index (PCI) of 0.92, and a Davies-Bouldin index (DBI) of 0.41, outperforming other stats of the arts methods.
Volume: 15
Issue: 5
Page: 4916-4932
Publish at: 2025-10-01

Job performance of human resource management graduates from the employers’ and graduates’ perspectives

10.11591/ijere.v14i5.31959
Dahlee Sadang-Pascua , Jennifer Montenegro-Villanueva
Graduates’ job performance reflects their academic orientation in pursuit of their degrees. Thus, academic institutions should prepare students to be competitive, match the needs of the industry, and become worthy of employment after graduation. This research determines the job performance of human resource management (HRM) graduates in terms of their job competencies, career skills, and team performance from the perception of the graduates and their employers. A quantitative research method with statistical tools such as frequency, percentage, weighted mean, and Mann-Whitney U Test was used. Findings revealed a significant difference in the respondents’ perception, specifically in conveying ideas, use of IT, values, quality work, communication skills, human relations, technical, research, leadership skills, and team performance. The result also shows that graduates perceived themselves as excellent performers, which is in contrast to their employers’ perceptions of them as good performers only regarding their job competencies, career skills, and team performance. The differences in perceptions of the performance of the graduates depicts a mismatch between the academe and the industry requirements that result in a recommendation of thorough review and revision of the HRM curriculum, the teaching methodology, and the strategy of the academic institutions to meet the needs of the industry.
Volume: 14
Issue: 5
Page: 3756-3764
Publish at: 2025-10-01

Systematic review: the application of ChatGPT on Arabic language text processing

10.11591/ijece.v15i5.pp4837-4847
Ali Mousa AlSbou , Fadzli Syed Abdullah , Ashanira Mat Deris
Over 420 million people speak Arabic, and it is the official language of 22 countries. Its complex morphology and dialectal diversity present unique challenges for natural language processing (NLP) models like ChatGPT. This systematic review investigates the application of ChatGPT in Arabic language text processing, examining its potential uses, accuracy, and limitations. Covering literature published between 2021 and 2024, this review synthesizes findings from 21 articles, addressing four key research questions: ChatGPT’s applications in Arabic text processing, its performance in terms of accuracy and reliability, the challenges and limitations encountered, and future directions to enhance its utilization. Results indicate that ChatGPT has potential in several applications, including educational tools, machine translation, text generation, and sentiment analysis. Despite current limitations, ChatGPT's potential in Arabic text processing is promising. While it shows high accuracy in structured tasks, it struggles with dialectal variations and cultural nuances, especially in complex text types. Primary limitations include a lack of high-quality Arabic datasets, difficulty handling dialects, and a need for more nuanced contextual understanding. Future research should focus on improving data quality, expanding dialectal coverage, fine-tuning models for specific linguistic tasks, and integrating AI with human teaching methods. Addressing these areas will enhance ChatGPT's accuracy and reliability for Arabic NLP.
Volume: 15
Issue: 5
Page: 4837-4847
Publish at: 2025-10-01

Proactive university students’ views on skills gained from a research colloquium

10.11591/ijere.v14i5.32973
Ma del Carmen Nolasco-Salcedo , Kleophe Alfaro-Castellanos , Diego Ulises Carranza-Sahagun , José Ávila-Paz , Angelica Patricia Ávila-Paz
Research is a key component of higher education, promoting deep learning, critical thinking, and problem-solving skills. The Academic Body of Engineering and Systems (InySis) organizes the colloquium of research initiation: proactive university students (CIIEUPA) each academic cycle (A and B) to foster research as a fundamental tool for students’ educational and professional development. This qualitative, descriptive study aimed to determine the skills acquired by students participating in CIIEUPA. The action-research methodology was employed, with participant observation used as the data collection technique. The sample consisted of second and third-semester students from the Software Engineering Competence Unit from the 2022A and 2022B cycles. The results revealed that participation in the colloquium enriched students with experiences that fostered teamwork, effective communication, critical thinking, and leadership. CIIEUPA, as an active collaborative learning methodology, allowed students to share their findings with the academic community, strengthening their commitment, and motivation toward research. Such initiatives contribute to learning and play a crucial role in the holistic development of students, promoting their growth in both academic and professional fields. This approach demonstrates the value of integrating research into the educational process, allowing students to engage meaningfully with their discipline while developing essential skills for their future careers.
Volume: 14
Issue: 5
Page: 3928-3934
Publish at: 2025-10-01

Optimizing internet of things based gas sensors: deep learning and performance optimization strategies

10.11591/ijece.v15i5.pp4813-4828
Mariam M. Abdellatif , Mehmet Akif Çifçi , Asmaa A. Ibrahim , Hany M. Harb , Abeer S. Desuky
The rapid growth of industrialization and internet of things (IoT) driven advancements in Industry 5.0 necessitates efficient and user-friendly engineering solutions. Gas leakage incidents in coal mines, chemical enterprises, and households pose significant risks to ecosystems and human safety, emphasizing the need for automated and rapid gas-type detection. Traditional detection methods rely on single-source data and focus on isolated spatial or temporal features, limiting accuracy. This paper proposes a multimodal artificial intelligence (AI) fusion technique combining pre-trained convolutional neural networks (CNNs), such as VGG16, with a deep neural network (DNN) model. The particle swarm optimization (PSO) algorithm optimizes CNN hyperparameters, outperforming traditional trial-and-error methods. The system addresses challenges posed by gases being odorless, colorless, and tasteless, which limit conventional human detection methods. By leveraging sensor fusion, the late fusion technique integrates distinct network architectures for unified gas identification. Experimental results demonstrate 95% accuracy using DNN with gas sensor data, 96% with optimized VGG16 using thermal imaging, and 99.5% through multimodal late fusion. This IoT-enhanced solution outperforms single-sensor approaches, offering a robust and reliable gas leakage detection system suitable for industrial and smart city applications.
Volume: 15
Issue: 5
Page: 4813-4828
Publish at: 2025-10-01

Enhancing learning outcomes through course redesign using self-assessment and inquiry models

10.11591/ijere.v14i5.32215
Fredy Martinez , César Hernández , Diego Giral
This study addresses the challenge of enhancing learning outcomes in propaedeutic education by redesigning an undergraduate deep learning course. To achieve this, the self-assessment and quality model (SQM) was combined with the community of inquiry (CoI) framework, which emphasizes cognitive, social, and teaching presence in online education. The redesigned course aligns with the guidelines of the Colombian Ministry of National Education and incorporates continuous feedback from students. Initial implementation led to improved student performance but revealed gaps in perceived learning experiences. Iterative adjustments were made to the course design based on CoI survey results, particularly focusing on increasing teacher involvement. The findings demonstrate that integrating SQM with a responsive, design-based approach can significantly improve learning outcomes and student satisfaction. This study highlights the importance of dynamic course design in higher education and offers a replicable model for other institutions.
Volume: 14
Issue: 5
Page: 3838-3845
Publish at: 2025-10-01

Enhanced torque control for horizontal-axis wind turbines via disturbance observer assistance

10.12928/telkomnika.v23i5.26805
Edwin; Fundación Universitaria Los Libertadores Villarreal-Lopez , Horacio; Universidad de San Buenaventura Coral-Enriquez , Sergio; Samara National Research University Tamayo-Leon
This paper presents an enhanced control strategy for optimizing energy capture in horizontal axis wind turbines operating in the partial-load region (region 2). The proposed approach builds upon conventional standard torque control (STC) by incorporating a generalized extended state observer (GESO) that follows the active-disturbance-rejection paradigm. Although traditional torque control methods have proven effective under steady wind conditions, they often lack robustness against disturbances, system faults, and model uncertainties inherent in wind energy systems. The proposed observer-assisted control scheme addresses these limitations by estimating and compensating for total disturbance signals, including non-modeled dynamics, parameter uncertainties, and actuator faults. The effectiveness of the proposed control strategy is validated through comprehensive simulations using a 5 MW wind turbine model subjected to realistic operational conditions. Simulation scenarios include turbulent wind speed profiles and actuator degradation to assess controller performance. The results demonstrate improved robustness and energy capture efficiency compared to the conventional control approach, while maintaining the simplicity of the implementation. This work contributes to the development of more reliable wind energy conversion systems (WECSs) by offering a practical solution that improves both performance and fault tolerance in partial load operation.
Volume: 23
Issue: 5
Page: 1395-1403
Publish at: 2025-10-01
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