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Indonesian tenth graders’ academic self-efficacy and English achievement admitted through zoning and achievement schemes

10.11591/ijere.v14i4.29110
Hartono Hartono , Ruseno Arjanggi , Kurniawan Yudhi Nugroho
The zone-based new student admission scheme for lower and secondary public schools has significantly changed education practices, not only in the admission policy but also in the teaching and learning practices. This study aims to describe and compare academic self-efficacy and the achievement of Indonesian tenth-graders admitted through the zoning and achievement admission schemes. Five public senior high schools were purposively selected as samples to represent different clusters of school preferences before the zoning scheme was implemented. Data were collected through an academic self-efficacy questionnaire specially prepared for the study and an achievement test conducted by the classroom teachers. A total of 483 tenth graders completed the questionnaire and an English achievement test; among them, 74.3% were admitted through the zoning scheme, 17.6% were through the achievement scheme, and the remaining 8.1%, were through affirmation, transfer of parent’s job, and other schemes. Data were analyzed descriptively and inferentially using SPSS. The tenth graders had a high level of academic self-efficacy. However, there was a significant difference in academic self-efficacy and English achievement between the tenth graders admitted through the zoning scheme and those admitted through the achievement scheme. The causes of the difference and the implications are discussed.
Volume: 14
Issue: 4
Page: 2500-2509
Publish at: 2025-08-01

Revolutionizing internet of things intrusion detection using machine learning with unidirectional, bidirectional, and packet features

10.11591/ijai.v14.i4.pp3047-3062
Zulhipni Reno Saputra Elsi , Deris Stiawan , Bhakti Yudho Suprapto , M. Agus Syamsul Arifin , Mohd. Yazid Idris , Rahmat Budiarto
Detection of attacks on internet of things (IoT) networks is an important challenge that requires effective and efficient solutions. This study proposes the use of various machine learning (ML) techniques in classifying attacks using unidirectional, bidirectional, and packet features. The proposed methods that implement decision tree (DT), random forest (RF), extreme gradient boosting classifier (XGBC), AdaBoost (AB) and linear discriminant analysis (LDA) work perfectly with all kinds of datasets and includes. It also works very well with data type-based feature selection (DTBFS) and correlation-based feature selection (CBFS). The experiment results show a significant improvement compared to previous studies and reveals that unidirectional and bidirectional features provide higher accuracy compared to packet features. Furthermore, ML models, particularly DT, and RF, have faster computing times compared to more complex deep learning models. This analysis also shows potential overfitting in some models, which requires further validation with different datasets. Based on these findings, we recommend the use of RF and DT for scenarios with unidirectional and bidirectional features, while AB and LDA for packet features. The study concludes that using the right ML techniques along with features that work in both directions can make an intrusion detection system for IoT networks becomes very accurate.
Volume: 14
Issue: 4
Page: 3047-3062
Publish at: 2025-08-01

Formation of key skills of the XXI century in the educational practice of a teacher

10.11591/ijere.v14i4.32968
Saltanat Beissembayeva , Zhanar Oshakbayeva , Gulfairuz Yerkibayeva , Karlygash Babayeva , Selime Chakanova
This study addresses the pressing need to develop key XXI century skills among teachers to effectively navigate the contemporary educational challenges that they are facing. Through interviews with 86 educators from Kazakh pedagogical universities, we identified several essential competencies, including digital literacy, critical thinking and collaboration, as being fundamental for successful teaching in modern contexts. The research proposes a multifaceted approach, employing innovative strategies such as active learning, project-based learning, and collaborative techniques, to seamlessly integrate these skills into the curriculum. The findings indicate that these methods not only enhance students’ practical experiences but also foster a supportive learning environment conducive to creativity and effective problem-solving. The study concludes by emphasizing the vital role of continuous professional development for teachers, ensuring they can adapt their pedagogical practices in response to the rapidly evolving demands of education today.
Volume: 14
Issue: 4
Page: 3125-3134
Publish at: 2025-08-01

Communication and collaboration competence within the digital competence framework: a bibliometric analysis

10.11591/ijere.v14i4.32183
Hue Hong Cao , Lai Thai Dao , Trung Tran , Huyen Thi Thanh Nguyen
This paper evaluates the development of research on communication and collaboration competence within the digital competence framework (CCC-DCF), an increasingly vital area in the digital era. Bibliometric techniques were applied to analyze 449 articles published in the Scopus database from 2000 to 2023. Using VOSviewer and Biblioshiny, publication trends were tracked, leading journals and high-productivity countries identified, as well as collaboration networks, prominent scholars, most-cited documents, and frequently used keywords. Our analysis revealed a steady increase in publications over the past 23 years, with a notable surge in the last 3 years due to the fourth industrial revolution and the COVID-19 pandemic. MDPI AG was the leading publisher, with the United States and Spain as the top-producing countries. Diana Andone and Mark Frydenberg were the most prolific authors, and the British Journal of Educational Technology was the most cited journal. The study also explored collaborations among authors and countries through visualization analysis. Key frequently appearing terms included digital competences, higher education, information and communication technologies, and collaborative learning. This research forms a basis for future studies to enhance communication and collaboration competence in the digital environment for students. It also provides policymakers and researchers with key authors and impactful studies for further exploration.
Volume: 14
Issue: 4
Page: 2652-2665
Publish at: 2025-08-01

Optimized pap-smear image enhancement: hybrid Perona-Malik diffusion filter-CLAHE using spider monkey optimization

10.11591/ijai.v14.i4.pp2765-2775
Ach Khozaimi , Isnani Darti , Wuryansari Muharini Kusumawinahyu , Syaiful Anam
Pap-smear image quality is crucial for cervical cancer detection. This study introduces an optimized hybrid approach that combines the Perona-Malik diffusion (PMD) filter with contrast-limited adaptive histogram equalization (CLAHE) to enhance pap-smear image quality. The PMD filter reduces the image noise, whereas CLAHE improves the image contrast. The hybrid method was optimized using spider monkey optimization (SMO PMD-CLAHE). Blind/reference-less image spatial quality evaluator (BRISQUE) and contrast enhancement-based image quality (CEIQ) are the new objective functions for the PMD filter and CLAHE optimization, respectively. The simulations were conducted using the SIPaKMeD dataset. The results indicate that SMO outperforms state-of-the-art methods in optimizing the PMD filter and CLAHE. The proposed method achieved an average effective measure of enhancement (EME) of 5.45, root mean square (RMS) contrast of 60.45, Michelson’s contrast (MC) of 0.995, and entropy of 6.80. This approach offers a new perspective for improving pap-smear image quality.
Volume: 14
Issue: 4
Page: 2765-2775
Publish at: 2025-08-01

Mapping the intellectual structure of mobile gaming in education: insights from bibliometric methods

10.11591/ijere.v14i4.32988
Lim Seong Pek , Rita Wong Mee Mee , Fatin Syamilah Che Yob , Walton Wider , Cathy Mae Toquero , Karen Joy Brillo Talidong
Mobile gaming in education encompasses using games on mobile devices to achieve educational goals, offering an interactive platform that can make learning more engaging and accessible. This study addresses the gap in understanding how mobile gaming can enhance educational outcomes by mapping the intellectual landscape of mobile gaming research in education through bibliometric methods. The problem is the growing need to adapt educational tools to students’ digital preferences, balancing engagement with academic rigor. A total of 247 articles were identified from the Web of Science (WoS) database. Through co-citation and co-occurrence analyses, the study identifies influential research themes and emerging trends, such as gamification, serious games, and augmented reality. The findings demonstrate that mobile gaming fosters engagement in promoting motivation and supporting problem-solving skills in educational contexts. However, it also highlights the importance of aligning mobile gaming with curriculum objectives and ensuring instructor readiness, supporting sustainable development goal 4: quality education, which aims to improve inclusive and equitable learning outcomes. It identifies emerging trends, including serious games, technology acceptance models, and the use of augmented reality in educational settings. This study provides a significant impact for educators and researchers seeking to incorporate mobile gaming into educational settings actively. The study suggests a balanced approach to mobile gaming, ensuring its introduction enhances educational goals while minimizing the potential for distraction, fostering innovation in line with sustainable development goal 9: industry, innovation, and infrastructure.
Volume: 14
Issue: 4
Page: 2956-2965
Publish at: 2025-08-01

Validity and reliability “K² REBT” group counseling module depression among students

10.11591/ijere.v14i4.32133
Nor Asikhin Ishak , Nurul Huda Ishak , Mohamad Sukeri Khalid
Depression among teenagers, especially students, is an increasing concern with serious consequences, including criminal behavior and suicide. This study develops a rational emotive behavior therapy (REBT) counseling module aimed at reducing irrational beliefs and enhancing the cognitive, emotional, and behavioral well-being of depressed students. The module comprises four sub-modules: self-acceptance, feelings, beliefs, and challenging, based on established REBT principles. The 30 students diagnosed with depression participated in reliability testing, while content validity was assessed by five experts, yielding a high validity quotient of 0.930. The module’s reliability was confirmed with a Cronbach’s alpha of 0.964, indicating strong internal consistency. These findings suggest that the REBT Module is a highly valid and reliable tool for improving coping strategies and alleviating depression among students, making it a valuable addition to school counseling programs.
Volume: 14
Issue: 4
Page: 3065-3077
Publish at: 2025-08-01

Transforming images into words: optical character recognition solutions for image text extraction

10.11591/ijai.v14.i4.pp3412-3420
Jyoti Wadmare , Sunita Patil , Dakshita Kolte , Kapil Bhatia , Palak Desai , Ganesh Wadmare
Optical character recognition (OCR) tool is a boon and greatest advancement in today’s emerging technology which has proven its remarkability in recent years by making it easier for humans to convert the textual information in images or physical documents into text data making it useful for analysis, automation processes and improvised productivity for different purposes. This paper presents the designing, development and implementation of a novel OCR tool aiming at text extraction and recognition tasks. The tool incorporates advanced techniques such as computer vision and natural language processing (NLP) which offer powerful performance for various document types. The performance of the tool is subject to metrics like analysis, accuracy, speed, and document format compatibility. The developed OCR tool provides an accuracy of 98.8% upon execution providing a character error rate of 2.4% and word error rate (WER) of 2.8%. OCR tool finds its applications in document digitization, personal identification, archival of valuable documents, processing of invoices, and other documents. OCR tool holds an immense amount of value for researchers, practitioners and many organizations which seek effective techniques for relevant and accurate text extraction and recognition tasks.
Volume: 14
Issue: 4
Page: 3412-3420
Publish at: 2025-08-01

Navigating the practice teaching odyssey: unveiling the well-being dynamics of student teachers

10.11591/ijere.v14i4.32798
Trixie E. Cubillas , Maricel D. Tabao , Jascha Kaye S. Cabalan , Kristienah Sastha D. Baron
Despite the growing emphasis on student well-being in educational policy and practice, there remains a need for more consensus on which domains should be studied, resulting in fragmented research. This study addresses this issue by gauging the well-being of student teachers at Caraga State University-Main Campus, Philippines, focusing on cognitive, psychological, social, and material dimensions based on the Organization for Economic Co-operation and Development (OECD) Programme for International Student Assessment (PISA) framework. The study employed descriptive-correlational research design and data were collected from 62 Bachelor of Elementary Education (BEEd) and 66 Bachelor of Secondary Education (BSEd) major in Science student teachers using stratified random sampling. Analysis methods included frequency counts, percentages, weighted means, independent sample T-test, and Pearson product-moment correlation. Results showed that most participants were female and from the BEEd program. Significant differences in well-being were found based on gender, while no significant differences were observed between the programs. Cognitive well-being was associated with psychological and social well-being, and material well-being was significantly linked to both psychological and social well-being. Proposed interventions include financial support, social network enhancement, and academic engagement promotion. These findings present novel insights into the importance of financial aid and robust social networks in improving student teachers’ well-being and academic success.
Volume: 14
Issue: 4
Page: 2530-2538
Publish at: 2025-08-01

Image analysis and machine learning techniques for accurate detection of common mango diseases in warm climates

10.11591/ijai.v14.i4.pp2935-2944
Md Abdullah Al Rahib , Naznin Sultana , Nirjhor Saha , Raju Mia , Monisha Sarkar , Abdus Sattar
Mangoes are valuable crops grown in warm climates, but they often suffer from diseases that harm both the trees and the fruits. This paper proposes a new way to use machine learning to detect these diseases early in mango plants. We focused on common issues like mango fruit diseases, leaf diseases, powdery mildew, anthracnose/blossom blight, and dieback, which are particularly problematic in places like Bangladesh. Our method starts by improving the quality of images of mango plants and then extracting important features from these images. We use a technique called k-means clustering to divide the images into meaningful parts for analysis. After extracting ten key features, we tested various ways to classify the diseases. The random forest algorithm stood out, accurately identifying diseases with a 97.44% success rate. This research is crucial for Bangladesh, where mango farming is essential for the economy. By spotting diseases early, we can improve mango production, quality, and the livelihoods of farmers. This automated system offers a practical way to manage mango diseases in regions with similar climates.
Volume: 14
Issue: 4
Page: 2935-2944
Publish at: 2025-08-01

The growth and trends information technology endangered language revitalization research: Insight from a bibliometric study

10.11591/ijece.v15i4.pp3888-3903
Leonardi Paris Hasugian , Syifaul Fuada , Triana Mugia Rahayu , Apridio Edward Katili , Feby Artwodini Muqtadiroh , Nur Aini Rakhmawati
Since United Nations Educational, Scientific and Cultural Organization (UNESCO) declared endangered languages, researchers have revitalized endangered languages in many fields. This study discusses a bibliometric analysis conducted to investigate research on the topic of revitalization of endangered languages in information technology. The study's aim is to assess research topics by identifying authors, institutions, and countries that influence research collaboration. The Scopus dataset (from 2002-2024) was obtained from journal articles (n=62) and conference papers (n=76) and visualized using VOSviewer 1.6.20. The analysis outcomes reveal a fluctuating trend with an increasing pattern. The United States, Canada, and China were identified as the top three countries in terms of publications. Meanwhile, the University of Alberta, Université du Québec à Montréal, University of Auckland, and University of Hawaiʻi at Mānoa are the most prolific institutions on this topic, with two authors from the Université du Québec à Montréal, Sadat and Le, being the most productive. The dominant research is related to computational linguistics. Meanwhile, topics such as phonetic posteriograms, integrated frameworks, and artificial intelligence are some of the potential research areas that can be explored in the future. Its implications for exposing the extent to which the development of endangered language revitalization can be accommodated in the field of information technology.
Volume: 15
Issue: 4
Page: 3888-3903
Publish at: 2025-08-01

Deep transfer learning for classification of ECG signals and lip images in multimodal biometric authentication systems

10.11591/ijai.v14.i4.pp3160-3171
Latha Krishnamoorthy , Ammasandra Sadashivaiah Raju
Authentication plays an essential role in diverse kinds of application that requires security. Several authentication methods have been developed, but biometric authentication has gained huge attention from the research community and industries due to its reliability and robustness. This study investigates multimodal authentication techniques utilizing electrocardiogram (ECG) signals and face lip images. Leveraging transfer learning from pre-trained ResNet and VGG16 models, ECG signals and photos of the lip area of the face are used to extract characteristics. Subsequently, a convolutional neural network (CNN) classifier is employed for classification based on the extracted features. The dataset used in this study comprises ECG signals and face lip images, representing distinct biometric modalities. Through the integration of transfer learning and CNN classification, improving the reliability and precision of multimodal authentication systems is the primary objective of the study. Verification results show that the suggested method is successful in producing trustworthy authentication using multimodal biometric traits. The experimental analysis shows that the proposed deep transfer learning-based model has reported the average accuracy, F1-score, precision, and recall as 0.962, 0.970, 0.965, and 0.966, respectively.
Volume: 14
Issue: 4
Page: 3160-3171
Publish at: 2025-08-01

Advancing practice-oriented education in the training of future pedagogic psychologists

10.11591/ijere.v14i4.32905
Gulmira Manashova , Natalya Mirza , Gulmira Beisenbekova , Saule Nurgaliyeva , Maral Korzhumbayeva
This research aims to explore the characteristics of practice-oriented training within universities and identify the psychological and pedagogical factors that influence the development of professional competencies in future specialists. A comprehensive combination of content analysis of training conditions for future professionals in higher education and an analytical review of the formation of professional competencies among teacher-psychologists in the context of educational system modernization were applied. The conclusions highlight the challenges of implementing practice-oriented training in Kazakhstan’s higher education system, detailing its forms and methods in preparing competitive and competent specialists. Additionally, the study addresses strategies for effectively organizing pedagogical conditions that foster the development of core competencies in future professionals within social and psychological fields. The findings are critical for educators educating future teacher-psychologists, as they emphasize practice-oriented methods during educational modernization.
Volume: 14
Issue: 4
Page: 3162-3170
Publish at: 2025-08-01

Unpacking the drivers of artificial intelligence regulation: driving forces and critical controls in artificial intelligence governance

10.11591/ijai.v14.i4.pp2655-2666
Ibrahim Atoum , Salahiddin Altahat
The burgeoning field of artificial intelligence (AI) necessitates a nuanced approach to governance that integrates technological advancement, ethical considerations, and regulatory oversight. As various AI governance frameworks emerge, a fragmented landscape hinders effective implementation. This article examines the driving forces behind AI regulation and the essential control mechanisms that underpin these frameworks. We analyze market-driven, state-driven, and rights-driven regulatory approaches, focusing on their underlying motivations. Furthermore, critical regulatory controls such as data governance, risk management, and human oversight are highlighted to demonstrate their roles in establishing effective governance structures. Additionally, the importance of international cooperation and stakeholder collaboration in addressing the challenges posed by rapid technological change is emphasized. By providing insights into the strengths, weaknesses, and potential synergies of different governance models, this study contributes to the development of equitable and effective AI regulatory frameworks that encourage innovation while safeguarding societal interests. Ultimately, the findings aim to inform policymakers, industry leaders, and civil society organizations in their efforts to foster a future where AI is utilized responsibly and equitably for the betterment of humanity.
Volume: 14
Issue: 4
Page: 2655-2666
Publish at: 2025-08-01

Revolutionizing autism diagnosis using hybrid model for autism spectrum disorder phenotyping

10.11591/ijece.v15i4.pp3904-3912
Vijayalaxmi N. Rathod , Rayangouda H. Goudar
The growing prevalence of autism spectrum disorder (ASD) necessitates efficient data-driven screening solutions to complement traditional diagnostic methods, which often suffer from subjectivity and limited scalability. This study introduces a hybrid ensemble model combining logistic regression (LR) and naive Bayes (NB) for ASD classification across four age groups (toddlers, children, adolescents, and adults) using behavioral screening datasets. By integrating statistical learning and probabilistic inference, the proposed model effectively captured behavioral markers, ensuring a higher classification accuracy and improved generalization. The experimental evaluation demonstrated its superior performance, achieving 94.24% accuracy and 99.40% area under the receiver operating characteristic curve (AUROC), surpassing those of individual classifiers and existing approaches. This artificial intelligence (AI)-driven framework offers a scalable, cost-effective, and accessible solution for both clinical and telemedicine-based ASD screening, facilitating early intervention and risk assessment. This study underscores the transformative potential of AI in neurodevelopmental diagnostics, paving the way for more efficient and widely deployable autistic screening technologies.
Volume: 15
Issue: 4
Page: 3904-3912
Publish at: 2025-08-01
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