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30,938 Article Results

Online collective efficacy and its relationship with organizational sustainable development in higher education

10.11591/ijere.v15i4.38721
Ashraf Ragab Ibrahim , Ibrahim Mohammed Ibrahim , Billal Mohamed Aboelhasayb , Mohammed Maher Mohammed , Ahmed Metwally Eissa , Mohamed Ali Nemt-allah
Digital transformation in higher education has intensified reliance on online collaboration, yet the role of shared digital capability beliefs in driving institutional sustainability remains underexplored. This study examined the relationship between online collective efficacy (OCE) and organizational sustainable development (OSD) among faculty members in Egyptian higher education. Using a quantitative correlational design, a purposive sample of 647 faculty members and teaching assistants from Al-Azhar University completed two validated instruments: the OCE scale and the OSD questionnaire. Pearson correlation and multiple regression analyses revealed exceptionally strong positive associations between all OCE dimensions and OSD outcomes (r=.887, p
Volume: 15
Issue: 4
Page: 2775-2785
Publish at: 2026-08-01

Reframing teacher evaluation in higher education: a three-pillar framework from Assam

10.11591/ijere.v15i4.40024
Arabinda Rajkhowa , Munmi Dutta
Teacher evaluation shapes the quality of classroom instruction and, through it, student learning outcomes; yet in Indian higher education the dominant single-source model, student feedback channeled through the internal quality assurance cell (IQAC), is widely critiqued as ritualistic and developmentally inert. To the authors’ knowledge, no prior study has integrated student feedback, structured self-evaluation, and peer review into a coherent operational framework for regional Global South contexts. Drawing on primary data collected between 2009 and 2025 from approximately 200 undergraduate arts and science students, principally at North Lakhimpur College (now North Lakhimpur University) and other institutions across Assam, this study employs a qualitative-descriptive design with thematic analysis and triangulation of open-ended questionnaires and semi-structured interviews. Two contributions emerge. First, it yields a culturally grounded fivefold taxonomy of good teaching from Assamese student articulations, including culturally distinctive expectations: the teacher’s public moral role in the community and the obligation of intellectual life beyond the syllabus, that standardized student evaluation of teaching (SET) instruments routinely miss. Second, it proposes a developmental three-pillar framework integrating reformed student feedback, disciplined teacher self-evaluation, and structured peer review to restore the formative function of evaluation and improve student learning outcomes. Both the taxonomy and the framework are scalable across comparable institutions in the Global South.
Volume: 15
Issue: 4
Page: 2840-2851
Publish at: 2026-08-01

Policy–governance–culture dynamics in Myanmar education reform: implications for transformational leadership practice

10.11591/ijere.v15i4.39057
Chi Che , Win Pa Pa Tun
Myanmar’s education reform faces a persistent enactment gap because policy intent is filtered through governance feasibility and culturally grounded legitimacy norms. This study examined whether transformational leadership functions as a mediation practice that buffers policy–governance misalignment and under what conditions that buffering is stronger. The novelty of this study lies in proposing and testing an integrated policy–governance–culture (PGC) leadership mediation model that explains reform enactment through the joint effects of structural misalignment, leadership buffering, and culturally conditioned legitimacy. An explanatory sequential mixed-methods design combined a two-wave time-lag survey of teachers and middle leaders from 48 schools (N=720) with semi-structured interviews to clarify mechanisms. Measurement models showed acceptable-to-strong fit (CFA: CFI=0.956, TLI=0.948, RMSEA=0.044, SRMR=0.041). Multilevel SEM (ICC_RE=0.11) indicated that policy–governance misalignment directly reduced reform enactment (β=−0.15, p=.003) while increasing leadership mediation practices (β=0.25, p
Volume: 15
Issue: 4
Page: 2959-2972
Publish at: 2026-08-01

The relationship between arithmetic proficiency and artificial intelligence-assisted learning

10.11591/ijere.v15i4.39461
Khalid Marnoufi , Imane Ghazlane , Fatima Zahra Soubhi , Bouzekri Touri
Amidst the rapid developments witnessed in educational environments, this study aims to investigate the dynamic relationship between the desire for artificial intelligence (AI) supported learning and proficiency in mental arithmetic, considering the latter a decisive factor in enhancing cognitive acquisition. The study focused specifically on the academic elite, represented by students in the mathematical sciences track at the qualifying secondary level. To ensure the accuracy of the results, the methodology relied focusing particularly on the arithmetic subtest within the Wechsler intelligence scale for children as an effective tool for measuring logical reasoning and working memory. The target sample consisted solely of adolescents, who were characterized by a similarity and a homogeneity in their developmental stages and ages. Selection and analysis criteria were based on two pillars, the general scores obtained in the arithmetic subtest, and a systematic evaluation of the students’ aptitude and inclination toward using AI tools. The results concluded that there is a close correlation between arithmetic ability and the quality of logical reasoning in AI contexts. Furthermore, statistically significant homogeneity confirmed that students proficient in AI skills demonstrate higher levels of creative thinking and the ability to apply logic in learning.
Volume: 15
Issue: 4
Page: 3292-3300
Publish at: 2026-08-01

Moral disengagement, character strengths, and maladaptive behavior among university students: a structural equation modeling approach

10.11591/ijere.v15i4.38470
Akhmad Syahri , Nimatul Dinawisda , Sri Afsinatun
The increasing integration of digital technology in higher education has raised concerns about students’ maladaptive behaviors (MB), including academic dishonesty and cyber aggression. This study aims to examine the structural relationships among moral disengagement (MD), character strengths (CS), and MB, as well as the mediating role of CS in digitally mediated learning environments. A cross-sectional quantitative design was employed using data collected from 500 undergraduate students at a State Islamic University in Indonesia through purposive sampling. Data were analyzed using partial least squares structural equation modeling (PLS-SEM). The results indicate that MD significantly predicts MB (β=0.830, p
Volume: 15
Issue: 4
Page: 3049-3059
Publish at: 2026-08-01

A model for flexible learning in graduate teacher education programs

10.11591/ijere.v15i4.39252
Marilyn U. Balagtas , Adonis P. David , Erminda C. Fortes , Arceli M. Amarles , Alvin B. Barcelona , Marla C. Pampango , Marjorie Naquita
This study aimed to develop a model for flexible learning (FL) appropriate to graduate teacher education programs (GTEP) based on the different practices of the graduate faculty and students in a teacher education institution (TEI) before and during the COVID-19 pandemic. A multimethods approach was employed, utilizing survey questionnaires, semi-structured interviews, and focus group discussions (FGD). Data were collected from 215 graduate students and 43 graduate faculty members who were selected through convenience sampling. The study resulted in the development of a model of FL for GTEP (MFL-GTEP), reflected in an outcome-based syllabus that highlights 10 areas of FL, all beginning with P: purpose, process, pedagogy, platform, people, place, pace, performance, product, and policy of learning. The MFL-GTEP promotes self-agency, self-regulation, and self-determination among education professionals pursuing GTEP. The challenges that graduate faculty and students experience in the implementation of FL are addressed in the (MFL-GTEP) to make the model more relevant, inclusive, and sustainable in a graduate teacher education program.
Volume: 15
Issue: 4
Page: 3193-3203
Publish at: 2026-08-01

The role of big data in precision medicine and healthcare monitoring using the MapReduce framework

10.11591/ijai.v15.i4.pp3852-3864
Meenakshi Sankarasubramanian , Meena Chavan , Govindan Manoharan Karthik , Jhansi Pandiri , Arumalla Nagaraju , Idimadakala Madhavilatha
Data analytics has become a cornerstone of precision medicine by enabling doctors and scientists to extract meaningful insights from vast, complex data sets. Most healthcare data are high-dimensional data that not only require longer computational time but also affect the accuracy of analysis. In order to overcome these issues, the MapReduce based big data healthcare monitoring framework is proposed. The proposed work comprises preprocessing, the MapReduce framework, and data classification. The preprocessing can be done using improved min-max normalization, and the big data can be handled using the improved support vector machine (SVM)-recursive feature elimination (RFE) method. Finally, the classification can be done using a deep Q-network (DQN). The performance of the proposed method is analyzed in terms of accuracy, precision, F-measure, and Matthew's correlation coefficient (MCC).
Volume: 15
Issue: 4
Page: 3852-3864
Publish at: 2026-08-01

Performance of majority voting transfer learning deep learning monkeypox disease detection

10.11591/ijai.v15.i4.pp3782-3791
Nur Nafiiyah , Muhammad Nurul Huda
Global health concerns have been raised by the advent of monkeypox following the COVID-19 epidemic, highlighting the need for reliable automated systems to support early skin disease screening. This study proposes a monkeypox skin disease classification framework using a majority voting ensemble based on transfer learning. The ensemble combines predictions from multiple pretrained convolutional neural networks (CNNs) to improve classification robustness. A publicly accessible dataset comprising four classes: monkeypox, chickenpox, measles, and normal skin was used for the experiments. The results indicate that while a single ResNet50 model achieved the highest overall accuracy (99.15%), the majority voting approach yielded higher precision than several individual models, demonstrating improved reliability in distinguishing monkeypox cases. These findings suggest that ensemble-based majority voting can enhance the robustness of monkeypox skin disease classification and may support computer-aided screening systems.
Volume: 15
Issue: 4
Page: 3782-3791
Publish at: 2026-08-01

Indirect adaptive neural network control for constant power conversion in wave energy system

10.11591/ijece.v16i4.pp2120-2133
Jesus de la Cruz-Alejo , Hugo Beatriz Cuellar , J. Antonio Lobato Cadena , Edwin Christian Becerra-Alvarez
The conversion of ocean wave energy into electrical energy occurs near beaches and is important for the design and implementation of wave energy conversion (WEC) systems. However, its generation depends on environmental conditions, which complicates the design and control of the devices. This work presents an approach to indirect adaptive control based on artificial neural networks to detect wave conditions for the proper functioning of WEC structures. The method involves generating a constant output voltage using a voltage boost converter and a direct current-alternating current (DC-AC) converter. Maintaining a constant output power despite variations in wave conditions to generate a voltage of 24 V with a current of 2 A is the primary proposal for the control design. The mechanical design integrates a rack and pinion system and a pulley transmission that connects a floating device to an electric generator. The implementation of control is carried out on an Arduino platform. The control system was implemented on an Arduino platform, occupying 48% of the available memory, with a convergence time of 4.29 ms, a mean squared error (MSE) of 0.13715, and a root mean squared error (RMSE) of 0.37034. These low values indicate that the proposed control system has greater accuracy. The experimental results validate the proposed control system, which reduces energy conversion errors and achieves greater efficiency.
Volume: 16
Issue: 4
Page: 2120-2133
Publish at: 2026-08-01

Deep learning for categorizing microsatellite stability in colorectal cancer

10.11591/ijai.v15.i4.pp3761-3769
Sofyan El Idrissi , Yassine Drider , Ikram Ben Abdel Ouahab , Mohammed Bouhorma , Fatiha El Ouaai
Cancer remains a significant global health challenge, with its incidence rising steadily in recent decades. In colorectal cancer (CRC), microsatellite instability (MSI), and microsatellite stability (MSS) are important biomarkers that influence treatment decisions and patient outcomes. Accurate MSI classification is critical but traditional methods can be costly and time-consuming. This study explores the potential of deep learning to classify MSI and MSS in CRC. A large dataset of CRC patients with confirmed MSI and MSS status was utilized, obtained through standard testing images. Deep learning models were applied to histopathological images, analyzing tissue features from digital slides. Convolutional neural network (CNN) and residual network (ResNet)-18 models demonstrated high accuracy in distinguishing between MSI and MSS CRCs. The best-performing model, which integrated genomic and histopathological data, achieved an area under the curve (AUC) receiver operating characteristic (ROC) of 0.85, indicating strong discrimination capability. The findings suggest that deep learning could be a valuable tool for clinical decision-making and personalized medicine in CRC.
Volume: 15
Issue: 4
Page: 3761-3769
Publish at: 2026-08-01

Efficient deep learning for automated corneal ulcer severity classification from fluorescein images

10.11591/ijai.v15.i4.pp3603-3613
Rodiah Rodiah , Indah Sinthya Permata Sari , Matrissya Hermita , Sarifuddin Madenda , Diana Tri Susetianingtias
Corneal ulcers can cause permanent vision loss if not diagnosed and managed promptly, particularly in settings with limited access to ophthalmology services. This study aims to develop an automated deep learning approach for classifying corneal ulcer severity from fluorescein slit-lamp images. An EfficientNetV2-S–based model is employed, incorporating corneal area masking to suppress non-relevant regions and class distribution–based augmentation to address data imbalance. To improve evaluation reliability, a leakage-aware data splitting strategy is applied before and after augmentation. Experimental results show that the proposed approach achieves a maximum validation accuracy of 95.93% under non-leakage conditions for the category classification scenario, while maintaining high training efficiency. These results demonstrate that the proposed method provides a robust and efficient solution for automated corneal ulcer severity assessment and has the potential to support clinical decision-making in ophthalmic practice.
Volume: 15
Issue: 4
Page: 3603-3613
Publish at: 2026-08-01

Intravenous immunoglobulin resistance prediction in Kawasaki disease using oversampled transformer embeddings

10.11591/ijai.v15.i4.pp3944-3954
Namitha Thattarassery Nanappan , Raghavendra Srinivasaiah , Vinith Rejathalal
Kawasaki disease (KD) is a leading cause of acquired heart disease in children under five. Although intravenous immunoglobulin (IVIG) treatment is usually effective, 10–20% of cases are resistant and at higher risk for coronary complications. Early prediction of IVIG resistance is critical but difficult due to the rarity of KD and imbalanced clinical data. To address this, we propose a novel technique called sentence transformer embeddings with synthetic minority over-sampling technique (SMOTE) oversampling (STESO), which leverages the complementary strengths of transformer-based representation learning and synthetic oversampling. Pretrained models such as paraphrase-MiniLM-L3-v2 are used to convert tabular clinical data into dense text-based embeddings, capturing deeper semantic relationships across features. By coupling these rich embeddings with SMOTE, we balance class distributions directly in the semantic space, enabling traditional machine learning (ML) models to more effectively detect minority (resistant) cases. This synergy yielded substantial improvements in sensitivity and F1-score, with random forest (RF) combined with STESO (RF-STESO) achieving the highest overall performance. Among the models evaluated, our proposed model attained best result as accuracy of 0.85, a sensitivity of 0.81, a specificity of 0.89, and an F1-score of 0.85. Our results underscore that the joint use of transformer embeddings and oversampling is more effective than either approach in isolation, offering a promising pathway for rare disease prediction tasks such as IVIG resistance prediction in KD.
Volume: 15
Issue: 4
Page: 3944-3954
Publish at: 2026-08-01

An empirical comparison of clustering approaches for recency, frequency, and monetary customer segmentation

10.11591/ijai.v15.i4.pp3402-3410
Upekkha Lau , Meditya Wasesa
This study evaluates effectiveness of three clustering techniques—k-means, hierarchical clustering, and density-based spatial clustering of applications with noise (DBSCAN)—applied to the recency-frequency-monetary (RFM) model for customer segmentation in the retail sector. Using sales transaction data from a distributor of computer accessories and printing products. The results show that k-means achieved the best clustering validation scores and effectively identified high-value customers, hierarchical clustering generated less meaningful groupings than k-means, and DBSCAN misclassified key customers as noise. These findings highlight k-means as the most suitable technique for RFM-based segmentation in this retail business context. The study offers practical insights for retail and distribution businesses aiming to adopt data-driven customer strategies and suggests future research to enhance segmentation robustness and refine the RFM framework.
Volume: 15
Issue: 4
Page: 3402-3410
Publish at: 2026-08-01

A machine learning framework for predicting and optimizing return on investment across marketing channels

10.11591/ijai.v15.i4.pp3528-3536
Chandra Chathura , Keerthan Saya , Sathishkumar Mani
In the current fast-paced competitive marketing environment, firms require data-centric methods to maximize their investments in several avenues. This research work applies machine learning techniques to estimate the return on investment (ROI) for marketing costs, which helps organizations in budgeting more effectively. Four models including random forest, extreme gradient boosting (XGBoost), gradient boosting, and linear regression were utilized for their accuracy in making predictions. Results showed that the highest accuracy was achieved by linear regression at 99.39%, random forest at 99.07%, gradient boosting at 99.01%, and XGBoost at 98.81%. It was further noted that digital marketing avenues such as social media and online stores gave the highest ROI, indicating that companies should prioritize digital marketing more than traditional marketing. On a practical level, this approach helps marketing team for choosing high performing channels since it estimates expected returns from each marketing channels and make smarter budget allocation. Yet, the study is done by using Kaggle dataset. In order to improve its generality, future research may use larger real-world datasets and extensive visualization techniques.
Volume: 15
Issue: 4
Page: 3528-3536
Publish at: 2026-08-01

Production scheduling using a hybrid approach based on genetic algorithm and convergent random search

10.11591/ijeecs.v43.i2.pp534-546
Belbachir Djelloul , Kadri Boufeldja
The flexible job shop scheduling problem (FJSSP) is a highly complex combinatorial optimization problem widely encountered in modern manufacturing systems. Its complexity arises from the simultaneous determination of operation sequencing and machine assignment, making it significantly more challenging than the classical job shop scheduling problem (JSSP). Recent advances in hybrid metaheuristics and intelligent optimization methods have improved solution quality; however, achieving an effective balance between global exploration and local exploitation remains a critical challenge. In this paper, a novel hybrid metaheuristic approach combining a genetic algorithm (GA) and convergent random search (CRS) is proposed to address the FJSSP. The proposed method exploits the global search capability of GA to explore the solution space, while CRS is employed as an adaptive local refinement mechanism applied to elite individuals. This hybridization strategy enhances convergence speed and avoids premature stagnation. Extensive computational experiments are conducted on well-known benchmark instances, including Brandimarte and Kacem datasets. The results indicate that the proposed GA–CRS approach significantly improves the makespan compared to classical GA and PSO based methods. In addition, the algorithm exhibits faster convergence behavior, reaching high-quality solutions in fewer iterations. Statistical analysis using non-parametric tests confirms the superiority of the proposed method. These findings demonstrate that the proposed hybrid GA–CRS algorithm provides a robust and efficient optimization framework for solving large-scale and complex FJSSP instances, outperforming several state-of the-art approaches.
Volume: 43
Issue: 2
Page: 534-546
Publish at: 2026-08-01
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