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30,907 Article Results

Enhancing emotional resilience in pre-service early childhood educators through digital learning environments

10.11591/ijere.v15i4.39637
Gulmira Mombiyeva , Ulbossyn Kyyakbayeva , Perizat Dautkaliyeva , Zhanna Zhunisbekova , Zhanar Saduova , Zhaxygul Issayeva
Emotional resilience (ER) is a critical competence for pre-service teachers, enabling them to manage stress, adapt to change, and maintain professional functioning. Digital learning (DL) environments present both challenges and opportunities for developing ER, yet empirical studies on structured interventions in these contexts remain limited, particularly in early childhood education (ECE). This study examined the effect of a structured DL intervention on the development of ER among pre-service teachers in ECE contexts in Kazakhstan. A quasi-experimental pretest-posttest design with non-equivalent groups was employed, involving 165 female pre-service teachers. The intervention included scaffolded digital tasks, reflective exercises, and instructor-led feedback. Baseline ER scores were comparable between groups. Following the intervention, participants in the experimental group (EG) demonstrated significantly higher ER compared to those in the control group (CG). These results indicate that structured DL interventions can effectively enhance ER by fostering adaptive coping, self-regulation, and problem-solving skills. Embedding scaffolded digital tasks combined with reflection and feedback into teacher education programs may support the preparation of resilient, reflective, and emotionally competent educators.
Volume: 15
Issue: 4
Page: 3279-3291
Publish at: 2026-08-01

Bridging the transition gap: the role of practical career education in enhancing independent living skills for adolescents with developmental disabilities in Vietnam

10.11591/ijere.v15i4.38936
Dao Thi Thu Thuy , Nguyen Thi Quynh Hoa , Nguyen Thi Kim Hoa , Nguyen Thi Huyen , Nguyen Thi Phuong Hoa
The transition to adulthood for adolescents with developmental disabilities (DD) in Vietnam is fraught with challenges, marked by a gap between rights-based policies and practical support. This study addresses the critical need to identify effective interventions for enhancing their independent living skills (ILS). Adopting the social cognitive career theory (SCCT) framework, we conducted a quantitative survey with 458 special education teachers and parents to evaluate the impact of three career education (CE) dimensions: practical teaching methods, teacher competence, and experiential environment. Multiple regression analysis revealed that practical teaching methods were the most significant predictor of socio-adaptive competence (β=.48). A key finding was the “will-skill” discrepancy, where students’ high motivation (M=3.35) contrasted with their limited functional skills (M=3.18). We conclude that in resource-constrained contexts like Vietnam, hands-on, task-based instruction is paramount. The study provides empirical evidence for policymakers to prioritize practical vocational training over theoretical approaches to effectively bridge the gap between students’ aspirations and their actual capabilities, fostering genuine autonomy.
Volume: 15
Issue: 4
Page: 2993-3003
Publish at: 2026-08-01

Digital divide and fairness perceptions of computer-based testing in Vietnam

10.11591/ijere.v15i4.37322
Tran Thi Hoa Tien , Phan Thi Thuy An , Do Huy Duc , Le Ha Giang , Ngo Thi Minh Hanh , Tran Thi Ngoc Anh
Equity concerns are central to large-scale shifts toward computer-based testing (CBT), yet evidence from developing systems remains limited on whether institutional supports can offset household digital inequalities. This study surveyed 1,197 Vietnamese high school students (grades 10–12) using a 20-item CBT acceptance scale (α=.94) and two single-item fairness indicators. Overall acceptance was moderately positive (M=3.54, SD=0.74), whereas fairness perceptions were markedly weaker: only 31.7%–38.2% agreed that CBT ensures fair scoring and 55.1%–66.7% worried that CBT could create inequality, reflecting unequal fairness perceptions across areas, with concerns strongest in rural settings. Group comparisons showed small but consistent advantages in acceptance for students with prior CBT experience and school-level computer access (g≈.16–.20), while home computer ownership had negligible association (g≈.01). Acceptance did not differ by residential area (η²≈.001), but fairness concerns varied across areas. These findings suggest that institutional exposure and governance practices—rather than household device ownership—are the most actionable levers for equitable CBT implementation. Practical implications include strengthening school-based access and practice opportunities, transparent proctoring and contingency procedures, and integrating fairness monitoring into CBT dashboards.
Volume: 15
Issue: 4
Page: 3075-3085
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

Developing a transdisciplinary design-based in-service science teacher training framework

10.11591/ijere.v15i4.38783
Joelash R. Honra , Ma. Kristina B. B Dela Cruz , Jermae B. Dizon-Yi , Raianne Joy V. Maulion , Sean Derrick M. Oliquiano , James C. Ollero , John Lorence A. Villamin
Contemporary science education requires teachers to facilitate learning that addresses complex, real-world problems beyond disciplinary boundaries. Yet, many in-service science teachers lack professional development that supports transdisciplinary problem-solving and innovative pedagogy. This qualitative study used a grounded theory (GT) approach to examine teachers’ experiences in a transdisciplinary, design-based training program and to develop a framework for effective professional learning. Participants engaged in sustained training grounded in design thinking and authentic problem contexts. Data were collected through semi-structured interviews, focus groups, reflective journals, training artifacts, and observations, and analyzed using constant comparative methods. Findings indicated shifts in teachers’ conceptions of problem-solving, enhanced capacity to integrate disciplinary and non-disciplinary perspectives, and changes in instructional planning and classroom practice. Design thinking functioned as a mediating process that helped teachers navigate ambiguity, collaboration, and iterative reflection. The resulting transdisciplinary design-based in-service science teacher training framework highlights key principles: authentic problem contexts, structured yet flexible design processes, collaborative inquiry, and iterative reflection. The study offers an empirically grounded framework with implications for teacher professional development, curriculum design, and policy.
Volume: 15
Issue: 4
Page: 2814-2822
Publish at: 2026-08-01

Soft skills development in future foreign language teachers: evidence from digital educational artifacts

10.11591/ijere.v15i4.38727
Akbike Boranbayeva , Gulnur Yerik , Svetlana Minasyan
In the context of digital transformation in teacher education, the development of soft skills among future foreign language teachers has become an important dimension of professional preparation. This qualitative case study aimed to identify the factors shaping soft skills development through the analysis of digital educational artifacts created in a technology-enhanced learning environment. The study involved 48 undergraduate students enrolled in a foreign language teacher education program during one academic semester. The data corpus included reflective essays, discussion posts, collaborative project outputs, multimedia assignments, and digital portfolios produced within a learning management system (LMS)-based course. Content analysis and thematic coding were used to identify recurring patterns in the artifacts. The findings revealed five interrelated groups of factors influencing soft skills development: pedagogical design and teaching methods, pedagogical strategies and learning activities, communication and collaboration in digital environments, organization and management of learning activities, and professional and personal development. The results show that digital educational artifacts provide rich evidence of authentic soft skills manifestation beyond traditional self-report methods. The proposed five-factor model may serve as a practical framework for teacher educators and curriculum designers in digitally mediated teacher education.
Volume: 15
Issue: 4
Page: 3518-3528
Publish at: 2026-08-01

Machine learning techniques for rainfall prediction: a systematic literature review

10.11591/ijai.v15.i4.pp3441-3451
Deepa Sharma , Anand Kumar Shukla , Punam Rattan
There are numerous aspects of human life in which knowing how much rain to expect might be beneficial. Heavy rainfall events such as flash floods and landslides, as well as droughts, can be predicted with effective rainfall forecasting. Because of reliable weather forecasts, the infrastructure required to capture rainwater and cultivate crops may be planned ahead of time. A variety of machine learning (ML) and deep learning (DL) algorithms enable accurate weather forecasting. This work seeks to provide a full overview of the numerous ML algorithms used for rainfall prediction by focusing on the technique, input parameters, and several performance measures. The review consists of 51 works divided into three sections. It is found that long short-term memory (LSTM), one of the DL algorithms, is mostly used by researchers for developing the model, but in recent years, ensemble learning and hybrid learning have also gained popularity among researchers as they give more accurate results. These methods need to be explored further.
Volume: 15
Issue: 4
Page: 3441-3451
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

Prioritizing ransomware indicators of compromise using algorithmic scoring for enhanced threat detection

10.11591/ijai.v15.i4.pp3865-3877
Krishna Prasad D. Subramanya , Prasanna Kumar H. Ramakrishna
Ransomware is a very serious cybersecurity threat. Its attack methods are continually evolving, which means that it is not very easy to detect it quickly. A big challenge faced in the ransomware defense is how to prioritize indicators of compromise (IoC) efficiently. This paper introduces a hybrid IoC ranking scheme based on static and dynamic analysis of the behavior of commonly circulating ransomware variants. Each IoC is given a weighted score according to its significance for investigations based on actual patterns of occurrence and contextual behavior. Experimental results clearly separate high-confidence indicators from low-confidence ones. Deleting shadow copy and connecting to Tor2web receive the highest rank scores of about 99.99, while older behaviors like locking screen show lower relevance, around 73.11. The proposed algorithm has a linear time complexity of O(n·m) for score calculation and a bounded space complexity of O(n·m), allowing it to scale for large IoC sets. The findings show that this ranked IoC framework enhances early ransomware detection and helps prioritize responses based on evidence in current security systems.
Volume: 15
Issue: 4
Page: 3865-3877
Publish at: 2026-08-01

Symmetry index-based gait improvement prediction using a CNN-LSTM-attention framework

10.11591/ijai.v15.i4.pp3625-3636
Pushpalatha Obanna , Premkumar Ramesh
The shortcomings of existing clinical assessments are addressed by using a deep learning (DL) framework. This study will assess the gait improvement of lower limb fractured patients using the publicly available GaitRec dataset. The dataset contains the vertical ground reaction force (GRF) data used to compute biomechanical kinetic features. This framework considers hip, knee, ankle, and calcaneus fractures as lower limb fracture class, and the symmetry indices computation is done between affected and unaffected limbs, and their temporal changes were examined to analyze and track the rehabilitation progress of individual subject. The framework successfully identifies the reduction in the asymmetry between left and right leg features. The overall gait improvement in patients is computed using initial and final session composite asymmetry index (ASI) values and if it is at least 30% then overall gait is improved, demonstrating its robustness and clinical relevance. The suggested convolutional neural network (CNN)-long short-term memory (LSTM)-attention hybrid model outperformed with regression metrics of root mean square error (RMSE) of 1.09, mean absolute error (MAE) by 0.64, and R2 score of 0.98 and classification metrics of accuracy 97.1%, precision of 97.6%, recall of 96%, and F1-score of 96.8% by capturing both the local patterns and temporal dynamics of the gait data.
Volume: 15
Issue: 4
Page: 3625-3636
Publish at: 2026-08-01

GWO-optimized sparse Bayesian least squares regression for direction-of-arrival estimation in MIMO networks

10.11591/ijai.v15.i4.pp3431-3440
Anne Gowda Aleri Byregowda , Babu Nallur Venkateshappa , Anughna Narayanaswamy
Accurate direction-of-arrival (DOA) estimation is a critical requirement for massive multiple-input multiple-output (MIMO) systems operating in fifth-generation (5G) and beyond (5G/B5G) wireless environments. Although sparse Bayesian learning (SBL)–based techniques have demonstrated improved robustness by exploiting signal sparsity, their performance is often limited by fixed hyperparameter selection, sensitivity to noise, and suboptimal residual error minimization. To address these challenges, this paper proposes an optimized sparse Bayesian least squares regression (SBLSR) framework in which grey wolf optimization (GWO) is employed to adaptively optimize Bayesian hyperparameters and regression coefficients. The proposed approach jointly enforces sparsity and minimizes estimation error, enabling robust DOA estimation under dynamic noise conditions and varying network density. Extensive simulations conducted in a massive MIMO environment demonstrate that the optimized SBLSR consistently outperforms conventional SBLSR and state-of-the-art benchmark techniques in terms of root mean square error (RMSE), closely approaching the Cramér–Rao lower bound (CRLB) across a wide range of signal-to-noise ratios, sensor configurations, and Monte Carlo trials. The findings validate that the suggested optimized SBLSR framework offers a noise-resilient solution for high-precision DOA estimation in practical massive MIMO and MIMO radar systems.
Volume: 15
Issue: 4
Page: 3431-3440
Publish at: 2026-08-01

Systematic review of fraud detection using AI and ML with an emphasis on telecommunication industry

10.11591/ijai.v15.i4.pp3269-3285
Soly Mathew , Sindi Rryta
The telecommunications industry is one of the top industries affected by fraudulent activities. Given the financial impact, on top of confidentiality breaches, security concerns, and reduced service quality as well as consumer dissatisfaction, there is an immediate need to implement effective fraud detection approaches. While there have been different fraud detection systems implemented, technological advancements as well as the improved techniques of fraudsters have made the traditional approaches no longer efficient. This paper aims to further investigate the use of artificial intelligence (AI) and machine learning (ML) to create efficient and advanced fraud detection models based on the strategy used, models applied, accuracy of the system, as well as future research work suggested. A systematic review of 50 papers was conducted. The most prevalent strategy was supervised one, majority of papers used software instead of hardware, and the most common ML models were artificial neural network (ANN), support vector machines (SVM), and decision tree.
Volume: 15
Issue: 4
Page: 3269-3285
Publish at: 2026-08-01

Metaheuristic optimization for atrial fibrillation detection: feature extraction, selection, and hyperparameter tuning

10.11591/ijai.v15.i4.pp3878-3887
Zaid Nouna , Hamid Bouyghf , Mohammed Nahid , Issa Sabiri
Atrial fibrillation (AF) detection from electrocardiogram (ECG) signals is crucial for early diagnosis and intervention. This study presents a multi-objective optimization approach for AF detection, focusing on feature extraction, selection, and neural network hyperparameter tuning. The methodology uses cross-validation during the training of the two concatenated ECG dataset features and simultaneously minimizes the error rate on the separate validation folds of each dataset and reduces the number of selected features, enhancing model generalization and efficiency. Particle swarm optimization (PSO), grey wolf optimization (GWO), and differential evolution (DE) algorithms were implemented to navigate this multi-objective space. While all three algorithms were explored, the final solution, demonstrating a superior trade-off between accuracy and feature reduction, was obtained using DE. This approach effectively identifies optimal feature subsets and neural network configurations, yielding a robust and compact AF detection model. The proposed approach has shown promising results, with the model achieving accuracies of 96.38% and 90.69%, and corresponding area under the curve (AUC) values of 0.99 and 0.96, for the first and second datasets, respectively, using 10 optimally selected features.
Volume: 15
Issue: 4
Page: 3878-3887
Publish at: 2026-08-01

Multi-stage hybrid YOLO-driven and MobileNetV2-CNN variants for robust fish freshness classification

10.11591/ijai.v15.i4.pp3660-3671
Raseeda Hamzah , Rosniza Roslan , Amni Munira Khidir , Lala Septem Riza
This study presents a multi-stage hybrid representation learning framework for robust fish freshness classification to address the critical challenge of reliable quality assessment in unpreserved food supply chains. The proposed pipeline operates in two stages: stage-1 employs you only look once (YOLO)v8n as feature-gated detector to validate inputs and eliminate non-fish images, while stage-2 leverages transfer-learned MobileNetV2 variants enhanced with convolutional neural network (CNN) layers, fine-tuning, adaptive learning rate schedulers, and expanded fully connected layers for hierarchical classification into three freshness classes i.e., highly fresh, fresh, and not fresh. The framework has been trained and evaluated on two curated datasets comprising 4,500 fish and non-fish images and 11,111 freshness-labeled including augmented dataset to increase diversity. The experimental results showed significant improvements. The best performance model transfer learning (TL)-MobileNetV2 + CNN + fine-tuning achieved 98.07% training accuracy and 67.72% validation accuracy on 80:20 split, and training accuracy of 97.57%, validation accuracy of 97.21% through 10-fold cross-validation. The comparative benchmarking confirmed that dual-stage design outperformed baseline MobileNetV2 and YOLOv5s models across precision, recall, and F1-score. The findings highlighted significant value of integrating detection-driven validation with transfer-learning classification, and propose new benchmark for intelligent freshness monitoring. For future work, this study aims to explore attention-based models, data integration, and species diversity.
Volume: 15
Issue: 4
Page: 3660-3671
Publish at: 2026-08-01

Hybrid deep learning model for enhanced short-term gold price forecasting

10.11591/ijai.v15.i4.pp3228-3239
Hoang Ha Nguyen , Minh Duc Nguyen , Cuong H. Nguyen-Dinh
Accurate short-term gold price forecasting is crucial for the financial decision-making. This paper introduces a short-term gold prediction network (STGP-Net), a novel hybrid deep learning model designed to enhance prediction accuracy by integrating one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM). STGP-Net leverages the 1D-CNN's ability to extract local temporal features and the LSTM capacity to model long-range dependencies within gold price time series. Various sliding window configurations are employed to generate input sequences for multi-step ahead prediction. Comprehensive experiments were conducted comparing STGP-Net against 1D-CNN + recurrent neural network (RNN) and 1D-CNN + bidirectional long short-term memory (BiLSTM) baseline models across three configurations using metrics like mean absolute error (MAE), root mean square error (RMSE), and determination (R²). The results demonstrated that STGP-Net consistently provided better performance and robustness, proving more effective for short-term gold price forecasting than the alternative hybrid models tested.
Volume: 15
Issue: 4
Page: 3228-3239
Publish at: 2026-08-01
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