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Digital ecopedagogy-based counseling for cyberbullying prevention among vocational high school students

10.11591/ijere.v15i4.39314
Nina Permata Sari , Hendro Yulius Suryo Putro , Muhammad Andri Setiawan
Cyberbullying has become a growing concern among adolescents in vocational high schools, particularly in Indonesian contexts where conventional counseling often struggles to address online aggression effectively. This study evaluated the effectiveness of digital ecopedagogy-based counseling (DEBC), a structured digital counseling intervention that combines interactive online modules, ecological reflection tasks, peer mentoring, and counselor-guided discussions, in preventing cyberbullying among vocational high school students in South Kalimantan, Indonesia. A quasi-experimental design involved 180 students and six school counselors from three vocational schools, with an eight-week intervention for the experimental group and conventional face-to-face counseling for the control group. Data were collected through pre-test and post-test cyberbullying behavior scales, supported by interviews, focus group discussions, and observations. The experimental group showed significantly greater reductions in cyberbullying behavior (N-Gain=0.45–0.67, p
Volume: 15
Issue: 4
Page: 3497-3507
Publish at: 2026-08-01

Procrastination trap: how personality traits fuel generative artificial intelligence over-reliance and erode academic performance

10.11591/ijere.v15i4.39382
Mohamad Rizal Abdul Hamid , Chen Jung Ku , Anath Rau Krishnan , Imran Mehboob Shaikh , Yoke Lian Lau , Mohd Zulkifli Muhammad , Saiful Bahri
This study investigates how long-term personality traits contribute to generative artificial intelligence (GenAI) use and whether these traits have an impact on academic procrastination and performance. The Big five personality model was used for this investigation, and a quantitative methodology was employed to analyze data from 200 undergraduate students in East Malaysia via partial least squares structural equation modeling (PLS-SEM). Findings indicated that while neuroticism and openness were associated with increased levels of GenAI use, conscientiousness was found to be a protective factor against GenAI dependency. In addition, results showed that when students excessively utilize GenAI, they experience increased levels of procrastination which leads to longer procrastination periods and decreased levels of deep learning engagement. Finally, procrastination was identified as a partial mediator between GenAI use and poor academic performance. Thus, GenAI dependency produces a ‘competence illusion’, causing a decline in students’ academic abilities over time. Ultimately, this study supports the need for interventions designed to help students learn self-regulation and develop critical AI literacy skills to enable technology to serve as a cognitive scaffold as opposed to a replacement for students’ own cognitive efforts.
Volume: 15
Issue: 4
Page: 3362-3374
Publish at: 2026-08-01

Effect of PhET simulations and YouTube videos on polytechnic students’ conceptual understanding in fluid mechanics

10.11591/ijere.v15i4.37002
Jean D'Amour Iradukunda , Lakhan Lal Yadav
Fluid mechanics is an important branch of engineering and science with various technological and scientific applications. However, students often struggle to develop a solid conceptual understanding of related concepts due to their abstract nature, which involves invisible forces and complex scientific phenomena. This study investigated the effect of physics education technology (PhET) interactive simulations combined with educational YouTube videos on students’ conceptual understanding in ten areas of fluid mechanics. Using a quasi-experimental pre-test and post-test design, 168 construction technology students from two Rwanda Polytechnic (RP) colleges were assigned to the experimental group (n=81) and the control group (n=87). The experimental group got instruction using PhET interactive simulations and YouTube videos, while the control group was taught using traditional methods. A validated test assessed students’ conceptual understanding before and after the intervention. Descriptive and inferential statistics analyses of the study show that the students in the experimental group demonstrated far enhanced conceptual understanding in different areas of fluid mechanics than those in the control group. Results using different measures (normalized learning gains and effect sizes, using different approaches) show that the experimental group gained significantly greater improvement in their level of conceptual understanding compared to that of the control group. For example, Cohen’s d for the post-test scores for the two groups was found to be 1.85; ratios of Hake’s normalized learning gains and Cohen’s d for post- and pre-test for the experimental group to the control group were respectively 2.2669 and 2.1623. Based on these findings, the study provided actionable recommendations for educational policy and practice for polytechnic colleges.
Volume: 15
Issue: 4
Page: 3025-3037
Publish at: 2026-08-01

Artificial intelligence technologies in teaching Russian as a foreign language

10.11591/ijere.v15i4.38077
Larissa Krymova , Navruz Khasanov , Zhamiila Arstanbekova , Ariya Azamatova , Nuraisha Bekeyeva
The present study aims to investigate the impact of integrating artificial intelligence (AI) technologies into teaching Russian as a foreign language (RFL) from the perspective of educators. Employing a mixed-methods research design, the study utilized several methodologies, including a teacher survey, an analytical-descriptive approach to data interpretation, and the development and evaluation of AI-based interventions. The study sample comprised 120 RFL instructors from three public universities in Kazakhstan. Preliminary findings revealed a considerable awareness among teachers regarding various AI technologies, such as chatbots, voice assistants, the ChatGPT neural network, educational platforms, gaming applications, and task design tools. Nonetheless, the practical utilization of these technologies varied significantly, with only a subset of teachers incorporating them into their regular teaching practices. The study culminated in the development of a conceptual framework for AI-driven educational interventions, incorporating platforms such as Coursera, Moodle, Open EdX, and eFront; game-based applications including Duolingo, Talk2Russia, and Russian Verbs Pro; and task creation tools such as Kahoot! and Quizlet. Following the integration of these interventions into the curriculum, post-implementation evaluations indicated that teachers generally perceived the tools as effective, with the average effectiveness rating surpassing 4.0 out of 5.0 across all assessed categories. The findings of this study have practical applicability; they can be used to enhance professional development programs for teachers of the Russian language and to formulate strategies for the integration of AI technologies into language education within the Central Asian region.
Volume: 15
Issue: 4
Page: 3422-3438
Publish at: 2026-08-01

Teachers’ perspectives on pedagogical challenges of AI integration in English language teaching

10.11591/ijere.v15i4.39226
Parthiban Ganesan , Karthikeyan Padmanathan , Thiyagu Kaliappan , Raja Kumar Subburaj , Durgaprasad Sahoo
Artificial intelligence (AI) tools are rapidly being integrated into English language teaching (ELT). However, limited empirical evidence exists regarding the pedagogical challenges teachers encounter during classroom implementation. This study investigates whether demographic variables influence ELT teachers’ perceptions of AI effectiveness and related pedagogical challenges. A quantitative survey design was employed, involving 200 ELT teachers with prior AI exposure. Data were collected using a validated Likert-scale instrument (α=0.81) and analyzed using independent samples t-tests, one-way ANOVA with Tukey post hoc analysis, and Chi-square (χ²) tests. Results revealed significant gender differences in perceived effectiveness (t=2.504, p=0.044) and pedagogical challenges (t=2.627, p=0.018), with female teachers reporting higher mean scores. Locality differences were significant only for perceived effectiveness (F=4.610, p0.05) or teaching experience (χ²=4.266, p>0.05). Educational level taught was significantly associated with perceived effectiveness (χ²=13.816, p
Volume: 15
Issue: 4
Page: 3439-3450
Publish at: 2026-08-01

Development of ergonomic skills of future teachers of preschool organizations based on klax pedagogy

10.11591/ijere.v15i4.39441
Feruza Abdrimova , Sholpan Kolumbayeva , Aliya Kosshygulova , Gulnur Amirzhanova , Saltanat Khassanova
Training future preschool teachers requires the systematic development of ergonomic skills due to the high physical and emotional demands of their professional activity. However, existing teacher education programs often emphasize theoretical knowledge while providing limited opportunities for the development of practical ergonomic competencies. This study addresses this gap by investigating the effectiveness of klax pedagogy, a movement-oriented and experiential approach, in dev eloping ergonomic competence among future preschool teachers. A quasi-experimental pre-test/post-test control group design was employed with 84 students (experimental group: n=42; control group: n=42). The experimental group participated in klax-based activities focused on posture control, movement coordination, spatial organization, and ergonomic awareness, while the control group received traditional instruction. The results revealed a statistically significant improvement in ergonomic skills in the experimental group (t=9.84, p
Volume: 15
Issue: 4
Page: 3489-3496
Publish at: 2026-08-01

Psychosocial factors of cyberbullying experienced by Malaysian public university undergraduates

10.11591/ijere.v15i4.39582
Siti Nurfathin Idris , Mastura Mahfar , Faizah Mohd Fakhruddin , Azlina Mohd Kosnin , Aslan Amat Senin , Halimah Mohd Yusof , Nur Azmina Paslan
Cyberbullying has emerged as a critical issue among university students in Malaysia, driven by the widespread use of digital technologies and associated with serious psychological consequences. Despite its increasing prevalence, limited research has explored how psychosocial factors shape cyberbullying victimization within the Malaysian higher education context. This study addresses this gap by exploring the psychosocial factors influencing cyberbullying victimization among university students in Malaysia. A qualitative case study approach was employed, involving five university students who had experienced cyberbullying. Data was collected through semi-structured, in-depth interviews and analyzed using thematic analysis. The findings suggest that cyberbullying victimization is influenced by an interplay of psychological and social factors. Psychological factors include passive behavior, low self-esteem, irrational beliefs and personality traits, while social factors encompass peer relationships, parenting styles, social media engagement, and online gaming environments. This study suggests that cyberbullying is a multifaceted phenomenon shaped by both individual vulnerabilities and environmental influences. These findings highlight the need for comprehensive, psychosocial-based interventions to support students’ wellbeing and promote safer digital environments in higher education institutions.
Volume: 15
Issue: 4
Page: 2795-2805
Publish at: 2026-08-01

Competency-based evaluation of scientific-pedagogical master’s programs

10.11591/ijere.v15i4.37191
Nurzada Kozhamberdiyeva , Aliya Kudaibergenova , Bakytgul Imanbekova , Sarash Konyrbayeva , Zhanar Bekturganova , Yücel Gelişli
The goal of this study is to conduct a comprehensive evaluation of the master’s degree programs at the Faculty of Philosophy and Political Science of Al-Farabi Kazakh National University from the perspective of the competency-based approach. The study involves a targeted sample of 120 participants directly involved in the implementation and evaluation of scientific and pedagogical master’s programs: 70 master’s students, 20 teachers, 20 graduates, and 10 employers. The study employs a mixed-method approach, including surveys of master’s degree students, faculty members, graduates, and employers, as well as qualitative analysis of interviews with participants. Course relevance received the highest rating. Faculty members positively evaluate the alignment of the programs with the competency-based approach. Graduates emphasize the applicability of their knowledge, while employers express satisfaction with the graduates’ level of preparation. Kruskal–Wallis test analysis does not reveal statistically significant differences between groups for most indicators, confirming a consistent perception of educational quality. The interviews reveal a generally positive attitude toward the programs but highlighted several shortcomings, such as uneven competency development, insufficient practical focus, and limited engagement with the professional environment. The novelty of the work lies in the comprehensive empirical evaluation of master’s programs based on a competency-based approach from the perspectives of students, teachers, graduates, and employers. The practical significance of the findings lies in justifying the need for stronger integration of theory and practice, greater transparency in competency assessment, and expanded collaboration with employers to enhance the quality of master’s degree training.
Volume: 15
Issue: 4
Page: 3375-3391
Publish at: 2026-08-01

AI literacy and academic engagement in higher education: the mediating role of foreign language anxiety

10.11591/ijere.v15i4.38082
Ashraf Ragab Ibrahim , Mohamed Megahed Nasreldeen , Hesham Hussein Yakout , Hanan Mohamed Elsied , Mohammed Saad Eltawela , Mohamed Ali Nemt-allah
The rapid integration of artificial intelligence (AI) in higher education has raised questions about how technological competencies influence student outcomes, particularly in foreign language learning where anxiety significantly affects performance. This study investigated whether foreign language anxiety (FLA) mediates the relationship between AI literacy (AIL) and academic engagement (AE) among 1,052 English as a foreign language (EFL) learners enrolled in foreign language programs at Al-Azhar University, Egypt. Participants completed three validated instruments: the artificial intelligence literacy scale (AILS), the short-form foreign language classroom anxiety scale (S-FLCAS), and the academic engagement scale (AES). Mediation analysis was conducted using PROCESS Model 4 with 5,000 bootstrap iterations to generate bias-corrected confidence intervals (CI). Correlation analyses revealed that AIL positively correlated with AE (r=.31, p
Volume: 15
Issue: 4
Page: 3613-3622
Publish at: 2026-08-01

Adaptive feature selection for credit scoring models

10.11591/ijeecs.v43.i2.pp507-521
Mostafa Mohamed SeifElnasr , Yasser Omar , Saleh Mesbah
Accurate credit scoring and delinquency prediction are critical for financial institutions, particularly when evaluating both banked and unbanked clients. Traditional feature selection approaches such as wrapper, filter, and embedded methods often require repeated retraining and may not efficiently explore optimal feature subsets in multi-class credit risk settings. This study proposes a reinforcement learning (RL)–based adaptive feature selection framework using tabular Q-learning to dynamically identify informative feature subsets for credit bucket classification and delinquency prediction. The framework was evaluated on two datasets, including proprietary credit datasets (24,533 records with 18–19 features). Using stratified 10-fold cross-validation, the proposed approach achieved 62.84% accuracy (F1=0.63) for 8-class credit bucket prediction, outperforming traditional wrapper-based methods by up to 4.2% while reducing computational cost compared to exhaustive subset evaluation. For delinquency prediction, the model achieved approximately 70% F1-score, demonstrating improved minority-class sensitivity. Compared to filter and embedded methods, the RL-based framework produced more compact feature subsets while maintaining competitive computational efficiency. These findings demonstrate that adaptive RL driven feature selection provides a practical and scalable mechanism for enhancing predictive performance in credit risk modelling, supporting automated and consistent decision making in financial institutions.
Volume: 43
Issue: 2
Page: 507-521
Publish at: 2026-08-01

Transformer-based sentiment modeling for identifying cross country fintech perception gaps

10.11591/ijeecs.v43.i2.pp522-533
Kayla Zhafira Ardinov , Muhardi Saputra , Riska Yanu Fa’rifah
Conventional sentiment analysis lacks granularity for capturing detailed user experiences and cross-country comparative insights in digital finance. This study identifies and maps perception gaps among ShopeePay users in Indonesia and Thailand using a topic-informed sentiment analysis pipeline inspired by aspect-based sentiment analysis (ABSA) principles. Adopting the knowledge discovery in databases (KDD) framework, over 170,000 web scraped reviews were preprocessed, automatically labeled through pseudo labeling, and balanced using random oversampling. To mitigate pseudo-label reinforcement, manual validation on 500 reviews per country achieved agreement rates of 98.80% (Indonesia) and 99.20% (Thailand) with Cohen’s Kappa above 0.97. The fine-tuned DistilBERT model achieved accuracies of 97.65% (Indonesia) and 98.38% (Thailand), though these figures should be interpreted within the pseudo-labeled evaluation context. Significant perception gaps were revealed: Thai users showed lower satisfaction with transactions (67.6% negative) while Indonesian users were more positive (93.0% positive). Process time received negative dominance in both countries, with Indonesia at 78.2% and Thailand at 50.8% negative. These findings demonstrate that user satisfaction is shaped by local infrastructure and cultural contexts, providing strategic insights for regional fintech development.
Volume: 43
Issue: 2
Page: 522-533
Publish at: 2026-08-01

A deep learning-based system for coral reef image segmentation using YOLOv8 with EfficientNet-B0 in Indonesian waters

https://ijeecs.iaescore.com/index.php/IJEECS/article/view/44980
Raden Sutiadi , Sparisoma Viridi , Giyanto Giyanto , Andarta F. Khoir , Rizkie S. Utama , Elsa D. Aulia , Tri A. Hadi , Ludi Parwadani Aji
Manual coral point count with excel extension (CPCe) analysis requires approximately 4–6 hours to process one 50-image station, limiting the scale of coral reef monitoring. This study presents an artificial intelligence-based workflow using YOLOv8 with an EfficientNet-B0 backbone to automate benthic cover estimation. A total of 8,147 underwater images from 151 transects across 39 Indonesian coral reef stations were annotated into eleven benthic categories for model training, while evaluation was conducted using the 2021 Derawan Islands dataset. During validation, YOLOv8 achieved an average mAP@0.5 of 0.58, recall of 0.79, and an average absolute percentage cover error of 9.8% compared with CPCe. The model processed each image in 3.13 seconds, equivalent to 156.47 seconds per 50-image station, representing a 92–138× speedup over manual CPCe analysis. These results show that the proposed workflow can support scalable and near-real-time coral reef monitoring across Indonesia.
Volume: 43
Issue: 2
Page: 628-639
Publish at: 2026-08-01

FireDetXplainer: an explainable artificial intelligence framework for wildfire detection

10.11591/ijeecs.v43.i2.pp576-585
Janjhyam Venkata Naga Ramesh , Bhargavi Peddi Reddy , Jillellamoodi Naga Madhuri Rajyalakshmi , Rajyalakshmi Uppada , Gaddam Venu Gopal , Rajesh Tulasi
Wildfires pose significant environmental, ecological, and socioeconomic threats, necessitating rapid and reliable detection systems for timely emergency response and disaster mitigation. Recent advances in deep learning have substantially improved wildfire detection accuracy; however, most existing models operate as black-box systems, limiting transparency and reducing user trust in safety-critical applications. This study proposes FireDetXplainer (FDX), an explainable artificial intelligence (XAI) framework designed to enhance the interpretability of deep learning-based wildfire detection while maintaining high predictive performance. The proposed framework integrates convolutional neural network (CNN)-based image classification with explainability techniques to identify the visual regions that contribute most to wildfire detection decisions. By generating intuitive visual explanations, FDX enables users to understand, validate, and trust the model's predictions, thereby supporting transparent and accountable decision-making. Experimental evaluation demonstrates that the proposed framework effectively distinguishes wildfire images from non-fire scenes while providing meaningful visual interpretations that improve model transparency without compromising detection performance. The findings highlight the potential of explainable AI to strengthen the reliability, usability, and practical deployment of intelligent wildfire monitoring systems for environmental surveillance, disaster management, and early warning applications.
Volume: 43
Issue: 2
Page: 576-585
Publish at: 2026-08-01

Low-power high-speed FinFET DRAM array using sleep transistors

10.11591/ijeecs.v43.i2.pp413-424
N Praveena , N Shylashree
Dynamic random-access memory (DRAM) is a fundamental memory technology widely employed in modern digital systems because of its high storage density and simple cell structure. However, conventional DRAM cells suffer from considerable power dissipation and propagation delay, which limit their suitability for high-speed and low-power applications. This paper presents three novel FinFET-based DRAM architectures incorporating sleep transistor techniques to reduce power consumption while improving operating speed. The proposed designs include a 2T-DRAM with sleep transistors and two configurations of 3T-DRAM with sleep transistors. The FinFET technology enhances switching performance and reduces propagation delay, whereas the sleep transistor technique effectively suppresses leakage, dynamic, and short-circuit power during memory operations. The proposed DRAM cells are designed and evaluated using the cadence virtuoso analog design environment. Simulation results demonstrate significant improvements over conventional DRAM architectures. The proposed 2T-DRAM achieves a 66% reduction in write delay, while the proposed 3T-DRAM achieves up to a 98% reduction in read delay. Furthermore, write power consumption is reduced by 56.8%, 63.02%, and 99.6% for the 2T, 3T-B, and 3T-C configurations, respectively. During read operations, power consumption is reduced by 99.8%, 99.5%, and 99.8%, respectively. These results demonstrate that the proposed FinFET DRAM architectures provide an effective solution for high-speed, low-power embedded memory applications.
Volume: 43
Issue: 2
Page: 413-424
Publish at: 2026-08-01

PID-based performance optimization of ultra-wideband microstrip patch antennas for indoor positioning systems

10.11591/ijeecs.v43.i2.pp450-459
Fredelino A. Galleto Jr. , Aaron Don M. Africa , Bettina Gaille H. Dayrit , Gia Kyla S. Guevarra , Chrismon Elijah Q. Mansilungan , Michael Angelo M. Obciana , Mariah Venice A. Rodriguez , Keane Dwight A. Sulit
Ultra-wideband (UWB) technology has become a key enabler for high accuracy indoor positioning systems (IPSs), where antenna performance directly influences localization accuracy, signal quality, and communication reliability. However, designing compact UWB microstrip patch antennas with wide bandwidth, low reflection loss, and stable radiation characteristics remains a significant challenge. This paper presents a PID-based performance optimization approach for UWB microstrip patch antennas to improve antenna characteristics for indoor positioning applications. The proposed methodology integrates MATLAB-based electromagnetic simulation with parameter optimization to refine antenna geometry while incorporating optimization concepts inspired by rough set theory and fuzzy logic to support efficient design parameter selection. Using MATLAB and the Parallel Computing Toolbox, the proposed approach significantly reduces computational complexity while accelerating the optimization process. Experimental results demonstrate substantial improvements in reflection coefficient (S11), voltage standing wave ratio (VSWR), radiation pattern, and antenna directivity, particularly around the target operating frequency of 10 GHz. Among the evaluated configurations, the optimized rectangular microstrip patch antenna consistently outperformed the triangular design in terms of impedance matching and radiation performance. The proposed optimization framework provides an effective and computationally efficient solution for enhancing UWB antenna performance, making it well suited for high-precision indoor positioning and next-generation wireless communication systems.
Volume: 43
Issue: 2
Page: 450-459
Publish at: 2026-08-01
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