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29,167 Article Results

University students’ perceptions on developing constructivist learning approach in classroom settings

10.11591/ijere.v14i6.35117
Cuc Thi Doan , Tuan Van Vu , Ai Nhan Nguyen
This study investigated tertiary students’ perceptions regarding constructivist learning in the context of higher education in Vietnam. It aimed to examine the general perceptions of university students towards constructivist learning and the effects of constructivist learning on students’ learning outcomes. It also examined the conditions that make students more likely to embrace or resist these approaches. The study evaluated the engagement of students in problem-solving activities through the use of constructivist learning methods. A mixed-methods approach was employed, combining both quantitative and qualitative data. Specifically, the study involved a survey of 384 students from Hanoi Law University, using a researcher-made Likert-scale questionnaire and semi-structured interviews of 20 students from the sample. While descriptive and inferential statistics were used to analyze the quantitative data, the qualitative data were thematically analyzed for common themes and patterns. The results indicate that although the participants acknowledge the benefits of constructivist methods, particularly in fostering critical thinking and problem-solving, there is still uncertainty about their ability to engage in a self-directed learning approach. The findings suggest that while the constructivist approach has been recognized, practical efforts have not been made in teaching practices, teacher training, and assessment methods to create an interactive, student-centered learning environment in Vietnam.
Volume: 14
Issue: 6
Page: 4264-4275
Publish at: 2025-12-01

Students’ and teachers’ perceptions and experiences of using social media in learning foreign languages

10.11591/ijere.v14i6.34732
Le Thanh Nguyet Anh , Bui Thanh Tinh , Nguyen Van Canh
The benefits of social media applications have emerged as an advancement in teaching and learning foreign languages. However, it falls short of studies investigating the perspectives and experiences of educators and learners about social media usage in Vietnamese tertiary education contexts in rural areas. Therefore, this study examined the perceptions and experiences of teachers and students regarding the application of social media in foreign language learning at a university in the Mekong Delta, Southern Vietnam. The study was conducted with the participation of 199 students and 20 teachers. This study employed a mixed-methods approach, collecting data from questionnaires, and an in-depth interview. The findings showed that most teachers and students had a positive attitude towards applying this mode to teaching and learning foreign languages. However, they indicated some problems when utilizing those methods. Additionally, they suggested some measures for blending direct teaching and learning with social media based on their experience. The research results provided more insights into this field in the literature, especially in local settings.
Volume: 14
Issue: 6
Page: 5197-5208
Publish at: 2025-12-01

Perceptions of audiovisual media in vocabulary acquisition among English learners: benefits and challenges

10.11591/ijere.v14i6.34852
Xuan Hong Nguyen Thi , Thanh Thai Nguyen
Learning vocabulary through English audiovisual materials has long been a popular method among students. With the advancement of digital technology, this approach has gained even more attraction, leading to a growing number of studies that have investigated its effectiveness. However, there is a notable scarcity of research addressing the challenges that students face; therefore, the current study aims to explore students’ perspectives on the challenges along with the benefits of using audiovisual media as tools for learning vocabulary. This study was done through a quantitative approach using a questionnaire that included both open-ended and closed-ended questions. With the participation of 132 senior English-major students at Thu Dau Mot University in Vietnam, the study collected 117 valid questionnaires that provided valid data for analysis. Through descriptive statistics, the results reveal the improvements in pronunciation and listening skills, enhanced understanding of slang and idiomatic expressions, and increased exposure to the natural use of the target language. However, the findings also reveal that this method poses challenges for students, including misunderstandings stemming from the use of formal or informal language and an over-reliance on audiovisual media. Therefore, the study emphasizes the need for structured guidance to foster language learning outcomes.
Volume: 14
Issue: 6
Page: 5209-5218
Publish at: 2025-12-01

Emotional intelligence in teaching: a key to performance and institutional climate in basic education

10.11591/ijere.v14i6.34924
Benjamin Maraza-Quispe , Victor Hugo Rosas-Iman , Giuliana Feliciano-Yucra , Atilio Cesar Martinez-Lopez , Elizabeth Katherine Ortiz-Corimaya , Walter Choquehuanca-Quispe , Frida Karina Coasaca-Hancco , Luis Elfer Nuñez-Saavedra
This study addresses the lack of understanding regarding the relationship between emotional intelligence (EI), teaching performance, and institutional climate (IC) in basic education. As a solution, the study proposes evaluating and strengthening teachers’ EI to enhance both their performance and the school environment. Using a quantitative, non-experimental, correlational design, the research analyzed a randomly selected sample of 145 teachers. Validated questionnaires measured dimensions such as self-awareness, self-regulation, motivation, empathy, and social skills, as well as teaching preparation and IC. The results reveal significant positive correlations between EI and IC (r=0.85) and between teaching performance and IC (r=0.78). This suggests that higher EI not only improves teaching effectiveness but also fosters a positive institutional environment. The study concludes that enhancing teachers’ EI can optimize both their performance and institutional dynamics, contributing to higher-quality education. The findings support the implementation of EI training programs as a key strategy to improve teaching performance and the school climate (SC).
Volume: 14
Issue: 6
Page: 5054-5066
Publish at: 2025-12-01

Language learning strategies in relation to advanced Chinese vocabulary and writing proficiency

10.11591/ijere.v14i6.31857
Xinqin Liu , Mohammed Y.M. Mai
The study investigated the relationship between the language learning strategies (LLSs) employed by international undergraduate students at universities in Qinghai Province, China, and their proficiency in advanced Chinese vocabulary and writing. Data was collected from 45 advanced-level students selected through purposive sampling, using Oxford’s strategy inventory for language learning (SILL), an advanced Chinese vocabulary knowledge test, and advanced Chinese writing test scores. The descriptive analysis revealed moderate language learning strategy usage, with a preference for speaking and listening development. This result indicates a limited strategy usage. The correlation analysis showed no significant relationship between strategy usage and advanced Chinese vocabulary or writing proficiency. However, a strong relationship was observed between advanced Chinese vocabulary and writing proficiency. The absent relationship between strategy usage and proficiency levels suggests insufficient Chinese language proficiency among the students. The significant relationship highlights the crucial role of vocabulary in enhancing Chinese writing skills. The results provide practical insights for enhancing the use of strategies and vocabulary teaching to improve advanced writing and Chinese proficiency among international undergraduate students.
Volume: 14
Issue: 6
Page: 4844-4853
Publish at: 2025-12-01

Tailoring collaborative learning with jigsaw and VARK: a case study in teaching physics with environmental protection

10.11591/ijere.v14i6.32823
Ngan Hai My Le , Anh Thi Kim Nguyen
Collaboration is a crucial 21st century skill, requiring learning environments that foster teamwork while leveraging students’ individual strengths. This study aimed to enhance collaboration using the jigsaw strategy, which was adapted to students’ learning styles based on the model: visual, aural, read/write, and kinesthetic (VARK). The study involved 27 tenth-grade students in Ho Chi Minh City and focused on the topic “physics with environmental protection.” Students were initially grouped by learning styles into expert groups and later reorganized into mixed jigsaw groups to collaboratively address tasks related to environmental issues. A quasi-experimental design was employed, utilizing pre- and post-test self-assessment surveys, video observations, and group discussions to assess collaborative performance. Quantitative data were analyzed using the Wilcoxon signed rank test, while qualitative data provided deeper insights. Results demonstrated a significant improvement in team support (p=0.030), suggesting that aligning learning tasks with students’ styles foster group cohesion. However, participation and contribution showed minimal improvement, with students preferring reading/writing styles facing challenges in adapting to group activities. While the integration of jigsaw and VARK proved effective in enhancing collaboration, the study underscores the need to develop strategies to accommodate diverse learning preferences. Future research should involve larger sample sizes and consider teachers’ perspectives to optimize the practical implementation of learning styles in collaborative learning environments.
Volume: 14
Issue: 6
Page: 4364-4374
Publish at: 2025-12-01

Attitude and intention to use chatbots in e-commerce: the moderating role of personal innovativeness

10.11591/ijict.v14i3.pp760-771
Indah Oktaviani Hardi , Ahmad Maki , Evi Rinawati Simanjuntak
Internet-based retailers employ artificial intelligence (AI) chatbots to facilitate customer communication. This research endeavored to evaluate consumers' intentions regarding the utilization of chatbots for customer service interactions, building upon the technology acceptance model (TAM). TAM-based chatbot adoption is the subject of an abundance of research. Conversely, the extent to which users' perception of the chatbot's response quality influences their intention to adopt remains uncertain. In addition to investigating the potential influence of chatbot response accuracy and completeness on users' intention to adopt the system, this study explored the relationship between users' personal innovativeness and adoption intention. A total of 312 usable responses were analyzed with PLS-SEM from survey data collected via convenience sampling from e-commerce customers. Perceived usefulness, convenience of use, accuracy, and completeness all influenced attitudes toward chatbots, as shown by hypothesis testing result. Attitude formation toward chatbots is most strongly influenced by perceived completeness. Personal innovativeness has a negative influence, which contradicts the hypothesis despite the fact that its moderating effect is statistically significant. Further comprehension of the key determinants of attitude towards chatbots is enhanced by these findings. It is advisable for organizations to empower the chatbot with the capability to conduct thorough and precise responses to inquiries.
Volume: 14
Issue: 3
Page: 760-771
Publish at: 2025-12-01

Comparative analysis of u-net architectures and variants for hand gesture segmentation in parkinson’s patients

10.11591/ijict.v14i3.pp972-982
Avadhoot Ramgonda Telepatil , Jayashree Sathyanarayana Vaddin
U-Net is a well-known method for image segmentation, and has proven effective for a variety of segmentation challenges. A deep learning architecture for segmenting hand gestures in parkinson’s disease is explored in this paper. We prepared and compared four custom models: a simple U-Net, a three-layer U-Net, an auto encoder-decoder architecture, and a U-Net with dense skip pathways, using a custom dataset of 1,000 hand gesture images and their corresponding masks. Our primary goal was to achieve accurate segmentation of parkinsonian hand gestures, which is crucial for automated diagnosis and monitoring in healthcare. Using metrics including accuracy, precision, recall, intersection over union (IoU), and dice score, we demonstrated that our architectures were effective in delineating hand gestures under different conditions. We also compared the performance of our custom models against pretrained deep learning architectures such as ResNet and VGGNet. Our findings indicate that the custom models effectively address the segmentation task, showcasing promising potential for practical applications in medical diagnostics and healthcare. This work highlights the versatility of our architectures in tackling the unique segmentation challenges associated with parkinson’s disease research and clinical practice.
Volume: 14
Issue: 3
Page: 972-982
Publish at: 2025-12-01

Quality of service optimization for 4G LTE upload and download throughput

10.11591/ijict.v14i3.pp1024-1033
Afrizal Yuhanef , Siska Aulia , Lefenia Indriani
Demand for mobile data services and people’s dependence on 4G LTE networks continue to increase. However, the quality of service (QoS) of this network still requires improvement, especially regarding the effect of QoS on throughput at specific frequencies. The research gap lies in the lack of indepth analysis of the impact of QoS parameters on network performance at frequencies of 2,100 MHz and 2,300 MHz. This study evaluates the effect of QoS parameters, such as delay, jitter, and packet loss, on throughput in 4G LTE networks at both frequencies. The research methodology uses an experimental approach with throughput, delay, jitter, and packet loss measurements in various network conditions. The results showed that delay (17.2174 ms to 37.0322 ms), jitter, and packet loss significantly influence throughput, ranging from 624.5 Kbps to 1,322.4 Kbps. The 2,100 MHz frequency tends to show better performance than 2,300 MHz. This study concludes that optimizing QoS parameters, especially delay and jitter, can significantly improve 4G LTE network performance. These findings provide practical contributions for mobile operators in improving network quality and customer satisfaction and open opportunities for further research on other frequencies or newer network technologies.
Volume: 14
Issue: 3
Page: 1024-1033
Publish at: 2025-12-01

Cross-cultural exploration of stylized performance: traditional Chinese training methods in drama education

10.11591/ijere.v14i6.34292
Jingying ZHANG , Syahrul Fithri Musa
This study explores the effectiveness of stylized performance training in cross-cultural drama education, focusing on how symbolic body language conveys emotions and character intent. Originating from traditional Chinese theatre, stylized training was adapted to help students from diverse cultural backgrounds achieve emotional resonance and cohesive character portrayal. Employing a participatory action research (PAR) design, the 16-week experiment involved performing arts students from six cultures, using iterative feedback and reflective practices to track changes in emotional expression and adaptation to symbolic movements. The training included three phases: basic training, emotional integration, and applied practice. Data was collected through classroom observations, interviews, feedback forms, and peer evaluations. Findings show that participants improved in non-verbal emotional expression, effectively conveying emotions across cultural barriers. Supported by Pavis’s “intercultural theatre” theory and Mead’s symbolic interactionism, the study highlights stylized performance’s potential to enhance cross-cultural emotional resonance in drama education.
Volume: 14
Issue: 6
Page: 4978-4991
Publish at: 2025-12-01

Stability analysis and robust control of cyber-physical systems: integrating Jacobian linearization, Lyapunov methods, and linear quadratic regulator control via LMI techniques

10.11591/ijece.v15i6.pp5276-5285
Rachid Boutssaid , Abdeljabar Aboulkassim , Said Kririm , El Hanafi Arjdal , Youssef Moumani
Stability issues in cyber-physical systems (CPS) arise from the challenging effects of nonlinear dynamics relation to multi-input, multi-output systems. This research proposed a robust control framework that combines Jacobian linearization, Lyapunov stability analysis, and linear quadratic regulator (LQR) control via linear matrix inequalities (LMIs). The robust methodology does the following: it applies linearization on the dynamics of the CPS; it establishes the stability of the system using Lyapunov functions and LMIs; and it designs an LQR controller. The proposed framework was validated through a comparison between the behavior of a linearized and nonlinear model. The autonomous vehicle application showed: a settling time of 20 seconds; an overshoot of 3.8187%; and a steady-state error of 2.688×10⁻⁷. The proposed framework is robustly demonstrated and has applications to areas in automation and smart infrastructure. Future work includes optimizing the design of weighting matrices and developing adaptive control features.
Volume: 15
Issue: 6
Page: 5276-5285
Publish at: 2025-12-01

Smart wearable glove for enhanced human-robot interaction using multi-sensor fusion and machine learning

10.11591/ijece.v15i6.pp5162-5172
Nourdine Herbaz , Hassan El Idrissi , Hamza Sabir , Abdelmajid Badri
Hand gesture recognition (HGR) using flexible sensors (flex-sensor) and the MPU6050 sensor has proved to be a key area of research in human-machine interaction, with major applications in biasing, rehabilitation, and assisted robotics. This paper proposes a wearable intelligent glove designed to operate a robotics arm in real time, relying on multi-sensor fusion and machine learning methods to enhance the system's responsiveness and precision. The proposed system enables the intuitive reproduction of hand movements and precise control of the robotic arm. In the context of Industry 4.0 and internet of things (IoT), the classification of gestures is necessary for maintaining operational efficiency. To guarantee gesture recognition, data signals from the smart glove are collected and trained by a recurrent neural network (RNN), which achieves 98.67% accuracy for real-time classification of seven gestures. Beyond industrial applications, the wearable smart glove can be exploited in a recognized circuit of all systems, including rehabilitation exercises that involve recording the progression of muscular activity for the assessment of motor functions and serve as a tool for patient recovery.
Volume: 15
Issue: 6
Page: 5162-5172
Publish at: 2025-12-01

Plant disease detection and classification: based on machine learning and Eig(Hess)-co-occurrence histograms of oriented gradients

10.11591/ijece.v15i6.pp5336-5346
El Aroussi El Mehdi , Barakat Latifa , Silkan Hassan
Agricultural districts provide high-quality food and contribute substantially to economic growth and population support. However, plant diseases can directly reduce food production and threaten species diversity. The use of precise, automated detection techniques for early disease identification can improve food quality and mitigate economic losses. Over the past decade, numerous methods have been proposed for plant disease classification, and in recent years the focus has shifted toward deep learning approaches because of their outstanding performance. In this study, we employ the Eig(Hess)-co-occurrence histograms of oriented gradients (CoHOG) descriptor alongside pre-trained machine-learning models to accurately identify various plant diseases. We apply principal component analysis (PCA) for dimensionality reduction, thereby enhancing computational efficiency and overall model performance. Our experiments were conducted on the popular PlantVillage database, which contains 54,305 images across 38 disease classes. We evaluate model performance using classification accuracy, sensitivity, specificity, and F1-score, and we perform a comparative analysis against state-of-the-art methods. The findings indicate that the approach we proposed achieves up to 99.83% accuracy, outperforming existing models. Additionally, we test the robustness of our method under various conditions to highlight its potential for real-world agricultural applications.
Volume: 15
Issue: 6
Page: 5336-5346
Publish at: 2025-12-01

Optimized passive and active shielding of magnetic induction generated by ultra-high-voltage overhead power lines

10.11591/ijece.v15i6.pp5144-5161
Salah-Eddine Houicher , Rabah Djekidel , Sid Ahmed Bessidek
This paper presents computational modeling to assess and limit the magnetic induction levels emitted by an extra-high-voltage (EHV) overhead transmission line of 750 kV using the fundamental principle of Biot-Savart law in magnetostatics. An optimization technique based on the grey wolf optimizer (GWO) algorithm is employed to determine the appropriate location of the passive and active loop conductors, and the associated parameters to shielding to achieve better compensation of magnetic induction in an interest zone. The resulting magnetic induction of the ultra high voltage (UHV) overhead power line exhibits a crest value of 27.78 μT at the middle of the right-of-way, which can be considered unacceptable by strict protection standards. Generally, the magnetic compensation loops optimally located under the phase conductors of the power transmission system reduce the magnetic induction levels along the transmission line corridor. The passive loop attenuates the maximum magnetic induction by a rate of 29.7%. Therefore, the performance of the active loop is better; it provides a greater reduction with a rate reaching 53.24%. The simulation results were tested with those derived by the elliptical polarization process. An excellent concordance was found, which made it possible to ensure the adopted method.
Volume: 15
Issue: 6
Page: 5144-5161
Publish at: 2025-12-01

Novel multilevel local binary texture descriptor for oral cancer detection

10.11591/ijict.v14i3.pp837-844
Vijaya Yaduvanshi , Raman Murugan
Categorizing texture medical images is an extensive job in most of the fields of computer vision, pattern recognition and biomedical imaging. For the past few years, the texture analysis system model, especially for biological images, has been brought to attention because of its ever-growing requirements and characteristics. This research shows its novelty by using a multilevel local binary texture descriptor (MLBTD) algorithm with support vector machine (SVM), k-nearest neighbor (KNN), and CT Classifiers to investigate the texture features of the oral cancer samples. The simulation work is done in MATLAB2021a environment by employing the MLBTD algorithm. A Mendeley dataset, containing 89 oral cavity histopathological images and 439 OSCC images in 100x magnification, is used. A statistical comparative study of local binary pattern (LBP) and MLBTD with linear SVM, KNN, CT classifier is performed in which results show the better performance of MLBTD and linear SVM with 89.94% of accuracy and by applying MLBTD algorithm over 90.57% accuracy is obtained whereas LBP algorithm only provides 86.16% of accuracy.
Volume: 14
Issue: 3
Page: 837-844
Publish at: 2025-12-01
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