Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

28,719 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

Assessment strategies in digital learning environments: insights from teacher education institutions

10.11591/ijere.v14i6.35270
Martin Jr., L. Nobis , Benjielen C. De Guzman , Alegria P. Cui , Jennifer G. Evardone , Elena M. Pulga , Catherine L. Caparroso , Lourdes Hyacinth A. Sabalza , Amabelle C. Nobis
This study explores the adaptation of assessment strategies in teacher education programs within digital learning environments (DLEs). The perspectives and experiences of faculty members from teacher education institutions (TEIs) were analyzed using an embedded mixed-methods approach. The findings reveal significant advancements in enhancing instructor digital literacy and student engagement with DLEs. However, challenges such as the authenticity of assessments and increased student workload were identified. Faculty members recommended frequent feedback and authentic evaluations to address these issues. The study also highlights areas for improvement, including the diversification of evaluation tools, broader technology integration, and additional administrative support. These insights are crucial for TEIs to refine their assessment processes in DLE-based teacher training programs and contribute to ongoing discussions in ASEAN teacher education and global educational development.
Volume: 14
Issue: 6
Page: 4724-4733
Publish at: 2025-12-01

Perceptions and institutional readiness for generative AI adoption in education using a multi-method approach

10.11591/ijere.v14i6.35245
Ken Gorro , Elmo Ranolo , Lawrence Roble , Adrian Ybañez , Anthony Ilano , Joseph Pepito , Rue Nicole Santillan , Cesar Ranolo , Emardy Barbecho , Purity Mata , Anna Marie Neiz
The rapid emergence of generative artificial intelligence (GenAI) tools like ChatGPT is reshaping educational practices, presenting both transformative opportunities and institutional challenges. This study offers a novel, integrative framework for understanding the adoption of GenAI tools in higher education by combining quantitative and qualitative analyses within a hybrid methodological design. Specifically, it is the first to incorporate the analytical hierarchy process (AHP), fuzzy decision-making trial and evaluation laboratory (Fuzzy DEMATEL), and the extended technology acceptance model (ETAM) in a unified model of adoption, augmented by thematic analysis of user experiences. A stratified random sample of 1,297 participants—comprising 1,191 students and 105 faculty members from various departments—ensured proportional representation across the university. AHP was employed to prioritize key adoption criteria, Fuzzy DEMATEL uncovered the causal interdependencies among constructs, and ETAM validated the direct and indirect effects influencing behavioral intention. Thematic analysis provided contextual depth regarding institutional barriers and individual perceptions. Findings reveal that attitude toward GenAI and intention to use (IU) are the strongest drivers of adoption. Notably, university support (US) emerged as a central enabler, significantly influencing both awareness and perceived usefulness (PU). This study contributes a comprehensive and multi-method framework that educational institutions can use to ethically, effectively, and equitably integrate GenAI technologies into academic ecosystems.
Volume: 14
Issue: 6
Page: 4770-4784
Publish at: 2025-12-01

Optimized ultra-low power and reduced delay GNR Ternary SRAM using a 7-transistor architecture

10.11591/ijict.v14i3.pp1044-1055
Ravikishore Gaddikoppula , Nandhitha Nathakattuvalasu Muthu
Greater need and evolution in electronics require a memory device that can go with a decreased power delay, SRAM plays an important role as a storage element in digital circuit design. Power and delay are vital problems faced by today’s RAM technology. It is necessary to lessen the power and increase the speed. There is a need to reduce power utilization and time delay. The proposed method is seen in the Electronics technical tool H-Spice technology. The technique proposed on DRG 7T- transistors SRAM consumes less power and delay. After the analysis and enhancement of the circuit, this approach gives the power delay product of the graphene nanoribbon (GNR) 7T SRAM as 80% at 0.7 V, 59% at 0.8 V, 34 % at 0.9 V, which is much less when compared to conventional SRAM power delay product.
Volume: 14
Issue: 3
Page: 1044-1055
Publish at: 2025-12-01

Does empathy and awareness of bullying affect the performance of Moroccan students in PISA?

10.11591/ijict.v14i3.pp860-867
Ilyas Tammouch , Abdelamine Elouafi , Soumaya Nouna
Socioemotional skills, such as empathy and bullying awareness, play a pivotal role in shaping students' personal and academic development. These skills are increasingly recognized as critical factors influencing educational outcomes, particularly in addressing challenges like bullying that can hinder learning. This study examines the impact of empathy and bullying awareness on the academic performance of Moroccan students, using data from the 2018 Programme for International Student Assessment (PISA). To ensure robust causal inference in high-dimensional data, the double/debiased machine learning (DML) technique is employed. The findings reveal that higher levels of empathy and awareness of bullying significantly enhance performance across reading, mathematics, and science, with the most notable improvements observed in reading. These results remain consistent across various demographic and socioeconomic groups, highlighting their robustness. The study underscores the importance of integrating socioemotional learning into educational practices to foster academic success and create supportive school environments. By contributing to the growing evidence on non-cognitive skills in education, this research offers valuable insights for educators and policymakers seeking to improve student outcomes.
Volume: 14
Issue: 3
Page: 860-867
Publish at: 2025-12-01

State space controller of SLCC and design analysis with MPPT approaches

10.11591/ijict.v14i3.pp791-801
Jeyaprakash Natarajan , Nivethitha Devi Manoharan , Mohanasanthosh Murugan , Karnati Venkata Lokeshwar Reddy , Thirumalaivasal Devanathan Sudhakar
Power systems with standalone properties like remote unit telecommunication network requires high negative DC supply voltage. In such remote places, solar photovoltaic (PV) are used to generate power. Maximum power point tracking techniques (MPPT) gives unregulated voltage from solar panel. This unregulated voltage is converted into regulated voltage by providing proper pulse width modulation (PWM) signal to self-lift cuk converter (SLCC). In comparison with classic cuk converter, SLCC reduces load voltage and load current ripples. This paper focuses on state space controller design and implementation of SLCC used in MPPT based PV system. The switching pulse of SLCC can be generated by perturb and observe (P&O), incremental conductance (IC) and also using fuzzy control. The simulation of SLCC has been performed using MATLAB/Simulink and its specifications in time domain has been compared.
Volume: 14
Issue: 3
Page: 791-801
Publish at: 2025-12-01

Practice of lateral dominance: an early evaluation strategy in children from the rural area of Puno

10.11591/ijere.v14i6.32729
Nelly Olga Zela-Payi , Haydee Clady Ticona-Arapa
The purpose of this study was to investigate the practice of lateral dominance as an early evaluation strategy in children from the rural area of Puno, Peru. Early assessment of lateral dominance is crucial for understanding a child’s cognitive and motor development. This study focused on children attending a school for children with learning disabilities in the rural Initial Educational Institution Camacani within the Local Educational Management Unit of Puno, located in the southern region of the Province of Puno, under the jurisdiction of the Platería District. A total of 100 children (50 boys and 50 girls), aged 5 years, were selected for the study. The research was descriptive-quantitative in nature, aimed at evaluating the dominance of four dimensions—hands, feet, ears, and eyes—using the Harris test. The findings revealed that lateral dominance in both boys and girls was characterized by poorly affirmed laterality in 60% of the cases and crossed laterality in 40%. This suggests that at the age of 5 years, the children were still in the developmental stage of lateralization, with no clear dominance of the left or right hemisphere. Based on the results, it was concluded that lateral dominance in these children was not yet fully established. Furthermore, the study emphasized the importance of incorporating psychomotor activities during early childhood development to promote the continuous improvement of laterality, motor skills, and spatial orientation.
Volume: 14
Issue: 6
Page: 5094-5104
Publish at: 2025-12-01

Three-phase power flow solution of active distribution network using trust-region method

10.11591/ijape.v14.i4.pp923-933
Rudy Gianto , M. Iqbal Arsyad , Managam Rajagukguk
Distribution systems or networks are inherently unbalanced. As a result, single-phase power flow methods are generally no longer valid for such systems. Therefore, to obtain accurate results, unbalanced systems should be analyzed using three-phase power flow methods, which are far more complicated than the single-phase methods. Moreover, at present, the penetration of distributed generation (DG) in the distribution network has significantly increased. DG integration will increase the complication of the power flow analysis as it changes the network's basic configuration from passive to active system. This computational burden will significantly be higher if the power flow calculation has to be conducted several times (for example, in feeder reconfigurations or service restorations). This paper investigates the utilization of the trust-region method in obtaining the solution to the three-phase power flow problem of an active distribution network (i.e., distribution network embedded with DG). Trust-region computation algorithm is robust and powerful since the optimization technique is employed in finding new solutions in the iteration process. Results obtained from three representative unbalanced distribution networks (i.e., 10-node, 19-node, and 25-node networks) verify the validity of the proposed method. The effects of DG installation on distribution network steady-state performances are also investigated in the present paper.
Volume: 14
Issue: 4
Page: 923-933
Publish at: 2025-12-01

Transmission line fault detection using empirical mode decomposition in presence of wind intermittency

10.11591/ijape.v14.i4.pp960-969
Venkata Krishna Bokka , E. R. Biju , Sai Veerraju Mortha , Majahar Hussain Mahammad , Shaik Mohammad Irshad
The regular fault detection approaches are failed to detect the faults in wind integrated transmission networks due to intermittency nature of the wind energy. More reliable schemes are required to accomplish the detection of faults in presence wind. This article proposed empirical mode decomposition (EMD) based fault detection scheme to detect various faults in wind integrated transmission lines during the normal and stressed conditions of the system. The instantaneous current measurements available at either sending or receiving end are processed through EMD to decompose it into a series of intrinsic mode functions (IMFs) and IMF2 is identified as a dominated IMF with numerous case wise investigations. 1/4th cycle moving window is used to calculate the absolute sum of the IMF2 coefficients to detect the faults with the support of a predefined threshold. The efficacy of the method is tested on different types of faults during the normal condition in presence of wind and later extended to stressed conditions such as power swing. The method is reliable during the typical cases and includes remote end and high resistance faults. All the experiments are carried out in Simulink to generate the measurement data and programs are executed in MATLAB.
Volume: 14
Issue: 4
Page: 960-969
Publish at: 2025-12-01

Predicting Emirati student academic outcomes: school tracks and standardized tests

10.11591/ijere.v14i6.33951
Fatima Al-Ali , John Rice
Global education systems apply grouping strategies to enhance academic outcomes. The United Arab Emirates (UAE) has developed school tracks to address performance gaps by offering more varied high-school tracks while also creating a local Emirates Standardized Tests (EmSAT) for measurement. This study examines the impact of educational tracks in Emirati schools and EmSAT scores on UAE university students’ academic performance. A quantitative multivariate analysis of 3,190 University of Sharjah students compared the outcomes across different high school tracks and analyzed the predictive power of EmSAT scores on university cumulative grade point average (CGPA). EmSAT scores vary significantly by tracks, with elite students performing best, followed by those in the advanced and scientific tracks. Arabic and mathematics EmSAT scores predict CGPA more strongly than English, which has a moderate effect. General track students achieve higher CGPAs compared to other tracks, even after controlling EmSAT performance and gender, suggesting a complex relationship between high school experiences and university success. The findings highlight the track model’s effectiveness, with the elite fostering strong academic pathways. However, the overlap in university achievement between the general and advanced warrants further research. The study provides insights for policymakers to refine educational strategies and enhance student outcomes.
Volume: 14
Issue: 6
Page: 4592-4603
Publish at: 2025-12-01

Practical instruction and mathematics academic achievement in selected Ugandan secondary schools

10.11591/ijere.v14i6.30273
Frank Murangira , Alphonse Uworwabayeho , Innocent Twagilimana
The challenges met in academic achievement of learners in mathematics at secondary school level are enormous. Taking a glance at the academic achievement of learners in mathematics in Uganda, poor academic achievement becomes more and more striking. It is a puzzle to understand the origin of the causes of this poor academic achievement despite the prominence and significance bestowed upon it by the Ugandan government and society at large. It is accepted that ways mathematics is taught have high impact on learners’ achievements. Therefore, an investigation on the effect of practical instruction (PI) on learners’ academic achievement in mathematics is timely. The study was carried out using a total of 383 senior three students from eight secondary schools both private and government aided schools. It involved a quantitative approach using mathematics achievement test (MAT). The results revealed that PI affects learners’ academic achievement positively in mathematics. They showed that learners that were taught using PI improved in their academic achievement more than their counterparts in the post-test examination. Considering these findings, the study recommends proactive measures to ensure the widespread adoption of PI strategies in secondary school classrooms. This includes revising curriculum standards, providing teacher training programmers, and allocating resources to support the implementation of PI, thereby fostering learning outcomes in mathematics.
Volume: 14
Issue: 6
Page: 4966-4977
Publish at: 2025-12-01

The impact of work concerns on teaching effectiveness: evidence from Chinese private universities

10.11591/ijere.v14i6.35367
Liang Mingyu , Mohd Khairuddin Abdullah , Connie Shin
Understanding how young teachers cope with work concerns is crucial for improving teaching quality in Chinese private higher education. This study investigates the relationship between different stages of such concerns and teacher effectiveness of young lecturers in private universities. These lecturers often face workload pressure andlack of career supports, which may influence their effectiveness and professional development. This research involved 416 full-time lecturers under the age of 40 from Shandong Province. The sample was determined using Krejcie and Morgan’s formula and selected through a multi-stage sampling method. Private universities were stratified into four categories, one university from each category was purposively selected, and participants were randomly sampled. Data were gatheredthrough a structured questionnaire adapted from the stages of concern (SoC) and the school teacher effectiveness questionnaire (STEQ). Pearson correlation, multiple regression, and structural equation modeling (SEM) were conducted for analysis. The results show that task concerns and impact concerns significantly influenced teacher effectiveness across instructional planning and strategies, assessment, and learning environment. In contrast, self-concerns showed weaker influence. These findings suggest that work concerns reflect not only stress but also deeper professional motivation, pointing to the need for more purposeful supports to increase teacher effectiveness and career growth.
Volume: 14
Issue: 6
Page: 4604-4613
Publish at: 2025-12-01

Multilingual hate speech detection using deep learning

10.11591/ijict.v14i3.pp1015-1023
Vincent Vincent , Amalia Zahra
The rise of social media has enabled public expression but also fueled the spread of hate speech, contributing to social tensions and potential violence. Natural language processing (NLP), particularly text classification, has become essential for detecting hate speech. This study develops a hate speech detection model on Twitter using FastText with bidirectional long short-term memory (Bi-LSTM) and explores multilingual bidirectional encoder representations from transformers (M-BERT) for handling diverse languages. Data augmentation techniques-including easy data augmentation (EDA) methods, back translation, and generative adversarial networks (GANs)-are employed to enhance classification, especially for imbalanced datasets. Results show that data augmentation significantly boosts performance. The highest F1-scores are achieved by random insertion for Indonesian (F1-score: 0.889, Accuracy: 0.879), synonym replacement for English (F1-score: 0.872, Accuracy: 0.831), and random deletion for German (F1-score: 0.853, Accuracy: 0.830) with the FastText + Bi-LSTM model. The M-BERT model performs best with random deletion for Indonesian (F1-score: 0.898, Accuracy: 0.880), random swap for English (F1 score: 0.870, Accuracy: 0.866), and random deletion for German (F1-score: 0.662, Accuracy: 0.858). These findings underscore that data augmentation effectiveness varies by language and model. This research supports efforts to mitigate hate speech’s impact on social media by advancing multilingual detection capabilities.
Volume: 14
Issue: 3
Page: 1015-1023
Publish at: 2025-12-01

Electric load forecasting using ARIMA model for time series data

10.11591/ijict.v14i3.pp830-836
Balasubramanian Belshanth , Haran Prasad , Thirumalaivasal Devanathan Sudhakar
Any country's economic progress is heavily reliant on its power infrastructure, network, and availability, as energy has become an essential component of daily living in today's globe. Electricity's distinctive quality is that it cannot be stored in huge quantities, which explains why global demand for home and commercial electricity has grown at an astonishing rate. On the other hand, electricity costs have varied in recent years, and there is insufficient electricity output to meet global and local demand. The solution is a series of case studies designed to forecast future residential and commercial electricity demand so that power producers, transformers, distributors, and suppliers may efficiently plan and encourage energy savings for consumers. However, load prognosticasting has been one of the most difficult issues confronting the energy business since the inception of electricity. This study covers a new one–dimensional approach algorithm that is essential for the creation of a short–term load prognosticasting module for distribution system design and operation. It has numerous operations, including energy purchase, generation, and infrastructure construction. We have numerous time series forecasting methods of which autoregressive integrated moving average (ARIMA) outperforms the others. The auto–regressive integrated moving average model, or ARIMA, outperforms all other techniques for load forecasting.
Volume: 14
Issue: 3
Page: 830-836
Publish at: 2025-12-01

The bootstrap procedure for selecting the number of principal components in PCA

10.11591/ijict.v14i3.pp1136-1145
Borislava Toleva
The initial step in determining the number of principal components for both classification and regression involves evaluating how much each component contributes to the total variance in the data. Based on this analysis, a subset of components that explains the highest percentage of variance is typically selected. However, multiple valid combinations may exist, and the final choice is often made manually by the researcher. This study introduces a novel yet straightforward algorithm for the automatic selection of the number of principal components. By integrating ANOVA and bootstrapping with principal component analysis (PCA), the proposed method enables automatic component selection in classification tasks. The algorithm is evaluated using three publicly available datasets and applied with both decision tree and support vector machine (SVM) classifiers. Results indicate that this automated procedure not only eliminates researcher bias in selecting components but also improves classification accuracy. Unlike traditional methods, it selects a single optimal combination of principal components without manual intervention, offering a new and efficient approach to PCAbased model development.
Volume: 14
Issue: 3
Page: 1136-1145
Publish at: 2025-12-01
1 2 3
...
1914 1915
Show 16 of 1915

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration