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

27,404 Article Results

Challenges of educational leaders’ utilization of educational portal information systems

10.11591/ijere.v14i3.31984
Hamed Hilal Nasser Al Yahmadi , Yousuf Nasser Said Al Husaini
The study aims to determine the challenges that hinder the adoption of educational portal information systems by Omani educational leaders, in order to explore the manner through which their capabilities can be improved. Moreover, the study uses quantitative research through the questionnaire as the main research instrument. The research population consisted of all educational leaders of the educational portal information systems in the Sultanate of Oman. The research sample included 96 individuals from the study population, selected using a convenience sampling method. Moreover, the study findings concluded that the challenges hinder the adoption of educational portal information systems obtained a moderate response degree, whereas the requirements for developing educational portal information systems obtained a very high response degree. Moreover, there were no statistically significant differences in the challenges hinder the adoption of educational portal information systems and the requirements for their development attributed to the variable of gender, years of experience, technological competency, and job position. Lastly, the study recommends the necessity to encourage leaders to participate in workshops and to keep educational leaders continuously updated on the latest improvements of the educational portal is necessary.
Volume: 14
Issue: 3
Page: 1961-1971
Publish at: 2025-06-01

The impact of leader motives in students: a systematic review

10.11591/ijere.v14i3.31418
Anil DCosta , Joseph Chacko Chennattuserry , G. S. Prakasha
Leader motives elucidate the driving forces behind leadership behavior and decision-making, which are pivotal for understanding effective leadership dynamics across diverse contexts. In this context, the systematic literature review (SLR) analyzed leader motives among students, providing insights into the underlying drivers shaping leadership behaviors within educational environments. This paper aims to understand how leader motives impact student behavior, academic performance, and social dynamics within educational environments. Based on McClelland’s needs theory as a conceptual framework, the review examines students’ prevalence and manifestations of achievement, power, and affiliation motives. This study systematically reviewed 16 papers, scholarly databases, and pertinent literature published between 2007 and 2024. A preferred reporting items for systematic reviews and meta-analysis (PRISMA) method was used to report the items. The findings underscore the importance of nurturing leader motives in educational settings, which contribute to positive student outcomes and foster leadership development through the lens of need theory. This study contributes to understanding how leader motives can elevate leadership behaviors and outcomes, offering valuable insights for policymakers and academic leaders aiming to enhance educational quality.
Volume: 14
Issue: 3
Page: 2144-2153
Publish at: 2025-06-01

Perceptions of the generative AI-enabled cognitive offload instruction in English writing

10.11591/ijere.v14i3.33138
Hui Hong , Poonsri Vate-U-Lan , Chantana Viriyavejakul
This study examines the students’ perceptions of the generative artificial intelligence (AI)-enabled cognitive offload instruction and its effectiveness in improving their critical thinking skills in writing English essays. This qualitative research collects data from 120 students through focus group discussions and is analyzed by Word Clouds to generate a visual representation of the word frequencies. The findings reveal that generative AI-enabled cognitive offload instruction had: i) an impact on critical thinking and writing skills; ii) effective features of Skywork, ability to generate relevant prompts and provide constructive feedback; iii) use of Skywork in developing stronger arguments; iv) promoting critical examination of different perspectives; v) interactive nature and motivation; vi) enhanced analytical skills; vii) impact on essay structuring and organization; viii) feedback and revision process; and ix) transferability of critical thinking skills. This study concludes that the highest frequency was Skywork, ability, writing, feedback, evidence, skills, thinking, arguments, essays, and peers. Students recommend in-depth explanations for complex topics, advanced tutorials, regular updates, collaboration features, advanced modules, and personalized learning paces to enhance Skyworks’s integration into instruction.
Volume: 14
Issue: 3
Page: 1761-1769
Publish at: 2025-06-01

An improved internal and external resilience framework for new high school teachers

10.11591/ijere.v14i3.31186
Wan Mohd Agil Mat Yamin , Lim Hooi Lian
The concept of resilience gained widespread recognition in the teaching profession as some new high school teachers are confronted with various challenges and pressures, which cause some of them to leave the profession during the first four to five years of their employment. By considering the guidance new high school teachers need to survive and retain their profession, this qualitative study aimed to identify resilient strategies used by new high school teachers. This study focuses on semi-structured interviews with twelve new high school teachers. After performing a thematic analysis, this study found internal and external resilience, with five strategies new high school teachers use to overcome challenges and pressures (internal: professional, emotional, and motivational; external: social and spiritual). This study validates the applicability of Mansfield’s four-dimensional teacher resilience frameworks (professional, emotional, motivational, and social resilience) to the resistance of new high school teachers in Malaysia. This study also improved Mansfield’s framework through its findings by considering a new dimension, spiritual resilience. The Malaysian Ministry of Education, specifically through public universities that train future high school teachers, can use these resilient strategies to develop intervention programs that enhance their resilience, thereby fulfilling the objectives of the Malaysia Education Development Plan (MEDP) 2013–2025.
Volume: 14
Issue: 3
Page: 1608-1620
Publish at: 2025-06-01

Enhancing educational outcomes using AlAfnan taxonomy: integrating cognitive, affective, and psychomotor domains

10.11591/ijere.v14i3.33147
Mohammad Awad AlAfnan
Following the introduction of AlAfnan’s taxonomy of educational objectives, this study offers a framework for educational development encompassing cognitive, affective, and psychomotor domains essential for nurturing well-rounded learners. The cognitive domain emphasizes knowledge acquisition, critical thinking, ethical reasoning, practical application, creativity, and lifelong learning skills. It prepares students to analyze, synthesize, and evaluate information effectively, fostering intellectual depth and adaptability in navigating complex challenges. The affective domain focuses on emotional intelligence, creativity, resilience, collaboration, and visionary thinking. By cultivating these attributes, educators create a supportive environment that encourages self-awareness, empathy, and ethical decision-making. This domain prepares students to excel academically and contribute meaningfully to society, emphasizing holistic personal development alongside academic achievement. The psychomotor domain enhances sensory perception, cognitive-motor integration, feedback responsiveness, creative motor expression, precision, and leadership through physical action. It equips learners with practical skills and dexterity, enabling them to effectively apply theoretical knowledge in real-world contexts. This domain emphasizes hands-on learning experiences that promote mastery, innovation, and leadership in various fields. The study emphasizes that integrating AlAfnan’s taxonomy into educational practices requires strategic alignment of instructional methods and assessment approaches tailored to each domain’s objectives. Educators are encouraged to utilize inquiry-based learning, collaborative projects, experiential activities, and reflective practices to foster comprehensive skill development across all learning styles. This shall foster students’ intellectual curiosity, emotional resilience, and practical competence. This framework promotes a balanced educational approach that prepares learners to thrive in diverse professional settings and contribute actively to global challenges.
Volume: 14
Issue: 3
Page: 2419-2437
Publish at: 2025-06-01

Multiple-cutoff regression discontinuity designs in educational evaluation

10.11591/ijere.v14i3.31081
HyeonJin Yoon , Keith Zvoch , Keith Smolkowski , Ben Clarke
The regression discontinuity design (RDD) can be used for evaluating cut-score based educational interventions. The design enables researchers and evaluators to estimate the local causal effect of an intervention provided to those who are most at-risk. In certain educational evaluations, an RDD with multiple cutoffs can also be applied, where different cutoffs across classrooms or schools are used to assign students to the intervention condition. The availability of multiple cutoffs allows estimation of a pooled average treatment effect across cutoffs and/or individual estimates at each cutoff location, allowing for the possibility of heterogeneous treatment effects. The purpose of this paper is to demonstrate the design and analysis of the multiple-cutoff regression discontinuity (MCRD) design for the evaluation of need-based educational programs. Using data obtained from a kindergarten mathematics intervention for low achieving students, we illustrate the utility of the MCRD design for examining the average and potential variability of the regression discontinuity (RD) treatment effect. Implications for evaluation research and practice in education are discussed.
Volume: 14
Issue: 3
Page: 1597-1607
Publish at: 2025-06-01

Sign language recognition and classification using blended ensemble machine learning

10.11591/ijai.v14.i3.pp2035-2043
Akash Rajan Rai , Sujata Rajesh Kadu
An efficient sign language recognition system (SLR) is the most significant for hearing-impaired people for communication. The body movements and hand gestures are utilized to characterize the vocabulary in dynamic sign language. The SLR is a challenging problem because the computational model requires simultaneous spatial-temporal modelling for a number of sources. To overcome this problem, this research proposes the blended ensemble machine learning (ML) approaches for SLR. Initially, the Indian sign language (ISL) dataset is collected for evaluating the effectiveness of the model. Then, the pre-processing is done by using data augmentation and normalization techniques. Then, the pre-processed data is provided to the segmentation process which is done by using multi-threshold entropy function. Then, VGG-16 is used for the feature extraction process to extract the features and finally, classification is carried out using ensemble ML. An effectiveness of the proposed method is validated based on accuracy, precision, recall, and F1-score, wherein it achieves better results of 99.57%, 0.92%, 0.95%, and 0.99% as compared to the existing works like support vector machine (SVM) and convolutional neural network (CNN).
Volume: 14
Issue: 3
Page: 2035-2043
Publish at: 2025-06-01

Modeling sentiment analysis of Indonesian biodiversity policy Tweets using IndoBERTweet

10.11591/ijai.v14.i3.pp2389-2401
Mohammad Teduh Uliniansyah , Asril Jarin , Agung Santosa , Gunarso Gunarso
This study develops and evaluates a sentiment analysis model using IndoBERTweet to analyze Twitter data on Indonesia’s biodiversity policy. Twitter data focusing on topics such as food security, health, and environmental management were collected, with a representative subset of 13,435 tweets annotated from a larger dataset of 500,000 to ensure reliable sentiment labels through majority voting. IndoBERTweet was compared to seven traditional machine-learning classifiers using TF-IDF and BERT embeddings for feature extraction. Model performance was assessed using mean accuracy, mean F1 score, and statistical significance (p-values). Additionally, sentiment analysis included word attribution techniques with BERT embeddings, enhancing relevance, interpretability, and consistent attribution to deliver accurate insights. IndoBERTweet models consistently outperformed traditional methods in both accuracy and F1 score. While BERT embeddings boosted performance for conventional models, IndoBERTweet delivered superior results, with p-values below 0.05 confirming statistical significance. This approach demonstrates that the model’s outputs are explainable and align with human understanding. Findings underscore IndoBERTweet’s substantial impact on advancing sentiment analysis technology, showcasing its potential to drive innovation and elevate practices in the field.
Volume: 14
Issue: 3
Page: 2389-2401
Publish at: 2025-06-01

Content based image retrieval using visual geometric group19 with Jaccard similarity measure

10.11591/ijaas.v14.i2.pp430-438
Rajath Arakere Narayanaswamy , Vidyalakshmi Krishne Gowda
Content-based image retrieval (CBIR) is an important research area that focuses on emerging techniques for handling large image collections and enabling efficient retrieval. The main challenge of image retrieval is to extract relevant feature vectors for image description. Therefore, visual geometric group 19 (VGG19) with Jaccard is proposed in this research for CBIR. The VGG19 allows to capture of hierarchical features, and it is appropriate for texture and fine detail characteristics. It enables to production of robust and discriminative feature representations because of numerous convolutional layers. The Jaccard is utilized as a similarity measure among feature vectors by comparing the union and intersection of feature sets. It is helpful to manage sparse and higher-dimensional data that provides a fast and accurate image retrieval process. The Caltech 256 and Corel 1K datasets are considered and it is preprocessed by image resizing and normalization. The raw images are resized to ensure dataset similarity and normalized into the range of 0 and 1. The metrics such as recall, f-measure, and average precision are used to calculate the VGG19-Jaccard performance. The VGG19-Jaccard achieves average precision of 99.0 and 99.8% for Caltech 256 and Corel 1K datasets compared to the two-stage CBIR technique.
Volume: 14
Issue: 2
Page: 430-438
Publish at: 2025-06-01

Road pavement deformation using remote sensing technique

10.11591/ijaas.v14.i2.pp345-351
Kishan Patel , Rajesh Gujar
The road surface reflects the status of the city’s infrastructure. Road safety and driving comfort can be affected by the rough surface. To minimize road hazards, pavement conditions must be periodically inspected for damaged surfaces. A quick and efficient data collection can be provided by the radar images. For a large spatial coverage, radar image provides a non-destructive data collection technique for analyzing road conditions and classifying distress. The surface distress can be correlated by analyzing the images collected from high-resolution cameras and satellites. This article outlines the applicability of synthetic aperture radar (SAR) and interferometric synthetic aperture radar (InSAR) based images to manage and monitor pavement infrastructure. Therefore, the detection of deteriorating surfaces can be improved by analyzing the radar images timely. The results showed the deficiencies on the surface that can be used to mitigate bad pavement conditions and allow road users to use good road infrastructure with safety and comfort.
Volume: 14
Issue: 2
Page: 345-351
Publish at: 2025-06-01

Hybrid semantic model based on machine learning for sentiment classification of consumer reviews

10.11591/ijai.v14.i3.pp2001-2011
Palaniraj Rajidurai Parvathy , Nagarajan Mohankumar , Rajendran Shobiga , Gour Sundar Mitra Thakur , Mamatha Bandaru , Velusamy Sujatha , Shanmugam Sujatha
Digital information is regularly produced from a variety of sources, including social media and customer service reviews. For the purpose of increasing customer happiness, this written data must be processed to extract user comments. Consumers typically share comments and thoughts about consumable items, technological goods, and services supplied for payment in the modern period of consumerism with simple access to social networking globe. Each object has a plethora of remarks or thoughts that demand special attention due to their sentimental worth, especially in the written portions. The goal of the current project is to do sentiment prediction on the Amazon Electronics, Kindle, and Gift Card datasets. In order to predict sentiment and evaluate utilizing many executions evaluates admitting accuracy, recall, and F1-score, a hybrid soft voting ensemble method that combines lexical and ensemble methodologies is proposed in this study. In addition to calculating a subjectivity score and sentiment score, this study also suggests a non-interpretive sentiment class label that may be used to assess the sign of the evaluations applying suggested method for sentiment categorization. The effectiveness of our suggested ensemble model is examined using datasets from Amazon customer product reviews, and we found an improvement of 2-5% in accuracy compared to the current state-of-the-art ensemble method.
Volume: 14
Issue: 3
Page: 2001-2011
Publish at: 2025-06-01

Bridging technology and healthcare: user acceptance of a surgical site infection system

10.11591/ijaas.v14.i2.pp523-532
Afan Fatkhur Akhmad , Maria Ulfa
Surgical site infections (SSI) continue to be a problem for surgeons, and unfortunately, SSI information systems are underutilized. This study analyzed the user acceptance of the SSI information system based on the extended technology acceptance model (TAM2). A cross-sectional questionnaire-based study. The variables studied intention to use (IU), perceived ease of use (PEOU), demographic factors (FD), subjective norm (SN), Image (I), job relevance (JR), output quality (OQ), result demonstrability (RD), perceived usefulness (PU). Data were collected by filling out questionnaires and then analyzed using smart-partial least squares (PLS). In total, 61 nurses were included. Most respondents are aged 31-35 (26.23%), and most working periods are between 11-15 years (27.87%). There were significant positive effects on SN to PU (β=0.12; p 0.05). This study concluded that PEOU is the most influential variable in the IU the SSI information system.
Volume: 14
Issue: 2
Page: 523-532
Publish at: 2025-06-01

A review of open-source energy system modeling tools

10.11591/ijaas.v14.i2.pp469-480
Nguyen Binh Khanh , Phuong Le Ngo , Luong Ngoc Giap , Truong Nguyen Tuong An , Trung Bui Tien , Tran The Vinh , Le Van Nghia , Tran Trong Dat
Nowadays, the transition to open markets, the rapid growth of renewable energy sources like wind and solar, and the shift towards electrification in transportation and industry for decarbonization have increased the demand for advanced energy system models with detailed spatial and temporal data. This paper utilizes a comprehensive literature review and selects a representative set of open-source tools for evaluation. A comparative analysis of 17 open-source energy system modeling tools and their commercial alternatives was conducted. The paper analyzes many open-source aspects such as code commits, updates, programming languages, license details, citations, and energy system modeling features such as power flows (PFs), continuation PF, dynamic analysis, short-circuit analysis, contingency analysis, transportation model, optimal PF (OPF), multi-period OPF, unit commitment (UC), investment optimization, and graphic user interface. Based on the results, the paper suggests appropriate tools used for according power/energy system analysis objective: MATPOWER for power system analysis and Python for power system analysis (PyPSA) for energy system analysis.
Volume: 14
Issue: 2
Page: 469-480
Publish at: 2025-06-01

A gamified online learning environment with comprehensive assessments and software integration

10.11591/ijaas.v14.i2.pp416-429
Swati Shilaskar , Shripad Bhatlawande , Rupali Deshpande , Shivam Shinde , Jyoti Madake , Anjali Solanke
The National Achievement Survey (NAS), conducted by the Ministry of Education, India, highlighted a concerning decline in mathematics proficiency among students in Maharashtra as they advance through grades. This trend is further aggravated by the limited availability of online resources in Marathi, hindering their learning progress. To address this, a pilot study was proposed to develop a specialized online platform tailored for Marathi medium students, integrating gamification and artificial intelligence (AI)-driven feedback to enhance engagement and comprehension. The pilot project, conducted at a Marathi medium school with approval from the principal, focused on polynomial division tests for 8th-grade students over four days. Results revealed that despite the easy level test's higher difficulty, students scored higher on the medium level test, possibly due to an adjustment period to the online platform. Notably, some students performed better on the hard-level test, indicating the platform's potential to improve performance. While promising, the study's limitations, including a small sample size, highlight the need for further research with a larger cohort and the integration of automatic suggestions for concept-specific games and assessments in future iterations to optimize the platform's effectiveness.
Volume: 14
Issue: 2
Page: 416-429
Publish at: 2025-06-01

A review on ischemic heart disease prediction frameworks using machine learning

10.11591/ijaas.v14.i2.pp361-372
Kabo Clifford Bhende , Tshiamo Sigwele , Chandapiwa Mokgethi , Aone Maenge , Venu Madhav Kuthadi
Ischemic heart disease (IHD) is a leading cause of mortality worldwide, calling for advanced predictive models for timely intervention. Current literature reviews on machine learning (ML)-based IHD prediction frameworks often focus on predictive accuracy but lack depth in areas like dataset diversity, model interpretability, and privacy considerations. Existing IHD prediction frameworks face limitations, including reliance on small, homogenous datasets, limited critical analysis, and issues with model transparency, reducing their clinical utility. This review addresses these gaps through a systematic, comparative analysis of popular ML models, such as random forest (RF) and support vector machines (SVM), noting their strengths and limitations. Key contributions include a qualitative examination of prevalent tools, datasets, and evaluation metrics, identification of gaps in dataset diversity and interpretability; and recommendations for improving model transparency and data privacy. Major findings reveal a trend toward ensemble models for accuracy but highlight the need for explainable artificial intelligence (AI) to support clinical decisions. Future directions include using federated learning to enhance data privacy, integrating unstructured data for comprehensive prediction, and advancing explainable AI to build trust among healthcare providers. By addressing these areas, this review aims to guide future research toward developing robust, transparent ML frameworks that can be more effectively deployed in clinical settings.
Volume: 14
Issue: 2
Page: 361-372
Publish at: 2025-06-01
1 2 3
...
1826 1827
Show 22 of 1827

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