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25,002 Article Results

Design of an educational platform based on an innovative model of research in secondary school students

10.11591/ijece.v15i2.pp2000-2021
Laberiano Andrade-Arenas , Janet Ivonne Corzo-Zavaleta , Ada Alvarado-Paucar , Cecilia Baldeón-Vilca , Luis Segovia-Fernández , Nelly Reyes-Vilca , Giovana López-Tolentino , Verónica Villarreal-Chumbes , Jhon Canturín-Narrea
The development of research skills worldwide is more emphasized in postgraduate programs; however, the training of these skills should be carried out from basic education; that is, from elementary school. In this sense, this research aims to formulate a proposal to develop research skills in secondary school students through an innovative model. The methodology was carried out through student surveys and interviews with teachers and authorities. The ATLAS.ti 22 software was used for network analysis and SPSS 23 for statistical analysis. The results obtained in the survey show that the dimensions of reading comprehension, writing and argumentation, academic writing, and scientific writing are within the acceptable average. However, in the interviews, some students show difficulties in scientific writing, but they show a critical position in their arguments. It is concluded that the authorities should incorporate the proposed model of research skills in the curricular plan, adding it to their annual plan; for this purpose, teachers should be trained to transmit it to their students. In addition, an innovative model is proposed during the 5 years of high school studies to develop students' research skills. The beneficiaries of the proposal are the entire educational community and therefore the country.
Volume: 15
Issue: 2
Page: 2000-2021
Publish at: 2025-04-01

Enhanced embedded system for various synthetic electrocardiogram generation using McSharry’s dynamic equation

10.11591/ijece.v15i2.pp1620-1631
Nada Fitrieyatul Hikmah , Rachmad Setiawan , Nafila Cahya Andanis , Aldo Pranata
n electrocardiogram (ECG) is a signal that describes the heart’s electrical activity. Signal processing techniques are necessary to extract meaningful information from ECG signals. Researchers often use large databases like the PhysioNet database to evaluate the performance of algorithms. However, these databases have limitations concerning the lack of temporal or morphological variations. This study addresses this limitation by introducing a synthetic ECG capable of producing both normal 12-lead ECG signals and abnormal ECG signals and implementing it into the microcontroller. The primary contribution involves developing a synthetic ECG model using McSharry's dynamic equation model and implementing it using Mikromedia 5 for STM32F4 Capacitive as a microcontroller. This model enables users to set the desired heart rate and accurately replicates ECG waveforms using parameters 𝑎𝑖, 𝑏𝑖, and 𝜃𝑖, each determines the peak’s magnitude, the peak’s time duration, and the angular velocity of the trajectory. The synthetic ECG was evaluated qualitatively and quantitatively, demonstrating waveform similarity to the ECG signals. This study implies that the synthetic ECG model serves as a valuable tool for researchers and practitioners in electrocardiography. It enables the generation of normal and abnormal ECG signals, aiding in algorithm development and potentially enhancing the understanding and diagnosis of heart conditions.
Volume: 15
Issue: 2
Page: 1620-1631
Publish at: 2025-04-01

4HAN: hypergraph-based hierarchical attention network for fake news prediction

10.11591/ijece.v15i2.pp2202-2210
Alpana A. Borse , Gajanan K. Kharate , Namrata G. Kharate
Fake News presents significant threats to both society and individuals, highlighting the urgent need for improved news authenticity verification. To deal with this challenge, we provide a novel strategy called the 4-level hierarchical attention network (4HAN), designed to enhance fake news detection through an advanced integration of hypergraph convolution and attention neural network mechanisms. The 4HAN model operates across four hierarchical levels: paragraphs, sentences, words, and contextual information (metadata). At the highest level, the model employs hypergraph-based attention and convolution neural networks to create a contextual information vector, utilizing a SoftMax activation function. This vector is then combined with a news content vector generated through word and sentence-level attention mechanisms. This architecture enables the 4HAN model to effectively prioritize the relevance of specific words and contextual information, thereby improving the overall representation and accuracy of news content. We evaluate the 4HAN model using the LIAR dataset to demonstrate its efficacy in enhancing Fake News prediction accuracy. Comparative analysis shows that the 4HAN model outperforms several of cutting-edge techniques, like recurrent neural networks (RNN), ensemble techniques, and attention mechanisms techniques. Our results indicate 4HAN model accomplishes a notable accuracy of 96%, showcasing its potential for significantly advancing fake news prediction.
Volume: 15
Issue: 2
Page: 2202-2210
Publish at: 2025-04-01

Stress detection through wearable sensors: a convolutional neural network-based approach using heart rate and step data

10.11591/ijece.v15i2.pp1880-1888
Rifki Wijaya , Gamma Kosala
With the current technological advancements, particularly in sensing technologies, monitoring various health aspects, including heart rate, has become feasible. The problem addressed in this study is the need for effective stress detection methods to mitigate the significant consequences of high-intensity or long-term stress, which impacts safety and disrupts normal routines. We propose a stress detection system developed based on the convolutional neural network (CNN) method to address this. The study involves university students aged 20–22, focusing on mental stress. The dataset encompasses parameters such as heart rate, footsteps, and resting heart rate recorded through a smartwatch with 149,797-row data. Our results indicate that the CNN model achieves an 84.5% accuracy, 80.9% precision, 79.8% recall, and an 80.4% F1-score, confirming its efficacy in stress classification. The confusion matrix further validates the model’s accuracy, particularly for classes 1 and 2. This research contributes significantly to the development of an effective and practical stress detection method, holding promise for enhancing well-being and preventing stress-related health issues.
Volume: 15
Issue: 2
Page: 1880-1888
Publish at: 2025-04-01

Predicting stock prices using ensemble learning techniques

10.11591/ijece.v15i2.pp1783-1792
Salma Elsayed , Ahmad Salah , Ibrahim Elhenawy , Marwa Abdellah
Stock price prediction has grown in importance due to its role in determining the future worth of business shares. There are several approaches for stock price prediction that can be classified into machine learning, deep learning, and ensemble learning methods. To predict stock prices, we proposed collecting a dataset for different well-known stocks, e.g., Microsoft. The utilized datasets consist of two parts; the first part contains a set of tweets for the stocks under investigation in this study which were collected from the X social media platform and the other part contains the stock prices. Sentimental features of the tweets were extracted and merged with the stock price changes. Then, we framed the problem as a regression task. we aim to analyze the performance gap between ensemble learning and other machine learning (ML) and deep learning (DL) models for predicting stock prices based on tweets. In this context, different ensemble learning models were proposed to predict the price change of each stock. Besides, several machine learning and deep learning models were used for comparison purposes. Several evaluation metrics were utilized to evaluate the performance of the proposed models. The experimental results proved that the stacking regressor model outperformed the other models.
Volume: 15
Issue: 2
Page: 1783-1792
Publish at: 2025-04-01

Enhancing curriculum development: a comprehensive framework for undergraduate competencies

10.11591/ijere.v14i2.30029
Ma Teresa Silos Alvarez , Sheila Y. Guinat
In the context of significant changes in higher education and the job market, there has been extensive discussion on what qualifies graduate competency and what shapes graduates’ labor market outcomes. Each university’s vision is to produce highly competitive and educated graduates with high competence and contribute to the country’s development. Graduate employability is a key issue for higher education. Ensuring their competency is vital in forming an educated graduate the industry is looking for. Their competency is honed based on the activities and curriculum of the program as embedded in the circular memorandum order (CMO) of each program. A descriptive research design was used and a questionnaire on structured institutionalized tracer instrument and CMO 17 s.2017 was adopted. Uses statistical treatment such as mean, frequency, percentages and t-tests. This study focuses to assess and evaluate the competency of our graduates in response to the needs of the industry and for curriculum enhancement. The results reveal that the bachelor of science in business administration (BSBA) graduate’s competency based on all the identified parameters was deemed "very effective" and useful in their respective workplace. Though the results highlighted research and extension as "very effective", their importance to employment shows that they are highly significant corresponding to the present trend. Despite all the training and exposure, the college provides them, still need improvement and commends to enhance the curriculum, improve instruction delivery, and upgrade graduate competencies.
Volume: 14
Issue: 2
Page: 1518-1527
Publish at: 2025-04-01

Evaluating the geometric thinking levels of generation Z pre-service mathematics teachers in Indonesia

10.11591/ijere.v14i2.32365
Muhammad Ammar Naufal , Hisyam Ihsan , Auliaul Fitrah Samsuddin , Zaid Zainal , Nur Wahidin Ashari , Muhammad Nasiru Hassan
Geometric thinking abilities remain crucial in mathematics education, particularly for pre-service teachers who will shape future generations’ understanding of geometry. This study evaluates and compares the geometric thinking levels of generation Z pre-service mathematics teachers at Universitas Negeri Makassar (UNM) and Universitas Mulawarman (UNMUL) using the Van Hiele model. A quantitative comparative design was employed, involving 233 UNM and 227 UNMUL students selected through purposive sampling. The geometric thinking test (GTT) assessed students across five levels: visualization, analysis, informal deduction, formal deduction, and rigor. Results indicated that UNM students excelled in analysis and informal deduction, whereas UNMUL students displayed a broader distribution across all levels, with notable frequencies at visualization and formal deduction levels. A statistically significant difference in overall geometric thinking scores were identified, using the Mann-Whitney U test, with UNM students scoring higher. These findings emphasize the importance of adopting student-centered instructional strategies aligned with the Van Hiele model to enhance geometric thinking. Incorporating hands-on activities and technology is recommended to better prepare pre-service teachers for effective geometric instruction. The study provides insights for educators and policymakers to improve mathematics education by fostering higher geometric thinking levels in future teachers.
Volume: 14
Issue: 2
Page: 918-925
Publish at: 2025-04-01

Challenges, opportunities, and effects of alternative assessment approaches in teaching practices: a systematic literature review

10.11591/ijere.v14i2.32283
Najdah Sanusi , Hafizhah Zulkifli , Mohd Isa Hamzah
Alternative assessment, encompassing methods such as portfolios, project-based evaluations, and peer assessments, aligns with 21st-century student-centered learning goals by holistically and authentically evaluating students’ progress. This research applies the systematic review literature method with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol. The study analyzed 47 peer-reviewed articles published between 2019 and 2023, sourced from Web of Science, Scopus, and Education Resource Information Centre (ERIC) databases. The thematic analysis revealed three main themes: i) challenges in implementing alternative assessments, including teacher readiness and assessment skills; ii) opportunities for enhancing creativity and variety in teaching approaches; and iii) positive effects on teachers’ motivation and understanding of student learning. The findings highlight the need for targeted professional development to support teachers in effectively implementing alternative assessments. This review contributes to the growing body of knowledge on innovative assessment practices and their impact on teaching and learning in contemporary educational settings.
Volume: 14
Issue: 2
Page: 1105-1113
Publish at: 2025-04-01

Kafka-machine learning based storage benchmark kit for estimation of large file storage performance

10.11591/ijece.v15i2.pp1990-1999
Sanjay Kumar Naazre Vittal Rao , Anitha Chikkanayakanahalli Lokesh Kumar , Subhash Kamble
Efficient storage and maintenance of big data is important with respect to assuring accessibility and cost-friendliness to improve risk management and achieve an effective comprehension of the user requirements. Managing the extensive data volumes and optimizing storage performance poses a significant challenge. To address this challenge, this research proposes the Kafka-machine learning (ML) based storage benchmark kit (SBK) designed to evaluate the performance of the file storage system. The proposed method employs Kafka-ML and a drill-down feature to optimize storage performance and enhance throughput. Kafka-ML-based SBK has the capability to optimize storage efficiency and system performance through space requirements and enhance data handling. The drill-down search feature precisely contributes through reducing disk space usage, enabling faster data retrieval and more efficient real-time processing within the Kafka-ML framework. The SBK aims to provide transparency and ease of utilization for benchmarking purposes. The proposed method attains maximum throughput and minimum latency of 20 MBs and 70 ms, respectively on the number of data bytes is 10, as opposed to the existing method SBK Kafka.
Volume: 15
Issue: 2
Page: 1990-1999
Publish at: 2025-04-01

The effect of convenience and self-efficacy on the satisfaction of learning management system usage

10.11591/ijere.v14i2.32065
Zulherman Zulherman , Abu Bakar Ahmad Mansor , Christoph Kulgemeyer
Universities widely use the learning management system (LMS) technology due to its flexibility and ease of use for lecturers when managing online learning with the LMS. The primary determinant of success is the admittance of students utilizing this technology based on the LMS. However, institutions have challenges when utilizing LMS systems. The study aims to evaluate the factors that impact student satisfaction (SS) when using the LMS. The study methodology employs the Delone McLean model technique, incorporating the elements of convenience (Co) and self-efficacy (SE) into the survey. Data was gathered from a sample of 178 undergraduate students. The data analysis conducted using structural equation modeling (SEM) partial least squares (PLS) entailed the testing of six hypotheses. The results found that only three hypotheses were supported: information quality (IQ) and system quality (SQ) had a positive impact on SS. Student satisfaction also harmed the use of LMS (LU). This research contributes to the knowledge that internal and external factors of the LMS system also play an important role in the satisfaction of LMS usage.
Volume: 14
Issue: 2
Page: 910-917
Publish at: 2025-04-01

Telecommunication project methods as an effective tool in modern education

10.11591/ijere.v14i2.31292
Marina Lebedeva , Vladimir Beketov , Marina Taranova
This article is devoted to the study of the problem of insufficient involvement of philology students in the educational process, which negatively affects their academic performance. To solve this problem, a telecommunication project was developed and implemented to improve learning efficiency and student engagement. The research was based on a pilot project with one group, and survey methods were also utilized to measure the level of student engagement in the learning process. Statistical analysis results indicate a noticeable improvement in students’ academic performance after the implementation of the innovative telecommunication course, with the average score increasing from 75.5 to 80.1, supported by a statistically significant level. Survey data on student engagement demonstrate a high level of positive attitude towards the use of technology in the educational process. The overall trend indicates a positive attitude towards new technologies in education. The practical significance of this article lies in highlighting telecommunication project methods as an effective tool in modern education. Applying the results of this research in educational practice can contribute to the development and implementation of new educational programs based on telecommunication projects.
Volume: 14
Issue: 2
Page: 1331-1339
Publish at: 2025-04-01

Adaptation and validation of academic resilience scale in Bengali

10.11591/ijere.v14i2.30113
Riya Ahmed , Bijoy Krishna Panda , Muktipada Sinha
The purpose of the current study was to adapt and validate the Academic Resilience Scale (ARS-30) in the context of West Bengal and other Bengali-speaking regions. The research included a total of 628 participants. The data analysis occurred in three stages. Initially, confirmatory factor analysis was employed to assess the factorial validity of the Bengali version of ARS-30 scale, revealing a poor fit for the original three-factor model. Subsequently, further exploratory factor analysis (EFA) suggested a more suitable two-factor structure. In the third stage, this newly derived two-factor structure was validated through confirmatory factor analysis (CFA) with an independent sample. The adapted scale, renamed ARS-19, measures two factors related to academic resilience: negative affect and emotional response (6 items) and positive adaptation (13 items). Results from validity and reliability analyses indicated that the ARS-19 is a valid and reliable tool for assessing academic resilience in the aforementioned context. This study contributes to the literature by proposing a valid and reliable academic resilience measurement for West Bengal as well as other Bengali-speaking regions, facilitating practitioners in assessing academic resilience among higher education students.
Volume: 14
Issue: 2
Page: 947-960
Publish at: 2025-04-01

Adult education in Greek municipalities during COVID-19 pandemic

10.11591/ijere.v14i2.29678
Athanasia Ntafou , Noelia N. Jiménez-Fanjul , David Gutiérrez-Rubio
State and local governments’ primary purpose is to provide services to their populations by shaping their social and economic life. Adult education is a good example of a public service that extends deeply into people’s everyday lives. It helps adults increase their social roles as employees, parents, and retiree, gain more fulfilment in their personal lives, and solve personal and community problems. The main research purpose of this paper is to investigate and describe adult education in Greek Municipalities during the COVID-19 pandemic, specifically in the Municipality of Piraeus. For this purpose, the research was conducted using the focus group research method. Two groups participated, the first consisting of four employees of the Center for Lifelong Learning of the Municipality of Piraeus and the second group of four adult learners representing the four courses held. The results of the survey show that the unprecedented situation of COVID-19, particularly affected adult education which had stopped for a while and then many courses were discontinued. The imperative to strengthen digital skills, the uncertainty of continuing education and the change of education as a result of the changing world were also identified.
Volume: 14
Issue: 2
Page: 1302-1309
Publish at: 2025-04-01

Impact of technology integration on students’ sense of belonging and well-being: a systematic review

10.11591/ijere.v14i2.30938
Novel Lena Folabit , Loyiso C. Jita , Thuthukile Jita
As an important tool, technology supports and enhances students’ educational experiences by fostering inclusive learning communities, bridging cultural gaps, and accommodating diverse learning styles. However, limited studies have demonstrated the impact of technology integration on students’ sense of belonging and well-being in the context of South African higher education. This study aimed to address this gap through a systematic literature review. Published scholarly peer-reviewed articles were used to examine the impact of technology on students’ sense of belonging and well-being. The technology acceptance model (TAM) and self-determination theory (SDT) were used to theorize the findings. The findings reveal that while technology provides self-directed learning, it also exacerbates inequalities and digital stress. In addition, factors affecting students’ sense of belonging and well-being include the digital divide, isolation-related stress, and psychological needs due to a lack of technology preparedness, compounded by socio-economic disparities and insufficient digital skills and technical support. It is recommended that global financial, technical, and intellectual stakeholders collaborate to ensure equal access to digital resources in education. These strategies should focus on supporting user-friendly initiatives that bridge the digital gap. The study limitations include reliance on existing literature and lack of direct student feedback.
Volume: 14
Issue: 2
Page: 1075-1084
Publish at: 2025-04-01

Factors affecting pre-service teachers to adopt augmented reality in science learning

10.11591/ijere.v14i2.30359
Iqbal Ainur Rizki , Nadi Suprapto , Denissa Putri Awwalina , Wilujeng Trismaya Wanti , Nadya Mazayu Nur Sabrina
The advancement of augmented reality (AR) technology and its application in education presents an opportunity for pre-service teachers to incorporate it into the learning process, particularly in science subjects with abstract and microscopic materials. However, the adoption of AR technology among pre-service teachers remains suboptimal. Therefore, this study aims to analyze the factors influencing pre-service teachers’ adoption of AR in science learning. By employing partial least squares structural equation modeling, we gathered 211 responses through a questionnaire. The developed model has met the criteria of validity and reliability. The study’s findings reveal that perceived control and learning content significantly influence behavioral intention, while visual attraction and knowledge-ability do not. Clearly, their focus is on pedagogically implementing AR technology rather than visually developing it. Thus, it is recommended to provide training for pre-service teachers to apply AR science because many of them need an understanding of integrating this technology as a science learning media. This research implies offering insightful analysis and practical suggestions for the successful integration of AR technology into science learning, especially by addressing the variables affecting its uptake.
Volume: 14
Issue: 2
Page: 862-870
Publish at: 2025-04-01
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