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27,404 Article Results

Gamification with self-determination theory to foster intercultural communicative competence and intrinsic motivation

10.11591/ijere.v14i3.29858
Su Min , Noor Azean Atan , Akhmad Habibi
In globalization, possessing intercultural communicative competence (ICC) is essential for individuals’ success. However, students face motivational barriers in online intercultural learning. Thus, this study aims to explore the effectiveness of integrating gamification with self-determination theory (SDT) to enhance the intrinsic motivation, ultimately aiding in the development of intercultural communication competence among Chinese vocational college students enrolled in online English intercultural learning. Employing a mixed-methods approach involving pre-post questionnaires and interviews, the study engaged 38 vocational college students from the automobile and rail transit faculty in a four-week online English intercultural learning module enriched with gamified elements such as points, badges, leaderboards, levels, and quests. The findings indicate that gamified learning effectively fulfills students’ needs for competence and autonomy, partially addressing their needs for relatedness and consequently fostering an upsurge in intrinsic motivation. Additionally, improvements were observed in knowledge, attitude, and skill, with marginal changes noted in awareness. It is concluded that gamified learning approaches based on SDT can positively contribute to the development of intrinsic motivation and intercultural communication competence. These findings hold practical implications for educational institutions and researchers to cultivate intrinsic motivation and ICC through online gamified learning.
Volume: 14
Issue: 3
Page: 1985-1994
Publish at: 2025-06-01

Development and validation of a cooperating teacher mentoring scale for student teachers

10.11591/ijere.v14i3.31565
Leemarc C. Alia , Ehlrich Ray J. Magday , Daisy R. Palompon
Teaching internship is a crucial component of teacher education to prepare student teachers for their future careers in education. This study developed and validated an instrument to measure and evaluate the performance of cooperating teachers in mentoring student teachers. Items capturing the concept of teacher mentoring were developed through literature review, interviews, and focus group discussions. The 110-item 5-point Likert scale was given to 265 randomly selected student teachers from higher education institutions in the Philippines. Validity and reliability of the cooperating teacher mentoring scale (CTMS) were tested using exploratory factor analysis (EFA) and reliability analyses. Moreover, EFA showed three-factor structure of the instrument regarding the CTMS. The study reported the average variance extracted (AVE), composite reliability, and Cronbach alpha coefficients. These findings confirmed that the extracted constructs possess convergent validity and meet the necessary requirements. The item remained in the factor loadings of less than 0.50 (instructional support and professional development: 20 items; supportive teaching and mentorship: 15 items; and effective mentoring and coaching: 15 items). This study has confirmed three-factor structure of the CTMS. Researchers, educators, administrators, and student teachers can use the CTMS to evaluate cooperating teachers’ mentoring skills and provide feedback on areas that need improvement.
Volume: 14
Issue: 3
Page: 2381-2388
Publish at: 2025-06-01

In the zone or out of bounds? How sports and physical activity anxiety affects life satisfaction among students

10.11591/ijere.v14i3.33530
Marlon A. Mancera , Eduard S. Sumera , Jr., Ruben L. Tagare , Gilbert E. Lopez , Irish M. Orgeta , Yashier T. Haji Kasan , Harold Deo Cristobal , Armand G. Aton , Gauvin Adlaon
This study aims to explore the relationship between sports and physical anxiety and life satisfaction among college students in a leading Philippine state university. Employing a quantitative research design, specifically descriptive correlation, data were collected from 2,043 respondents using simple random sampling. The research utilized the physical activity and sport anxiety scale and the life satisfaction index to measure the respective constructs, with analyses conducted using Spearman’s rho correlation coefficient to assess relationships between variables. Results indicated a significant relationship between sports and physical anxiety and life satisfaction, revealing that higher levels of anxiety corresponded to lower life satisfaction. These findings highlight the importance of addressing sports and physical anxiety to improve overall well-being. Implications suggest that institutions should implement mental health and wellness initiatives aimed at reducing anxiety and promoting supportive environments in physical education settings. By fostering a culture that prioritizes psychological well-being alongside physical engagement, institutions can enhance students’ life satisfaction and overall quality of life.
Volume: 14
Issue: 3
Page: 1844-1855
Publish at: 2025-06-01

General trends on the impacts of evidence-based university accreditation on quality assurance enhancement

10.11591/ijere.v14i3.31271
Nurali Kairanbayev , David Arulraj David
Traditional accreditation process although has relevant impacts on quality assurance in higher education. Research and practices have shown the value of emerging evidence-based university accreditation. The study therefore aims to understand the impacts of evidence-based university accreditation on quality assurance enhancement. The research relied on literature review and document analysis as suitable methods. The study’s results demonstrated that the final decision for academic accreditation should be based on evidence that all stakeholders took part in quality assurance, namely staff and students. This study also explores the university accreditation practices in the United Kingdom (UK), United Arab Emirates (UAE), and Kazakhstan. The analysis presented here allows us to compare and discuss the practices of three different quality assurance practices. The three cases Quality Assurance Agency for higher education (QAA), Commission for Academic Accreditation (CAA), and Independent Kazakh Agency for Quality Assurance in Education (IQAA) indicate relevant use of evidence-based approaches to university accreditations that support quality assurance enhancement, given the explicit approaches grounded in data and evidence. The future of evidence-based approach will be furthered with the support of technology and sophisticated tools that will support explicit policies and practices. This research is expected to benefit researchers, policy makers and practitioners in quality assurance.
Volume: 14
Issue: 3
Page: 1939-1948
Publish at: 2025-06-01

The potential impact of generative AI on the future of higher education: a game-changer or a danger to academic integrity

10.11591/ijere.v14i3.32148
Pritam Kumar , Amarjeet Singh Mastana , Chainarong Rungruengarporn , Donyawan Chantokul
Artificial intelligence (AI) has the potential to improve education by substantially modifying knowledge acquisition. While the research on AI’s incorporation into higher education is growing, significant gaps exist in understanding its responsibilities, potential, implications for ethics, and privacy problems in educational settings. This study investigates AI’s transformative impact on higher education using a total of four essential objectives: the ever-growing capabilities of AI within customized instruction, the prospective use of smart tutoring platforms, AI-driven review and input procedures in learning evaluation, and the ethical and privacy issues inherent in these technologies. A systematic review of the literature (SLR) was carried out to answer research questions established utilizing population, intervention, comparison, outcome, and context (PICOC) criteria, resulting in a structured analysis of pertinent articles. To conduct a thorough literature search, the Publish or Perish (version 8) application and an API key were used to systematically access the Scopus database. Initial keyword searches yielded 567 articles, which were reduced to 29 following predetermined relevant screening, restricted access sorting, repetition removal, and content validation. The findings show that AI technologies are increasing personalized education by adapting instructional content to individual needs while also improving decision-making, resources deployment, and administrative duties. However, the integration of AI raises issues such as data privacy, potential redundancies of human educators, and ethical obstacles. These findings highlight AI’s immense potential for higher education, underlining the importance of tackling these problems regarding responsible and inclusive integration, furthering future research, and developing processes for responsible AI use in educational environment.
Volume: 14
Issue: 3
Page: 1731-1742
Publish at: 2025-06-01

Modeling English teachers’ intention to use ICT: technology acceptance and TPACK

10.11591/ijere.v14i3.30444
Li Cao , Mohamad Sattar Rasul , Marlissa Omar , Hutkemri Zulnaidi
Teachers’ acceptance of technology in the teaching setting is significantly influenced by their behavioral intention to utilize information and communication technology (ICT). A considerable amount of study has been done on the use of ICT in teaching English as a foreign language (EFL). Nevertheless, there exists a significant lack of deep studies among EFL teachers in Chinese vocational colleges. Drawing on the technology acceptance model (TAM) and technological pedagogical content knowledge (TPACK) theoretical frameworks, this current study aimed to ascertain whether EFL teachers’ TPACK levels could predict their intention to adopt ICT. A quantitative study was conducted with the participation of 440 EFL instructors from vocational schools in Shandong Province. The seven components met the scale’s validity and reliability requirements and the partial least squares (PLS) approach was utilized to describe the structural model and examine the relationships among significant components. The findings revealed that EFL teachers’ perceived usefulness (PU), perceived ease of use (PEU), and attitudes towards use (ATCU) significantly impacted their behavioral intention to use (BIU) ICT. Moreover, the TPACK framework exerted a substantial influence on their acceptance of ICT. The study’s findings may provide insights and resources for subsequent theoretical research and teaching approaches centered on enhancing the integration of technology in EFL education.
Volume: 14
Issue: 3
Page: 2314-2326
Publish at: 2025-06-01

Deep learning approach for monkeypox virus prediction: leveraging DensetNet-121 and image data

10.11591/ijaas.v14.i2.pp439-453
Kishor Kumar Reddy Chinthala , Vijaya Sindhoori Kaza , Pilly Ashritha , Mohammed Shuaib , Shadab Alam , Mohammad Fakhreldin
The Mpox virus, sometimes referred to as monkeypox, causes flu-like symptoms and rashes. The variola virus, which causes smallpox, is linked to the virus that causes monkeypox. Smallpox symptoms are more severe than those of Mpox, and the illness is rarely deadly. There is no connection between Mpox and chickenpox. The variola virus of smallpox and the vaccinia virus being used in the smallpox vaccine both belong to the Orthopoxvirus genus, which also includes the uncommon viral disease known as monkeypox. This study aims to increase the effectiveness of monkeypox virus (MPV) identification by utilizing global historical records. This study examines several approaches and determines which produces the best results for the input data. Performance metrics have been used to compare the efficiency to current models. The underlying patterns and correlations in the data are then taught to Dense-Net-121 through the use of the training set. The remarkable results are as follows: accuracy at 96.12%, precision at 93.2%, recall at 90%, F1-score at 91%, the area under the curve-receiver operating characteristic (AUC-ROC) at 94.5%, and specificity at 94%, outperforming the existing methods.
Volume: 14
Issue: 2
Page: 439-453
Publish at: 2025-06-01

Impact of natural-white and red-blue light-emitting diode lighting on hydroponic basil growth and energy efficiency

10.11591/ijaas.v14.i2.pp406-415
Chaiyant Boonmee , Warunee Srisongkram , Wipada Wongsuriya , Patcharanan Sritanauthaikorn , Paiboon Kiatsookkanatorn , Napat Watjanatepin
Advanced phosphor-converted white light-emitting diodes (pc-WLEDs) have been developed to mimic the natural sunlight spectrum, potentially enhancing plant growth compared to traditional red-blue (R-B) LEDs. This study aimed to compare the effects of natural-white pc-WLED (nsW-pcLED) and conventional R-B LED (R:B 3.24) on the growth, yield, and energy efficiency of hydroponically grown sweet basil. It was cultivated in a deep-water culture system under identical conditions with a photosynthetic photon flux density (PPFD) of 200±10 µmol·m⁻²·s⁻¹ and a 16/8 light/dark photoperiod over 28 days. Key growth parameters, including plant height, stem diameter, leaf number, and plant fresh weight (PFW), were measured, while energy consumption was recorded to assess efficiency. Results indicated that nsW-pcLED significantly enhanced growth, with plants achieving an average height of 44.30±1.51 cm, stem diameter of 6.68±0.21 mm, and a PFW of 34.20±6.12 g, compared to 35.88±4.05 cm, 4.66±0.88 mm, and 23.02±5.26 g under R-B LED (p <0.05), respectively. The nsW-pcLED treatment produced an average net growth of 1,221 g·m⁻² versus 536.43 g·m⁻² for R-B LED and delivered 33.05 g·m⁻²·kW·h⁻¹ compared to 11.17 g·m⁻²·kW·h⁻¹, while consuming 23% less energy. These findings highlight nsW-pcLED’s superior performance for indoor hydroponic cultivation. Future studies should explore its application in large-scale systems and across diverse crop species.
Volume: 14
Issue: 2
Page: 406-415
Publish at: 2025-06-01

Advanced classification techniques for weed and crop species recognition using machine learning algorithms

10.11591/ijaas.v14.i2.pp300-309
Sathya Rajendran , Thirunavukkarasu KS
This study proposes an intelligent machine learning framework integrating image analysis and environmental data for precision weed management. The framework leverages efficient feature extraction techniques combined with supervised machine learning algorithms to accurately classify multiple species. Features such as color, texture, and shape characteristics are utilized for model training, enabling high-precision classification while maintaining low computational complexity. The experimental results demonstrate the robustness of the approach, achieving an average classification accuracy of 94.3% across ten weed and crop species in diverse agricultural environments. The system also achieved a 90% reduction in herbicide application compared to traditional methods, showcasing its potential for sustainable farming. Real-time testing confirmed the framework’s efficiency, processing images in under 1.5 seconds per frame, making it suitable for deployment in drones and autonomous farming equipment. These results underscore the practical and scalable nature of the proposed system in automating weed management and advancing sustainable agricultural practices.
Volume: 14
Issue: 2
Page: 300-309
Publish at: 2025-06-01

Contraction control factor-based gorilla troop optimizer for features in intrusion detection systems

10.11591/ijaas.v14.i2.pp373-383
Shalini Sharma , Supriya Khaitan , Gayatri Hegde , Divya Rohatgi , Nusrat Parveen Mohammad Rafique , Suhas Janardan Lawand
Internet of things (IoT) has evolved into a large-scale network due to the increasing number of connected devices and massive amount of data they generate. IoT networks produce massive amounts of heterogeneous data from various devices, making it difficult to identify relevant features for intrusion detection. Hence, this research proposes the contraction control factor-based gorilla troop optimizer (CCF-GTO) for feature selection and multiple parametric exponential linear units based long short-term memory (MPELU-LSTM) approach for classification of intrusion detection system (IDS) in IoT. CCF-GTO. It uses adjustable parameters to prioritize relevant information while eliminating unnecessary features, making the model more efficient and resulting in better classification accuracy. The experimental results demonstrate that the MPELU-LSTM approach achieves better accuracy of 99.56% on the UNSW-NB15 dataset as compared to the earlier approaches like convolutional neural network with LSTM (CNN-LSTM) and optimized deep residual convolutional neural networks (DCRNN). These findings suggest that the MPELU-LSTM method significantly enhances the accuracy and robustness of IDS in IoT environments by addressing issues like the identification of relevant features and feature redundancy, contributing to more effective and secure systems. This research has valuable implications for enhancing the security bearing of IoT infrastructure.
Volume: 14
Issue: 2
Page: 373-383
Publish at: 2025-06-01

Sentiment analysis of vaccine data using enhanced deep learning algorithms

10.11591/ijaas.v14.i2.pp562-579
Monika Verma , Sandeep Monga
This paper investigates and experiments with an approach to improve sentiment analysis on vaccine datasets with deep learning. It evaluates random forest (RF), naïve Bayes (NB), and recurrent neural network (RNN) models across a variety of configurations, i.e., vector dimensions, pooling techniques, as well as evaluation methods, hierarchical SoftMax vs negative sampling. The results show that the model we proposed prevailed with an accuracy of 99.05% on a learning rate equal to 0.001, outperforming all other models based on metrics including precision, recall, and F1-score for benign/malignant cases. The results suggest that higher vector dimensions, average pooling, lowering the dropout rate, and employing hierarchical SoftMax for output significantly improve model performance. Hierarchical SoftMax performs better than negative sampling, whereas a lower dropout rate decreases overfitting and leads to improved generalization. Our results demonstrate the necessity to apply more sophisticated deep-learning tools around capturing nuances of public vaccine-related sentiment, which may be crucial for informing communication strategies and supporting decision-making in a real-world health emergency. The findings indicate that the performance of sentiment analysis with regard to COVID-19 vaccine deployment policy design and public monitoring could be enhanced by advanced deep learning algorithms.
Volume: 14
Issue: 2
Page: 562-579
Publish at: 2025-06-01

Optimized control strategy for enhanced stability in grid-connected photovoltaic-wind hybrid energy systems

10.11591/ijaas.v14.i2.pp609-617
Madhu Babu Thiruveedula , Asokan Kaliyamoorthy , Kosara Sravani , Murgam Sharath Yadav , Petturi Satish Kumar , Routhu Akash
To improve stability in grid-connected photovoltaic-wind (PV-wind) hybrid energy systems, this research presents optimized model predictive control (MPC) and proportional resonant (PR) control algorithms. The proposed MPC strategy enhances power management by forecasting future system behavior and optimizing control actions accordingly, while the PR controller effectively handles grid-synchronized voltage regulation and harmonic compensation. Together, these advanced control techniques significantly improve grid stability, ensure optimal utilization of renewable energy resources (RER), and maintain power quality under varying operating conditions. The performance of the hybrid system is evaluated through extensive simulations that consider a range of real-world scenarios, including fluctuating load demands and diverse climatic conditions. The results confirm the effectiveness of the proposed MPC and PR-based control in dynamically adjusting power output from wind and photovoltaic sources, thereby ensuring reliable and efficient grid integration. These findings highlight the potential of intelligent control systems in enabling the secure, stable, and long-term adoption of renewable energy within modern power grids.
Volume: 14
Issue: 2
Page: 609-617
Publish at: 2025-06-01

A deep learning-based myocardial infarction classification based on single-lead electrocardiogram signal

10.11591/ijaas.v14.i2.pp352-360
Annisa Darmawahyuni , Winda Kurnia Sari , Nurul Afifah , Bambang Tutuko , Siti Nurmaini , Jordan Marcelino , Rendy Isdwanta , Cholidah Zuhroh Khairunnisa
Acute myocardial infarction (AMI) carries a significant risk, emphasizing the critical need for precise diagnosis and prompt treatment of the responsible lesion. Consequently, we devised a neural network algorithm in this investigation to identify myocardial infarction (MI) from electrocardiograms (ECGs) autonomously. An ECG is a standard diagnostic tool for identifying acute MI due to its affordability, safety, and rapid reporting. Manual analysis of ECG results by cardiologists is both time-consuming and prone to errors. This paper proposes a deep learning algorithm that can capture and automatically classify multiple features of an ECG signal. We propose a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) for automatically diagnosing MI. To generate the hybrid CNN-LSTM model, we proposed 39 models with hyperparameter tuning. As a result, the best model is model 35, with 86.86% accuracy, 75.28% sensitivity and specificity, and 83.56% precision. The algorithm based on a hybrid CNN-LSTM demonstrates notable efficacy in autonomously diagnosing AMI and determining the location of MI from ECGs.
Volume: 14
Issue: 2
Page: 352-360
Publish at: 2025-06-01

Analysis of ice creams from goat milk kefir and red dragon fruit

10.11591/ijaas.v14.i2.pp481-489
Aman Santoso , Lilik Eka Radiati , Evi Damayanti , Armaini Armaini , Amiroh Nabilah Mujahidah , Eli Hendrik Sanjaya , Muntholib Muntholib , Muhammad Roy Asrori
Ice cream from goat milk kefir is lower lactose than cow milk kefir. Combining goat milk kefir with red dragon fruit in ice cream formulations can improve the quality of the product. This study aims to determine the sensory characteristics, total solid, total flavonoid, and antioxidant activity of goat kefir-based ice cream flavored red dragon fruit as quality evaluation. The study used a completely randomized design with 4 treatments and 3 replications. The treatment was the ratio of goat kefir and dragon fruit, including 40:60, 50:50, 60:40, and 70:30 in the ice cream mixture. 30 panelists participated in the organoleptic test. Total solid testing referred to SNI 01-3713-1995. Determination of total flavonoid content was carried out by forming AlCl3 complexes spectrophotometrically and assaying antioxidant activity used 2.2-diphenyl-1-picrylhydrazyl (DPPH) method. The results showed that there was no significant difference in the organoleptic test for taste, color, and texture. The results of the total solids test showed that the higher the addition of goat kefir to ice cream, the lower the total solids produced. While the addition of goat kefir increased the total flavonoids in ice cream. The antioxidant activity with the best formulation of 50:50 was categorized as moderate level, which is 136.59 ppm.
Volume: 14
Issue: 2
Page: 481-489
Publish at: 2025-06-01

Techno-economic analysis and optimization of solar energy systems: a case study at Ar-Raniry State Islamic University

10.11591/ijaas.v14.i2.pp322-335
Fahmy Rinanda Saputri , Ricardo Linelson , Muhammad Salehuddin , Muhammad Dzaky Al-Haidar
This research examines the implementation of a solar power generation system at Ar-Raniry State Islamic University (UIN Ar-Raniry), specifically focusing on the Faculty of Tarbiyah and Keguruan building. The study aims to enhance energy efficiency, assess economic feasibility, and reduce environmental impacts by optimizing solar energy potential through variables such as local meteorological conditions, panel orientation, tilt angles, and system efficiencies. Utilizing PVSyst software for simulations, the research evaluates technical performance, life cycle costs, and carbon dioxide (CO₂) emission reductions. The results indicate that the solar Photovoltaic (PV) system can generate 251,214 kWh annually while reducing CO₂ emissions by 173,095 kg. Economically, the investment is deemed feasible, with a payback period of 7.8 years, a lower cost of energy (LCOE) compared to State Electricity Company (PLN) tariffs, a positive net present value (NPV), and a high internal rate of return (IRR). Although there are minor losses in thermal and module quality, the system remains effective. This study contributes significantly to sustainable energy policies in higher education and recommends further long-term performance monitoring and exploration of additional renewable energy technologies on campus.
Volume: 14
Issue: 2
Page: 322-335
Publish at: 2025-06-01
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