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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

A Delphi study on factors influencing school students’ adoption of social media as a learning platform in Malaysia

10.11591/ijere.v14i3.32939
Imelda Hermilinda Abas , Nawanantiny Krishnamurthi , Amran Rasli , Meria Ultra Gusteti
The increasing use of social media platforms among students offers potential for both academic and personal information exchange. However, the factors influencing its adoption for learning by school students remain underexplored. This study aims to identify and rank the key factors that affect the use of social media for learning among primary school students. Utilizing the Delphi method, data were collected in two rounds from 30 expert participants, who were primary school teachers, using purposive sampling. In the first round, thematic analysis identified six key factors influencing social media adoption. In the second round, these factors were ranked in order of importance, with Kendall’s W of 0.364 and a p-value of 0.000 confirming consensus. In addition, an intraclass correlation coefficient (ICC) value of 0.923 indicated reliability. The top three factors identified were learning transformation, technology reform, and long-term prospects for students. The findings suggest that schools should prioritize these factors in strategic planning. Future research could expand this study to include private and international educators, and qualitative studies like tracer research could further enrich the understanding of social media’s role in learning.
Volume: 14
Issue: 3
Page: 1743-1751
Publish at: 2025-06-01

Development and validation of the principals’ digital leadership instrument using Rasch measurement model

10.11591/ijere.v14i3.32214
Peng Yuanyuan , Bity Salwana Alias , Azlin Norhaini Mansor , Mohd Rashid Ab Hamid
This study addresses the critical need for robust measurement tools in digital leadership (DL) within educational settings—a topic of increasing relevance but limited research. Using the Rasch model measurement analysis, the study aims to develop and validate an instrument tailored to assess principals’ digital leadership (PDL) in China. The questionnaire, based on the five dimensions of the International Society for Technology in Education (ISTE) for education leaders—equity and citizenship advocate (ECA), visionary planner (VP), empowering leader (EL), systems designer (SD), and connected learner (CL)—was adapted to reflect Chinese cultural contexts. Following expert validation, the 33-item instrument was piloted with 188 teachers from higher vocational and technical colleges in Sichuan Province. The Rasch analysis, performed using Winsteps 3.72.3, assessed item fit, unidimensionality, local independence, reliability, separation index, and item-person mapping. The findings revealed that 26 items met all assumptions, demonstrating the strong reliability, validity, and psychometric robustness of the instrument. In conclusion, the validated PDL instrument is a reliable tool for assessing the DL of principals within the Chinese educational context, offering insights into professional development, and sets the stage for future research and policy development in the field of educational leadership.
Volume: 14
Issue: 3
Page: 1577-1589
Publish at: 2025-06-01

Trend analysis of machine learning techniques for traffic control based on bibliometrics

10.11591/ijai.v14.i3.pp2402-2411
Hilda Luthfiyah , Eko Syamsuddin Hasrito , Tri Widodo , Sofwan Hidayat , Okghi Adam Qowiy
Machine learning in traffic control for intelligent transportation systems (ML-ITSTC) aims to enhance user coordination and safety within transportation networks, ultimately improving overall traffic system performance. ML-ITSTC is achieved by leveraging data to execute machine learning algorithms in intelligent transportation management and optimizing traffic flow to prevent or reduce congestion. This paper conducts bibliometric analysis to explain the research status, development trajectory, and challenges of ML-ITSTC, drawing insights from literature in the Scopus database literature covering 2013 to November 2023. The bibliometric analysis of ML-ITSTC includes: performance analysis, science mapping analysis, and citation analysis. The evaluation of ML algorithm trends over the 10-year span indicates that traffic prediction (TP), neural networks, and deep learning are frequently used keywords. Further, an examination of keywords used over the entire period and in 2023 (up to November) shows that reinforcement learning (RL) is the latest popular approach for traffic control in transportation. The results provide a comprehensive view of the opportunities and challenges in ML-ITSTC, covering data, models, and applications, offering researchers insights into the current and future directions of ML-ITSTC research.
Volume: 14
Issue: 3
Page: 2402-2411
Publish at: 2025-06-01

Identification of potential depression in social media posts

10.11591/ijai.v14.i3.pp2096-2103
Munawar Munawar , Yulhendri Yulhendri
The widespread use of social media to convey emotions (including depression) can be used to identify suspected depression in social media posts by examining the language that they have used on social media. This study aims to develop a system for detecting suspected depression in social media posts using sentiment analysis. This study collected data from X (Twitter) for three months using the keywords depression, mental health, and mental disorders. 1,502 data were generated due to the cleaning process of the 5,000 data collected. The findings of employing the validated by psychologist valence aware dictionary and sentiment reasoner (VADER) and Indonesian sentiment (InSet) lexicons demonstrate that VADER is more accurate (95.1%) than Inset (76.9%). The results of modeling with random forest, naive Bayes, and support vector machine (SVM) showed that random forest had the highest accuracy (83.3%), followed by naive Bayes (80.5%) and SVM (80.4%). Predicting social media data using lexicons and machine learning has limits that can be addressed by validation from clinical psychology. The frequency, timing, and idiom of posts on social media can reveal signs of depression. Depression seems to be best described by words like melancholy, stress, sadness, worthlessness, and depression.
Volume: 14
Issue: 3
Page: 2096-2103
Publish at: 2025-06-01

Managing cooperative learning and digital competences in secondary education: a systematic review

10.11591/ijere.v14i3.30449
Virginia A. Samane-Cutipa , Juan Carlos Callacondo Velarde , Fabian Hugo Rucano Paucar , Fabiola Talavera-Mendoza
The COVID-19 pandemic led most schools to opt for distance education, resulting in challenges in the educational field. However, the increased use of digital technology prompted studies on strategies to help reduce the digital divide concerning two key 21st-century skills: cooperation and digital competencies. This article aims to analyze the study of cooperative learning in relation to the achievement of digital competencies in secondary education. It was developed through a systematic literature review (SLR) using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, retrieving scientific information from the Web of Science (WoS) and ERIC databases, published from 2018 to 2024. The results and findings emphasize the existence of strategies aimed at improving teaching and learning, academic performance, and students’ communication and social skills through task management, the formation of cooperative teams, and conflict resolution with shared leadership. Additionally, it highlights the development of digital competencies such as information retrieval, digital interaction, virtual object creation, digital security, and responsible citizenship. The conclusions focus on using cooperative learning strategies to make the teacher’s role more efficient in interactive spaces.
Volume: 14
Issue: 3
Page: 2088-2098
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

Need analysis: development of a teaching module for enhancing higher-order thinking skills of primary school students

10.11591/ijere.v14i3.30335
Hamidah Mat , Toto Nusantara , Adi Atmoko , Yusuf Hanafi , Siti Salina Mustakim
This research identified a pressing need to create specialized teaching modules for electrical topics within the science curriculum that target students’ higher-order thinking skills (HOTS). Despite the recognized significance of HOTS in improving students’ educational achievements, science educators encounter obstacles when attempting to effectively teach these skills. To tackle this challenge, the study utilized a qualitative research methodology, conducting semi-structured interviews with six science teachers from diverse Malaysian schools. The primary objective was to pinpoint the necessity for developing instructional modules that enhance students’ HOTS in primary school science subjects. This study revealed four key themes arising from the needs assessment: the importance of HOTS knowledge, obstacles in teaching HOTS, effective teaching strategies, and the actual teaching of HOTS. This study underscores the critical need for enhanced professional development opportunities for teachers to effectively impart HOTS and stresses the importance of providing suitable teaching resources. By developing these tailored modules, students’ critical thinking and problem-solving skills can be nurtured, paving the way for their academic and professional success. Consequently, the study’s recommendations offer valuable insights for policymakers, educators, and researchers seeking to create impactful teaching modules that cater to students’ HOTS in primary school science subjects.
Volume: 14
Issue: 3
Page: 1643-1650
Publish at: 2025-06-01

Semantic based medical visual question answering with explainable artificial intelligence

10.11591/ijai.v14.i3.pp2169-2177
Sheerin Sitara Noor Mohamed , Kavitha Srinivasan , Raghuraman Gopalsamy
The medical visual question answering (MVQA) system takes the advantage of both computer vision (CV) and natural language processing (NLP) to accept the medical image and corresponding question as input and generates the respective answer as output. One step further, the MVQA system capable of generating the answer based on the semantics has a distinct place and hence semantic based medical visual question answering (SMVQA) system is proposed in this research. In SMVQA, the semantics for input image and question are generated using layerwise relevance propagation explainable artificial intelligence (LRP XAI) technique and the answer is derived using deductive reasoning method. For this, seven MVQA datasets are used for model creation, testing and validation. The training phase of the SMVQA system is implemented using VGGNet, long short-term memory (LSTM), LRP XAI, ResNet and bidirectional encoder representations from transformers (BERT) to generate a model file. Then the inference is derived in the testing phase based on the generated model file for the test set. Finally, the answer is derived from the inference using natural language toolkit (NLTK) library, term frequency-inverse document frequency (TF-IDF), cosine similarity, best match25 (BM25) techniques along with deductive reasoning. As a result, the proposed SMVQA system gives improved performance then the existing MVQA system especially for abnormality type samples.
Volume: 14
Issue: 3
Page: 2169-2177
Publish at: 2025-06-01

Enhancing tabular data analysis for classification of airline passenger satisfaction using TabNet deep neural network

10.11591/ijece.v15i3.pp3362-3372
Rachid Kaidi , Hicham Omara , Mohamed Lazaar , Mohammed Al Achhab
In an era of air travel, understanding and enhancing passenger satisfaction are pivotal to the success of airlines and the overall passenger experience. Analyzing airline passenger satisfaction using tabular data can pose various challenges, both when employing classical statistical methods and when leveraging machine learning and deep learning techniques. On the one hand, statistical approaches pose various challenges including limited feature engineering techniques, the assumption of linearity of the data sets and limited predictive power. Then again, the use of machine learning and deep learning techniques may face other challenges such as the problem of overfitting, difficulty in interpreting data, intensive resource requirements, and the generalization problem in deploying machine learning-based methods. This paper presents a novel deep learning approach utilizing TabNet to classify airline passenger satisfaction. Leveraging a comprehensive dataset comprising various passenger-related attributes, our TabNet-based model demonstrates exceptional performance in distinguishing between satisfied and dissatisfied passengers. Our model’s robustness in handling tabular data, underscores its power as a valuable tool for the aviation industry. Comparing out results to recent papers show that out model outperforms these studies in terms of accuracy, precision, recall and area under the curve. The results show that our TabNet Network model outperforms all implemented machine learning models by reaching respectively the following results: 96.47%, 96.41% and 96.24% for accuracy, F1-score and G-mean score.
Volume: 15
Issue: 3
Page: 3362-3372
Publish at: 2025-06-01

The impact of COVID-19 on e-commerce: a cross-national analysis of policy implications

10.11591/ijeecs.v38.i3.pp1946-1956
Jia Qi Cheong , Wong Hock Tsen , Samsul Ariffin Abdul Karim , Jeffrey S. S. Cheah
The field of e-commerce research has evolved over recent decades, but the coronavirus disease 2019 (COVID-19) pandemic significantly accelerated its prominence, as evidenced by extensive literature. The pandemic underscored the pivotal role of e-commerce in driving the digital transformation of the global economy. However, there remains a lack of comprehensive reviews in this area, particularly comparative analyses of how different countries leveraged e-commerce to navigate the pandemic’s challenges. This paper addresses this gap by examining the literature on e-commerce adoption and its implications during COVID-19, focusing on select countries, including China, Malaysia, and several European nations. The case of China, as a major economic power in Asia, offers particularly valuable insights.
Volume: 38
Issue: 3
Page: 1946-1956
Publish at: 2025-06-01

Monkey detection using deep learning for monkey-repellent

10.11591/ijece.v15i3.pp3238-3245
Nur Latif Azyze Mohd Shaari Azyze , Teow Khimi Quan , Ida Syafiza Md Isa , Muhammad Afif Husman
Animal intrusion has caused many issues for human beings, especially monkeys. Monkeys have caused many problems such as yield crop damage, damage to human facilities and assets and stealing food. This study aims to investigate the use of deep learning to detect monkey presence accurately and use a proper repellent system to scare them away. A deep learning algorithm is constructed with supervised learning, which includes the monkey dataset with appropriate labelling of the object of interest. The detection of the monkey comes with a bounding box with respective confidence of detection. The result is then used to evaluate the accuracy of monkey detection. The accuracy of the trained model is assessed by evaluating its performance under varying conditions of camera quality and distances. The study focuses on proving the reliability of deep learning to detect monkeys and automatically perform corresponding actions like repelling monkeys. Hence this may reduce the reliance of manpower to secure a large space and improve safety issues.
Volume: 15
Issue: 3
Page: 3238-3245
Publish at: 2025-06-01

Exploring the effectiveness of multiclass decision jungle for internet of things security

10.11591/ijece.v15i3.pp3095-3106
Smitha Rajagopal , Abhik Sarkar , Venkat Narayanan Manjunath
Network intrusion detection systems (NIDS) are vital in protecting computer networks against cyber security incidents. The relationship between NIDS and internet of things (IoT) security is pivotal and NIDS plays a significant role in ensuring the security and reliability of IoT ecosystems. Ensuring the security of IoT devices is critical for several reasons. It helps safeguard sensitive information, guarantees the dependability of crucial infrastructure, meets regulatory obligations, and fosters user confidence. As the IoT ecosystem expands, prioritizing security is essential to minimize risks and maximize the benefits of connected devices. Given the ever-expanding cyber threat landscape, the multiclass classification task is essential to empower the NIDS with an ability to distinguish between various attack patterns in less computational time. The multiclass decision jungle algorithm is investigated to optimize the performance of NIDS. The research has considered permutation feature importance to include only the relevant features from the data. Using a contemporary dataset such as CICIOT 2023, the study has demonstrated an impressive attack detection rate of over 90% for 20 modern attack types. This research has investigated the effectiveness of IoT security measures and its prospective contributions to the field of cyber security.
Volume: 15
Issue: 3
Page: 3095-3106
Publish at: 2025-06-01

Improved YOLOv10 model for detecting surface defects on solar photovoltaic panels

10.11591/ijece.v15i3.pp3319-3331
Phat T. Nguyen , Loc D. Ho , Duy C. Huynh
Surface defects greatly affect the performance and service life of photovoltaic (PV) modules. Detecting these defects is important to improve the management, repair and maintenance of PV panels. With the development of artificial intelligence, computer vision brings higher accuracy and lower labor costs than traditional inspection methods. This paper introduces an improved PV you only look once v10 (YOLOv10) model for detecting surface defects of PV modules. The improvement includes adding an exponential moving average (EMA) attention mechanism to the neck, using a cycle generative adversarial network (GAN) to enhance the data, and replacing the YOLOv10 head with a YOLOv9 head to retain non-maximum suppression (NMS). Experiments show that the proposed model outperforms state-of-the-art methods such as YOLOv10s, n, x, b, l, and e, achieving superior detection accuracy. Despite the increased computational cost, the proposed method improved mAP@0.5 and mAP@0.5:0.95 by 5.1% and 6.5% over the original YOLOv10s.
Volume: 15
Issue: 3
Page: 3319-3331
Publish at: 2025-06-01

Higher education instructors’ and students’ attitudes toward distance learning

10.11591/ijere.v14i3.29383
Yousef M. Arouri , Yousef M. Alshaboul , Diala A. Hamaidi , Asia Y. Alshaboul
This study aimed at exploring the attitudes of higher education instructors and university students regarding distant learning during the COVID-19 epidemic. It took place at a higher education institution of Jordan. Using a mixed method approach, the researchers developed a two-part questionnaire and a semi-structured interview. The questionnaire was distributed questionnaire to 167 instructors and 349 students from the University of Jordan (UJ). The findings showed that the participants have a moderately favorable attitude toward distant learning. Additionally, the findings revealed no statistically significant differences (α=.05) in the attitudes of UJ instructors and students toward distance learning during the COVID-19 pandemic attributedto the study variables. Furthermore, the interviews revealed several themes that the university instructors and students identified as influencing the general effectiveness of their distance learning experience, including access to online platforms and professional training, offering electronic equipment, and protecting the integrity of exams. The study recommends that higher education institutions reconsider the concept of distance learning, considering lessons acquired from the era of compulsory distance learning.
Volume: 14
Issue: 3
Page: 1949-1960
Publish at: 2025-06-01
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