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27,404 Article Results

Factors affecting engineering students’ self-perceived employability in Morocco

10.11591/ijere.v14i3.31797
Zineb Sabri , Ahmed Remaida , Benyoussef Abdellaoui , Abdessalam Ait Madi , Aniss Qostal , Fatima Ezzahra Chadli , Youssef Fakhri , Aniss Moumen
In a dynamic socio-economic world, perceiving a career opportunity and job prospects has become complex. The number of unemployed individuals is rising despite the increasing number of students pursuing higher education. This study is suggested to enhance students’ professional insertion, guide their career development initiatives, and help them acquire the skills demanded by prospective employers, thereby increasing their likelihood of employment. For this goal, this study investigates the determinants impacting self-perceived employability (SPE) among engineering students. Following a quantitative approach to explain how personal and contextual factors impact perceived employability, more than 350 Moroccan engineering students responded to a questionnaire for data collection, which had an internal consistency of 0.90. Data analysis employing advanced statistical techniques using structural equations modeling (SEM) to conduct descriptive, regression, and mediation analysis. The findings highlight that academic performance, university contribution, and personal circumstances significantly influence perceived employability, while generic skills have a minor effect. Furthermore, personal determinants are identified as stronger than contextual ones. The results provide several recommendations to stakeholders such as university administrations, teaching staff, employers, the Ministry of Education, and graduates. Additionally, they offer an insightful exploration of the intricate interactions among factors that enhance employability.
Volume: 14
Issue: 3
Page: 2132-2143
Publish at: 2025-06-01

Leveraging active learning practices for effectiveness of higher education: performance based investigation

10.11591/ijere.v14i3.30325
Vignesh Saravanan Kirubakara , Swarna Sudha Muppudathi , Jothi Thilaga Paul Ayyadurai , Sakthi Priya Gomathi Nayagam
Engineering graduates in India struggle with employability due to outdated curricula and ineffective teaching methods, which limit their ability to apply knowledge and think critically. A performance-based study investigated the impact of active learning (AL) techniques in technical education using methods like concept mapping, role-playing, virtual labs, and collaborative coding in computer science and engineering courses. The findings showed a 35% to 40% improvement in academic results compared to traditional methods, along with significant boosts in student engagement, comprehension, and critical thinking. Student feedback and performance evaluations strongly favored AL. Cluster analysis revealed fewer slow learners, highlighting its effectiveness in meeting diverse needs. The study concludes that integrating AL can better prepare students for the job market and enrich their educational experience.
Volume: 14
Issue: 3
Page: 2327-2336
Publish at: 2025-06-01

Pioneering educational frontiers: South Korea-ASEAN synergy in big data integration and future innovations

10.11591/ijere.v14i3.31828
Catherine Joy T. Escuadra , Ella Joy Avellanoza Ponce
This study examines the evolving trends in publication collaboration and research topics related to big data and education in South Korea and the Association of Southeast Asian Nations (ASEAN) region, analyzed through the lens of international relations (IR). Using scientometric methods, the study analyzed 2,427 publications from Web of Science (WoS) through R Studio and VOSViewer, highlighting a marked increase in publication volume, citation, and collaboration in recent years. The research focuses on key areas such as the integration of big data in teaching and performance assessment, the intersection of big data with artificial intelligence (AI), and the varying implementation frameworks across different countries. The findings reveal that while significant progress has been made, there is a need for more structured collaborative efforts. To enhance future research output and collaboration, the study recommends establishing international research networks, organizing joint projects, facilitating exchange programs, and investing in necessary infrastructure. Additionally, it suggests developing policy frameworks and securing funding to support these initiatives. Engaging industry partners and expanding collaborative networks are crucial for advancing the field and optimizing the application of big data in education.
Volume: 14
Issue: 3
Page: 2007-2017
Publish at: 2025-06-01

A systematic review on innovations in computer aided design in engineering education

10.11591/ijere.v14i3.30250
Margarita F. Murillo Manrique , Jorge Augusto Sanchez Ayte
curricula up-to-date with advanced computer-aided design (CAD) technologies to understand their practical application in various industrial and research contexts. Given this need, a systematic review was proposed following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method to evaluate the most recent innovations in CAD systems and their effective application in engineering education. The results reveal that emerging technologies such as real-time virtual collaboration, 3D design, and virtual reality are being incorporated into educational environments, significantly enhancing students’ technical and collaborative skills. The research concludes that these technologies not only reconfigure engineering education, emphasizing a practical and updated approach, but also facilitate a more interactive and effective learning experience. This study highlights the urgent need to adapt educational programs to incorporate these innovations, thus ensuring that future engineers are better prepared to face the challenges of modern industry.
Volume: 14
Issue: 3
Page: 1804-1814
Publish at: 2025-06-01

Addressing fraction comprehension: global perspectives and Malaysian educational strategies

10.11591/ijere.v14i3.33092
Syed Azman Syed Ismail , Siti Mistima Maat , Fariza Khalid
Understanding fractions is a significant challenge in mathematics education globally, including in Malaysia, where students often struggle with core concepts. These difficulties hinder their progression into advanced areas like ratios, proportions, and algebra. This paper proposes a conceptual framework to enhance students’ understanding of fractions, with a focus on the Malaysian education system. Drawing on literature and practices from international contexts, this paper emphasizes the importance of visual models, manipulatives, technology integration and real-world applications in teaching fractions. As a concept paper, it synthesizes key insights from educational theories to develop strategies for improving fraction education. The framework highlights the need for alignment with both local and international curriculum. Key findings suggest that the use of manipulatives, visual models and technology can significantly improve fraction comprehension. By comparing global strategies, this paper offers insights into how these methods can be adapted to diverse learning environments, including low-resource settings. The framework implies that curriculum reforms, professional development for teachers and revised assessments are crucial to enhancing student outcomes in fraction education.
Volume: 14
Issue: 3
Page: 2107-2117
Publish at: 2025-06-01

Effectiveness evaluation and application of large language model in data-driven teaching decision-making

10.11591/ijere.v14i3.33374
Binrui Jiang , Qingchang Fan , Jiuyan Zhou , Linping Li
This study aims to examine teachers’ perceptions of the effectiveness of large language models (LLM) in supporting data-driven decision-making in educational contexts. Specifically, the study explores the influence of technological pedagogical knowledge, technological content knowledge, and technological pedagogical content knowledge on teachers’ utilization of LLMs for informed decision-making. Additionally, it investigates the moderating role of ethical considerations in the use of LLMs. A survey-based methodology was employed to collect data from university teachers in Chengdu, Sichuan, China, resulting in a sample of 319 respondents, which was analyzed using Smart PLS 4. The findings indicate that technological pedagogical knowledge, technological content knowledge, and technological pedagogical content knowledge for LLM use significantly impact data-driven decision-making in teaching. Moreover, ethical considerations were found to significantly moderate the relationship between these knowledge domains and data-driven decision-making. This study contributes novel insights by addressing the evaluation and application of LLM effectiveness from teachers’ perspectives, ultimately fostering the advancement of quality education.
Volume: 14
Issue: 3
Page: 2263-2277
Publish at: 2025-06-01

Quick response code generation for e-invoicing in Saudi Arabia

10.11591/ijeecs.v38.i3.pp1980-1989
Abdelrazek Wahba Sayed , Zeinab Rabea
In the digital era, the emergence of quick response (QR) code technology has become a vital tool for enhancing the efficiency of electronic invoice management and promoting security and transparency in financial transactions, while reducing costs and ensuring compliance with regulations. This study focuses on QR code technology and electronic invoice requirements in the Kingdom of Saudi Arabia, by exploring the generation of QR codes for electronic invoices. The study begins by analyzing QR code technology and its role in encoding and decoding information. Subsequently, the electronic invoice requirements in Saudi Arabia are reviewed, with a focus on the applicable systems and regulations. The research also includes details on generating QR codes for electronic invoices, considering factors such as data encoding, security protocols, and compatibility standards using the Python programming language. Various steps of this process are explained. The study aims to provide a comprehensive understanding of the technology and requirements related to electronic invoices in Saudi Arabia and to develop a program for creating QR codes for electronic invoices to improve and develop the financial and technological infrastructure in the Kingdom of Saudi Arabia, thereby contributing to supporting the digital economy and promoting sustainable development.
Volume: 38
Issue: 3
Page: 1980-1989
Publish at: 2025-06-01

The variety of phosphor Ca2MgSi2O7:Eu2+ emission color affect white light LEDs

10.11591/ijeecs.v38.i3.pp1471-1478
Pham Van De , My Hanh Nguyen Thi
The conventional phosphor-converted white light emitting diode (WLED) suffers from several drawbacks relevant to heat generation and low rendered performance. Thus, using ultraviolet LEDs was introduced as a solution. It is essential to choose the phosphors with high stability that can activated under 350-410 nm to be compatible with the chips. Rare-earth-doped silicate phosphor is among the most reserched materials for solid-state light devices, thanks to its high stability and low-cost production. This work presents the Eu2+ -doped Ca2MgSi2O7 green phosphor to serve the pursuit of comprehensively enhancing the WLED performances. The f–d transitions and Eu2+ ions mixture take possession of two seperate cation spots in main grids with the help of two emission peaks, one at 465 nm and another at 520 nm. The composition of YAG:Ce3+ and Ca2MgSi2O7:Eu2+ phosphors, and a near-UV chip of 370 nm were utilized to compose WLEDs. Results show that by increasing the Ca2MgSi2O7:Eu2+ phosphor amount, the lumen output, correlated color homogeneity, and color rendering factors can be improved. The paper emphasizes the necessity for the optimal selection of the Ca2MgSi2O7:Eu2+ phosphor concentration, which would be about 10 wt%. The phosphors could be promising in making green-induced white luminous materials for white pc-LEDs with near UV-base.
Volume: 38
Issue: 3
Page: 1471-1478
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

Challenges and opportunities in strategic educational planning: a systematic literature review

10.11591/ijere.v14i3.32750
Semail Endo , Abdul Halim Busari , Dayang Kartini Abang Ibrahim
Strategic educational planning is essential for adapting to the evolving landscape of education, driven by socio-economic, technological and exceptional global health crisis. This systematic literature review explores the complex challenges and opportunities in strategic educational planning, synthesizing insights from diverse studies to provide a comprehensive understanding. The problem statement addresses the necessity for effective strategic planning to ensure educational resilience, quality and inclusivity amidst changing external conditions. To achieve this, we conducted an extensive search of scholarly articles from reputable databases such as Scopus and Web of Science, focusing on studies published between 2020 and 2024. The flow of study based on preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework. The database found (n=33) final primary data was analyzed. The finding was divided into three themes which is: i) educational strategies and innovations; ii) organizational and strategic management in education; and iii) impact and adaptation to external challenges in education. The review indicate that strategic educational planning must prioritize flexibility, stakeholder engagement and continuous improvement to navigate future challenges effectively. This review underscores the fundamental role of strategic planning in transforming educational systems to be more adaptive, inclusive and forward-thinking, ultimately enhancing their capacity to meet the diverse needs of learners in an ever-changing global context.
Volume: 14
Issue: 3
Page: 1621-1632
Publish at: 2025-06-01

A modified learning by design approach to support preservice teachers’ technology integration into teaching

10.11591/ijere.v14i3.32507
Tang Junhong , Jia Wei Lim , Meng Yew Tee
Integrating technology into instructional practices continues to pose a substantial challenge for preservice teachers, a challenge that has not been sufficiently addressed by existing teacher education programs. This study seeks to bridge this gap by implementing, evaluating, and refining a modified learning by design (mLBD) approach through a two-cycle design-based research (DBR) methodology. The research was conducted with 27 preservice teachers in the first cycle and 29 in the second cycle. Data were collected through semi-structured interviews, group lesson plans, micro-teaching sessions, peer feedback, and group discussions. The content analysis and constant comparative analysis identified two key impediments to effective technology integration: i) a superficial understanding of teaching methods and approaches and ii) a lack of targeted instructional guidance. Conversely, three facilitating factors were found to support preservice teachers’ technology integration: i) a deep understanding of teaching methods and approaches; ii) targeted guidance from the instructor; and iii) authentic experiences in collaborative curriculum design and redesign. These findings suggest that the mLBD approach offers valuable insights for enhancing teacher education programs’ capacity to support preservice teachers in effectively integrating technology into their instructional practices.
Volume: 14
Issue: 3
Page: 2079-2087
Publish at: 2025-06-01

Internet of things based autonomous robot system architecture for home automation and healthcare services

10.11591/ijeecs.v38.i3.pp1624-1633
Bhimunipadu Jestadi Job Karuna Sagar , Garapati Swarna Latha , Sreenivasulu Bolla , Jyotsna Amit Nanajkar , Pattabhirama Mohan Patnala , Praveen Mande , Mukund Ramdas Kharde , Jonnadula Narasimharao
The internet of things (IoT) is playing a major role in the development of the health industry by enabling more accessible and affordable virtual and distant patient contacts through applications that are easy to use. The IoT and automated homes are becoming more popular in recent days. A network of connected devices, including hardware, equipment, and technical support, is known as the IoT. Their purpose is to allow data exchange with other systems through the internet. This paper presents, internet of things based autonomous robot system architecture (IoT-ARSA) for home automation and healthcare services. The primary goal of this secure home automation system is to help the elderly and disabled people by allowing them to operate home appliances. Additionally, the system uses a cloud server to predict the health conditions of patients and the elderly people, providing information to a guardian. The patient's health condition is determined using sensors like temperature, pulse, blood pressure, and oxygen level. Ultrasonic sensor and face detection are used for home automation. Each sensor will interact with the Raspberry Pi 4 to record data, which will then be processed and stored in the cloud. From results it is clear that described (IoT-ARSA) for home automation and healthcare services model is very efficient with high accuracy and high security. Health monitoring is achieved with this model continuously with great efficiency.
Volume: 38
Issue: 3
Page: 1624-1633
Publish at: 2025-06-01

CGDE-YOLOv5n: a real-time safety helmet-wearing detection algorithm

10.11591/ijeecs.v38.i3.pp1765-1781
Wanbo Luo , Ahmad Ihsan Mohd Yassin , Khairul Khaizi Mohd Shariff , Rajeswari Raju
Due to numerous parameters and calculations, existing safety helmetwearing detection models are challenging to deploy on embedded devices. Therefore, this paper proposed a you only look once (YOLO) v5n-based lightweight detection algorithm called CGDE-YOLOv5n to address the shortcomings in the following areas: (i) the YOLOv5n algorithm was selected to minimize the model’s parameters and calculations, reducing the hardware cost. (ii) The convolutional block attention module (CBAM) was integrated into the backbone to enhance the network’s feature extraction capability. (iii) The neck was improved using the efficient re-parameterized generalized feature pyramid network (efficient RepGFPN) to enhance the multi-scale object detection capability. (iv) The C3 module was improved using the deformable ConvNets v2 (DCNv2) module to enhance the network’s adaptability to geometric changes of objects. (v) The complete intersection over union (CIoU) loss was replaced with focal-efficient IoU (focal-EIoU) loss to reduce the missed detection rate. Experimental results demonstrated that the customized gradient descent estimation (CGDE)- YOLOv5n achieved a mean average precision (mAP) 50 of 89.5% and recall of 84%, which is 1% and 0.8% higher than the YOLOv5n. In particular, the recall of workers not wearing safety helmets increased by 1.7%. Furthermore, the improved model achieved a detection speed of 68.5 frames per second (FPS), meeting the real-time requirements.
Volume: 38
Issue: 3
Page: 1765-1781
Publish at: 2025-06-01

TextBugger: an extended adversarial text attack on NLP-based text classification model

10.11591/ijeecs.v38.i3.pp1735-1744
Sanjaikanth E. Vadakkethil Somanathan Pillai , Srinivas A. Vaddadi , Rohith Vallabhaneni , Santosh Reddy Addula , Bhuvanesh Ananthan
Recently, adversarial input highly negotiates the security concerns in deep learning (DL) techniques. The main motive to enhance the natural language processing (NLP) models is to learn attacks and secure against adversarial text. Presently, the antagonistic attack techniques face some issues like high error and traditional prevention approaches accurately secure data against harmful attacks. Hence, some attacks unable to increase more flaws of NLP models thereby introducing enhanced antagonistic mechanisms. The proposed article introduced an extended text adversarial generation method, TextBugger. Initially, preprocessing steps such as stop word (SR) removal, and tokenization are performed to remove noises from the text data. Then, various NLP models like Bi-directional encoder representations from transformers (BERT), robustly optimized BERT (ROBERTa), and extreme learning machine neural network (XLNet) models are analyzed for outputting hostile texts. The simulation process is carried out in the Python platform and a publicly available text classification attack database is utilized for the training process. Various assessing measures like success rate, time consumption, positive predictive value (PPV), Kappa coefficient (KC), and F-measure are analyzed with different TextBugger models. The overall success rate achieved by BERT, ROBERTa, and XLNet is about 98.6%, 99.7%, and 96.8% respectively.
Volume: 38
Issue: 3
Page: 1735-1744
Publish at: 2025-06-01

Improving farming by quickly detecting muskmelon plant diseases using advanced ensemble learning and capsule networks

10.11591/ijeecs.v38.i3.pp2090-2100
Deeba Kannan , Nagamuthu Krishnan Sundarasrinivasa Sankaranarayanan , Shanmugasundaram Venkatarajan , Rashima Mahajan , Brindha Gunasekaran , Pandi Maharajan Murugamani , Karthikeyan Dhandapani
In modern agriculture, ensuring plant health is essential for high crop yields and quality. Plant diseases pose risks to economies, communities, and the environment, making early and accurate diagnosis crucial. The internet of things (IoT) has revolutionized farming by enabling real-time crop monitoring and using drones and cameras for early disease detection. This technology helps farmers address challenges with precision and sustainability. This research propose an ensemble learning model incorporating multi-class capsule networks (MCCN) and other pre-trained model with majority voting system is implemented to predict plant diseases and pests early. The research aims to develop a robust MCCN-based ensemble prediction model for timely disease identification. To evaluate the performance of the ensemble model, various key metrics, including accuracy, and loss value, are assessed. Furthermore, a comparative analysis is conducted, benchmarking the MCCN model against other well-known pre-trained models such as residual network-101 (ResNet101), visual geometry group-19 (VGG19), and GoogleNet. This research signifies a substantial stride towards the realization of IoT-driven precision agriculture, where advanced technology and machine learning contribute to the early detection and mitigation of plant diseases, ultimately enhancing crop yield and environmental sustainability.
Volume: 38
Issue: 3
Page: 2090-2100
Publish at: 2025-06-01
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