bluuebunny/crossref_metadata_2025 huggingface dataset:
Controlling a vehicle braking and longitudinal acceleration using a seeking control approach
Authors: Salman Saad A. , Shallal Abidaoun H. , Sabry Ahmad H.
Published: December 2023

Traditional methods for tracking the paths of driverless vehicles use plant models to determine the corresponding control laws. Due to the intricate interactions between the road and the tires, time-varying characteristics, and unidentified disturbances. It is challenging to create an accurate vehicle model. As a result, data-driven controllers, which are independent of a predetermined plant model are becoming more and more well-liked. This work implements adaptive cruise control (ACC) by employing a control approach called extremum seeking technique (EST), which is a model-free control (MFC), to control a vehicle braking and longitudinal acceleration. The main aim here is to create an ego vehicle that travels at a specific speed with maintaining a secure space with respect to a guide vehicle. A car including an ACC technique called ego car, exploits radar to determine relative velocity and relative space relating to the guiding car. The ACC technique is considered to keep maintain a relatively secure space or a preferred cruising velocity concerning the guiding vehicle. The developed model succeeded to determine the relative velocity and relative space according for the ego car to another guiding car with acceleration not more than ±2 m/s2 and spacing error less than 6 m.
Stability analysis of a сlosed non-linear system “FC-BM” of the electric drive of an electric vehicle
Authors: Z. Yerlan Amangaliyev , I. Yuri Shadkhin
Published: December 2023

The article presents the analysis and stability program of the closed non-linear system frequency converter-brushless motor “(FC-BM)”, which differs significantly from the analysis of linear systems. First of all, this is because the stability property of a nonlinear system depends on the initial conditions and external influences: for some input signals, the system will be stable, while for others it becomes unstable. Consequently, the stability criteria developed in the linear theory cannot be applied to their analysis. The stability of a non-linear automatic control system means that small changes in the input signal or disturbances, initial conditions, or plant parameters will not take the output variable beyond a sufficiently small neighborhood of the equilibrium point or limit cycle. Since several equilibrium positions can exist for a non-linear system, stability should be analyzed in the vicinity of each of them. This complicates the task of research.
Ensemble learning classifiers hybrid feature selection for enhancing performance of intrusion detection system
Authors: Ali Al Essa Hasanain , S. Bhaya Wesam
Published: December 2023

Feature selection (FS) plays an important role in the construction of efficient ensemble classifiers; particularly for intrusion detection system (IDS). An IDS is a utilized in a network architecture to protect the availability of sensitive information. However, existing IDSs suffer from redundancy, high dimensionality, and high false alarm rate (FAR). Also, lots of models are constructed for outdated datasets, which makes them less flexible to deal with new assaults. Therefore, this paper proposes a new IDS relies on hybrid FS and ensemble classifiers. A hybrid FS approach consists of two techniques, hard-voting and mean. In contrast to recent papers, we use three different FS approaches: extra tree classifier importance as an embedded FS, recursive feature elimination (RFE) as a wrapper FS, and mutual information (MI) as a filter FS. Then, a hard-voting technique has been used to fuse output of these approaches and obtain a reduced subset of features. Since each feature has three weights, a mean technique has been utilized to assign one weight to each feature and obtain an optimal subset of features. The experimental outcomes, utilizing the modern InSDN dataset, confirm that the proposed hybrid FS with ensemble soft voting classifier achieves better results than other ensemble and individual classifiers due to several measures.
Performance analysis of convolutional neural network architectures over wireless capsule endoscopy dataset
Authors: Kaur Parminder , Kumar Rakesh
Published: December 2023

Wireless capsule endoscopy is one of the diagnostic methods used to record the video of the gastrointestinal tract. The endoscopy capsule stays in the digestive system for at least eight hours. It is difficult for gastroenterologists to examine such a lengthy video and identify the ailment. Convolutional neural networks (CNN) are a powerful solution to several computer vision problems. CNN can speed up the reviewing time of the recorded video by classifying video frames into various categories. The primary emphasis of this research paper is to examine and evaluate the performance of three different CNN architectures-VGG, inception, and MobileNet-in classifying the disease. Experimental results demonstrate that MobileNetV2’s accuracy is 91%, whereas InceptionV3 and VGG16 have an accuracy of 94% which is better than the accuracy of MobileNetV3. However, MobileNeV2 performed relatively better than the other CNN models in terms of computational time and cost. The model’s F-score, precision, and recall values are computed and compared also.
Optimization of solar powered air conditioning system using alternating Peltier power supply
Authors: Salman Mustafa Mohammed , Mahdi Mahmoud Mustafa , Ahmed Majida Khalil
Published: December 2023

Solar-powered thermoelectric air conditioning systems offer distinct advantages over traditional cooling methods, including thermal comfort, absence of moving parts, and eco-friendliness as they operate on solar energy. Despite these benefits, they exhibit a lower coefficient of performance (COP) compared to conventional systems. In this study, a solar-powered thermoelectric air conditioning system based on the Peltier effect was experimentally investigated in Baghdad during September (39 °C to 32 °C). The system was designed to cool a small 1 m³ test room. The six Peltier modules were divided into groups, each powered by a different electrical source with varying ON/OFF intervals. The highest COP achieved was 0.649, with an optimal outlet air temperature of 22-23 °C and a 20 minute switching cycle. Notably, the inlet air velocity directly influenced COP and outgoing air temperature. The study also indicated improved performance at reduced air flow, making Peltier air coolers ideal for hot regions.
Improving frequency regulation for future low inertia power grids: a review
Authors: Wamukoya Brian K. , Muriithi Christopher M. , Kaberere Keren K.
Published: December 2023

The modern power system is witnessing an unprecedented increase in the penetration of renewable variable generation (VG) sources. Increased uptake of converter interfaced VG like solar PV and wind power while replacing conventional synchronous generators (SGs) introduces new challenges to grid operators in terms of dynamically handling frequency stability and regulation. Reducing the number of SGs while increasing non-synchronous, inertia-less converter interfaced VG reduces grid natural inertia, which is critical for maintaining frequency stability. To cure inertia deficiency, researchers, broadly, have proposed implementing supplemental control strategies to VG sources or energy storage systems to emulate natural inertia (virtual inertia (VI)). Alternatively, VG sources can be operated below their maximum power point (deloaded mode), making available a reserve margin which can rapidly be deployed in case of a contingency with the help of power electronic devices, to provide fast frequency response. This paper reviews recent solutions proposed in literature to address the low inertia problem to improve frequency stability. Additionally, it highlights the formulation of an optimization problem for VI sizing and placement as well as techniques applied in solving the optimization problem. Finally, gaps in literature that require further research were identified
Performance evaluation of generative adversarial networks for generating mugshot images from text description
Authors: Bahrum Nur Nabilah , Setumin Samsul , Othman Nor Azlan , Fitri Maruzuki Mohd Ikmal , Abdullah Mohd Firdaus , Che Ani Adi Izhar
Published: December 2023

The process of identifying photos from a sketch has been explored by many researchers, and the performance of the identification process is almost perfect, particularly for viewed sketches. Suspect identification based on sketches is one of the applications in forensic science. To identify the suspect using these kinds of methods, a face sketch is required. Hence, the methods require skilled artists to sketch the suspect based on descriptions provided by eyewitnesses. However, the skills of these artists are different from one another, which results in different rendered sketches. Therefore, this work attempts to propose a new identification method based only on forensic face-written descriptions. To investigate the feasibility of the proposed method, this study has evaluated the performance of some text-to-photo generators on both viewed and forensic datasets using three different models of GAN which are SAGAN, DFGAN, and DCGAN. Then, the generated images are compared to the real photo contained within those datasets to evaluate how well the proposed method recognizes the faces. The results demonstrated that the recognition rate for the generated photos by the DCGAN models is better than the other two models which achieve a 38.3% recognition rate at rank-10 for mugshot identification.
Improving skin diseases prediction through data balancing via classes weighting and transfer learning
Authors: El Gannour Oussama , Hamida Soufiane , Lamalem Yasser , Mahjoubi Mohamed Amine , Cherradi Bouchaib , Raihani Abdelhadi
Published: December 2023

Skin disease prediction using artificial intelligence has shown great potential in improving early diagnosis and treatment outcomes. However, the presence of class imbalance within skin disease datasets poses a significant challenge for accurate prediction, particularly for rare diseases. This study proposes a novel approach to address class imbalance through data balancing using classes weighting, coupled with transfer learning techniques, to enhance the performance of skin disease prediction models. Two experiments were conducted using a tuned EfficientNetV2L based classifier. In the first experiment, a default dataset structure was utilized for training and testing. The second experiment involved employing classes weighting approach to balance the dataset. The effectiveness of the proposed approach is evaluated using the ISIC 2018 dataset, which comprises a diverse collection of skin lesion images. By assigning appropriate weights to different classes based on their prevalence, the proposed method aims to balance the representation of rare disease classes. To evaluate the performance of the proposed methodology, several performance evaluation metrics, including accuracy, precision, and recall, were employed. These findings revealed that the balanced dataset achieved enhanced generalization, mitigating the biases associated with class imbalance. As a result, the efficacy of artificial intelligence models is enhanced.
Design of a novel control hysteresis algorithm for photovoltaic systems for harmonic compensation
Authors: Obulesu Dakka , Swarupa Malladi Lakshmi
Published: December 2023

Solar photovoltaic (PV) system design and integration with an existing AC grid is growing very fast in recent years and used by many of them as they are pollution-free, structure is limited and maintenance free. From the factors considering, the performance of PV system depends upon the inverter output voltage tested for linear, non-linear, with harmonic, and without harmonic loads. Generated due to the nonlinear loads. Better inverter control techniques are developed to maintain grid power quality. This article discusses the analysis and comparison of pulse width modulation (PWM) converters with unique and state of the art nonlinear control schemes and various modulation approaches. The primary objective of this research is controlling an active filtering hysteresis PWM converter with no sensors. A simple structure with hysteresis current control method total harmonic distortion (THD) is lower when compared with the sinusoidal pulse width modulation (SPWM) method. The said claims are supported by employing computer simulations using MATLAB/Simulink and different control approaches, such as proportional plus integral and artificial neural network controllers.
Tomato pest recognition using convolutional neural network in Bangladesh
Authors: Polin Johora Akter , Hasan Nahid , Habib Md. Tarek , Rahman Atiqur , Vasha Zannatu Nayem , Sharma Bidyut
Published: December 2023

The tomato is one of the most popular and well-liked veggies among Asians. It is interesting to note that in Bangladesh, it is the second most significant vegetable consumed. Moreover, tomato is served not only as a vegetable, but it is also served as sauce, jam, etc., and used in making different types of cuisines. But the fact is due to the pests, thousands of tons of tomatoes are harmed every year in Bangladesh. The production of tomatoes in Bangladesh is harmed by a number of dangerous pests. We develop a solution to recognize pests at an early stage. Five different pest types, including aphids, red spider mites, whiteflies, looper caterpillars, and thrips, have been studied in this research. To identify tomato pests, we curated image datasets from online and offline repositories and processed them using a convolutional neural network (CNN) model. We used features from CNN layers for three machine learning algorithms: Random Forest (RF), support vector machine (SVM), and K-Nearest Neighbors (K-NN). This comprehensive approach allowed a thorough comparison of these algorithms in tomato pest recognition. For recognizing tomato pests, our methods generate excellent results. The accuracy of our experiment is 95.49% which indicates the successful completion of the experiment.
Bayesian probabilistic modeling in robosoccer environment for robot path planning
Authors: Steffi Diana , Mehta Shilpa , Venkatesh Kanyakumari Ayyadurai
Published: December 2023

The main goal of a route planning approach is to find a trajectory that safely transports the robot from one site to the next. Furthermore, it should provide an energy-efficient path so the computer can calculate it rapidly. This study develops a path-planning system for robots to approach the ball without collision. The Bayesian optimization algorithm (BOA) is used to identify the shortest path between the robot and the ball. BOA employs a probabilistic model to seek the optimum of an uncertain objective function efficiently. The performance of the BOA-based path planning system is compared to other optimization algorithms such as genetic algorithm, ant colony optimization, and firefly algorithm. BOA’s acquisition functions such as expected improvement, probability of improvement (PI), and upper confidence bound, are investigated. The exact locations of the robots and the ball are fed into optimization problems to discover the optimum path. The results reveal that the BOA system outperforms other systems in terms of computational time for planning the optimum path in dynamic situations and BOA-PI is the fastest algorithm.
Malaysian views on COVID-19 vaccination program: a sentiment analysis study using Twitter
Authors: Mohamed Ariff Mohamed Imran , Shuhada Zubir Nurul Erina , Azilawati Azizan Azilawati Azizan , Ahmad Samsiah , Noreen Izza Arshad Noreen Izza Arshad
Published: December 2023

This study aimed to analyze the opinions and emotions of Malaysians towards the COVID-19 vaccination program, as expressed on Twitter. By collecting data from the Twitter network and utilizing the machine learning life cycle technique. The results show that Malaysians have a mostly neutral viewpoint of the COVID-19 vaccination, with an accuracy score of 93%, an F1-score of 94%, a recall measurement of 94%, and a precision measure of 93%. These findings emphasize the significance of understanding public sentiment and perception towards crucial issues such as the COVID-19 vaccine and can be utilized to support healthcare professionals, policymakers, and the public in making informed decisions regarding the COVID-19 vaccination program.
Toward enhanced skin disease classification using a hybrid RF-DNN system leveraging data balancing and augmentation techniques
Authors: Hamida Soufiane , Lamrani Driss , El Gannour Oussama , Saleh Shawki , Cherradi Bouchaib
Published: December 2023

Significant health concerns are associated with skin diseases, and accurate and timely diagnosis is essential for effective treatment and patient management. To improve the classification of cutaneous diseases, we propose a novel hybrid system that incorporates the strengths of random forest (RF) and deep neural network (DNN) algorithms. The system employs data augmentation and balancing techniques to enhance model performance and generalizability. The HAM10000 dataset of diverse dermatoscopic images is used for training and evaluation in this study. In the hybrid system proposed, the RF model provides an initial diagnosis based on patient-reported symptoms, while the DNN analyzes images of skin lesions, resulting in more precise and efficient diagnoses. Using hyper-parameter optimization, we fine-tune the system for optimal performance. The evaluation demonstrates the accuracy of the hybrid model, which achieves a classification accuracy of 96.8% overall. According to our findings, the hybrid system demonstrates exceptional efficacy in six of seven skin disease classes. Variations in sensitivity and reliance on data quality and quantity are however cited as limitations. Nevertheless, this hybrid system has the potential to revolutionize skin disease diagnosis and treatment.
Assessing the performance of YOLOv5, YOLOv6, and YOLOv7 in road defect detection and classification: a comparative study
Authors: Mohd Yusof Najiha ‘Izzaty , Sophian Ali , Mohd Zaki Hasan Firdaus , Bawono Ali Aryo , Embong Abd Halim , Ashraf Arselan
Published: December 2023

Road defect inspection is a crucial task in maintaining a good transportation infrastructure as road surface distress can impact user’s comfortability, reduce the lifetime of vehicles’ parts, and cause road casualties. In recent years, machine learning has been adapted widely in various fields, including object detection, thanks to its superior performance and the availability of high computing power which is generally needed for its model training. Many works have reported using machine-learning-based object detection algorithms to detect defects, such as cracks in buildings and roads. In this work, YOLOv5, YOLOv6 and YOLOv7 models have been implemented and trained using a custom dataset of road cracks and potholes and their performances have been evaluated and compared. Experiments on the dataset show that YOLOv7 has the highest performance with mAP@0.5 score of 79.0% and an inference speed of 0.47 m for 255 test images.
A deep learning-based intelligent decision-making model for tumor and cancer cell identification
Authors: Durga Putta , Godavarthi Deepthi
Published: December 2023

In the current era, the prevalence of common ailments is leading to an increasing number of fatalities. Various infections, viruses, and other pathogens can cause these illnesses. Some illnesses can give rise to tumors that seriously threaten human health. Distinct forms of tumors exist, including benign, premalignant, and malignant, with cancer being present only in malignant forms. Deep learning (DL) algorithms have emerged as one of the most promising methods for detecting cancers within the human body. However, existing models face criticism for their limitations, such as lack of support for large datasets, and reliance on a limited number of attributes from input images. To address these limitations and enable efficient cancer detection throughout the human body, an intelligent decision-making approach model (IDMA) is proposed. The IDMA is combined with the pre-trained VGG19 for improved training. The IDMA analyses convolutional neural network (CNN) layer images for signs of malignancy and rules out false positives. Various performance indicators, like sensitivity, precision, recall, and F1-score, are used to assess the system's performance. The suggested system has been evaluated and proven to outperform similar current systems, achieving an impressive 98.67% accuracy in detecting cancer cells.
Ultrasonic sensor decision-making algorithm for mobile robot motion in maze environment
Authors: Khaleel Hind Zuhair , Oleiwi Bashra Kadhim
Published: December 2023

An autonomous mobile robot is one that can move from one location to another without the intervention of a human. A maze environment is a complex environment since it contains many obstacles and the major problem is moving through it. To avoid obstacles while moving through the maze, the mobile robot must be designed with an algorithm. This work proposes a decision-making system with an ultrasonic sensor to allow the developed autonomous mobile robot to avoid obstacles in any maze setting through its movements. The maze was designed with a size of 100×200 cm2 for the case study. Due to the height dimension of the barrier (20 cm) and the height dimension of the mobile robot including the ultrasonic sensor (20 cm), a distance of 20 cm was taken between the wall (obstacle) and the sensor. The result distance between the (wood wall) object and the sensor indicates that it is a reasonable distance chosen for the mobile robot to move and turn with flexibility as it travels through the maze environment from 0 cm to 300 cm. This mobile robot path took 1 minute to finish at a speed of 5 cm/sec, indicating that it is a quick algorithm as compared with related work.
Challenges in implementing free software in small and medium-sized enterprises in the city of Montería: a case study
Authors: Baena-Navarro Rubén , Vergara-Villadiego Juan , Carriazo-Regino Yulieth , Crawford-Vidal Richard , Barreiro-Pinto Francisco
Published: December 2023

This study investigates challenges and opportunities in adopting free and open-source software (FOSS) in small and medium-sized enterprises (SMEs) in Monteria, Colombia. The research reveals that around 77.5% of SMEs prefer free software, yet surprisingly, 80% are unaware of the benefits of open-source licenses, with nearly 45% not adopting them due to lack of knowledge. Implementing FOSS in SMEs offers legal and economic advantages, including reduced software acquisition costs, compliance with data protection and privacy regulations, and fostering innovation. However, adoption barriers persist, necessitating further research for enhancing implementation in Colombian SMEs. Notably, Colombia's ethical framework for AI serves as a guide for ethical AI and open-source software deployment, aligned with sustainable development goals. This study highlights free software usage prevalence in Monteria's SMEs and critical factors hindering full adoption. Addressing challenges and leveraging potential benefits can improve efficiency, regulatory compliance, and contribute to sustainable development. Continued research in this field can promote broader and stronger implementation of FOSS in Colombian SMEs.
Securing laboratories through internet of things networks: an ocomprehensive approach for ensuring safety and efficiency
Authors: Abderrahmane Tamali , Amardjia Nourredine , Mohammed Tamali
Published: December 2023

The design of a secure, intelligent laboratory that incorporates internet of things (IoT) devices and applications is a complex process. One of the main goals is to create a process monitoring system that can collect and analyze data from connected devices such as temperature and pressure sensors, smart locks, and access control systems. This system must operate in real time to ensure that equipment is within reference values. This reduces the risk of contamination and increases reliability. In addition, computer network security is paramount and it is imperative that certain measures such as encryption, multi-factor authentication, and intrusion detection systems are implemented. These measures help to ensure the safety and security of critical information and protect against potential risks. Physical security is also essential to protect scientific equipment and data. This paper provides a comprehensive overview of the critical factors involved in designing a secure, intelligent laboratory. It discusses the benefits of the integration of IoT devices and applications, and the security challenges that must be addressed. The paper also provides recommendations for designing and implementing a secure smart lab.
Multi-objective optimization of distributed energy resources based microgrid using random forest model
Authors: Vaish Jayati , Tiwari Anil Kumar , Kaimal Seethalekshmi
Published: December 2023

Microgrids (MG) in integration with distributed energy resources (DERs) are one of the key models for resolving the current energy problem by offering sustainable and clean electricity. This research presents a novel approach to address the complex challenges of optimizing a DERs based microgrid while considering multiple objectives. In this paper, the utilization of a popular machine learning algorithm, random forest (RF) model is proposed to optimize the DERs based MG configuration. The research commences by collecting historical data on energy consumption, renewable energy production, electricity prices, weather conditions, and other relevant factors of Bengaluru City (Karnataka, India) for different seasons. This research covers the conflicting objectives by finding optimal seasonal sizing of the battery, minimum generation cost, and reduction in battery charging cost. The optimization and analysis are done using an ensemble learning-based RF model. The findings from the RF model are compared with meta-heuristics and artificial intelligence (AI) methods such as particle swarm optimization (PSO) and artificial neural networks (ANN) for different seasons, i.e., winter, spring and autumn, summer, and monsoon.
Incorporating financial literacy in video clips amongst high school students
Authors: Yin Yin Khoo , Ong Eng Tek , Abd. Hadi Fatimah Salwa
Published: December 2023

Young adults with enough financial knowledge can commonly make good financial decisions, whereas those who lack financial literacy are always burdened by student loans and credit card debts. Acquiring adequate financial knowledge has become crucial among young adults. The present study investigated the effectiveness of incorporating financial literacy into videos among high school students. The video clips were incorporated into financial literacy in the economics curriculum to engage students and facilitate their learning of financial literacy. This study employed the mixed-methods explanatory design. The data analysis procedures in this design involved collecting the quantitative data, analyzing the data, and using the data to guide the follow-up interviews. A total of 327 students participated in this study. The results showed a significant change in students’ behavior regarding financial literacy (F=271.847, p<.05). Financial literacy should be instilled to students from primary school through tertiary education to ensure long-term sustainability effects.
Correlates gender traits and mindset on the choice of physics subject at high school in Kenya
Authors: Mugendi Eunice Ruraa , Gitonga Ciriaka , Rugano Peter , Ndwiga Zachary Njagi
Published: December 2023

Higher education and training in science, technology, engineering, and mathematics (STEM) are important for the industrial growth and development of any nation. Physics subject provides the basics for training in many STEM areas. However, failure to pursue physics to the end of high school denies learners opportunities in STEM courses. This research employed a correlational design and survey method to examine the relationship of gender traits (GT) and mindset (MS) with learners’ intention to choose physics among 378 high school students randomly sampled. The study adopted the gender traits test and mindset questionnaires. The Chi-square test for the relationship of GT and MS with the intention to choose a physics subject yielded p>0.05. These results revealed that no student is deprived of an opportunity to pursue physics to higher levels on account of their GT or MS. Thus, attempts to increase the number of learners who can potentially pursue STEM courses by studying physics in high school should focus on other known factors other than GT and MS.
Development of Android-based virtual laboratory media at vocational school: effects on students’ cognitive skills
Authors: Niswah Aisyah Khilawatun , Roemintoyo Roemintoyo , Rejekiningsih Triana
Published: December 2023

Vocational school students are not only required to master competency skills but also cognitive skills. Engineering mechanics learning is a basic calculation lesson that students should master. This research aimed to develop and identify the effectiveness of Android-based virtual laboratory (V-lab) media to improve students’ cognitive skills in learning engineering mechanics at vocational school in Surakarta. The subjects comprised 70 students of first grade vocational school majoring in civil engineering in Surakarta. This research was a research and development (R&D) study to develop an Android-based virtual laboratory media. Testing the N-Gain analysis in the experimental and control classes was to determine the effectiveness of virtual laboratory media in improving students’ cognitive thinking skills. Data were obtained from questionnaires and pretest and post-test test sheets. The developed virtual laboratory media was considered ‘very appropriate’ by media, material, and practitioner experts. The average value of N-Gain for the experimental class was 78.19% which was included in the effective category. Meanwhile, in the control class, the average N-Gain value reached 54.07%, which was included in the less effective category. This indicates that the use of Android-based virtual laboratory media for learning is more effective than conventional learning media.
The evaluation of problem-solving oriented e-module in learning computer-based subject
Authors: Che Lah Noor Hidayah , Hashim Mashitoh , Harun Jamalludin , Abdullah Yusri
Published: December 2023

It is tough and challenging to learn several computer-based subjects via an online learning setting during COVID-19 for students from the education field. It required a lot of skills, techniques, understanding among students, and the ability to think critically to solve problems. To date, a lack of studies provides digital content as learning approaches to computer-based subject students for their learning via online learning. Thus, the problem-solving oriented e-module in learning computer-based subject via Facebook for Higher Education was proposed. An exploratory sequential research design (qualitative and then quantitative approach) has been used in this study. Research samples were selected based on purposive sampling (10 students) to find out what they needed from the e-module. Then, the module was developed based on ADDIE Model and problem-solving learning strategy. There were nine experts validated the e-module in terms of the ADDIE model approach, problem-solving approach, and content. This module was tested on 34 undergraduate students towards students’ performance and social presence for quantitative part. The t-test showed that the e-module significantly improved student performance (p 0.00; t=5.52) with Cohen’s d=0.944. Interestingly, the results from the content analysis show that this module fosters social presence via Facebook learning groups such as interactive, affective, and cohesive.
Teachers’ digital literacy overview in secondary school
Authors: Rahmawati Sherly , Abdullah Ade Gafar , Widiaty Isma
Published: December 2023

This study explored the teachers’ digital literacy in secondary school and deciphered the most suggested way of enhancing secondary teachers’ digital literacy, overview levels, and the trends factors in the last five years of research. The methodologies in this study employed the traditional systematic literature review (SLR), which compiled some previous studies to be examined. The initial studies were only selected from an article published in a reputable journal indexed by Scopus. Of the 118 articles reviewed, 46 were selected to be included in the analysis. Significant findings summarize the fact that most of the last five-year research about teachers in secondary schools was impacted by the rapid development of technology and the demand for 21st-century skills. To summarize, as it is at the intermediate level, most secondary school teachers are digitally literate. However, future research may evaluate digital literacy training programs for teachers to assess what improvements should be made to delivery as well as existing frameworks used to test teachers’ digital literacy.
Learning from COVID-19: how Indonesian English teachers see computer-assisted language learning?
Authors: Maharddhika Rizkiana , Prasetianto Mushoffan
Published: December 2023

The COVID-19 outbreak has transformed various parts of life, including teaching and learning methods. Teachers, especially Indonesian English teachers, were taken aback by the abrupt transition from offline to online instruction because it was unanticipated and unprepared. This paper attempted to investigate how teachers implement their pedagogical knowledge in the online environment during the pandemic and how they improve on using computer-assisted language learning (CALL) during the sudden shift from offline to online instruction. The findings revealed that teaching methods are being adapted to accommodate the features of the online platform. In order to adjust to this new situation, teachers use their creativity to adapt the stages of cooperative learning, collaborative learning, and class discussion in an online context. Participating in virtual workshops and training also helps teachers improve their digital skills concerning internet material. In the future, the results of the present study can be applied to improve the teacher education curriculum. Specifically, it is suggested to construct a course for the teacher education curriculum that places pre-service teachers in diverse and challenging conditions.
The role of social support on vocational school students’ career choices
Authors: Suyitno Suyitno , Jatmoko Dwi , Primartadi Aci , Ratnawati Dianna , Abizar Haris
Published: December 2023

Strengthening career choices for vocational students is essential in preparing them to enter the world of work. Many studies highlighted the importance of career choices that are by the areas of expertise for vocational students and even examine the mechanism for strengthening their career choices. However, limited studies still focused on testing models for forming career choices by involving social support, career self-efficacy, and vocational identity in an integrative manner. Therefore, this study investigated the model of developing student career choices by applying for social support, career self-efficacy, and vocational identity. This study used structural equation modelling (SEM) analysis with Amos 18 software. The study results revealed that social support, career self-efficacy, and vocational identity factors directly influence vocational students’ career choices. In addition, career self-efficacy mediates the effect of social support on career choice. Finally, vocational identity moderates the interaction of the independent variables (social support and career self-efficacy) and the dependent variable (career choice). The results of this study provided important implications for lecturers to implement learning strategies that aim to strengthen vocational students’ career choices.
Investigating pre-service teachers’ perceptions on readability tools: a case study in Indonesia
Authors: Basori Basori , Wihardini Diah , Rofiki Imam , Maharany Elva Riezky
Published: December 2023

The readability tools allow us to measure the readability of a text. The previous studies have elaborated the use of readability tools to analyze reading materials, but it is relatively sparse regarding the teaching of readability tools especially involving the second language teaching education (SLTE) program as a context. Therefore, this study aimed to explore the acceptance of pre-service English as a foreign language (EFL) teacher upon readability text tools. It covers their acceptance towards the tools, the extent to how the pre-service teachers accept the Web-FX and Readability Analyzer as readability tools and seeks for participants’ concerns using the tools. The study was conducted at one English Language Education Study Program at in East Java, Indonesia, involving 33 third-year undergraduate students taking pre-teaching program. Having incorporated a qualitative data analysis approach on the students’ text analysis reports and interviews, the study revealed that the participants have positive acceptance of the tools for several reasons. Some final concerns from pre-service teachers after readability tools teachings are presented, including suggestions for future research related to readability tools.
Visualization of students’ cognitive knowledge in digital concept mapping
Authors: Stefkova Gabriela , Michalkova Jana , Dimunova Lucia , Halasz Beata Gress
Published: December 2023

Meaningful learning through the creation a digital concept map is the easiest way to gain cognitive knowledge, new information, and possibility how to expand among learners. Only a few empirical studies have been conducted in the field of nursing regarding learning through concept mapping to achieve higher levels of knowledge and digital skills. The study evaluated the student’s ability to visualize cognitive knowledge by creating a digital concept map. The objectives were to identify the perception using digital technologies and conceptual mapping, differences and relationships between gender, age, years of study, and branches. We used the ContextMinds program map creation. There were 92 Slovak students of nursing, public health and physiotherapy participated in the study. Data were collected and analyzed using IBM SPSS version 25.0. Perception of learning using digital concept mapping strategy was assessed using descriptive statistics, where paired t-test, analysis of variance (ANOVA) and Chi-square were used. The study found a positive perception of creating the digital concept map, but a low level of digital skills. First-year students took one or more weeks to submit the final version of the concept map in comparison to third-year students who took one or more hours. For second-year students, a creation of a concept map was less problematic than for first-year students. Students prefer standard teaching and learning methods. Healthcare absolvents are required to be proficient in digital literacy. Applying cognitive knowledge to create a digital concept map is a challenging strategy procedure.
Technology-based learning interventions on mathematical problem-solving: a meta-analysis of research in Indonesia
Authors: Ulya Himmatul , Sugiman Sugiman , Rosnawati Raden , Retnawati Heri
Published: December 2023

Mathematical problem-solving is important for learning mathematics and is needed in the 21st century. In the 21st century, education technology has been complementing every learning activity. Research on learners’ mathematical problem-solving improvement increased rapidly over the last few decades. This study examined the effectiveness of technology-based mathematics learning interventions on learners’ mathematical problem-solving at all levels of education in Indonesia. The researchers only took meta-analysis research from 2015 to 2023 from indexing databases, such as Scopus, Web of Science, Google Scholar, and Science and Technology Index (SINTA) indexers. The collected research articles were from only national journals in Indonesia. The screened data became the research results, containing the mean, standard deviation, number of samples (N), and the scale used in the research. This research had 19 independent studies in this meta-analysis. The data analysis applied meta-analysis, specifically the mean effect size value. The results of data analysis using Jeffrey’s Amazing Statistics Program (JASP) software showed the effective implementation of innovative and fun technology-based mathematics learning interventions. These findings highlighted the importance of incorporating technology into mathematics education and its potential for improving learners’ problem-solving skills.
Indonesian English language teachers’ conceptions of critical thinking: challenge and strategy
Authors: Bachtiar Bachtiar , Juhana Juhana , Pratiwi Widya Rizky
Published: December 2023

Critical thinking (CT) has already been acknowledged as a significant skill in education, including in the field of English language learning. However, very few studies have investigated the challenges and strategies in enhancing students’ CT skills in English language subjects. This qualitative research aims to investigate the Indonesian English as a foreign language (EFL) teachers’ conception of the necessity of implementing CT in EFL subject. This research is also intended to find out the challenges and strategies in encouraging students to use CT in their learning process. This research employed two research tools for data collection: questionnaires and semi-structured interviews. The study revealed that EFL teachers believe in the value of promoting CT in their teaching classroom activities. However, CT has been practically rarely unnoticed in the implementation of English language classes due to the EFL teachers’ concentration on linguistic skills, grammar instruction, and English communicative skills. The findings also found some challenges faced by the EFL teachers in implementing CT, as well as some strategies in promoting the skill. This research recommends the importance of involving Indonesian EFL teachers in a training on CT to enhance their understanding and allow them to stimulate CT among their students.
The study of digital literacy components for youths during the COVID-19 pandemic in Bangkok, Thailand
Authors: Siriwattanarat Ruthairat , Meethongjun Kittikhun , Tsailekthim Donsak , Wongroj Weerawich
Published: December 2023

This research paper investigates digital literacy components for youths in Bangkok. The first phase used a qualitative method. The key informants were eight experts in information and communication technology (ICT), human resource (HR), and education. The second phase was to develop the measurement of digital literacy components using the confirmatory factor analysis (CFA) approach with a sample consisting of 1,362 youths in Bangkok. Data collection was conducted through a questionnaire with 52 questions. The results revealed that digital literacy criteria for youths in Bangkok consisted of four factors containing 13 indicators. The first factor and its related access components was three subcomponents, which are i) use of tools and devices; ii) data collection in the cloud; and iii) internet connection. The second factor was understanding components consisting of evaluation, ethics, and legal literacy. The third factor was use of components consisting of safeguarding, search, sharing, and innovation. The fourth factor was creating components consisting of creating weblogs/applications, presenting on website/application, and safeguarding. CFA was employed to test the construct validity of the research latent variables that revealed the harmony correlation of empirical data contained in this research model. These results were employed to develop a digital literacy for youth during the COVID-19 pandemic in Bangkok, Thailand.
The psychometric properties of students’ attitudes, coping strategies, and psychological symptoms
Authors: Ahmad Roslee , Hidayat Dede Rahmat , Fitri Susi , Mohd Tahir Lokman , Kasmani Mohd Faizal , Sawai Rezki Perdani , Hisham Hizral Tazzif , Mahmud Mohd Izwan
Published: December 2023

This study examined the psychometric elements of psychological symptoms, attitudes and coping strategies and the impact of COVID-19 on learning among the university students in Malaysia. The design employed in this study was survey method, that centered on convenience sampling method. A total of 489 students from eight undergraduate program of year 1 to year 4 participated in the study. They enrolled in various disciplines ranging from chemical and food science, medicine, engineering, psychology, communication to religious studies program. Data was analyzed using exploratory factor analysis (EFA) with all items being grouped within the study’s constructs, which subsequently, were tested using the establishment of measurement model of confirmatory factor analysis. The instrumentation for this study was adapted from Weiner’s Attribution Theory, Expectations and Values Theory. Comparatively, in measuring the students’ coping strategic approach, items were adopted from Greenaway. Additionally, the Counseling Center Assessment of Psychological Symptoms (CCAPS-62) scale was adapted to measure the eight constructs namely anxiety, social anxiety, academic distress, eating anxiety distress, family distress, hostility, and smoking abuse. The Cronbach’s alpha values for all three constructs have indicated the accepted values. Findings revealed that the loading factors for the attitude, coping strategies and psychological constructs are at the accepted values and acceptable composite reliability values. Findings also indicate that the measurement model for the construct attitude, coping strategies and psychological have loading estimates that are statistically significant.
Validity of instrument to measure mathematics teachers’ perceptions towards problem-based learning activities
Authors: Zakaria Mohamad Ikram , Abdul Hanid Mohd Fadzil , Hassan Roslizam
Published: December 2023

Problem-based learning (PBL) is a proposed method for teaching mathematics in primary education in the education 4.0 era. This method enables teachers to conduct effective instruction by allowing students to explore alternative solutions to problems. This study was conducted to develop and validate an instrument for evaluating primary school mathematics teachers’ perceptions of PBL activities. This research is quantitative, and a questionnaire was employed to collect data. The data obtained were analyzed descriptively utilizing Cronbach’s alpha and exploratory factor analysis (EFA). It is determined that Cronbach’s alpha is 0.885, with a factor eigenvalue greater than 1. The Kaiser–Meyer–Olkin (KMO) value for each construct is 0.50, whereas Bartlett’s test value is statistically significant (<0.5). Each item has a factor loading of 0.50 or higher and a standard deviation of ≥60%. This study’s results indicated that this instrument can be used to investigate primary school mathematics teachers’ perceptions of PBL activities involving group division, generating ideas and learning issues, identifying problems, self-directed learning, synthesis and application, reflection, and assessment.