bluuebunny/crossref_metadata_2025 huggingface dataset:
Classification of human grasp forces in activities of daily living using a deep neural network
Authors: Padilla-Magaña Jesus Fernando , Sanchez-Suarez Isahi , Peña-Pitarch Esteban
Published: September 2024

The study of human grasp forces is fundamental for the development of rehabilitation programs and the design of prosthetic hands in order to restore hand function. The purpose of this work was to classify multiple grasp types used in activities of daily living (ADLs) based on finger force data. For this purpose, we developed a deep neural network (DNN) model using finger forces obtained during the performance of six tests through a novelty force sensing resistor (FSR) glove system. A study was carried out with 25 healthy subjects (mean age: 35.4±11.6) all right handed. The DNN classifier showed high overall performance, obtaining an accuracy of 93.19%, a precision of 93.33%, and a F1-score of 91.23%. Therefore, the DNN classifier in combination with the FSR glove system is an important tool for physiotherapists and health professionals to determine and identify finger grasp forces patterns. The DNN model will facilitate the development of tailored and personalized rehabilitation programs for subjects recovering of hand injurie and other hand diseases. In future work, prosthetic hand devices can be optimized to more accurately reproduce natural grasping patterns.
Development and evaluation of a network intrusion detection system for DDoS attack detection using machine learning
Authors: Ramachandra Bharathi , Surekha T. P.
Published: September 2024

Distributed denial of service (DDoS) attacks involves disrupting a target system by flooding it with an immense volume of traffic originating from numerous sources. These attacks can disrupt online services, causing financial losses and reputational damage to various organizations. To combat this threat, the proposed network intrusion detection system (NIDS) utilizes machine learning (ML) algorithms trained on the KDDCup99 dataset. This dataset encompasses a diverse array of network traffic patterns, bounded by both regular traffic and various attack types. By training the NIDS on this dataset, it becomes capable of accurately identifying DDoS attacks based on their distinctive patterns. The NIDS model is constructed using ML approaches like random forest (RF), support vector machines (SVM), and naive Bayes (NB). The developed NIDS is evaluated using performance metrics such as accuracy, precision, recall, F1-score, and receiver operating characteristic (ROC) curve. The proposed method demonstrates the NIDS’s accuracy of about 93%, precision of 99% and recall of 92% in detecting DDoS attacks, transforming it into a valuable tool for network security in comparison with the current methods. The study contributes to the domain of network security by providing an effective NIDS solution for detecting the DDoS attacks in the wireless sensor network.
Securing IoT edge device communication with efficient ECC middleware for resource-constrained systems
Authors: Mohamed Yusoff Zainatul Yushaniza , Ishak Mohamad Khairi , AB Rahim Lukman
Published: September 2024

The internet of things (IoT) rapidly grows into various parts of life. However, it has significant obstacles during setup and deployment, particularly in terms of network segmentation, administration, and security at all tiers, from physical to application. While IoT provides several advanced features and benefits, it is also vulnerable to security threats and flaws that must be thoroughly investigated to avoid misuse. Cryptographic approaches are routinely used to address these security concerns. Message queuing telemetry transport (MQTT), an application layer protocol, is vulnerable to various known and undisclosed security flaws. Integrating encryption techniques within the MQTT protocol to provide secure data flow is a potential strategy for increasing security. This study provides a middleware broker that improves authentication processes, securing connections between cloud servers and resource-constrained devices. Using a Java Servlet and the elliptic curve cryptography (ECC) technique, the study creates a system for creating encrypted identification keys within a web-based transaction framework. This system intends to provide asymmetric authentication that is energy and resource-efficient, with a focus on cost minimization. It also includes a security feature to protect users from common internet threats. The system's efficacy, including its low energy usage of only 4 mJ per device, is thoroughly tested, proving it meets the original protocol criteria.
Application of smart hydrogels scaffolds for bone tissue engineering
Authors: Owida Hamza Abu , Alnaimat Feras , Al-Nabulsi Jamal I. , Al-Ayyad Muhammad , Turab Nidal M.
Published: September 2024

Recent attention in the biomedical and orthopedic sectors has been drawn towards bone defects, emerging as a prominent focus within orthopedic clinics. Hydrogels, due to their biocompatibility, elevated water content, softness, and flexibility, are increasingly acknowledged in tissue regeneration research. Advanced biomaterials offer numerous advantages over traditional materials, notably the capacity to respond to diverse physical, chemical, and biological stimuli. Their responsiveness to environmental cues, such as three-dimensional (3D) morphology and phase conditions, holds promise for enhancing the efficacy of localized bone lesion repairs. This paper aims to revolutionize the treatment of severe bone abnormalities by providing a comprehensive examination of hydrogels capable of morphological adaptation to environmental changes. It delineates their classification, manufacturing principles, and current research status within the field of bone defect regeneration.
The analysis of soft error in static random access memory and mitigation by using transmission gate
Authors: Kadir Farhana Mohamad Abdul , Julai Norhuzaimin
Published: September 2024

As the progress of technology continues in accordance to Moore’s law, the density and downsizing of circuitry presents a significant vulnerability to the effects of soft errors. This study proposed a novel method to mitigate soft errors by increasing the robustness of complementary metal oxide semiconductor (CMOS) technology against soft errors via the use of transmission gates within the memory nodes of static random access memory (SRAM) which functioned as a low pass filter that disallowed the occurrence of data corruption. The proposed SRAM was tested against parameter variation of supply voltage and temperature. The critical charge was observed to increase with supply voltage increase, with the opposite being true of the increase in temperature. The increase in critical charge of up to 88.63% was achieved with regards to parameter variation for the transmission gate SRAM in comparison to the 6T SRAM.
Dynamics and kinematics of complex mechanical systems harnessing multibody dynamic program
Authors: Kaidash Mykhailo , Selevych Serhii
Published: September 2024

Understanding the behavior and performance of engineering applications like machines, transport machines, manipulators, and mechanisms like gears relies heavily on the study of the dynamics and kinematics of complex mechanical systems. This article provides a comprehensive overview of recent findings and advancements in this field. The purpose of this work is to provide an in-depth introduction to the theoretical and practical considerations involved in assessing the dynamic and kinematic properties of such complex systems. Understanding forces, torques, displacements, and velocities is highlighted as crucial to the design and study of complex mechanical systems, and the underlying mathematical models and concepts that control their motion are investigated. This paper also evaluates and critiques the most current developments in modeling and simulation approaches such as finite element analysis (FEA), computational dynamics, and optimization strategies. The multidisciplinary aspect of the topic and its potential to progress numerous engineering, robotics, and industrial applications constitute the topic's scientific uniqueness. The results include various advanced modeling and simulation techniques like FEA, computational dynamics, and multibody dynamics simulation. In conclusion, this article compiles a lot of information on the dynamics and kinematics of sophisticated mechanical systems, such as machines, transport machines, manipulators, and mechanisms.
Implementing advance control strategies to improve the performance of a microgrid
Authors: Rajput Isha , Verma Jyoti , Ahuja Hemant
Published: September 2024

Integration of flexible and non-dispatchable renewable energy production will influence the operation and future expansion of prevailing power systems. Because of the variations in performance responses between microgrid (MG) and regular generators, including renewable energy sources-based (RES-based) MG into the electrical system may have an influence on stability analysis. The reduction in switching frequency induced by these energy processors electronic interconnected electricity producing sources has a detrimental impact on the system’s structural analysis, potentially leading to stability issues. Power infusion from RES-based MG, on the other hand, increases damping efficiency, reducing transmission line congestion and power shortage. As a result, in light of expanded MG information, it is important to analyse more complex stability problems and regulate the production of a power grid. This study will examine the effect of RES-based MG on the structural analysis and controller of a multimachine multi-area device in various scenarios This paper defines the growth of a one-of-a-kind proportional-integral-derivative (PID-based) power system stabilizer (PSS) type2 fuzzy partial order based on a meta-heuristic hybrid technique for refining the efficiency and robustness of harmonic currents.
Potential and economic feasibility analysis of solar-biomass-based hybrid system for rural electrification
Authors: Channi Harpreet Kaur , Giri Nimay Chandra , Sandhu Ramandeep , I. Abu El-Sebah Mohamed , Syam Fathy Abdelaziz
Published: September 2024

A significant portion of the population lives in rural regions where the grid cannot provide them with enough power. Rising power demand, fossil fuel prices, limited fossil fuels such as coal, and environmental issues are the key drivers driving the usage of renewable energy resources for generating electricity. As a result, an alternate option for electricity generation in such remote places is required. Using renewable resources as alternatives would undoubtedly aid in mitigating the effects of global warming. The hybrid energy system combines electric power production with renewable sources such as solar, biomass, wind, biogas, hydro, and diesel generators (DGs). In light of this, a feasibility study on hybrid renewable energy was carried out for a specified remote region. This research investigates the efficacy of a solar-biomass-based hybrid power generation for rural electrification. The effective and sustainable alternative is found in a standalone hybrid version based on solar biomass. Electricity produced from the hybrid model proposed is $0.603.555 per unit, which is almost free of emissions of greenhouse gas (GHG), equally economical, and cleaner than the traditional supply. This system can be beneficial to electrify other adjacent remote zones.
Prediction of palm oil production using hybrid decision tree based on fuzzy inference system Tsukamoto
Authors: Tundo Tundo , Saifullah Shoffan , Yel Mesra Betty , Irawansah Opi , Mubarak Zulfikar Yusya , Saidah Andi
Published: September 2024

This research addresses the challenge of optimizing rule creation for palm oil production at PT Tapiana Nadenggan. It deals with the complexity of diverse agricultural variables, environmental factors, and the dynamic nature of palm oil production. The existing problem lies in the limitations of conventional decision tree models—J48, reduced error pruning (REP), and random—in capturing the nuanced relationships within the intricate palm oil production system. The study introduces hybrid decision tree models—specifically J48-REP, REP-Random, and Random-J48—to address this challenge via combination scenarios. This approach aims to refine and update the rule creation process, enabling the recognition of nuanced performance processes within the selected decision tree combinations. To comprehensively tackle this challenge and problem, the study employs Tsukamoto’s fuzzy inference system (FIS) for a sophisticated performance comparison. Despite the complexity, intriguing results emerge after the forecasting process, with the standalone J48 decision tree achieving 85.70% accuracy and the combined J48-REP excelling at 93.87%. This highlights the potential of decision tree combinations in overcoming the complexities inherent in forecasting palm oil production, contributing valuable insights for informed decision-making in the industry.
Enhance the accuracy of malicious uniform resource locator detection based on effective machine learning approach
Authors: Alqahtani Haifa , Abu-Khadrah Ahmed
Published: September 2024

Phishing attacks are increasing with the rise in web users. Addressing them requires understanding the techniques and employing effective response strategies. Phishing websites mimic authentic ones to deceive users into divulging personal information like bank account details, national insurance numbers, and passwords. Therefore, victims face financial loss from breached information security, constituting high-level internet fraud. Detecting phishing websites necessitates an intelligent model capable of recognizing suspicious features. To that purpose, this paper examines three classification methods for detecting phishing website attacks. This analysis allows to reconsider our awareness of phishing attacks and prevent the damage caused by phishing attempts in advance. Phishing website detection algorithm using three classification algorithms is proposed in this paper. It achieves high phishing website detecting accuracy, because three classification algorithms random forest (RF), support vector machine (SVM), and Bagging are combined in one system. The result of this research is found accuracy on validation set is 92.33%, the precision on validation set is 92.13%, the recall is 92.09% and F1 score is 92.10%. That prove that the result obtained in this research is more accurate than all the results of all the algorithms were applied in the same dataset that was train the proposed algorithm on it.
Solar photovoltaic integrated load frequency control of power system using variable structure fuzzy controller
Authors: Masikana Sboniso , Sharma Gulshan , Sharma Sachin
Published: September 2024

The incorporation of renewable energy sources into modern power systems is on the upswing, intending to produce and deliver cost-efficient electricity to meet the ever-increasing demands of today’s world. Solar energy stands out as a plentiful and robust solution for meeting current electricity requirements. However, integrating solar photovoltaic (PV) generated power into the contemporary power system introduces complexity, necessitating the development of suitable control design to ensure effective regulation of load frequency control (LFC). This research paper concentrates on the mathematical modeling and integration of solar PV generated electricity into a hydrothermal system. In addition, this study also evaluates the performance of the variable structure fuzzy (VSF) control with reduced rule base for hydrothermal system concerning varying degrees of disturbances in one or in both regions of the power system. Moreover, the research reveals that the integration of PV power into hydro-thermal systems can improve the LFC outputs and mitigate system deviations in the face of different disturbance scenarios.
Exploring COVID-19 vaccine sentiment: a Twitter-based analysis of text processing and machine learning approaches
Authors: Khalaf Ban Safir , Hamdan Hazlina , Manshor Noridayu
Published: September 2024

In the wake of the 2020 coronavirus disease (COVID-19) pandemic, the swift development and deployment of vaccines marked a critical juncture, necessitating an understanding of public sentiments for effective health communication and policymaking. Social media platforms, especially Twitter, have emerged as rich sources for gauging public opinion. This study harnesses the power of natural language processing (NLP) and machine learning (ML) to delve into the sentiments and trends surrounding COVID-19 vaccination, utilizing a comprehensive Twitter dataset. Traditional research primarily focuses on ML algorithms, but this study brings to the forefront the underutilized potential of NLP in data preprocessing. By employing text frequency-inverse document frequency (TF-IDF) for text processing and long short-term memory (LSTM) for classification, the research evaluates six ML techniques K-nearest neighbors (KNN), decision trees (DT), random forest (RF), artificial neural networks (ANN), support vector machines (SVM), and LSTM. Our findings reveal that LSTM, particularly when combined with tweet text tokenization, stands out as the most effective approach. Furthermore, the study highlights the pivotal role of feature selection, showcasing how TF-IDF features significantly bolster the performance of SVM and LSTM, achieving an impressive accuracy exceeding 98%. These results underscore the potential of advanced NLP applications in real-world settings, paving the way for nuanced and effective analysis of public health discourse on social media.
Handwritten digit recognition using a column scheme-based local directional number pattern
Authors: Aouine Mohammed , Gattal Abdeljalil , Djeddi Chawki , Abbas Faycel
Published: September 2024

One of the most well-known challenges in computer vision and machine learning is the recognition of handwritten digits. This study presents an advanced approach to improving isolated-digit recognition through the use of advanced feature extraction techniques. For example, digit recognition is commonly used to read numbers on forms and checks in banks. This paper introduces a novel method of extending the local directional number pattern (LDNP) to a column scheme using two different masks and their resolutions. A new descriptor of the LDNP column scheme is being proposed that combines derivative Gaussian and Kirsch masks in order to enhance textural analysis and capture more detailed local textual information. This approach is highly efficient and robust, able to handle variations in size, shape, and slant. Additionally, the support vector machine (SVM) is employed as a classifier, which has been shown to make better decisions. The empirical investigation is carried out using the CVL dataset, resulting in recognition rates that are comparable with the latest advancements in the field. The overall precision of 96.64% is achieved, outperforming existing similar works.
An optimization based deep learning approach for human activity recognition in healthcare monitoring
Authors: Kalyanasundaram Aparna , Panathula Ganesh
Published: September 2024

Medical images are comprised of sensor measurements which help detect the characteristics of diseases. Computer-based analysis results in the early detection of diseases and suitable medications. Human activity recognition (HAR) is highly useful in applications related to medical care, fitness tracking, and patient data archiving. There are two kinds of data fed into the HAR system which are, image data and time series data of physical movements through accelerometers and gyroscopes present in smart devices. This study introduced crayfish optimization algorithm with long short term memory (COA-LSTM). The raw data is obtained from three datasets namely, WISDM, UCI-HAR, and PAMAP2 datasets; then, pre-processing helps in removal of unwanted information. The features from pre-processed data are reduced using principal component analysis and linear discriminant analysis (PCA-LDA). Finally, classification is performed using COA-LSTM where, the hyperparameters are fine-tuned with the help of COA. The suggested method achieves a classification accuracy of 98.23% for UCI-HAR dataset, whereas the existing techniques like convolutional neural network (CNN), multi-branch CNN-bidirectional LSTM, CNN with gated recurrent unit (GRU), ST-deep HAR, and Ensem-HAR obtain a classification accuracy of 91.98%, 96.37%, 96.20%, 97.7%, and 95.05%, respectively.
Power system stability and control: a comprehensive review focusing on the rotor angle case
Authors: Mohamad Murad Nor Syaza Farhana , Kamarudin Muhammad Nizam , Md Rozali Sahazati , Zakaria Muhammad Iqbal
Published: September 2024

This paper provides a review of power system stability, focusing on the rotor angle case. To gain a preliminary understanding of the stability studies, the discussion begins with an overview of generators in power system generation. The distinguishing parameters of synchronous generators as compared to their counterparts such as induction generators, inductor alternators, and direct current generators are also emphasized. The discussion that is not bounded within their stability issues and control strategies is briefly assessed. The shortcomings and advantages of various modeling approaches are also discussed therein. To extend the thoughts, this review includes a thorough discussion and classification of power system stability, which includes rotor angle stability, frequency stability, and voltage stability. The stability of the rotor angle is important as it ensures frequency stability and voltage stability. This paper also presents the power system modeling approach that is able to facilitate the rotor angle stability studies. This paper also aims to review the established rotor angle stabilizers and algorithms developed by previous researchers.
Multispectral imaging and deep learning for oil palm fruit bunch ripeness detection
Authors: Shiddiq Minarni , Saktioto Saktioto , Salambue Roni , Wardana Fiqra , Dasta Vicky Vernando , Harmailil Ihsan Okta , Rabin Mohammed Fisal , Arpyanti Nisa , Wahyudi Dilham
Published: September 2024

Oil palm fresh fruit bunches (FFBs) are the raw material of crude palm oil (CPO) on which ripeness levels of FFBs are essential to obtain good quality CPO. Most palm oil mills use experienced graders to evaluate FFB ripeness levels. Researchers have developed rapid and non-destructive methods for ripeness detection using computer vision (CV) and deep learning. However, most of the experiments used color cameras, such as a webcam or a smartphone, limited to visible wavelengths, and used still FFBs on–trees or on the ground. This study developed a light-emitting diode (LED)-based multispectral imaging system with deep learning for rapid and real-time ripeness detection of oil palm FFBs on a moving conveyor. The ripeness levels used were unripe and ripe. We also evaluated the spectrum of reflectance intensities for the ripeness levels. The ripeness detection system employed a two-class you only look once version 4 (YOLOv4) detection model using a dataset of 2000 annotated unripe and ripe FFB multispectral images and a video of 30 moving FFBs for real-time testing. The results show a promising method to detect oil palm FFB ripeness with an average accuracy of 99.66% and a speed range of 3.32-3.62 frame per second (FPS).
Enhancing performance of slotted ALOHA protocol for IoT covered by constellation low-earth orbit satellites
Authors: Chabou Zakaria , Addaim Adnane , Ait Madi Abdessalam
Published: September 2024

Recently, constellation of satellites has drawn a lot of interest from academia and industry as potential solution for extensive coverage of wide range of internet of things (IoT). In this work, IoT devices was assumed to be covered by constellation of low-earth orbit (LEO) satellites where the medium access control (MAC) technique called slotted ALOHA is employed. In this article, we use a constellation of satellites to reduce the collision domain and enhancing performance in order to obtain maximum results. We have carried out some modeling and simulations to optimize the number of satellites with different erasure probabilities with respect to IoT devices in order to enhance throughput and stability of slotted ALOHA protocol using the network simulator 2 (NS2). The numerical results have shown an improvement in terms of throughput and stability. And the simulation of the same system using NS2 is conducted and shows a good correlation with the theoretical study. Where the throughput reached 0.82% instead of 0.52%. Our findings offer proof that this method helps to use large number of IoT, and reduce collisions compared to conventional slotted ALOHA.
Dissipative soliton generation with sidebands using Bismuth Telluride (Bi2Te3) in erbium doped fiber laser
Authors: Haris Hazlihan , Awalin Lilik Jamilatul , Muhammad Ahmad Razif , Mustaffa Siti Nasuha , Markom Arni Munira , Hasnan Megat Muhammad Ikhsan Megat , Harun Sulaiman Wadi , Tan Sin Jin , Saad Ismail
Published: September 2024

In this work, the demonstration of dissipative soliton (DS) was observed in erbium doped fiber laser (EDFL) using of Bismuth Telluride (Bi2Te3) nanosheets saturable absorber (SA). The prepared SA was deposited on a fiber ferrule using optical deposition method. Interestingly, the DS generatered was accompanied with sidebands and the number of sidebands grew with laser diode pump power. Sidebands were observed as a result of modulation instability (MI) process, which arises from the interaction between DS and nonlinear gain in the fiber laser cavity. Signal to noise ratio (SNR) of 58 dB was attained, confirming the stability of the generated pulse. This work proved the capability of Bi2Te3 as SA for generating DS with sidebands in an EDFL.
Comparison of 5G performance post-merger between two network operators using field tests in urban areas
Authors: Chatchalermpun Surachai , Daengsi Therdpong , Sriamorntrakul Pakkasit , Phanrattanachai Kritphon
Published: September 2024

In late Q1/2023, DTAC and TRUE officially completed their merger. Consequently, this study was initiated to ascertain whether their respective fifth-generation (5G) networks had been seamlessly integrated several months following the merger. The investigation involved conducting drive tests along two predefined routes within the urban areas of Bangkok, employing the G-NetTrack pro tool for testing and data collection. Additionally, stationary tests were conducted in two crowded places using an application called Speedtest. Subsequently, an array of quality of service (QoS) metrics, including reference signal received power (RSRP), reference signal received quality (RSRQ), signal to noise ratio (SNR), download (DL), upload (UL) speeds and latency, were meticulously analyzed and presented. The findings of this study unveiled that, despite the successful completion of the DTAC and TRUE merger from a business standpoint, the technical integration of their respective 5G networks had not been finalized, although there were no significant differences between DTAC and TRUE for DL (p-value=0.542) and UL (p-value=0.090). Notably, significant differences were found between DTAC and TRUE for four metrics, including RSRP, RSRQ, SNR and latency (p-values0.05). Remarkably, roaming functionalities were still operational between the two networks.
Closing the digital divide in education: an innovative review of demand and supply side policies in four ASEAN member states
Authors: McFarlane Daniel , Mieruch Yannik , Wann Si Nang Lao
Published: September 2024

The rapid adoption of digital technologies in education highlights the importance of closing the digital divide in education. However, research on school connectivity policy regimes to bridge the digital divide in education in the Association of Southeast Asian Nations (ASEAN) region is lacking. The authors conducted a systematic review of policies in Cambodia, Malaysia, Thailand, and the Philippines following a framework synthesis approach. Through desk research, data was identified based on a supply and demand side framework and included policy documents from relevant ministries and departments, reports by international development organizations and non-governmental organizations, and academic literature. The analysis reveals an imbalance between supply and demand-side policies and regulatory approaches with governments in the analyzed countries favoring demand side policies.
Validity and reliability of the discrepancy evaluation instrument for measuring inequality in the online learning
Authors: Sugiarta I Made , Ariawan I Putu Wisna , Ardana I Made , Divayana Dewa Gede Hendra , Sukawijana I Komang Gede , Sugiharni Gusti Ayu Dessy
Published: September 2024

This study aimed to demonstrate a valid and reliable discrepancy evaluation instrument. It was for measuring inequality in the online learning process for mathematics education research subjects. The approach to this research was instrument development, which focuses on several stages, including defining variables, describing variables into more detailed indicators, compiling instrument items, conducting instrument trials, and analyzing the results of instrument trials. The subjects involved in the content validity test were two experts, while the reliability test was 40 students. The result analysis of the content validity test used the Gregory formula. The result analysis of the reliability test results used the Cronbach alpha formula. It indicated that the results of the instrument content validity were very good, and the reliability test results were in the moderate category. The resulting impact of this study on educational evaluation development was new knowledge, especially for evaluators of mathematics education, about the importance of content validity and reliability of the discrepancy evaluation instrument used to measure inequality in the online learning process in mathematics education research subjects.
Virtual reality technologies for learning English: an example of using Immerse
Authors: Lobanova Olga , Fedorova Elena , Vobolevich Alena , Minakova Polina , Rybakova Lyudmila
Published: September 2024

This study aims to examine the effectiveness of using the Immerse virtual reality technology as an additional tool for learning English. The sample consisted of 120 students randomly selected from the lists of third-year students studying English as a foreign language according to their bachelor’s degree curriculum. The overall sample was divided into two groups. Immerse was a virtual reality technology used for training in an experimental group for three months. The results showed that in the experimental group, there was a significant difference between the pre-test and the post-test (0.35<0.05). In the control group, there were no relevant changes. In particular, these data show that the experimental group significantly outperformed the control group in terms of academic performance in English. However, some of the tasks that tested writing skills showed unsatisfactory results. In general, although virtual reality (VR) does not affect students’ writing skills, its use is crucial for their long-term memorization.
Implementing an online course in the MOOC environment with a focus on science and mathematics education
Authors: Kazakova Julia , Shastina Elena , Gibadulina Ilzira
Published: September 2024

The study focused on the development and evaluation of an online course in the massive open online courses (MOOC) environment, specifically targeting science and mathematics education. The goal was to utilize pedagogical design principles, particularly those derived from the analysis, design, development, implementation, and evaluation (ADDIE) model, to create an effective learning experience for students in different years of study. The online course was hosted on the edX platform, which is a popular platform for MOOCs. The assessment of the online course covered various criteria: interface, interaction, and design. The criteria of interface, interaction, and design received high marks from students of the two groups. The study found that the participants were highly engaged during the training, which is a positive outcome. The analysis of interest in online learning among first- and fourth-year students shows that first-year students had an average level of interest in online learning of 4.50 with a standard deviation of 0.32. Fourth-year students had an average level of interest of 4.45 with a slightly larger standard deviation of 0.41. Student engagement is an important factor in effective learning, as it reflects their active involvement and motivation in the learning process.
Primary teachers’ readiness to use project: investigate teachers’ efficacy and attitude toward project-based learning
Authors: Ariyani Yusinta Dwi , Wilujeng Insih , Wangid Muhammad Nur , Wijaya Dhina Puspasari , Wahyudi Andi , Istiqomah Istiqomah
Published: September 2024

The purpose of our study was to investigate primary teachers’ readiness to use project-based learning (PBL) using teachers’ efficacy and teachers’ attitude toward PBL. A correlational descriptive design was set as the research design on 109 primary teachers from various schools who had participated in collaborative work projects in 2023. To collect data, teachers’ efficacy and teachers’ attitude scale were used as instrument. The results found that the level of use of PBL was influenced by teachers’ attitude toward PBL for each subscale except on the knowledge dependence and system restriction. The study also found that the level of use of PBL has a positive relationship with each subscale of teachers’ efficacy and the level of teachers’ efficacy is influenced by teachers’ attitude toward PBL for each subscale, except for the knowledge dependence. Implications of this study are expected to be considerations for professional development that focus on more concrete practical training.
Digital hygiene skills and cyberbullying reduction: a study among teenagers in Kazakhstan
Authors: Berdi Dinara , Niyazova Gulzhan , Bayterekova Nurbanu , Koshanova Gulnazira , Usembayeva Indira
Published: September 2024

Cyberbullying is a growing concern among teenagers, leading to adverse psychological and emotional consequences. To address this issue, this study aimed to identify the role of digital hygiene skills in reducing cyberbullying experiences among teenagers in Kazakhstan. A quantitative research approach was employed, and the data were collected using a questionnaire with items related to digital hygiene skills and cyberbullying experiences, which were measured on a 5-point Likert scale. A stratified random sample of 238 students from town and district middle schools in the Turkistan region participated in the study. The data were analyzed using partial least squares structural equation modeling with SmartPLS 4.0. The findings revealed that privacy protection, critical thinking, phishing and scam awareness, and digital footprint awareness were significant factors associated with reduced cyberbullying experiences. However, safe reporting, positive online behavior, and online etiquette did not significantly impact cyberbullying reduction. This study underscores the importance of promoting specific digital hygiene skills to create a safer and more supportive digital environment for teenagers. Policy recommendations are provided to enhance cyberbullying prevention efforts and foster a positive online culture.
Principal decision-making in implementing Merdeka Curriculum in elementary schools: a review
Authors: Wijayanti Dian Marta , Pramono Suwito Eko , Handoyo Eko
Published: September 2024

This research uses a literature review that examines current articles on principals’ decision-making in implementing the Merdeka Curriculum in elementary schools. The literature review method is assisted by Open Knowledge Maps (OKM) and literature searches using Google Scholar, ERIC, ScienceDirect, and PubMed databases. The findings of this research suggest that principals’ decision-making in curriculum implementation has a significant impact on the quality of education in schools. By considering these aspects, principals can ensure that the curriculum implemented in their schools meets the needs of students and contributes to the achievement of broader educational goals. Principals’ decision-making in implementing the Merdeka Curriculum requires a deep understanding of education, leadership skills, and the ability to collaborate. The implementation of the Merdeka Curriculum is a significant change in Indonesia’s education system that aims to tailor education to local needs better, develop students’ character, and promote competency-based learning. However, successful implementation requires adequate support, training, and resources for schools and educators. This research is limited to a literature review only. So, in the future, it is necessary to carry out field research in order to reveal the principal’s decision-making in implementing the Merdeka Curriculum with various new methods and adapted to current issues.
Leadership and its impact on educational institutions: a systematic review
Authors: Huangal-Scheineder Sebastián , Cieza-Sánchez Jaime , Diaz-Paredes Manuel , Arriaga-Delgado Miguel , Marchena-Tafur Aurora
Published: September 2024

Leadership, in the educational field, is an important tool to improve the functioning of an educational institution and student learning. For this, trained leaders are required to exercise the most appropriate changes and manage the joint work of workers, in order to meet the proposed goals. In this context, this work had the objective of this article was to explore the role of directors and teachers in achieving the objectives of educational institutions, identify the most recommended leadership style for educational institutions, and identify the importance of having strong leadership within them. With this aim in mind, a systematic review of the literature based on the PRISMA method was carried out, selecting 50 publications. As a result, it was concluded that good leadership is fundamental for the academic development of institutions at any educational level, and that much of this responsibility falls on managers. In addition, the transformational leadership style is highlighted as the most effective to be used within schools and universities, and to provide a better educational experience for students.
Spiritual leadership and lecturer performance: mediating role of work motivation
Authors: Subhaktiyasa Putu Gede , Gede Agung Anak Agung , Jampel I Nyoman , Dantes Kadek Rihendra
Published: September 2024

Spiritual leadership developed from intrinsic motivation is based on ethical and moral values that align with the education system. However, the research is limited in the context of higher education institutions. Therefore, this study aims to provide an understanding of the effect of spiritual leadership on lecturer performance through work motivation. The research design was quantitative, with 120 samples of permanent lecturers in private higher education institutions who have obtained lecturer certificates. Data was collected through a structured questionnaire and analyzed using Smart-PLS. The results showed that spiritual leadership affects lecturer performance and work motivation. In addition, work motivation plays a mediating role with a high effect size. Hence, these findings can serve as a basis for higher education institutions to develop more inclusive and inspiring leadership strategies that integrate spiritual values and create a more motivating work environment for lecturers.
The relationship between principal inclusive leadership, teacher efficacy and teachers’ innovative behavior
Authors: Dongxiao Mo , Abdul Kadir Suhaida , Hassan Roshafiza , Kang Mohd Sufian Kang Enio
Published: September 2024

The objective of this study was to explore the relationship among principals’ inclusive leadership, teachers’ sense of efficacy, and teachers’ innovative behaviors. A sample of 671 middle school teachers from Guangxi, China was utilized. The study revealed that the practice of inclusive leadership by principals had a positive effect on teacher efficacy. Furthermore, teacher efficacy appeared to have a beneficial effect on teacher innovative behavior. Additionally, the study showed that principal inclusive leadership significantly influenced teacher innovative behavior. Lastly, it proved that teacher efficacy played a mediating role in the relationship between principal inclusive leadership and teacher innovative behavior. The study offers strategies for enhancing teacher’s propensity for innovative conduct. When designing programs to promote teacher innovative behavior, it is important to take into account the impact of inclusive leadership by principals and the magnitude of teacher efficacy.
Enhancing pre-service teachers’ TPACK through the integrating of community of practice and lesson study
Authors: Cojorn Kanyarat , Seesom Chaweewan
Published: September 2024

This study explores the impact of a combined community of practice (CoP) and lesson study (LS) approach on fostering technology pedagogical content knowledge (TPACK) in student teachers. TPACK encompasses various knowledge areas, including technological knowledge (TK), content knowledge (CK), pedagogical knowledge (PK), pedagogical content knowledge (PCK), technology content knowledge (TCK), and technology pedagogical knowledge (TPK). The study employs combined CoP-LS practices in two distinct CoPs: CoP A characterized by varied experiences, and CoP B comprising members with comparable experiences. Data gathered through questionnaires and group discussions. The findings reveal that the CoP-LS activities effectively enhance TPACK among student teachers, leading to improved confidence in designing learning activities. Both CoP A and CoP B show overall enhancements in TPACK, with CoP A exhibiting superior development across all dimensions. Further analysis demonstrates that CoP A exhibits greater advancements in PK and PCK, transitioning from the adapting level to the advancing level. Moreover, TCK and TPK demonstrate progress from the exploring level to the advancing level within CoP A. In contrast, CoP B showcases advancements in TK from the exploring level to the advancing level, while no notable changes are observed in CK, PK, PCK, TCK, and TPK.
The effect of ethnography-based outdoor learning methods on elementary students’ activities and learning outcomes
Authors: Hendratno Hendratno , Wiryanto Wiryanto , Istiq'faroh Nurul , Primaniarta M. Gita
Published: September 2024

The cultural approach as a social studies learning resource can be carried out using ethnography-based outdoor learning methods for elementary school students. It adopts a quantitative research methodology employing experimental techniques. The population used is class V (fifth-grade) students in a sub-district. The number of students identified was 172. The sampling technique used was purposive sampling. Purposive sampling was utilized to select participants, dividing them into experimental and control groups. The quasi-experimental approach employed a posttest-only design with a nonequivalent group procedure. Hypothesis testing was selected to further examine the relationship between the independent and dependent variables. Multivariate analysis of variance (MANOVA), was employed to analyze questionnaire responses and learning results, yielding a significant value of 0.000, which is less than 0.005, thus accepting the alternative hypothesis (Ha). The findings indicate statistically significant influence of ethnography-based outdoor learning activities for both students’ engagement and academic performance in fifth-grade classrooms. These results underscore the potential of integrating indigenous knowledge-based learning innovations into elementary education.
Critically evaluating university mission and vision statements
Authors: Sakellarios Marc D , Gann David
Published: September 2024

The purpose of this article is to better understand the nature of mission and vision statements issued by universities and whether these statements reflect the values of the institution’s leaders, faculties, and students. The researchers, being employed as professors in a Japanese science university, sought to analyze their own institution’s mission statement using open, axial, and selective (OAS) coding as an evaluation methodology. The themes that emerged were those of the great man, elite education, and alumni exclusivity. The overarching theme of the mission statement was elite serving the elite. Although presented as a mission statement, the prominence of the theme of a visionary man suggests this document stylistically adheres as much to the characteristics of a vision statement as it does to a mission statement. This paper may assist teachers in deriving meaning from their institution’s mission or vision statements, and critically assessing whether the institution’s mission or vision comports with the beliefs, standards, and educational philosophies evidenced by their institution in practice.
Modelling school principals’ soft skills with sustainable administrative effectiveness
Authors: Awodiji Omotayo Adewale , Katjiteo Ancia
Published: September 2024

Effective school leadership is pivotal to the success of educational institutions. While traditional leadership models have emphasized technical skills and administrative competencies, the significance of soft skills in educational leadership has gained increasing attention in recent years. This study explores the direct and indirect relationship between principals’ soft skills and sustainable administrative effectiveness (SAE). The objectives that guided this study are to investigate the direct and indirect influence of soft skills (critical thinking, empathy, communication, adaptability, and leadership) on the SAE of school principals. Stratified and random sampling techniques were adopted to select the participants. Questionnaires, school principals’ soft skills questionnaire (SPSSQ) and sustainable administrative effectiveness questionnaire (SAEQ), were administered to about 432 teachers. The findings revealed a strong positive correlation between principals’ soft skills and their SAE. Therefore, all soft skills directly relate to SAE except critical thinking skills. Empathy, communication, adaptation and innovation, and leadership skills were found to have direct effect on SAE. Whereas critical thinking has no direct effect on SAE but could influence indirectly SAE through other factors. This ultimately results in the achievement of SAE with indirect relationships. Based on the research outcomes, this study suggests that school administrators should invest in professional development programs to enhance principals’ soft skills to achieve SAE.