Analysis of learning effects of flipped learning linked to subject-non-curriculum for competency-based major education

Abstract:
Objectives This study analyzed the learning effects of flipped learning linked to subject-matter subjects to strengthen the major abilities of students in the Department of Police Administration at K University. Specifically, flipped learning was used as a teaching and learning method in the 'Forensic Investigation' class, a major subject in the Department of Police Administration, and was linked to a non-curricular program operated by the Teaching and Learning Development Center on campus.
 Methods To this end, flipped learning using scientific investigation videos of actual crime cases was conducted for 13 weeks in the forensic science major subjects, and as a subject-related extracurricular program, a forensic investigator from the K Local Police Agency was invited to give a special lecture on the major, and on-campus teaching and learning was conducted. It is designed to enable participation in learning communities or tutoring conducted by the development center. In order to comprehensively analyze the major abilities of participating stu-dents, we conducted lecture evaluations and special lecture satisfaction surveys on flipped learning using criminal investigation series (forensic video), and conducted in-depth interviews about learning experiences using Colaizzi's phenomenological method. It was carried out.
 Results The lecture satisfaction level for ‘Forensic Investigation’ using flipped learning and subject-related ex-tra-curricular classes was 4.65, which was improved from the previous year’s class that did not apply the sub-ject-connected extra-curricular program, showing that there was an educational effect on the learning experience. In addition, as a result of analyzing the learning effect of flipped learning, four central meanings were derived: class attitude, communication ability, class performance, and improvements. Lastly, as a result of analyz-ing the satisfaction level and in-depth interviews with the non-curricular program linked to the major curriculum, it was confirmed that it was helpful in motivating police officers to join the police force and cultivating criminal in-vestigation capabilities.
 Conclusions This study conducted an analysis of the learning effect of 'Forensic Investigation' in the Department of Police Administration at K University, which was found to have a positive effect on improving the ability to utilize professional knowledge related to criminal investigation, self-directed learning ability, convergence ability, and communication ability, which are major abilities. It is valuable in that it contributed to breaking down the bounda-ries between major subject classes by overcoming the constraints of limited class time and enabling individual in-depth learning by linking flipped learning, a learner-centered teaching and learning method, with extra-curricular programs.


The Validation of the Korean Version of Executive Functioning Scale

Abstract:
Objectives In this study, the adult executive functioning scale ADEXI(Adult Executive Functioning Inventory) de-veloped by Holst and Thorell (2017) was adapted and validated into Korean to measure the executive function ing of adults.
 Methods An executive functioning scale was selected through a review of previous research literature, permission to use the scale was obtained from the original author, and the Korean Adult Executive Functioning Scale (KADEXI) was constructed through translation and back-translation. Confirmatory factor analysis was conducted on 237 adults aged 19 or older.
 Results Confirmatory factor analysis was performed, and convergent and criterion validity were verified. The Korean adult executive functioning scale was ultimately composed of 14 items each for self-report and other-re-port, and the subfactors were 9 items for working memory and 5 items for inhibition, identical to the original scale. A test-retest performed 2 weeks later was found to be reliable.
 Conclusions By presenting specific difficulties in daily life through the validation of a Korean scale measuring adult executive functioning, it appears to be of great clinical utility in that it can provide guidance for therapeutic inter-vention and change. In addition, when implementing the executive functioning scale, cross-verification with self-report and meaningful other person's report can be used to ensure validity in identifying the degree of execu-tive functioning deficiency.


The Effect of In-School Work Experience on First Job Satisfaction among College Graduates: Applying Causal Forests

Abstract:
Objectives This study was conducted to verify the effect of in-school work experience on job satisfaction in the first job among college graduates and to explore how the purpose of work experience while in school and the rele-vance of the major affect job satisfaction in the first job.
 Methods To do this, causal forests were applied to the 2020 Graduates Occupational Mobility Survey (GOMS) to examine the average treatment effect of in-school work experience on the job satisfaction of the first employment. Additionally, heterogeneous treatment effects were explored based on personal backgrounds, col-lege factors, job preparation, and characteristics of the first job among college graduates. Furthermore, hier-archical regression analysis was applied to control for variables that were significant in the heterogeneous treat-ment effects and to verify the effects of the purpose of work experience and the relevance of the major on job sat-isfaction in the first job.
 Results First, the average treatment effect of work experience during college had a positive effect on job sat-isfaction in the first job. Second, the heterogeneous treatment effect of college location was significant, with some regions showing a decreasing effect of in-school work experience on job satisfaction in the first job. Third, livelihood type work experience has a negative effect on job satisfaction in the first job. Fourth, the relevance of work experience to the major had a positive effect on job satisfaction in the first job.
 Conclusions Based on the findings, implications and policy implications of the study were discussed.


Forecasting Indihome Users By Using Trigonometrics, Box Cox, Transformation, Arma Error, Trend, And Seasonal (TBATS) Methods

Abstract: The rapid growth in the use of telecommunication services, particularly IndiHome, has created a pressing need to comprehend and forecast the dynamics of user growth. The swift development of IndiHome services in Pematangsiantar underscores the importance of efficiently forecasting and managing user growth. This research aims to predict future IndiHome users using the TBATS method (Trigonometrics, Box Cox Transformation, Arma Error, Trend, and Seasonal). User data for IndiHome services were obtained from Telekomunikasi Indonesia (Telkom) Pematangsiantar. The collected data underwent analysis and interpolation using the TBATS method. The choice of the TBATS method is attributed to its capability to handle complex patterns of seasonality, trends, and variability in telecommunication service usage data. The TBATS method involves model optimization and model validation using the Mean Absolute Percentage Error (MAPE). The forecasted results for IndiHome users in the next 5 years, with the highest IndiHome users reaching 426,9432, indicate a reasonable forecasting performance. The accuracy of the forecast, as measured by the MAPE calculation, ranges around 30.5%, signifying that the TBATS method demonstrates a satisfactory forecasting capability for IndiHome user data.


Pemanfaatan Media Sosial Sebagai Media Komunikasi Massa Di Kalangan Pelajar

Abstract: Learning using text or conventional media makes it difficult for students to understand the material presented by the teaching staff. So now students use the YouTube application as a support in their learning activities. This research aims to find out more about the benefits of YouTube media for students and the use of YouTube social media as a place to share learning material. This research uses a descriptive method with a qualitative approach and this research uses data collection techniques including library research. The data used in this research is secondary data. The results of the research show that students who use YouTube as a mass communication medium make it very easy to find the information or knowledge they need and it is easier for students to understand the material contained in videos compared to material delivered directly by teachers or lecturers either online or face to face because of videos. on the YouTube application can be played back at any time to re-study the teaching material presented.


Implementasi Artificial Intelligence (AI) di Obrit Future Academy

Abstract: The government is obliged to provide the best educational services for the younger generation in order to create superior quality human resources as regulated by Law of the Republic of Indonesia Number 20 of 2003 concerning National Higher Education. The Ministry of Education, Culture, Research and Technology established the Independent Campus Orbit Future Academy (OFA) program which is an institution that is a pioneer in developing adaptive and innovative learning models. There are two main dimensions carried out by OFA to commit to improving the quality of life, namely providing special skills learning and the application of Artificial Intelligence (AI). Skills development at OFA aims to solve complex problems in the context of retail inventory management. Therefore, this study will discuss further the implementation of AI in retail inventory management with computer vision carried out at OFA. This research includes descriptive qualitative research with data collection techniques in the form of observation and research carried out directly at Orbit Future Academy. Data were analyzed using library research techniques. The findings show that the application and modeling of AI systems can be applied in retail inventory management through Python programming. The existence of AI can be applied in developing applications that have a real impact on the business sector and solving retail investment problems. Specifically, Computer Vision was developed as a smart and efficient solution for managing inventory. The resulting technology is a system that can see and recognize products automatically from visual images, minimize slow stock updates, optimize operational efficiency and reduce human error. Thus, overall the application of AI through Computer Vision in OFA can be an innovative solution developed as the latest technology to improve retail inventory.


bSDD FOR ARTWORKS IN HBIM OPEN AND STANDARD-ORIENTED DOCUMENTATION

Abstract: Abstract. This contribution shows part of a research activity aimed at supporting data documentation related to artworks and decorative apparatuses that characterize the architectural heritage using bSDD as a methodological tool. The proposal approach aims to provide specific information in SCAN-to-BIM processes, implementing HBIM models of decorative apparatuses or artworks with data on the state of conservation and morphological-structural composition according to the material of each component of the system (Lanzara and Scandurra, 2023). The main goal of this activity is the creation of open and detailed information models shareable and consultable via IFC exchange format (ISO 16739:2018), supporting conservation, prevention, maintenance and restoration of historical-artistic heritage. The use of codes, manuals, vocabularies and thesauri, and the multidisciplinary knowledge of specialists, shared within the theoretical and operational framework of the restoration and conservation of cultural heritage, informally contribute to supplement existing codes and to implement/define new datasets for information about deficient and not codified conservation states for specific materials. The structuring of the Data Dictionary is supported by ACCA software's usBIM.bSDD application, accessible via a collaborative web-based platform (Scandurra and di Luggo, 2023). According to the proposed workflow, shared terms and definitions can be integrated within the graphic model of the manufacts and structured within a CDE – Common Data Environment. The system, which has already been tested in the context of historical architecture, is currently being tested on movable and immovable decorative apparatuses and artworks, selected for their constituent materials and compositional complexity. This contribution presents the experimentation on the decorative apparatus of the major altar of the Church of Santa Maria di Costantinopoli in Naples.


EVALUATION OF NERF 3D RECONSTRUCTION FOR ROCK ART DOCUMENTATION

Abstract: Abstract. Digital documentation of rock art traditionally relies on a point cloud captured by a terrestrial laser scanner (TLS) or derived from an oriented image obtained using photogrammetry. In modern photogrammetry, the dense point cloud is generated using multi-view stereo (MVS) and subsequently used to generate a photorealistic 3D model. A recent method to reconstruct 3D models from images is Neural Radiance Fields (NeRF), which uses volume density to render the scenes through neural networks. The advantage of NeRF is that it can construct 3D models faster without using high computer processors and memory. NeRF has been studied in various applications, including cultural heritage, but not specifically for rock art documentation. Therefore, this paper evaluates three-dimensional (3D) reconstruction techniques using NeRF on Nerfstudio platform on two rock art datasets and compares them with the point cloud and 3D mesh models obtained from TLS and photogrammetry/MVS. The results have shown that NeRF does not match MVS in achieving geometric precision and texture quality. However, its learning-based approach accelerates reconstruction and offers potential enhancements to complement photogrammetric workflow.


A New Approach for Software Cost Estimation with a Hybrid Tabu Search and Invasive Weed Optimization Algorithms

Abstract: Due to the ever-increasing progress of software projects and their widespread impact on all industries, models must be designed and implemented to analyze and estimate costs and time. Until now, most of the software cost estimation (SCE) has been based on the analyst’s experiences and similar projects and these models are often inaccurate and inappropriate. The project will not be finished in the specified time and will include additional costs. Algorithmic models such as COCOMO are not very accurate in SCE. They are linear and the appropriate value for effort factors is not considered. On the other hand, artificial intelligence models have made significant progress in the cost estimation modeling of software projects in the past three decades. These models determine the correct value for effort factors through iteration and training, providing a more accurate estimate compared to algorithmic models. This paper employs a hybrid model incorporating the Tabu Search (TS) algorithm and the Invasive Weed Optimization (IWO) algorithm for SCE. IWO algorithm solutions are improved using the TS algorithm. The NASA60, NASA63, NASA93, KEMERER, and MAXWELL datasets are used for the evaluation. The proposed model has been able to reduce the MMRE rate compared to the IWO algorithm and the TS algorithm. The proposed model on the NASA60, NASA63, NASA93, KEMERER, and MAXWELL datasets obtained values of MMRE of 15.43, 17.05, 28.75, 58.43, and 22.46, respectively.


Evaluasi Penerapan Sistem Informasi Manajemen Rumah Sakit (SIMRS) dengan Metode Hot Fit di RSU Mitra Sejati Medan

Abstract: The Hospital Information System (SIMRS) is a system that integrates the collection, processing, reporting and use of data information needed to increase the efficiency and effectiveness of health services through better management at various levels of health services. The purpose of this research is to evaluate the implementation of the hospital management information system (SIMRS) at Mitra Sejati General Hospital with the Human Organization Technology (HOT) Fit Model method. The type of research used in this research is qualitative research with the Hot-Fit model method. The results of this study are that when viewed in terms of the human aspect, it is still not optimal, there are still many obstacles that occur in the field including the absence of an RME obligation, The organizational aspect is good, the hospital management has conducted training and is always holding retraining. The technological aspect is satisfactory enough for HR. Another obstacle that often occurs is related to the discipline of human resources for data input that is not in real time, SIMRS which is applied at Mitra Sejati General Hospital still has application deficiencies, the hospital management does not provide support in the form of repairs or strengthening the hospital network. Suggestions for hospitals need to develop medical records, HR discipline, develop existing SIMRS and improve network constraints.


Key Research Ideas of Artificial Intelligence in Higher Education: Bibliometric Analysis and Information Mapping

Abstract: This research aims to describe and map information and research ideas that can be developed in the application of artificial intelligence in higher education. The method applied in this research is Preferred Reporting Items For Systematic Reviews And Meta-Analyses (PRISMA) which uses scientific articles or proceedings sourced from the Scopus database which are analyzed using Vos Viewer and Publish Or Perish as well as Google Trends and Dimensions. The research results reveal that artificial intelligence research covers three aspects that can be applied in higher education: development of AI tools, application of AI in learning, and digitalization of higher education.


The Impact of AI-Driven Educational Simulations and Ideation With AI on Technopreneurship Education Learning Objectives: The Integration of ChatGPT in Education

Abstract: This research analyzes the effects of implementing Artificial Intelligence (AI) technologies, particularly ChatGPT for AI-DES (AI-Driven Educational Simulations) and Ideation with AI (Artificial Intelligence), on the learning outcomes of a technopreneurship course taught at a university in Bandung. Data was collected from a sample of 128 students who participated in the technopreneurship course. The results suggest that the application of AI-DES and Ideation with AI has an interesting effect on Lecture, thus supporting the usefulness of AI technologies as valuable instruments to help improve classroom learning. The finding indicates the application of Ideation with AI tends to influence different learning processes. The results of studies show that lectures hold an important role in creating favorable project outcomes and facilitating students’ accomplishing learning objectives. The research findings highlight the importance of implementing AI technology into technopreneurship education and using Lecture as an important facilitator for achieving learning outcomes.


Development of Soft Skills in Future Preschool Teachers Using ICT in Their Professional Training

Abstract: The study explores enhancing future preschool teachers’ training by developing soft skills through information and communication technologies. A proprietary method was devised, incorporating Mentimeter, Kahoot, and other digital tools across three stages. Evaluation employed J. Phillips’ model and various coefficients. Notably, critical thinking (0.92), creativity (0.90), competitiveness (0.87), and social competence (0.84) improved. Organizational skills were well developed (74%), impacting original content delivery. Using J. Phillips’ model, high performance was observed in both preschoolers (Group 1) and students (Group 2). This method showcases potential for optimizing education through digital tools. Future research might assess contemporary digital technologies’ effectiveness in cultivating soft skills among future teachers.


Efek Perlindungan Mangsa dan Daya Dukung Variabel pada Sistem Mangsa-Pemangsa dengan Fungsi Respon Beddington-DeAngelis

Abstract: Several species in the world have experienced extinction. To save species from extinction, a system needs to be formulated, one of which is protecting prey. Changes in physical and biological processes also have a role in the environment, namely resulting in dynamically changing carrying capacity. The rate of prey consumption by the average predator, i.e. the response function is also important in the system formulation. In this study, a prey-predator system was constructed with a Beddington-DeAngelis response function that considers prey protection and variable carrying capacity as well as a predation process that takes into account the predator population. In this research, the equilibrium point for prey-predator extinction, predator extinction, and coexistence was determined and then continued to analyze its stability. With a large prey protection value, the predator extinction equilibrium point is asymptotically stable and the coexistence equilibrium point is asymptotically stable, if and only if within a certain range of prey protection parameter values. In addition, simulations were carried out and it was concluded that at certain prey protection values, changes occurred in the stability of the system which depended on the limits of its carrying capacity.


Studi Literatur: Meningkatkan Keterampilan Berkomunikasi dengan Public Speaking

Abstract: Communication is an activity that cannot be separated from humans as social creatures, both between individuals and groups. Communication is a person's way of conveying ideas, messages, or thoughts and opinions involving two or more people. This research aims to explain the need to improve communication or public speaking skills as well as solutions for overcoming problems when communicating in public. This research method is a literature study by collecting data or information through books and scientific journals which are analyzed based on existing problems. Sources of information were obtained from research conducted by Taufik R. Talalu, Panir Selwen et al., Ayi Najmul Hidayat and Hasbullah Iau, as well as books written by Dr. Anna Gustina Zainal. Topics discussed in this research include the importance of learning public speaking, inhibiting factors, and solutions to overcome these problems. The research results show that by learning public speaking, a person's self-confidence can grow, and this is very much needed in forming human resources who are skilled at speaking in public both in the academic and professional fields


Peranan Cerita Rakyat Nusantara dalam Meningkatkan Literasi Budaya dan Kewargaan Siswa

Abstract: Cultural literacy skills are important to have so that students are not easily influenced by foreign cultures that enter. By improving their cultural literacy skills, they will better appreciate, maintain, and preserve the local culture found in the Indonesian community. The aim of writing this article is to see the role of Indonesian folklore in increasing student literacy, especially elementary school students. The research method used is a qualitative method with a library study approach. Data obtained by searching for journals, e-books and articles related to keywords and discussion topics. The results of the research show that folklore can improve students' literacy skills, especially by increasing character values and knowledge of the unique characteristics of each region in Indonesia


Student Adoption of Asynchronous Learning

Abstract: The educational process has been reorganized due to the epidemic, requiring educators to adopt new approaches to remote and virtual learning techniques. Future educators will employ strategies based on the use of social media, the production of videos, and the usage of virtual platforms. To develop learning videos that support online learning practicum activities or distance learning in Introduction to Databases Practicum courses, this research aims to investigate student acceptance of asynchronous learning video recordings of online learning practicum of introduction to databases in the COVID-19 pandemic. This study adopted the DeLone and McLean’s Information System Success Model (DL&ML model), the Technology Acceptance Model (TAM), and the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) theories as the conceptual framework for examining the student adoption of video recordings in the context Introduction to Databases Practicum course. Using SmartPLS 4 software, the data were modeled and evaluated utilizing the Partial Least Square Structural Equation Model (PLS-SEM). According to the 126-sample data, Perceived Ease of Use, Perceived Usefulness, Video Quality, and Information Quality affected behavioral intention to use Video Recordings during the Introduction to Databases Practicum when Covid-19 was a pandemic. The behavioral intention influences the use behavior of Video Recordings. It is suggested that the government create an adequate infrastructure to support online learning and help lecturers gain more knowledge and expertise in using technology, particularly when developing, implementing, and conducting evaluations of online learning.


Moduli of smoothness, <i>K</i>-functionals and Jackson-type inequalities associated with kernel function approximation in learning theory

Abstract: We give investigations on a kernel function approximation problem arising from learning theory and show the convergence rate from the view of classical Fourier analysis. First, we provide the general definition for a modulus of smoothness and a K-functional, and show that they are equivalent. In particular, we give explicit representation for some moduli of smoothness. Second, we establish some Jackson-type inequalities for the approximation error associated with some non-radial kernels. Also we apply these results to some concrete classical kernel function spaces and give Jackson-type inequalities for some concrete RKHS approximation problems. Finally, we apply these discussions to learning theory and describe the learning rates with the moduli of smoothness. The tools we used are Fourier analysis and the semigroup operator. The results show that the Jackson-type inequalities of approximation by some radial kernel functions on compact set with nonempty interiors cannot be expressed with the classical moduli of smoothness.


Meta-analysis of the effect of project-based learning on enhancing 21st century skills

Abstract: The research objective is to investigate the effect of project-based learning on improving 21st-century skills. The research method used is a survey with documentation techniques on journals available in Indonesia One Search, which is then analyzed by meta-analysis by calculating the effect size of each research variable. From the journal search results, 25 journals were obtained, consisting of 9 journals on critical thinking skills, 10 on creative thinking skills, and six on problem-solving skills. The research findings revealed that the effect of project learning on students' necessary skills was 88%, the impact of project learning on students' creative skills was 82%, and the effect of project learning on students' problem-solving was 92%.


Distributed Cognition in Fractured Ecologies

Abstract: In this article we present a conversation analytic case study of a video-mediated teleconsultation in which the participants face a problem with the audio connection. During the problem-solving process the interactants need to direct one another’s attention and action in relation to technological artefacts to solve the problem. Video mediation limits physical access to distant participants’ physical ecology, which is overcome by fitting interactional practices to the communicative medium available at a given moment. Drawing on insights from the distributed cognition perspective and the CA perspective on participation, multimodality, and epistemics, we propose specifications to existing theories of problems solving, seeking to develop integrative approaches to real-world problem solving.


Between the clicks

Abstract: Adaptive learning technology has the potential to tailor learning to suit individual students’ needs, desires and competence level. However, qualitative close up studies of students’ interaction with adaptive technology are rare. This study explores 4th grade mathematics students’ use of adaptive learning technology through screen recordings supplemented with analysis of data generated and tagged by the adaptive engine. The study explores how different types of students, i.e. students with varying mathematical competence levels, motivation and self-efficacy towards mathematics, interact with an adaptive learning material, what learning paths emerge for different types of students in their interaction with the adaptive learning resource and how student self-efficacy is affected by the interaction. Implications for both future design and teacher use of adaptive learning materials are discussed.


The role of AI chatbots in scaffolding: Linking learning outcomes with assessment

Abstract: Emerging discussions highlight the role of chatbots in education, which centres around fundamental perspectives concerning technology as either beneficial or an unnecessary disruption to the learning and assessment processes. However, it is crucial to recognise that no technology can be deemed value-neutral, and technological euphoria often blinds us to the unintended consequences of its use. Hence, there is a need for a practical understanding informed by value-based perspectives to comprehend how the encoded experiences created by ChatGPT transform the teaching of students. This article makes a significant contribution to the field of educational research, as only limited attention has been given to higher education students’ reflections on the pedagogical use of artificial intelligence (AI) chatbots for learning and assessment. The article discusses, through empirical findings, the constructive alignment between learning objectives and assessment when employing AI chatbots. The data collection process revolves around students’ reflective experiences while utilising the potential of ChatGPT, which are subsequently analysed through focused group interviews. The paper, informed by these observational data, presents a conceptual framework for teachers to employ and assess ChatGPT as a means to scaffold learning among students.


Studi Literatur: Meningkatkan Kemampuan Membaca Anak SD Melalui Buku Cerita Bergambar

Abstract: The aim of this research is to describe improving reading ability through picture story books. The research method uses the literary study method. Where the data collection system in this research comes from books or journals which are analyzed based on existing problems. The results of this research indicate that picture story books are effective in improving students' reading skills. This is possible because when reading story books students are very motivated, because story books are a favorite among students in general. So, when the lesson material is changed into the form of a picture story, it really attracts students' attention


ABAC as Access Control Solution for Digital Evidence Storage

Abstract: Digital evidence is content that must be protected against access and use by parties who should not have the authority to do so. Some protection parameters for access to digital evidence must be implemented to ensure its integrity and authenticity. Access to digital evidence is not enough to be facilitated only with authorization and authentication mechanisms but must also be facilitated with other aspects of access by users according to their level of authority. One approach is to use the concept of access control. The study of access control to digital evidence is essential. However, studies on this matter are still limited. Among the many access control models, the application of access control based on attribute variations is a concept that can be applied to the context of access to digital evidence. This paper discusses policy design and modeling using attribute-based access control (ABAC) with four attributes: subject, resource, action, and environment. Then, it implements and tests various requests to the system based on attribute variations and possible algorithms. This study supports the security of digital evidence storage systems through access control to the resources it manages using the Policy Statement, Policy Modeling, Policy Implementation, and Policy Validation approaches. The application of the access control design shows that the ABAC concept has been successfully applied as an access control solution for digital evidence stored in digital evidence storage systems. The built policy design was successfully validated using ACPT Tools, concluding that there was no inconsistency or incompleteness.


KESIAPAN SUMBER DAYA MANUSIA DINAS PERPUSTAKAAN DAN KEARSIPAN KOTA PALEMBANG DALAM MENGHADAPI TRANSFORMASI DIGITAL

Abstract: This research aims to test the readiness of human resources in facing digital transformation. The method used is quantitative, the object of research is employees of the Palembang City Library and Archives Service. The respondents in this research were 94 employees of the Palembang City Library and Archives Service, collecting data using a questionnaire. Sampling in this study used a saturated sampling technique, namely sampling carried out as a whole from the total population. The analysis technique used in this research is logistic regression, which is a type of regression analysis used to describe the relationship between independent variables and dependent variables, with the dependent variable (Y) being assumed to be binary qualitative/having two categories. The data analysis technique was carried out using SPSS version 20. Based on the results of the binary logistic regression analysis, it was found that significant factors in the readiness of human resources in facing digital transformation were age, length of work, personality and skills. Meanwhile, factors that were not found to be significant in the readiness of human resources in facing digital transformation were innovation & optimism. It is hoped that this research can add information and reading references, thereby increasing knowledge about the digital transformation process.


Facial Expression Recognition Based on Deep Learning: A Review

Abstract: This review paper provides a comprehensive analysis of recent advancements in Facial Expression Recognition (FER) through various deep learning models. Seven state-of-the-art models are scrutinized, each offering unique contributions to the field. The MBCC-CNN model demonstrates improved recognition rates on diverse datasets, addressing the challenges of facial expression recognition through multiple branches and cross-connected convolutional neural networks. The Deep Graph Fusion model introduces a novel approach for predicting viewer expressions from videos, showcasing superior performance on the EEV database. Multimodal emotion recognition is explored in the EEG and facial expression fusion model, achieving high accuracy on the DEAP dataset. The Spark-based LDSP-TOP descriptor, coupled with a 1-D CNN and LSTM Autoencoder, excels in capturing temporal dynamics for facial expression understanding. Vision transformers for micro-expression recognition exhibit outstanding accuracy on datasets like CASMEI, CASME-II, and SAMM. Additionally, a hierarchical deep learning model is proposed for evaluating teaching states based on facial expressions. Lastly, a visionary transformer model achieves remarkable recognition accuracy of 100% on SAMM dataset, showcasing the potential of combining convolutional and transformer architectures. This review synthesizes key findings, highlights model performances, and outlines directions for future research in FER.


Clustering Research on Islamic Empowerment Model using R

Abstract: This bibliometric research was conducted to provide a more in-depth picture of the "Islamic Empowerment Model" by analyzing a number of relevant articles that have been published in the Dimensions database. A total of 233 imlia publication documents in the period 2022-2023 became the object of this research. Using bibliometric analysis methods with the help of Biblioshiny-R software, this study seeks to reveal publication trends over time, identify the most prolific authors in this field, explore journals that have a significant impact in related literature, and reveal the main themes that consistently appear in these scientific works. The results show that the number of publications has increased significantly since 2016, reaching a maximum of 52 publications in 2021 and 2022. 2011 was the year with the highest average number of citations throughout the observation period. The Journal of Social Science Research, is the top publishing journal. There are several authors associated with this journal, such as Huda N, Alhifni A, and Ahwarumi B. The next important finding is that there are 3 research clusters including, Islamic empowerment models through Islamic financial instruments, women's empowerment models in Islam, and Islamic empowerment models through pesantren education. The results of this study are expected to provide valuable insights for various parties, including researchers who want to better understand the development of Islamic empowerment literature, practitioners involved in this field, and academics who want to explore this topic in more depth.


Perception on Bitcoin: A Senti-Strength Application

Abstract: Since 2008, the number of new virtual currencies, as well as the consumer base and frequency of transactions, have all increased dramatically. However, policymakers, economists and entrepreneurs face significant difficulties and opportunities as a result of monetary market turmoil. The purpose of this study is to analyze the sentiment generated from researchers' opinions through published articles related to bitcoin. The data analyzed consisted of 99 research articles indexed by Scopus in 2022. The method used is a qualitative approach with descriptive statistics from the study of bitcoin-themed literature. The results showed that neutral sentiment was the highest result with a percentage of 48% followed by negative sentiment of 23%, positive sentiment 22%, high positive sentiment 4%, and high negative sentiment 3%. Bitcoin has no centralized authority to distribute coins or keep track of who owns coins by design. As a result, creating currency and verifying transactions is much more complicated than in traditional bookkeeping systems. The diversity of sentiment results is obtained because of the pros and cons of bitcoin's existence.


Upaya Meningkatkan Hasil Belajar dengan Model Cooperative Learning Tipe Student Teams Achievement Division Kelas VII SMP Negeri 6 Rokan IV Koto

Abstract: The problem in this research is the low learning outcomes of class VII students at SMP Negeri 6 Rokan IV Koto in Mathematics subjects. To improve student learning outcomes, researchers applied the Student Teams Achievement Divisions (STAD) Cooperative Learning Model in Mathematics learning in class VII of SMP Negeri 6 Rokan IV Koto. This research is classroom action research, carried out in two cycles with a total of 23 students, each cycle consisting of four stages, namely planning action implementation, observation and reflection. From the results of the first cycle of action, 16 students got a score of more than 65 or 69.6% of the 23 students with an average score of 67.3, while 7 students got a score of less than 65 or 30.4% of the first cycle. 23 students. The results of the second cycle of action showed that 21 students got a score of more than 65 or 91.1% of 23 students with an average score of 83.3, while 2 students got a score of less than 65 or 8.9% of 23 students. student. Thus it can be concluded that the application of the Student Teams Achievement Divisions (STAD) Cooperative Learning Model can improve the learning outcomes of class VII students at SMP Negeri 6 Rokan IV Koto.


Detection of sarcasm in tweets using hybrid machine learning method

Abstract: When one wants to express the contrary of what they mean, especially when insulting someone, sarcasm is used. One of the challenges associated with the control and management of content on social networking sites such as Twitter and other social media sites, is the recognition of sarcasm. Because sarcasm is purposefully conveyed through ambiguous word choice, even for humans it can be challenging to identify. Existing methods for automatically detecting sarcasm rely heavily on lexical as well as linguistic clues the majority of the time. On the other hand, these methods have not yielded a large or even a minor enhancement in terms of the correctness of the mood. The purpose of this study is to increase the accuracy of sentiment analysis by proposing a system that is both reliable and effective at identifying sarcasm. In this investigation, three different types of features are focussed: lexical, sarcastic, and context features. These feature sets are utilised in the process of categorising tweets as either sarcastic or not sarcastic. This research presents a sarcastic feature set coupled with an efficient hybrid machine learning approach, which ultimately results in improved accuracy. The results of the experiments reveal that the recommended hybrid machine learning method achieves 97.3% accuracy for sarcastic feature sets which is better when compared to existing machine learning techniques namely k-nearest neighbor, random forest, support vector machine and decision tree for the selection of the appropriate features.


THE INFLUENCE OF CORPORATE SOCIAL RESPONSIBILITY, GOOD CORPORATE GOVERNACE AND ENVIRONMENTAL PERFORMANCE ON FINANCIAL PERFORMANCE

Abstract: The purpose of this study was to identify the influence of corporate social responsibility, good corporate governance and environmental performance on financial performance partially or simultaneously.This research uses quantitative research methods. The population in this research relies on manufacturing companies in the industrial sector listed on the Indonesia Stock Exchange for the 2017-2021 period. This study used a sampling technique that is purposive sampling in order to obtain a sample of 6 companies. Methods of data analysis using multiple linear regression analysis method. The results of this study indicate that; (1) CSR partially has no effect on financial performance, (2) Audit Committee partially has no effect on financial performance, (3) Board of Commissioners partially has no effect on financial performance, (4) Managerial ownership partially affects company financial performance, (5) Environmental Performance simultaneously influences financial performance, (6) CSR, Managerial Ownership, Audit Committee, Board of Commissioners and Environmental Performance simultaneously affect Financial Performance.


Detecting Spam E-mails with Content and Weight-based Binomial Logistic Model

Abstract: Spam e-mails are continuously increasing and are a serious threats to a network and its users. Several efficient methods are available regarding this context, but still, it is evolving randomly. Considering this, the proposed approach addresses the problem of spam detection by combining traditional content-matching criteria with the modified version of the binomial logistic algorithm. The work generates seven categories for content-matching, which begins from three basic categories, namely: special words, adult content, and specific symbols and digits. The remaining four categories are derived from various possible combinations of these basic categories. The words selected for each category are carefully curated based on the human psychology of action and reaction. Then, a weight is assigned to each of the categories to signify their importance and a threshold criterion is deployed before implementing the binomial logistic algorithm, which not only increases the efficiency of the proposed algorithm but also reduces the rate of misclassification. The proposed model is tested on six separate datasets of Enron Spam Corpus, where 98.31% and 92.575% are the maximum and minimum accuracies achieved, respectively, in spam e-mail classification. The AUC_ROC scores for the entire Spam Corpus range between 0.927 and 0.983. A comparison is also carried out between the proposed algorithm and the other methods of spam detection that have logistic regression. Finally, the suggested method can adequately handle a large sample size without compromising the efficacy, which is measured using accuracy, precision, recall, F-measure, and AUC_ROC score.


Fine-grained Sentiment-enhanced Collaborative Filtering-based Hybrid Recommender System

Abstract: Developing online educational platforms necessitates the incorporation of new intelligent procedures in order to improve long-term student experience. Presently, e-learning recommender systems rely on deep learning methods to recommend appropriate e-learning materials to the students based on their learner profiles. Fine-grained sentiment analysis (FSA) can be leveraged to enrich the recommender system. User-posted reviews and rating data are vital in accurately directing the student to the appropriate e-learning resources based on posted comments by comparable learners. In this work, a new e-learning recommendation system is proposed based on individualization and FSA. A hybrid framework is provided by integrating alternating least square (ALS) based collaborative filtering (CF) with FSA to generate an effective e-content recommendation named HCFSAR. ALS attempts to capture the learner’s latent factors based on their selections of interest to build the learner profile. Three FSA models based on attention mechanisms and bidirectional long short-term memory (bi-LSTM) are suggested and used to train twelve models in order to predict new ratings from learner-posted book reviews based on the extracted learner profile. HCFSAR used multiplication word embeddings for stronger corpus representation that were trained on a dataset generated for an educational context and showed a better accuracy of 93.39% for the best model entitled MHAM based ABHR-2 with multiplication (MHAAM), which performed better than other models. A tailored dataset that has been created by scraping reviews of different e-learning resources is leveraged to train different proposed models and validate against public datasets.


Extended Reality Platform for Metaverse Exhibition

Abstract: This paper proposes a web-based extended reality (XR) platform integrating exhibitions based on augmented reality (AR) and virtual reality (VR). The proposed platform includes an XR cloud for managing and sharing data in XR spaces, a VR client for users, and an AR client for real-world users. The XR cloud is a web-based VR service responsible for managing objects and spatial information related to real and virtual spaces while simultaneously facilitating real-time information sharing and synchronization between AR and VR users. The VR client offers a virtual exhibition environment based on the virtual space and information for VR users. The AR client overlays virtual information over objects in a real space, allowing for intuitive interaction with them. The proposed XR platform was implemented with Oculus Quest 2 for VR and HoloLens 2 for AR and was evaluated in a small exhibition environment. In this experimental environment, VR and AR users were able to participate in the same exhibition from different spaces and share their experiences in real time.


Extreme Learning Machine Classification Method based on Twin Strategy Salp Swarm Algorithm

Abstract: Extreme Learning Machines (ELM) are a type of Single Hidden Layer Feedforward Neural Network (SLFN). In recent years, they have attracted attention for their powerful approximation ability and fast learning speed. Compared with traditional neural network algorithms, ELM have the advantages of simple structure, fast learning speed, and good generalization performance. However, since the input weights and biases of ELM are randomly generated, there may be some suboptimal or unnecessary input weights and biases. In addition, ELM may require more hidden nodes, which may slow down its response to unknown test data. To address these issues, a twin strategy Salp Swarm Algorithm (TSSA) is proposed to increase the population diversity to improve the convergence speed of the algorithm through chaotic initialization, while shock inertia weights and learning paradigms are introduced into the follower position updating to enhance the stochasticity of the particles. Classification tests are conducted on six datasets using the proposed TSSA, and the experimental results show that the proposed TSSA-ELM has higher accuracy and better generalization performance than some existing ELM variants.


Entropy Error-based Convolutional Neural Network Sparse Optimization and Application

Abstract: Convolutional Neural Networks (CNNs), as quintessential representatives of deep neural networks, have found widespread applications in numerous domains such as image recognition, object detection, and image generation, owing to their robust nonlinear mapping capabilities. However, the practical deployment of CNNs faces challenges such as low learning efficiency and subpar accuracy due to the intricacies in the network structure. This paper introduces the smoothing L1 regularization term atop the conventional entropy loss function, effectively enhancing both the learning efficiency and algorithmic precision of Convolutional Neural Networks. The efficacy of the improved algorithm is substantiated through numerical simulations.


PPBRFL: Privacy-Preserving Byzantine-Robust Federated Learning

Abstract:
Federated learning is a distributed machine learning approach that allows the neural network to be trained without exposing private user data. Despite its advantages, federated learning schemes still face two critical security challenges: user privacy disclosure and Byzantine robustness. The adversary may try to infer the private data from the trained local gradients or compromise the global model update. To tackle the above challenges, we propose PPBRFL, a privacy-preserving Byzantine-robust federated learning scheme. To resist Byzantine attacks, we design a novel Byzantine-robust aggregation method based on
 cosine similarity, which can guarantee the global model update and improve the model’s classification accuracy. Furthermore, we introduce a reward and penalty mechanism that considers users’ behavior to mitigate the impact of Byzantine users on the global model. To protect user privacy, we utilize symmetric homomorphic encryption to encrypt the users’ trained local models, which requires low computation cost while maintaining model accuracy. We conduct the experimental assessment of the performance of PPBRFL. The experimental results show that PPBRFL maintains model classification accuracy while ensuring privacy preservation and Byzantine robustness compared to traditional federated learning scheme.


ES Data Aggregation Scheme based on Personalized Local Differential Privacy

Abstract: In modern society, with the rapid development of technology, research on Epidemiological Survey Data (ESD) has become increasingly crucial. As various diseases continue to evolve and spread, there is a growing need for a profound understanding of the patterns and trends of disease transmission. Epidemiological investigation, as a key method, provides fundamental data support for devising effective prevention and control strategies by tracking cases, contacts, and potential infectees. However, accompanying this progress is the issue of dealing with a large amount of privacy-sensitive data. Once these data are compromised, it may pose severe harm to individuals and society. To address this challenge, we propose a uOUE-based epidemiological survey data aggregation scheme for the collection and processing of ESD, aiming to enhance the efficiency and accuracy of data coding. We ensure the secure, efficient, and accurate aggregation processing of ESD. Through rigorous validation and comparative analysis, our algorithm complies with the requirements of local differential privacy and unbiased estimation. It demonstrates good practicality and accuracy in ESD collection, providing users with a reliable privacy protection effect.


Digital Death

Abstract: This paper reflexively unpicks digital ethnographic methods employed during ongoing online fieldwork on ‘digital deaths’. To do so, this research delves into the digital afterlife, exploring the fate of online traces and social media profiles after death, and how social media has changed our relationship with death and grieving. Anthropological studies of online death and grief faced new challenges even before COVID-19 moved research projects online. These include shared vulnerabilities and the ethnographer’s position, online field sites, omnipresent online traces and posthumous personhood, and ethical algorithms and duty to the dead. By transparently detailing my research methods whilst conducting research with Facebook and Instagram users navigating loss, this article contributes an honest and extensive debate on processes, challenges, ethics, and research collaboration. Guided by visual and media anthropology, I advocate for a set of methods rooted in shared anthropology (Rouch 1995) which fosters ongoing dialogue with participants. Thus, this article offers a new perspective on digital death, rooted in collaborative storytelling and reflexive methodologies, facilitating discussions on a still-contentious subject in certain societies. Leveraging the benefits of digital ethnography’s multi-sited nature, the research widens its geographical reach and comments on the sociocultural impacts of digital death. Keywords: digital afterlife, digital death, digital ethnography, social media, reflexive ethnography, shared anthropology, grief, methodology


Correlation Avoidance in Single-Photon Detecting Quantum Random Number Generators by Dead Time Overestimation

Abstract: Abstract In the case of quantum random number generators based on single photon arrivals, the physical properties of single-photon detectors, such as time-tagger clocks and dead time, influence the stochastic properties of the generated random numbers. This can lead to unwanted correlations among consecutive samples. We present a method based on extending the insensitive periods after photon detections. This method eliminates the unwanted stochastic effects at the cost of reduced generation speed. We calculate performance measures for our presented method and verify its correctness with computer simulations and measurements conducted on an experimental setup. Our algorithm has low complexity, making it convenient to implement in QRNG schemes, where the benefits of having uncorrelated output intervals exceed the disadvantages of the decreased rate.


Learning Guarantee of SDP Relaxation on Directed Stochastic Block Models

Abstract: Community detection in networks has been a focal point in various scientific domains, but the study of directed networks remains relatively under-explored despite their prevalence and importance in capturing real-world systems. This work addresses this research gap by focusing on Directed Stochastic Block Models (DSBMs), a natural extension of traditional Stochastic Block Models (SBMs) to directed graphs. The inherent complexity of directionality in DSBMs makes them challenging to analyze, requiring new mathematical frameworks and computational approaches. We introduce an augmented matrix to encapsulate the directional relationships within these networks, providing a nuanced perspective for further analysis. In this work, we prove the information-theoretical threshold for exact recovery in the DSBMs and propose an SDP relaxation that can achieve this threshold, thereby contributing to the theoretical understanding of community detection in the realm of directed graphs.


Observation of the non-Hermitian skin effect and Fermi skin on a digital quantum computer

Abstract: Abstract Non-Hermitian physics has attracted considerable attention in the recent years, in particular the non-Hermitian skin effect (NHSE) for its extreme sensitivity and non-locality. While the NHSE has been physically observed in various classical metamaterials and even ultracold atomic arrays, its highly-nontrivial implications in many-body dynamics have never been experimentally investigated. In this work, we report the first observation of the NHSE on a universal quantum processor, as well as its characteristic but elusive Fermi skin from many-fermion statistics. To implement NHSE dynamics on a quantum computer, the effective time-evolution circuit not only needs to be non-reciprocal and non-unitary, but must also be scaled up to a sufficient number of lattice qubits to achieve spatial non-locality. We show how such a non-unitary operation can be systematically realized by post-selecting multiple ancilla qubits, as demonstrated through two paradigmatic non-reciprocal models on a noisy IBM quantum processor, with clear signatures of asymmetric spatial propagation and many-body Fermi skin accumulation. To minimize errors from inevitable device noise, time evolution is performed using a trainable optimized quantum circuit produced with variational quantum algorithms. Our study represents a critical milestone in the quantum simulation of non-Hermitian lattice phenomena on present-day quantum computers, and can be readily generalized to more sophisticated many-body models with the remarkable programmability of quantum computers.


Prediction of turbulent flows behind cylinder arrays via Echo State Networks

Abstract: Abstract This study aims at the prediction of the turbulent flow behind cylinder arrays by the application of Echo State Networks (ESN). Three different arrangements of arrays of seven cylinders are chosen for the current study. Three different wake flow regimes are represented by these arrays: single bluff body flow, transient flow, and co-shedding flow, which allows inherently similar, yet very different, turbulent flows to be studied. The data is reduced by Proper Orthogonal Decomposition (POD), and then the Time Coefficients of the POD Modes (TCPM) are predicted by the ESN. The ESN is optimized with respect to its three main hyperparameters, Input Scaling (INS), Spectral Radius (SR), and Leaking Rate (LR), in order to produce the best predictions in terms of Weighted Prediction Score (WPS). In general, the ESN is capable of imitating the complex dynamics of turbulent flows even for long periods. Furthermore, the mutual interdependencies of the TCPM are well preserved. However, the ESN’s performance in different hyperparameters depends on the flow characteristics. Generally, as flow dynamics become faster and more intermittent, larger LR and INS values make better predictions, whereas less clear trends in SR values tend to emerge.


SMART EDUCATION TECHNOLOGIES IN HIGHER EDUCATION: POSSIBILITIES AND PROSPECTS OF APPLICATION IN UKRAINE

Abstract: The Ukrainian education system is actively engaged in the processes of Eurointegration, prompting leaders, researchers, and educators from many domestic educational institutions to explore the possibilities and prospects of implementing SMART education technologies in the learning process. This issue is relevant as it aligns with the contemporary trends in the development of higher education in Ukraine and globally. SMART education allows for the high-level resolution of a wide range of issues related to the quality management of educational services.


ENHANCING ICT CURRICULUM FOR A MASTER'S DEGREE PROGRAMME IN PROFESSIONAL EDUCATION AND COMPUTER TECHNOLOGIES IN UKRAINE

Abstract: The Russian war against Ukraine and the restrictions of the COVID-19 pandemic have forced the Ukrainian education system to make an urgent transition to distance learning [1]. This has highlighted the urgent need for teachers and students to use information and communication technologies (ICTs) effectively for teaching, communication, and collaboration. Professional education programmes must equip graduates not only to use ICT, but also to integrate it seamlessly into their work [2]. This study focusses on improving the ICT curriculum for a Master's Degree Programme in Professional Education and Computer Technologies in Ukraine to meet the pressing needs of distance education.


METHODS OF IMPLEMENTATION OF FLIPPED LEARNING TECHNOLOGY IN THE PROCESS OF TRAINING STUDENTS IN INSTITUTIONS OF HIGHER AND PROFESSIONAL PRE-HIGHER EDUCATION

Abstract: The article examines the problem of substantiating the expediency of using the "flipped learning" technology in the process of training future specialists and introducing innovative methods into teaching activities. The main models of the flipped classroom are considered, as well as the main approaches to solving the problem of organizing the educational process with the involvement of the "flipped learning" technology to ensure the quality of providing educational services in the process of training future specialists. The article also analyzes the main advantages of this technology in the conditions of mixed and distance learning.


Analisis Tone Warna Dalam Sinematografi Dalam Menciptakan Efek Bahagia Pada Film Dokumenter “ Permata Di Tengah Danau Toba” Sutradara Andi Hutagalung

Abstract: This research investigates the role and impact of the use of color tones in documentary films. Through analysis of a number of leading documentary films, this study aims to understand how choosing color tones can influence the audience's experience and convey messages more effectively. The research results show that color tones can create atmosphere, highlight themes, and influence audience emotions. These findings provide additional insight for ocumentary filmmakers to enhance the power of their visual narratives through intelligent use of color palettes


Giulia Pepe, New migrations, new multilingual practices, new identities: The case of post-2008 Italian migrants in London. Cham: Springer, 2022. Pp. 219. Hb. £89.99.

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Nuria Lorenzo-Dus, Digital grooming: Discourses of manipulation and cyber-crime. Oxford: Oxford University Press, 2022. Pp. 240. Hb. £64.


Greedy‐based user selection for federated graph neural networks with limited communication resources

Abstract: Abstract Recently, graph neural networks (GNNs) have attracted much attention in the field of machine learning due to their remarkable success in learning from graph‐structured data. However, implementing GNNs in practice faces a critical bottleneck from the high complexity of communication and computation, which arises from the frequent exchange of graphic data during model training, especially in limited communication scenarios. To address this issue, we propose a novel framework of federated graph neural networks, where multiple mobile users collaboratively train the global model of graph neural networks in a federated way. The utilization of federated learning into the training of graph neural networks can help reduce the communication overhead of the system and protect the data privacy of local users. In addition, the federated training can help reduce the system computational complexity significantly. We further introduce a greedy‐based user selection for the federated graph neural networks, where the wireless bandwidth is dynamically allocated among users to encourage more users to attend the federated training of neural networks. We perform the convergence analysis on the federated training of neural networks, in order to obtain some more insights on the impact of critical parameters on the system design. Finally, we perform the simulations on the coriolis ocean for reAnalysis (CORA) dataset and show the advantages of the proposed method in this paper.


Persepsi Kesiapan, Sikap dan Motivasi Belajar Mandiri terhadap Pembelajaran Matematika secara Kolaboratif Berbasis Online

Abstract: The research aims to describe the readiness, attitude and motivation of self-learning towards collaborative online based mathematics learning during the covid-19 pandemic. The researcher applies a quantitative descriptive methods in this study. Data collection has been done by giving three kinds of questionnaires including readiness, attitude and self learning motivation during covid-19 pandemic, and has distributed to 80 students in 3 Senior High School and 1 Vacational High School at Nuha district. The result of this research shows that the students are ready for Mathematics learning collaborative online based during covid-19 pandemic. That is supported by their ability to operate the computer and the usage of internet as well as their online learning experience they have been through for 2 years in online learning situation. Students' motivation towards learning mathematics through the implementation of online based collaborative learning is inconsistent even though it is in moderate level of motivation. Some students prefer the face to face learning process to online learning process and think that the previous learning will provide better learning outcomes. This is a challenge for teachers to prepare for the new normal phase with more interesting teaching material, strategies and learning methods by the development of digitalization of learning


ARVMex: A web application for obtaining acoustic reference values for forensic studies in Spanish

Abstract: Abstract Forensic speech experts using the auditory-acoustic-phonetic approach face different challenges when dealing with recordings collected under different technical conditions. We developed ARVMex tool to provide acoustic reference values to describe variation due to technical conditions in recordings. The tool employs a corpus of Spanish speakers, generating descriptive statistics based on different attributes, including speaker characteristics, different sounds, and on two technical conditions: face-to-face interviews and telephone conditions. The results indicate that, for the fundamental frequency, the mean values of the interview attribute accurately depict the behavior of each vowel by gender, whereas the mean values of the telephone attribute exhibit deviation from the general average. In relation to vowel formants, reference values per vowel, gender, and technical conditions must be established considering variations among vowels and the overall mean. The tool presented here aims to assist experts by providing insights into the variations in the measurements of acoustic properties due to technical conditions.


Pembuatan Peta Resiko Bencana di Desa Kubang Tangah Kota Sawahlunto dengan Metode Arc Toolbox

Abstract: Landslide movement is the activity of a balance disturbance process that causes the movement of masses of soil and/or rock from higher to lower areas. The cause of landslides is that the driving force on the slope is greater than the resisting force. Several factors cause landslides such as rainfall, steep slopes, soil density, rock type, land use and vibration. Sawahlunto City is one of 19 cities/regencies in West Sumatra Province that has a high level of vulnerability to landslides. Areas prone to landslides can be identified by utilizing remote sensing data and geographic information systems. This service is carried out by making maps of areas prone to landslides using parameters that cause landslides, including rainfall, soil type, height, slope and land use. These parameters will be processed and analyzed with the help of the ArcToolbox method, where ArcToolbox can be used to detect landslide areas in Kubang Tangah Village. The analysis process is carried out by using the spatial analysis tools feature found in ArcGIS software. Community service produces a slope map that can illustrate the vulnerability of landslides. Obtain results in the form of a landslide vulnerability level map which has 4 vulnerability classes, including non-prone vulnerability class, low vulnerability class, medium vulnerability class, and high vulnerability class.


ANALYSIS OF STUDENTS’ DIFFICULTIES IN TRANSLATING DESCRIPTIVE TEXT FROM INDONESIAN INTO ENGLISH

Abstract: The aims of this research was to find out the students’ difficulties into English and to know how the students were cope with the difficulties in translating descriptive text from Indonesian into English at the eighth grade students of junior high school Tri Dharma Palembang. The descriptive qualitative research was used in this sample of this research. The sample of this research was the eighth grade students at Junior High School Tri Dharma Palembang. There were fifteen participants of students and there was one participant of their English teacher. The data was taken by using test and interview as the instrument. The research finding showed that the highest percentage of students’ difficulties in translating was grammar (27%). the highest number of the students’ grammar difficulties was phrase (45%). The second difficulty faced by students was clarity and cohesion & naturalness (24%). The third difficulty face by students was spelling & style (14%) and the last difficulty was realizing & understanding (11%). Based on the result of students and their English teacher interview matrix, it showed that students asked their friends and teacher to help solving their problem. It could be concluded that students had difficulties in translating Indonesian descriptive text into English was Grammar and the students asked their friends or teacher to give them a help.


Pengelompokan Provinsi di Indonesia Menggunakan Time Series Clustering pada Sektor Ekspor Nonmigas

Abstract: Indonesia's export activities are dominated by non-oil and gas exports consisting of four sectors, namely the processing industry, agriculture, mining, and others. The government must pay attention to non-oil and gas exports for each province because exports can play an essential role in a country's economic growth. This study was conducted to cluster provinces in Indonesia using time series clustering in the non-oil and gas export sector based on data patterns concerning Dynamic Time Warping (DTW) distance. The sectors used in this study are the manufacturing industry sector and the agricultural sector in 34 Indonesian provinces in the period 2017 - 2021. Time series clustering analysis uses the average linkage method with DTW distance and the selection of the optimum number of clusters using the silhouette coefficient method. The results of the analysis in the processing industry sector resulted in 3 optimum clusters, namely cluster 1 consisting of 1 province that has high processing industry exports, cluster 2 consisting of 8 provinces that have medium processing industry exports, and cluster 3 consisting of 25 provinces that have low processing industry exports. As for the agricultural sector, it produces 2 optimum clusters, namely cluster 1 consisting of 5 provinces that have high agricultural industry exports, and cluster 2 consisting of 29 provinces that have low agricultural industry exports. The clustering results in the processing industry sector and the agricultural sectors have a silhouette coefficient value of 0.778 and 0.798, so it is said to have a strong cluster structure.


A Conversation Analytic Study on Clicks in Mandarin Chinese

Abstract: From the perspective of Conversation Analysis, this paper analyzes clicks in daily Chinese communication. Data analysis shows that clicks generally appear in negative contexts and play an important role in conveying the speaker’s negative attitudes or feelings. This article first discusses the main ways that the linguistic resource of clicks helps the speaker convey his/her negative attitudes: presupposing the click-producer’s disdain, presupposing the speaker’s dissatisfaction, presupposing the speaker’s impatience, and strengthening the speaker’s negative attitude. On this basis, the author explores the main two functions of clicks in Chinese conversation, that is serving as a signal of topic transition and maintaining harmonious social relations.


The Impact of Nutritional School Based Feeding Program on Academic Achievement of Selected Elementary Learners in West Philippines

Abstract: This study investigates the impact of nutritional school-based feeding programs on the academic performance of 45 elementary learners. This research method uses a quasi-experimental design with a pretest and posttest structure; a two-part questionnaire was the primary research tool. The first part gathered demographic information, while the second assessed participating students’ nutritional status and academic performance. Researchers administered the questionnaire to parents or caregivers, selected students systematically, traced household addresses, and conducted home interviews. The study results showed that the general weighted average increased noticeably and statistically significantly after implementing school-based feeding programs. Subsequent investigation, however, revealed a fascinating finding: the gains in academic achievement did not correspond consistently with the varied profiles of respondents, including age, gender, socioeconomic status, and educational attainment. The research highlights the necessity for additional investigation into the impact of nutrition on academic achievement.


End-to-end VVC encoding for energy-aware HDR rendering

Abstract: The production, transmission and display of video content requires significant amounts of energy. Whether broadcasting or streaming a video, its display on modern televisions is responsible for a significant proportion of the energy consumption. This paper proposes a framework for analyzing and processing High Dynamic Range (HDR) video frames that allows a modern screen to use less energy when displaying these frames. The content is analyzed prior to encoding and transmission, generating metadata that are attached to the content. After reception by a compatible display, the metadata is then used along with display parameters and user settings to adapt the peak brightness of the content. Picture quality is assessed objectively and subjectively, while in the meantime, we analyze the influence of the peak brightness over the power consumption of the screen. All these analysis provide us new insights to propose an energy-aware HDR rendering.


3D rooms map concept based augmented reality for green library

Abstract: Abstract Green libraries are an exciting topic for creating environmentally friendly buildings. Libraries are synonymous with book lending services and comfortable reading rooms. The green library concept will be in line with increasing the comfort of library visitors. Libraries with large buildings need room maps for visitors to find room information. This research aims to create a room map with the concept of Augmented Reality (AR). Room maps generally use 2D images that provide information or instructions for all rooms in a building. The Augmented Reality approach as a 3D-based room map will undoubtedly provide a different experience to library visitors. Creating an AR room map will apply the Multimedia Development Life Cycle (MDLC) method. This method is suitable for application in multimedia product development. However, the resulting product is not only used as room map information but can also be used as a library promotional tool.


An Analysis of the Arts Curriculum Framework in India According to the ‘National Curriculum Framework for School Education 2023’ Document

Abstract: The purpose of this study is to explore and analyze the arts curriculum framework, including music subjects, in India's newly announced 'National Curriculum Framework for School Education 2023', in order to examine various implications for Korea's music curriculum. For this purpose, this paper utilizes a literature research method to examine India’s curriculum documents, focusing on items such as 'document structure', 'competency', 'rationale and aims', 'content system and achievement standards' and ‘teaching & learning and assessment’ according to the components of Korea's 2022 revised music curriculum. As a result of the analysis, the characteristics of each item and the differences with Korea's 2022 revised music curriculum were presented, and based on this, implications related to the design method and content of Korea's music curriculum were derived.


The Analysis of Word Formation Process in Song Album “Traumazine” by Megan Thee Stallion

Abstract: Songs can be used to express ideas or emotions, or even as a way for people to communicate with one another. One area of linguistics, morphology, deals with how language structure might be used to analyze meanings or feelings in song lyrics. In this study, the word formation process for the song Megan Thee Stallion from the album “Traumazine” will be analyze. Descriptive research is the qualitative method employed in this study. The word formation hypothesis of George Yule (2019) is used by the researcher. The 18 songs on Megan Thee Stallion’s album “Traumazine” provided the source of the data used in this research. The “traumazine” album by Megan Thee Stallion contains 8 of the 14 different kinds of word formation process that were examined in this study. This study discovered 34 data on word formation process. There are three data from borrowing, seven from ccompounding, three from blending, ten from clipping, one each from hypocorisms and backformation, eight from acronyms, and one from multiple processes. Based on the findings, Megan Thee Stallion’s song albu ‘Traumazine” most dominantly uses the clipping process to create words


Determination of Radiation Value by Month Using Artificial Neural Network Model; Ankara, Sivas, Erzurum example

Abstract: This research examines the estimation of solar radiation using artificial neural network (ANN) models in Turkish cities with similar latitude values such as Ankara, Sivas and Erzurum. The aim of this study is to investigate whether cities at similar latitudes exhibit similar trends in solar radiation values, despite their geographical differences. In this study, solar radiation prediction was made for 3 cities with a single-layer neural network. Monthly solar radiation intensity was estimated for the 10-year period between 2012 and 2022 with a total of 4764 samples taken from the General Directorate of State Meteorology. An artificial neural network model was developed with 8 units in the first hidden layer and 4 units in the second hidden layer. The optimizer used in compiling the model was determined as Adam, the loss function as 'mean_squared_error' and the metric as 'mse'. ReLU activation function was used in the input layer and hidden layers. A 10-year solar radiation intensity value was used in the output layer. 70% of the data set is reserved for training and 30% for testing data set. As a result, similar solar radiation trends were obtained in the same latitude regions, the results were confirmed by meteorological data.


A Comparative Study on Hadoop MapReduce and Apache Spark Framework for Big Data Analytics

Abstract: In today internet world, due to the current advent of new technologies, mobile devices, and communication media like social networking sites, the amount of data generated every year is growing at a very high rate.The growth of this generated data is beyond our imagination.It is impossible to store these huge data sets in RDMBSs like MySQL, as there are no specific formats of the data and that can be in either text or image formats.It requires the need of technologies which can easily manage and process huge volumes of structured and unstructured data in real-time and can protect data privacy and security.Big data technologies like MapReduce, Apache Flume, and Apache Spark can capture, store, and analyze this huge amount of data in very efficient and less costly manner.Spark and MapReduce programming frameworks provide an effective open-source solution for managing and analyzing the Big Data.MapReduce is a high-performance distributed Big Data programming framework.It processes the data in batch processing environment.On the other hand, Apache Spark is a scalable distributed inmemory data processing engine.It processes the data in both batch and real time environment.It uses Resilient Distributed Datasets (RDD) and Directed Acyclic Graph (DAG) for data processing.In this paper, a review on Hadoop MapReduce and Apache Spark have been made by comparing them on various parameters like performance, streaming, fault tolerance, storage, language support, and reliability.


Deep Gaussian Covariance Network with Trajectory Sampling for Data-Efficient Policy Search

Abstract: Probabilistic world models increase data efficiency of model-based reinforcement learning (MBRL) by guiding the policy with their epistemic uncertainty to improve exploration and acquire new samples. Moreover, the uncertainty-aware learning procedures in probabilistic approaches lead to robust policies that are less sensitive to noisy observations compared to uncertainty unaware solutions. We propose to combine trajectory sampling and deep Gaussian covariance network (DGCN) for a data-efficient solution to MBRL problems in an optimal control setting. We compare trajectory sampling with density-based approximation for uncertainty propagation using three different probabilistic world models; Gaussian processes, Bayesian neural networks, and DGCNs. We provide empirical evidence using four different well-known test environments, that our method improves the sample-efficiency over other combinations of uncertainty propagation methods and probabilistic models. During our tests, we place particular emphasis on the robustness of the learned policies with respect to noisy initial states.


Improving Line Search Methods for Large Scale Neural Network Training

Abstract: In recent studies, line search methods have shown significant improvements in the performance of traditional stochastic gradient descent techniques, eliminating the need for a specific learning rate schedule. In this paper, we identify existing issues in state-of-the-art line search methods, propose enhancements, and rigorously evaluate their effectiveness. We test these methods on larger datasets and more complex data domains than before. Specifically, we improve the Armijo line search by integrating the momentum term from ADAM in its search direction, enabling efficient large-scale training, a task that was previously prone to failure using Armijo line search methods. Our optimization approach outperforms both the previous Armijo implementation and tuned learning rate schedules for Adam. Our evaluation focuses on Transformers and CNNs in the domains of NLP and image data. Our work is publicly available as a Python package, which provides a hyperparameter free Pytorch optimizer.


Pengaruh Subtitle pada Motion Graphics "Affordable" terhadap Pemahaman Siswa Kelas VII SMPN 7 Bogor

Abstract: The aim of this study is to assess the influence of subtitles in the "Affordable" motion graphics on the comprehension of 7th-grade students at SMPN 7 Bogor. This research utilizes a quantitative descriptive approach. In this study, samples are obtained through non-probability sampling, and respondents are selected using purposive sampling. Simple linear regression is employed as the data analysis technique. The results of the simple linear regression analysis reveal that the "Affordable" motion graphics have a positive and significant impact on the comprehension of 7th-grade students at SMPN 7 Bogor, accounting for 24.46%, while the remaining 75.54% is influenced by other variables.


Humble Leadership Style as Green Human Resource Management: Nurses’Organizational Trust and Work Resilience

Abstract: Background: The cultivation of humble leadership skills is crucial for fostering and preserving thedirect organizational connection among nurses while also bolstering organizational trust and promotingthe resilience of nurses in their work.The research aimed to examine the relation between humbleleadership style, organizational trust, and nurse work resilience. Design: A cross-sectional analyticalresearch design was utilized to conduct this study. Setting: The study was conducted at Minia UniversityHospital and an obstetric and pediatric hospital which are affiliated with the Ministry of HigherEducation and Scientific Research. Subjects: A convenient sample was used in the current study; theirtotal number was 222 nurses. Tools: Humble leadership style practices scale, Organizational Trust Scale,and Resilience at Work Scale (RAW-S). Results: The indicated findings revealed that the majority ofnurses exhibited an elevated disposition towards a humble leadership style, nearly two-thirds had amoderate level of organizational trust, and most of them demonstrated resilience in their workenvironment. Also, a fair, positive association was found between organizational trust and workresilience. Conclusion: A humble Leadership Style as green human resource management effectpositively affects organizational trust and work resilience. This is achieved through the establishment oftrust among staff nurses, which in turn serves as an indicator for fostering healthy relationships andpromoting resilience in the workplace. Recommendation: Managers, nurses, and human resourcesprofessionals can all benefit from advice on how to establish rapport with patients and coworkers in ahealthcare setting, and administrators of healthcare facilities should prioritize fostering positiverelationships between different departments.


Predicting root numbers with neural networks

Abstract: We report on two machine learning experiments in search of statistical relationships between Dirichlet coefficients and root numbers or analytic ranks of certain low-degree $L$-functions. The first experiment is to construct interpretable models based on murmurations, a recently discovered correlation between Dirichlet coefficients and root numbers. We show experimentally that these models achieve high accuracy by learning a combination of Mestre-Nagao type heuristics and murmurations, noting that the relative importance of these features varies with degree. The second experiment is to search for a low-complexity statistic of Dirichlet coefficients that can be used to predict root numbers in polynomial time. We give experimental evidence and provide heuristics that suggest this can not be done with standard machine learning techniques.


Towards Industry 4.0: Innovation of Digital-Based Academic Administration System in Islamic Religious Universities

Abstract: The Industry 4.0 era demands digitalization in all fields, including public services in the higher education sector. This research aims to develop a digital-based academic administration service system. The research was conducted at the Faculty of Tarbiyah and Keguruan Sciences, Institut Agama Islam Negeri Palopo. The development method used was Rapid Application Development (RAD), with stages of requirements planning, system design, development and feedback collection, and product implementation. This research involved staff or education personnel and web developer experts. Data were collected through observation and validation questionnaires and analyzed descriptively using percentage techniques to determine validity. Product validity was tested by expert web developers through analysis using the Aiken V formula. This research provides an important understanding of the importance of digitalization in improving the efficiency and quality of academic administration services in the digital era. The results of this study provide a significant contribution to developing a digital-based academic administration service system and can be a foundation for further research and development in the future. This research significantly impacts academic administration practice by contributing to a digital-based service system. These findings can be the basis for further research and development on optimizing academic administration services in the digital era.


PENDIDIKAN KESEHATAN PHBS, TERAPI PIJAT BAYI DIARE DALAM UPAYA MENURUNKAN FREKUENSI BAB PADA ANAK DI RUMAH SAKIT

Abstract: Abstrak: Diare adalah suatu kondisi dimana seseorang buang air besar dengan konsistensi lembek atau cair, bahkan dapat berupa air saja dan frekuensinya lebih sering. Pijat diare adalah terapi sentuh yang digunakan untuk mengatasi masalah diare dan menurukan frekuensi BAB. Tujuan Kegiatan ini ntuk mengetahui perilaku hidup bersih sehat dan tingkat pengetahuan ibu tentang pijat diare pencegahan diare pada anak. Metode yang digunakan pada kegiatan penyuluhan ini dilakukan melalui cuci tangan 6 langkah dengan praktik langsung dan terapi pijat diare pada anak yang mengalami diare dengan terapi pijat diare. Sebelum dilakukan kegiatan orang tua diberikan kuesioner dengan 10 pertanyann diare pada anak. Setelah mengetahui anak yang mengalami diare orang tua berkumpul untuk mengikuti penkes diare pada anak. Kegiatan ini dilakukan 40 menit. Setelah itu orang tua diberikan kuesioner diare, Kegiatan ini diikuti oleh 20 peserta orang tua. Hasil kegiatan berupa pengaruh pengetahuan orang tua tentang PHBS dan terapi pijat diare dalam penurunan frekuensi BAB dilihat dari kuesioner sebelum pendidikan Kesehatan dilakukan didapat hasil Tingkat pengetahuan sebesar 40% dan setelah kegiatan terjadi pengaruh pengetahuan diare sebanyak 98,8%.Abstract: Diarrhea is a condition where a person defecates with a soft or liquid consistency, it can even be just water and the frequency is more frequent. Diarrhea massage is a touch therapy used to treat diarrhea problems and reduce the frequency of defecation. Activity Objective: To determine healthy clean living behavior and the mother's level of knowledge about diarrhea massage to prevent diarrhea in children. Activity Method: This outreach activity is carried out through 6-step hand washing with direct practice and diarrhea massage therapy for children who experience diarrhea with diarrhea massage therapy. Before carrying out the activity, parents were given a questionnaire with 10 questions about diarrhea in children. After learning that the child had diarrhea, the parents gathered to take part in the child's diarrhea health program. This activity takes 40 minutes. After that, parents were given a diarrhea questionnaire. This activity was attended by 20 parent participants. Activity results: the influence of parents' knowledge about PHBS and diarrhea massage therapy in reducing the frequency of defecation seen from the questionnaire before the health education was carried out, the results showed that the level of knowledge was 40% and after the activity the influence of diarrhea knowledge was 98.8%.


PROGRAM KAMPUNG TANGGUH BERBASIS TEORI HIRARKI MASLOW UNTUK RECOVERY LITERASI DAN NUMERASI ANAK-ANAK PENYINTAS KEBAKARAN

Abstract: Kendala yang dialami oleh anak-anak di Kota Sorong, tepatnya di Kelurahan Malabutor Distrik Sorong Manoi yang menjadi pusat kebakaran hebat yang terjadi pada tanggal 22 Maret 2023 menjadi persoalan serius, setiap anak penyintas kebakaran di daerah ini mengalami trauma hebat yang menyebabkan hilangnya minat dan semangatnya untuk belajar ke sekolah sehingga menjadi salah satu penyebab kurangnya kemampuan anak dalam bidang literasi dan numerasi. Tujuan kegiatan pengabdian ini antara lain meningkatkan pengetahuan dan keterampilan literasi dan numerasi anak, serta meningkatkan minat belajar dan mengurangi tingkat kecemasan anak. Metode pelaksanaan kegiatan pengabdian ini, yaitu kegiatan diawali dengan melakukan pre-test untuk mengetahui kemampuan awal peserta, setelah itu dilakukan implementasi kegiatan selama dua minggu dengan membagi peserta menjadi dua kelompok, yaitu kelompok jenjang dasar dan kelompok jenjang madya sesuai dengan hasil pre-test, setelah dilakukan implementasi kegiatan selanjutnya dilakukan post-test untuk mengetahui kemampuan akhir peserta. Hasil yang telah dicapai dari kegiatan pengabdian ini, yaitu peningkatan kemampuan literasi sebanyak 85,93%, peningkatan kemampuan numerasi sebanyak 86,67%; dan peningkatan minat belajar anak sebanyak 85,75%.Abstract: The obstacles experienced by children in Sorong City, specifically in Malabutor Village, Sorong Manoi District, which was the center of the major fire that occurred on March 22 2023, are a serious problem. Every child who survived the fire in this area experienced significant trauma, which lead to lose interest and enthusiasm to study at school. This is one of the reasons why the children in this area lack of ability in literacy and numeracy. This service project's objective to increase children's curiosity to minimize their anxiety levels alongside further developing their literacy and numerical capabilities. The method for implementing this service activity is that the activity begins by conducting a pre-test to determine the participants' initial abilities, after which the activity is implemented for two weeks by dividing the participants into two groups, namely the elementary level group and the intermediate level group according to the results of the pre-test, After implementing the activity, a post-test is then carried out to determine the final abilities of the participants. The results that have been achieved from this service activity are increasing literacy skills by 85.93%, increase in numeracy skills by 86.67%; and increasing children's interest in learning by 85.75%.Abstract: The obstacles experienced by children in Sorong City, specifically in Malabutor Village, Sorong Manoi District, which was the center of the major fire that occurred on March 22 2023, are a serious problem. Every child who survived the fire in this area experienced significant trauma, which lead to lose interest and enthusiasm to study at school. This is one of the reasons why the children in this area lack of ability in literacy and numeracy. This service project's objective to increase children's curiosity to minimize their anxiety levels alongside further developing their literacy and numerical capabilities. The method for implementing this service activity is that the activity begins by conducting a pre-test to determine the participants' initial abilities, after which the activity is implemented for two weeks by dividing the participants into two groups, namely the elementary level group and the intermediate level group according to the results of the pre-test, After implementing the activity, a post-test is then carried out to determine the final abilities of the participantsThe method for implementing this service activity consists of: 1) FGD carried out by all 6 team members; 2) Subdistrict FKK followed by the team and head of Malabutor Subdistrict and the subdistrict employee in charge of the district where the fire occurred; 3) Program socialization, attended by the team, KKSS Secretary, IWSS representatives, literacy resource persons, residents of RW 01 village of Makassar; 4) The pre-test was attended by 37 children; 5) implementation of activities carried out for two weeks; 6) Post-test attended by 37 children; and 7) evaluation and monitoring carried out through the supervision of LP3M Education University Muhammadiyah Sorong. The results that have been achieved from this service activity are increasing literacy skills by 85.93%, increase in numeracy skills by 86.67%; and increasing children's interest in learning by 85.75%.


FUTURE TECHNOLOGICAL EDUCATION OF PROFESSIONAL TEACHERS’ COMPETENCE OF FORMATION MECHANISMS

Abstract: This article serves to increase the efficiency of the mechanisms of formation of professional competence of highly qualified technological education teachers in pedagogical institutions of higher education.


OPPORTUNITIES FOR THE DEVELOPMENT OF EDUCATIONAL SERVICES BASED ON CREATIVITY PEDAGOGY

Abstract: The article explores new opportunities for the development of educational services based on the pedagogy of creativity. As the education sector undergoes transformational changes, there is growing recognition of the importance of developing students' creativity. This topic delves into the theoretical foundations of creativity pedagogy, explores its practical applications, and examines potential ways to expand educational services in which creativity is a cornerstone. The goal is to shed light on how using creativity ineducation can lead to more dynamic and effective learning environments.


EDUCATIONAL AND METHODOLOGICAL SUPPORT FOR THE DEVELOPMENT OF PROFESSIONAL QUALITIES IN THE STUDY OF ENGINEERING COMPUTER GRAPHICS

Abstract: This article outlines the role of the use of modern pedagogical and innovative technologies in teaching engineering graphics. It is stated that the main purpose of using modern pedagogical technologies is to achieve students' efficiency for the easiest mastering the material and advanced training.


DEVELOPMENT OF STUDENTS' GRAPHIC COMPETENCE USING INTERACTIVE METHODS

Abstract: This article provides methodical recommendations for developing graphic competence by increasing students' motivation with the help of interactive methods.


OPTIMIZING PEDAGOGICAL CONDITIONS FOR STUDENT INTERNATIONALIZATION IN HIGHER EDUCATION INSTITUTIONS

Abstract: As higher education institutions increasingly globalize, the importance of providing optimal pedagogical conditions for students engaged in internationalization becomes paramount. This article explores various organizational strategies and pedagogical approaches to enhance the educational experience of students in the context of internationalization. Drawing on existing literature and empirical evidence, this paper discusses the significance of cultural competence, language proficiency, intercultural communication skills, and supportive learning environments in fostering successful internationalization initiatives. It also highlights the role of technology, collaborative learning, and experiential learning opportunities in facilitating cross-cultural understanding and academic success among students in higher education institutions. Overall, this article aims to provide insights and recommendations for educators and administrators to effectively organize pedagogical conditions that promote the internationalization goals of their institutions.


INNOVATIVE TECHNOLOGIES IN IMPROVING THE ASSESSMENT COMPETENCE OF FUTURE TEACHERS

Abstract: This article discusses effective innovative technologies and their importance in improving the assessment competence of future teachers.