Reviewerly/exHarmony huggingface dataset:
A Study of Factors Influencing Successful Entry into the Labor Market for College Graduates

Authors:
raw_author_name: Gil Jae Lee
raw_author_name: Tae Yang Park

Abstract: Objectives The purpose of this study is to analyze the factors influencing the employment outcomes that indicate successful entry into the labor market for college graduates. By doing so, the study aims to derive policy implications for future higher education.&#x0D; Methods To achieve this, data from the 5th to 12th surveys of the Korean Education Longitudinal Study 2005 (KELS 2005) were utilized, tracing the experiences of students from the 1st year of middle school in 2005 until 6 years after university graduation. The Korean Education Longitudinal Study investigates intellectual and moral growth, development, and educational experiences of students from middle school entry through high school graduation, tracking them up to the age of 28, including their initial career acquisition processes, graduate school enrollment, and more. The dependent variable in this study is the employment status of participants collected in the 12th survey. Logistic regression analysis was applied to identify the independent variables influencing this dependent variable.&#x0D; Results The analysis revealed that, except for gender, personal characteristics of students had no significant impact on employment prospects. Factors such as passion for education, academic achievement in the middle school stage, type of university attended, metropolitan area university enrollment, major GPA, and collaborative skills were identified as positively influencing the likelihood of employment.&#x0D; Conclusions The findings of this study highlight that the desire for education and academic achievement during the middle school stage, as well as the type of university attended, metropolitan area university enrollment, major GPA, and collaborative skills during the higher education stage, contribute to the increased likelihood of successful entry into the labor market for the young population who have completed higher education.&#x0D;
A Study on the Faculty Perceptions of the ChatGPT Use in Higher Education: The Case of K University

Authors:
raw_author_name: Jiyoung Lee
raw_author_name: Johye Hwang

Abstract: Objectives The purpose of this study is to present the direction of ChatGPT introduction and teaching and learning plans of universities by analyzing how ChatGPT perception and use behavior differ according to the characteristics of the faculty group in general situations and teaching and learning.&#x0D; Methods For this purpose, 341 faculty at K University were analyzed for their general perception of ChatGPT and perception of educational use according to their major field, digital literacy competency, and experience.&#x0D; Results First, in general, faculty' experience in using ChatGPT is high, and they use it for daily purposes such as searching for information and curiosity, and less for teaching. The reason why chat was not used was because they did not know how to use it and did not feel the need for it. Second, in terms of teaching and learning applications, the translation and summarization function was viewed most positively, and the spread of disinformation was viewed most negatively. ChatGPT utilization capabilities were high in the order of media exploration, research utilization, instructional design, and instructional application. Third, in the field of university support, ethics education for AI use and ChatGPT case sharing were the most important.&#x0D; Conclusions In order to raise faculty' perception of ChatGPT and spread the right culture, it is necessary to establish an ethical foundation at the university, support digital education, and develop and present new technology convergence classes.&#x0D;
Elementary school teachers’ perception of digital literacy education: Based on analysis of digital literacy teaching and learning materials shared on ‘Indischool’

Authors:
raw_author_name: Deul Roh
raw_author_name: Hyounjin Ok

Abstract: Objectives This study focuses on examining the perception of digital literacy education by analyzing teaching and learning materials related to digital literacy education produced and shared by elementary school teachers.&#x0D; Methods To this end, we analyzed teaching and learning materials related to digital literacy shared through ‘Indie School’, a representative communication platform for elementary school teachers, and through this, we investigated when, what content, and why they are providing digital literacy education.&#x0D; Results First, teachers were mainly conducting digital literacy education using creative experiential activity time for upper grades, and the amount of teaching and learning materials showed a rapid increase starting in 2020. This can be said to show that teachers' interest in digital literacy education has increased rapidly since COVID-19, and that they recognize that upper grades are appropriate targets for digital literacy education. Second, it was confirmed that the learning content covered in the teaching and learning materials was concentrated on some content elements of digital literacy, and that some materials were not well differentiated from content elements of related concepts such as news literacy, media literacy, and digital citizenship. This suggests that teachers need a more specialized understanding of digital literacy concepts and characteristics. Third, teachers' digital literacy education objectives were broadly categorized into supporting digital literacy learning activities presented in textbooks, learners' low digital literacy levels, and preventing and resolving problems that appear in digital spaces.&#x0D; Conclusions Based on these results, this study focused on the need for teachers to have a more professional understanding of the concept of digital literacy, the need for support in various aspects to develop teaching and learning materials related to digital literacy, the need for development and distribution of teaching and learning materials for evaluation purposes and the need for research into appropriate teaching and learning methods.&#x0D;
Chinese primary art education and traditional art culture: focusing on the content analysis of art textbooks

Authors:
raw_author_name: Eunkyung Kim

Abstract: In order to explore the teaching content of traditional art and culture in Chinese elementary art education, this research investigated the contents of 12 elementary school 『Fine Art』 textbooks from “People's Education Publishing House(人民敎育出版社).” The educational objectives of traditional art and culture presented in the art curriculum were aimed at promoting Chinese ethnic pride based on the love of excellent ethnic cultural heritage. Based on this, the results of the analysis of the art textbooks are as follows. Firstly, the total number of learning units in the 12 art textbooks is 230 units, and the total number of illustrations presented is 520. Among them, the number of artworks related to Chinese culture is 401, that is 77.1% of the total. And in terms of chronological classification, a significant number of 120 artworks was related to traditional Chinese art and culture before modern times, that is 29.7% of total. Secondly, as for the works related to traditional art and culture, 57.5% of the 120 works was included in the ‘appreciation and criticism’ learning area. Thirdly, the artistic fields to which the works belong to were quite diverse, starting with hieroglyphics, and including ceramics, sculpture, bronze, painting, crafts, architecture, gumbo, theatre, stamps, maps, and musical instruments. Fourthly, the main educational content through the works of each period is as follows: in the case of the Neolithic and Advanced Periods, the background and transformation process of the emergence of ceramic and bronze arts and the sculptural features and values of individual works were mentioned; Mausoleum of the First Qin Emperor in the case of the Qin(秦) Dynasty, the paintings and Buddha sculptures of the Donhuangseokgul in the Tang(唐) Dynasty, the ink-colored portraits of the Song(宋) Dynasty, the architectural arts of the 'Peking Palace' in the Ming(明) Dynasty, and the works related to ceramics and ink and light-colored painting in the Qing(淸) Dynasty, the meaning and value in terms of art history and sculpture were covered. Especially in the works of the Ming Dynasty, we can see that aesthetic issues were also mentioned.
Effects of STEAM-based cooking activities on children’s social skills and scientific problem-solving skills

Authors:
raw_author_name: Hyun-Kyung Lee

Abstract: Objectives This study aimed to reveal the effects of STEAM-based cooking activities on young children's social skills and scientific problem-solving skills.&#x0D; Methods This study conducted STEAM-based cooking activities developed for this study through single-group experimental treatment and pre- and post-tests on 20 5-year-old children attending M early childhood education institution located in G Metropolitan City on April 10, 2023. It was conducted a total of 21 times, approximately 40 minutes each time, 3 times a week for a total of 9 weeks from January 1st to June 9, 2023. In order to examine the effectiveness of the developed STEAM-based cooking activity, data were collected on the social abilities and scientific problem-solving skills of participating children through pre- and post-tests before and after participating in the STEAM-based cooking activity. The collected data were compared to the paired sample t- Verification was conducted and the effect was analyzed.&#x0D; Results First, STEAM-based cooking activities showed statistically significant differences in the pre- and post-scores of children's overall social skills and sub-elements. Second, STEAM-based cooking activities showed statistically significant differences in pre- and post-scores of children's scientific problem-solving skills.&#x0D; Conclusions The significance of the study is that it predicts the development direction, application, and effectiveness of STEAM educational activities for young children and can be used as basic data for the development of various educational activities.&#x0D;
C-PBL Driven Architectural Design Education - Creating Social Values through Self-Directed Learning -

Authors:
raw_author_name: Mi Jeong Kim

Abstract: Solving multidisciplinary problems is one of the core capabilities required in the Fourth Industrial Revolution. Contemporary society also requires such talent with skills. For this reason, many fields of education have tried applying Problem-Based Learning. However, architectural design education still remains in the traditional approach, which focuses on the physical environment itself rather than design problems that may arise due to other factors such as social changes, technological developments, and climate issues. This paper discusses the potential of the Industry-Coupled Problem-Based Learning (IC-PBL) developed by Hanyang University, South Korea, particularly from an architectural design education perspective. For this, the paper first introduces the architectural design class adapting the IC-PBL program in terms of class development and problem identification. It then evaluates the performance of the class according to the project outcomes, focusing on self-directed learning skills and social values. From the class assessment, this paper identifies the potential benefits of architectural design education applied to the IC-PBL, focusing on students' perspectives engaging with social issues. Consequently, this study provides insights into considerations for future curricula for architectural design education to ensure that students meet the requirement of modern society: problem-solving skills. These insights will be beneficial to both learners and educators in the architectural design education area.
AI Model Stability in Industrial IoT Intrusion Detection: Leveraging the Characteristics Stability Index

Authors:
raw_author_name: Love Allen Chijioke Ahakonye
raw_author_name: Cosmas Ifeanyi Nwakanma
raw_author_name: Jae Min Lee
raw_author_name: Dong-Seong Kim

Abstract: In Industrial Internet of Things (IIoT) environments, the reliability and adaptability of machine learning models are crucial for accurate decision-making. This paper introduces the Characteristic Stability Index (CSI) to monitor and ensure the stability of models in the context of heterogeneous IIoT sensor data. The CSI quantifies the variations in feature importance rankings, enabling the early detection of data drift and shifts. The experimentation results validate the performance of the decision tree algorithm to provide actionable insights, facilitating domain experts' adaptability and enhancing decision-making while minimizing operational risks and costs in the choice of intrusion detection systems model.
GAZE DIRECTION MONITORING MODEL IN COMPUTER SYSTEM FOR ACADEMIC PERFORMANCE ASSESSMENT

Authors:
raw_author_name: Olesia Barkovska
raw_affiliation_string: Kharkiv National University of Radio Electronics

Abstract: This paper focuses on the research and application of eye movement and gaze direction analysis in online testing systems. The practical novelty of the proposed model of monitoring the direction of gaze in a computer-based knowledge control system lies in the possibility of automated remote control over a large audience of students. The practical significance lies in creating the same conditions for computer testing for all students and increasing the correspondence of knowledge level to the received test results. The implemented and tested system is relevant and necessary in higher educational institutions, particularly in Ukraine, where remote education has emerged as the safest means of acquiring knowledge. This is especially true for fields of study where practical tasks and laboratory work do not necessitate a student's physical presence at the institution. That is why the application of the latest information technologies is extremely important. The dynamic authentication based on the sequence of eye movements proposed in the model allows error-free user's eye area detection for further analysis of the test subject's behavior. The proposed authentication method excludes the need to enter passwords or CAPTCHAs, ensures the speed of determining the user's presence. Further analysis of the user's direction of vision includes responding to the information received, such as skipping a question or the need for re-authorization. Question skipping occurs when the system decides that the user has not been looking at the screen for an extended period (looking sideways, down, up for more than 30 seconds). Re-authentication becomes necessary if the user exits the test or if there is a user replacement. Real-time gaze control capability is implemented by using massive parallel processing system (NVIDIA GeForce GTX 1650 graphics card) for calculations. The analysis of the results obtained shows that the proposed approach allows gaze detection at different illumination ranges (from 0.3 lux to 10,000 lux), as well as to detect states of the user's eyes that violating test rules for further system response (skipping a question or re-authentication request).
Advancing Digital Transformation in Chinese Education: A Comprehensive Analysis of Current Trends, Opportunities, Challenges, and Strategic Imperatives

Authors:
raw_author_name: Xiaomei Shi

Abstract: This study assesses the progress and identifies the pivotal advancements in China's digital education, including infrastructure improvements, the growth of online learning platforms, policy support, and technological innovations. It underscores digital education's significance in promoting educational equality, updating teaching methods, raising educational standards, expanding resources, and encouraging lifelong learning. Nevertheless, digital education's expansion encounters significant challenges: the digital divide, educators' insufficient digital skills, inconsistent quality of educational content, difficulties in developing students' self-learning skills, and concerns over data security and privacy. To address these issues, the study outlines strategic directions specific to China's educational context: enhancing infrastructure and technology access, increasing digital literacy among teachers and students, supporting personalized and adaptive learning approaches, improving digital resources and evaluation methods, integrating online and offline learning experiences, and expanding international cooperation. These strategies are designed to address existing challenges and stimulate ongoing innovation and improvement in China's digital education sector.
Optimizing Recommender Model: Integrating Knowledge Graph Information Fusion and Attention Mechanism

Authors:
raw_author_name: Ethan Chan
raw_affiliation_string: Memorial University of Newfoundland

Abstract: Abstract Graphs are versatile for capturing relationships between objects, as seen in social networks, enabling the implementation of algorithms like community discovery and clustering. The growing interest in deep learning within the graph domain has led to the development of various graph neural network algorithms in recent years. These algorithms offer an effective solution to address graph learning challenges by incorporating graph operations into traditional deep learning models and leveraging both graph structure and attribute information to handle the intricacies of graph data. Graph neural network algorithms represent an extension of traditional deep learning methods, like convolution, into the realm of graph data. These algorithms incorporate the concept of data propagation to formulate deep learning approaches specifically designed for graphs. Notably, they have demonstrated success in diverse domains such as social networks, recommendation systems, knowledge graphs, and others. In addressing the aforementioned challenges, this paper introduces a novel approach utilizing a graph neural network. To overcome the lack of temporal information in the recommendation network's neighbor structure, an ordered input-based gated cyclic unit is incorporated for state aggregation and updating. The model leverages the unit's capacity for capturing contextual relationships, thereby enhancing its ability to capture temporal features in the neighbor structure and improve predictions on new datasets. Additionally, the paper places emphasis on the shallow output of the intermediate layer. A multi-headed attention mechanism is employed to integrate information from multiple layers of output, ensuring that the shallow structural features provided by the intermediate layer play a more significant role in the scoring prediction task. This enhancement further refines the application of graph neural networks in recommendation systems.
Escalating High-dimensional Imaging using Combinatorial Channel Multiplexing and Deep Learning

Authors:
raw_author_name: Leeat Keren
raw_affiliation_string: Weizmann Institute of Science
raw_author_name: Omer Bar-Tal
raw_affiliation_string: Weizmann Institute of Science
raw_author_name: Christian M. Schürch
raw_affiliation_string: University Hospital and Comprehensive Cancer Center Tübingen
raw_author_name: Shai Bagon
raw_affiliation_string: Weizmann Institute of Science

Abstract: Abstract Understanding tissue structure and function requires tools that quantify the expression of multiple proteins at single-cell resolution while preserving spatial information. Current imaging technologies use a separate channel for each individual protein, inherently limiting their throughput and scalability. Here, we present CombPlex (COMBinatorial multiPLEXing), a combinatorial staining platform coupled with an algorithmic framework to exponentially increase the number of proteins that can be measured from C up to 2 C − 1, and is applicable to any mass spectrometry based or fluorescence-based microscopy platform. In CombPlex, every protein can be imaged in several channels, and every channel contains agglomerated images of several proteins. These combinatorically-compressed images are then decompressed to individual protein-images using deep learning and optimization. We perform feasibility experiments in silico and achieve accurate (F1=0.98, R=0.99) reconstruction for compressing the stains of twenty-two proteins to five imaging channels. We test our approach experimentally and obtain accurate (F1=0.97, R=0.93) images of seven proteins using three channels, both in fluorescence microscopy and in mass-based imaging. We demonstrate that combinatorial staining coupled with deep-learning decompression can serve to escalate the number of proteins measured using any imaging modality, without the need for specialized instrumentation. Coupling CombPlex with instruments for high dimensional imaging could pave the way to image hundreds of proteins at single-cell resolution in intact tissue sections.
Evaluation of Instagram's Neural Machine Translation for Literary Texts: An MQM-Based Analysis

Authors:
raw_author_name: Mozhgan Ghassemiazghandi
raw_affiliation_string: School of Languages, Literacies and Translation
Universiti Sains Malaysia
raw_author_name: Manjet K. M. Singh
raw_affiliation_string: Universiti Sains Malaysia, Malaysia

Abstract: Addressing the global increase in social media users, platforms such as Instagram introduced automatic translation to broaden information dissemination and improve cross-cultural communication. Yet, the accuracy of these platforms' machine translation systems is still a concern. Therefore, this paper aims to explore the potential of Neural Machine Translation utilized by Instagram in producing high-quality translations. In doing so, this study attempts to scrutinize the reliability of Instagram's "See Translation" feature in the translation of literary texts from Arabic to English. A selection of auto-translated Instagram captions is analyzed through the identification, classification, and assignment of error types and penalty points, utilizing the MQM core typology. Subsequently, the Overall Quality Score of the error-based analysis is calculated automatically using the ContentQuo platform. Furthermore, the study investigates whether Instagram Neural Machine Translation can effectively convey the intended message within literary texts. From 30 purposively selected Instagram captions with literary content, the study found Instagram's machine translation lacking in 90% of cases, particularly in accuracy, fluency, and style. Among these, 61 errors were identified: 26 in fluency, 25 in accuracy, and 10 in style, adversely affecting the quality and failing to convey the original message. The findings suggest a need for enhanced algorithms and linguistic architecture in Neural Machine Translation systems to better recognize linguistic variants and text genres for more accurate and fluent translations.