Reviewerly/exHarmony huggingface dataset:
A Study on the Utilization of Aesop's Fables in Moral Education: Focusing on the Revised 2022 Elementary School Moral Education Curriculum

Objectives The purpose of this study is to explore the utilization of Aesop's Fables in moral education, with a focus on the revised 2022 elementary school moral education curriculum. Methods To this end, I would like to present the individual fables of Aesop’s Fables by linking the content system of the 2022 revised elementary school moral education curriculum. In addition, it was intended to provide information and reasons for fables to be careful when using Aesop’s Fables in elementary school moral subject education. Furthermore, in order to pursue the formality of moral development in moral education, I tried to illustrate major learning questions and simple utilization measures through Aesop’s Fables. Results Aesop’s Fables were related to 202 in the “Relationship with oneself” section, 85 in the “Relationship with Others,” 46 in the “Relation with Society and Community” section, and 25 in the “Relation with Nature” section, which is the content system for the 2022 revised elementary school moral education curriculum. In detail, Aesop’s Fables could be linked to 19 content systems of questions and achievement standards. In addition, from Aesop's fables, I was able to extract 192 fables to be careful about in terms of “sincerity”, “consideration”, “justice”, “responsibility”, which are the core values of the moral education curriculum, and “sexual ethics”. And, moral questions for each fable were proposed for an approach to the formality of moral development of moral education. Finally, I was able to select ‘Wolf and Dog’ fable and present some examples of using them as a formal approach to moral development. Conclusions Aesop's Fables, which has been used as a moral story resource for a long time, can be effectively utilized as a story material for the revised 2022 elementary school moral education curriculum's content system and achievement standards and approaches to moral development forms. However, caution should be taken regarding non-moral elements, and further detailed discussions on specific utilization strategies will be necessary in the future.
Development and Performance Evaluation of Personalized Book Recommendation Engine based on LightFM for Elementary School Students

Objectives In this study, a personalized book system is designed for elementary school students based on machine learning technology, and a hybrid recommendation engine is developed. In addition, the purpose is to verify the performance through performance analysis of the developed recommended engine. Methods To achieve the above purpose, a toaping reading service with 9,000 members was used. 5,979,479 reading and reading activities were targeted, and a book recommendation system was developed using LightFM. The ranking for the activities was applied by applying the weight of each activity to the processing of Korean and various reading activities were applied. Results Performance analysis of the developed system was conducted. Performance analysis was conducted on AUC Score, Accuracy, Precison, Recall, and F1-Score, and as a result, 99.9%, 95.1%, 92.5%, 86.3%, and 89.3%, respectively. It was confirmed that the recommended book reflects the student's interest. Conclusions Through the results of the performance analysis, it was confirmed that the books selected by individual students and the books recommended by the developed recommendation engine were similar. It was confirmed that for elementary school students, book recommendations according to each student's area of interest should be made in addition to book recommendations by experts or related organizations. In addition, in order to improve the completeness of the system developed through this study, additional analysis and reflection of big data on book loans by public institutions are needed, and various additional studies are needed to recommend customized books.
Study on the characteristics of the online studying room as a learning space: Focusing on secondary students' learning experiences

Objectives The purpose of this study is to study the characteristics of the online studying room as a learning space focusing on secondary students' learning experiences. In this study, secondary students' learning experiences in the online studying room were analyzed, and based on this, the characteristics of the online studying room as a learning space were derived. Methods The research methods applied in this study were in-depth interviews targeting seven secondary students. In this study, Ecological Systems Theory was selected as an analysis framework for online studying room. Results First, secondary students' learning experiences in the online studying room were as follows. In the early stage of learning, learning in the online studying room as learning start stage, based on personal characteristics of expectations and curiosity for learning, was affected by the macrosystem of the influence of strengthening distance education due to COVID-19, Z culture and the microsystem of the reaction of parents and the reaction of friends. It was found that secondary students started learning in the online studying room in their free time. In the middle stage of learning, learning in the online studying room as learning execution stage, based on personal characteristics of self-control and competition for learning, was affected by the microsystem of the interaction of online studying room function and the interaction of friends. It was found that secondary students continued to learn in the online studying room. In the late stage of learning, learning in the onlinestudying room as learning assessment stage, based on personal characteristics of confidence and self-reflection in learning, was affected by the microsystem of the improvement of learning grades at school and the improvement of learning efficiency in the online studying room. It was found that secondary students formed learning habits in the online studying room. Second, the characteristics of the online studying room as a learning space are as follows. In the early stage of learning, the online studying room appeared as a open learning space selected by socio-cultural context, in the middle stage of learning, a learning space to share reflecting advanced science and technology, and in the late stage of learning, a self-learning space where effective and efficient learning is possible. Conclusions First, policy support is needed for revitalization of online learning space outside of school for secondary school students. Second, it is necessary to establish a social support system of linkage between secondary schools and homes centering on the online learning space outside of school. Third, it is necessary to educate secondary school students about self-directed learning in the online learning space outside of school.
Class Support Status of University Students with Deaf and Hard-of-Hearing in a pandemic situation

Objectives The purpose of this study was to find out the differences in support for distance learning classes that changed over time due to COVID-19, the pros and cons of taking classes according to class types, and difficulties outside of classes. Methods The study participants were 10 University students with Deaf and Hard-of-Hearing who were enrolled in a university and took lectures at the university in 2020 and 2021. The research method was a focus group interview using semi-structured questions. The interview contents were categorized and organized through qualitative analysis. Results As a result, both instructors and students have adapted to the distance learning system, and some universities have introduced shorthand support for changes in distance learning class support. As for the pros and cons of taking classes according to the type of class, face-to-face classes have the advantage of immediate exchange, but there were also difficulties in communication due to wearing a mask. The advantages of remote classes include convenience and reproducibility without restrictions of time and place. In terms of difficulties outside of class, difficulties in disconnecting exchanges and difficulties in performing assignments outside of classes appeared. Conclusions In order to improve these points, ‘quality improvement of support inside and outside of classes’, ‘teaching method utilizing the advantages of the instructor's class type based on Universal Design Learning’, and ‘education to understand hearing impairment for teachers and students taking classes together’ were proposed.
A study of Convergence Learning Method Using Metaverse: Focused on a Jeju Folk Tale, Cheonjiwangbonpuri

Objectives This study aims to investigate the convergent learning method using a Metaverse platform that integrates four subjects, such as Korean, English, Science and Music, based on the choice of common learning topics of a myth of Jeju creation, Cheonjiwangbonpuri. Methods This study examined the characteristics and functions of Metaverse as edu-tech through bibliographic research and carried out the case study about community-based convergence learning that could use the functions of Metaverse effectively. Based on the bibliographic research results, the study found out useful ways of community-based convergence learning, by building a platform suitable for convergence learning of four subjects, and implementing it. Results In this paper, the learner-centered convergent learning plan through Metaverse was designed and made it applicable to the classrooms appropriately. In order to study the myth Cheonjiwangbonpuri, learning topics and learning objectives were selected, and the major topics of learning activities and class organization were designed and they were presented in a sequence of six steps, such as warm-up, understanding, deepening, transforming, expressing, and closing respectively. Conclusions The theme-based convergence learning using an online Metaverse platform makes students determine self-directed learning processes beyond the limited time and space of classroom practices, and it helps them do inquiry activities related to a variety of themes connected with several programs. Thus, it is significant that this study moves towards the autonomous design and management of the local curriculum, by understanding and deepening the contents of Cheonjiwangbonpuri under the integration of four subjects.
The Relationship between the Latent Profile of Teaching Competency of Pre-service Elementary School Teachers and Influence Factors

Objectives The purposes The purpose of this study is to examine the relationship between various latent profiles of teaching competencies and influence factors of elementary school preservice teachers. Methods For this, we applied the Latent Profile Analysis (LPA) based on the data collected through an online survey targeting a total of 537 4th grade preservice teachers enrolled in national universities of education. In particular, IC index, entropy, significance of LMR_LRT and BLRT were used to determine the number of latent profiles. Results As a result of applying LPA, the final five latent profiles were found to be the most suitable. : The teaching competency-high group (26.8%) with the highest level in all sub-factors constituting all teaching competency, class management-high group (6.7%) with excellent class reflection and criticism, class atmosphere creation and class management, teaching competency-medium group (45.1%) with average levels of all sub-factors, teaching competency-low group (18.9%) with all sub-factors below the middle level, and teaching competency-lowest group (3.2%) with all sub-factors at the lowest level. To the next, as a result of verifying the variables that affect the classification of the latent profile for teaching competency, it was found that teacher belief and cooperative competency had a significant effect. Conclusions This study has empirical implications in that it identified the existence of various sub-latent profiles according to the teaching competencies of pre-service elementary teachers, and investigated the effect of internal and professional variables on profile classification comprehensively. Finally, the significance and limitations of this study were discussed.
The Longitudinal Effects of Information Literacy and Self-Regulation on Online Learning Effectiveness Perceived by Adult Learners

Objectives The purpose of this study was to identify changes in information literacy, self-regulation, and online learning effectiveness of adult learners, and to explore the structural relationship between the three variables. Methods A potential growth model was used by using data surveyed in 2014 (9th), 2016 (10th), 2018 (11th), and 2020 (12th) of the Korean Education Longitudinal Study 2005 (KELS 2005). The trend of change over time and the longitudinal relationship was verified. Results As a result of examining the trend of change over time, the average of information literacy, self-regulation, and online learning effectiveness did not change significantly, and the average of online learning effectiveness was relatively high. In addition, both information literacy and self-regulation statically predicted the initial value of the online learning effectiveness, and the rate of change was also significantly predicted. Finally, it was found that information literacy was greater than self-regulation in terms of the influence on the longitudinal change of online learning effectiveness. Conclusions Discussions and suggestions were presented on the significant effects of information literacy and self-regulation on the longitudinal changes in online learning effectiveness of adult learners. To ensure stable online learning effectiveness, prior training to improve competencies such as information literacy and self-regulation is required from the beginning, and support is especially required so that learners can acquire and adapt to new technologies. Through the results of this study, it will be possible to seek ways to develop information literacy and self-regulation skills to increase the effectiveness of online learning in a situation where the importance and application fields of online learning are expanding.
The Effect of the Initiative Personality of College Students Majoring in Fashion Design in China on Career Preparation Behavior through Career Decision Self-Efficacy

Objectives This study analyzed the mediating effect of career decision self-efficacy in the influence of initiative personality on career preparation behavior of Chinese college students majoring in fashion design. Methods Initiative personality, career decision self-efficacy, and career preparation behavior were measured for 539 college students majoring in fashion design who are attending universities in Guangxi and Hunan provinces, China, and career decision self-efficacy in the path leading from initiative personality to career preparation behavior. Results As a result of the analysis, there were significant differences in career preparation behavior according to general characteristics according to grade and major grades, and statistically significant static correlations were confirmed between all variables. The direct effect of the initiative personality of Chinese college students majoring in fashion design on career preparation behavior was not confirmed. Conclusions The initiative personality has no direct effect on career preparation behavior, but has an indirect effect through career decision self-efficacy. Therefore, it is necessary to enable college students to carry out specific and practical career preparation behaviors through the formation of career decision self-efficacy based on the leading personality.
Comprehending the Gossips: Meme Explanation in Time-Sync Video Comment via Multimodal Cues

Recent years have witnessed the booming of online social media platforms with embracing the popular service called “Time-Sync Comment”, which supports the viewers to share their time-sync opinions along with video content. In this way, we observe that numerous semantically-altered terms, or “Memes”, were created by niche users to express their unique ideas and emotions, and further attracted a large group of viewers with better activity and enthusiasm. Unfortunately, since the memes were created based on domain-specific knowledge and semantically varied depending on the multimodal context in videos, newcomers may fail to comprehend the semantic connotation of memes, which may severely impair their user-experiences. To deal with this issue, in this article, we propose a novel meme explanation framework, called ProMDE, to automatically capture and comprehend the memes in time-sync comments, which could further benefit the viewers with meme explanation service. Specifically, we first iteratively reconstruct the original time-sync comments compared with visual embedding to detect the semantically-altered terms as meme candidates. Afterward, based on the guides from the domain-specific corpus, visual and textual features will be fused to represent the context-aware multimodal cues. Moreover, to accurately describe the commonly-seen homophones in memes, i.e., they have the same pronunciation but different word-spelling expressions, we integrate the phonetic symbols as an additional modality to enhance the framework. Finally, we utilize a Transformer-based decoder to generate the natural language explanation for captured memes. Extensive experiments on a large real-world dataset prove that our framework could significantly outperform several state-of-the-art baseline methods, demonstrating the efficacy of modeling multimodal context and pronunciation for meme detection and explanation.
Coherence at first sight

Ito et al. observe the evolution of hybrid light-matter states called “Floquet-Bloch” states with unprecedented temporal resolution. They map the transient electronic band structure in exquisite detail, revealing that Floquet-Bloch bands emerge upon optical driving within just one period. Ito et al. observe the evolution of hybrid light-matter states called “Floquet-Bloch” states with unprecedented temporal resolution. They map the transient electronic band structure in exquisite detail, revealing that Floquet-Bloch bands emerge upon optical driving within just one period.
Enabling text comprehensibility assessment for people with intellectual disabilities using a mobile application

In research on Easy Language and automatic text simplification, it is imperative to evaluate the comprehensibility of texts by presenting them to target users and assessing their level of comprehension. Target readers often include people with intellectual or other disabilities, which renders conducting experiments more challenging and time-consuming. In this paper, we introduce Okra , an openly available touchscreen-based application to facilitate the inclusion of people with disabilities in studies of text comprehensibility. It implements several tasks related to reading comprehension and cognition and its user interface is optimized toward the needs of people with intellectual disabilities (IDs). We used Okra in a study with 16 participants with IDs and tested for effects of modality, comparing reading comprehension results when texts are read on paper and on an iPad. We found no evidence of such an effect on multiple-choice comprehension questions and perceived difficulty ratings, but reading time was significantly longer on paper. We also tested the feasibility of assessing cognitive skill levels of participants in Okra , and discuss problems and possible improvements. We will continue development of the application and use it for evaluating automatic text simplification systems in the future.
SHLPM: Sentiment Analysis on Code-Mixed Data Using Summation of Hidden Layers of Pre-trained Model

Code-mixing refers to the blending of languages. The most serious issue with code-mixing is that people move between languages and type in English instead of transcribing words in Dravidian scripts. Traditional NLP models are trained on large monolingual datasets. Code-mixing is difficult since they can’t manage data code-mixing. A significantly less percentage of research is going on code-mixed data. In this paper, a Kannada-English code-mixed dataset is used. In this dataset, YouTube comments are categorized into a negative, positive, unknown state, not-intended language (not-Kannada) and mixed feelings, and categories based on message-level polarity. The summation of hidden layers of the pre-trained model (SHLPM) like BERT and XLM-RoBERTa classification models was used for this complex and challenging task. The SHLPM-XLM-RoBERTa has given F1-score of 64% for the Kannada-English YouTube comments dataset and outperforms other existing models.
Design of Integrated English online education system based on Artificial Intelligence

With the widespread use of artificial intelligence technology in the field of education, it is bound to have a significant impact on all aspects of college English education. This paper aims to use artificial intelligence technology to design an integrated English online education system. First, it deeply analyzes the existing problems in current English education, carefully reads relevant materials through literature, compares the research results of other researchers with the teaching situation, and solves the problems in combination with the actual situation. Secondly, through the integration of English online education system, formulate appropriate education reform methods, teaching in the experimental class for one year. Finally, the practice data and teaching methods before and after the reform are analyzed. The purpose of data comparison is to summarize the successful events and experiences of English education reform according to information methods. The experiment shows that the teaching results of the online System in this paper are good, and the passing rate of the examination is increased by about 20%, and the satisfaction of teachers and students is over 90.
Natural Language Understanding of Systems Engineering Artifacts

Abstract This paper examines in close relation two fields of growing importance: model‐based systems engineering (MBSE) and natural language processing (NLP). System models provide a structured description of engineering data, whose inherent semantics often remains hard to explore. Natural language understanding, (i.e., the machine analysis of texts produced by humans) an important field of NLP, focuses on semantic text comprehension but cannot directly account for structured information sources. In this paper, we investigate natural language understanding of MBSE artifacts as they appear in industrial scenarios. In this context, the wide and heterogeneous knowledge space and user base calls for novel techniques to facilitate information retrieval from complex system models. We thus propose to leverage domain‐specific text generators to transform models into a descriptive text corpus on which we apply state‐of‐the‐art semantic search and analysis techniques. We illustrate the approach on relevant MBSE examples by performing a qualitative evaluation of intuitive text search and comprehension in textual descriptions obtained from system models.