Reviewerly/exHarmony huggingface dataset:
A Comparative Study on the Educational Effect between the Class Using Havruta and Debate Class: Focusing on Core Competencies of 3C

This study conducted the debate class and class using Havruta targeting the students who were taking a general education course called &lt;Thought and Expression 1&gt; in C university. In the results of comparing the improvement of self-directed competency, creative/convergent competency, and considerate/collaborative competency, the class using Havruta showed the higher educational effects than debate class.&#x0D; Such results of this study must be originated from the fact in which Havruta could induce students’ active participation in class more than debate; Havruta could improve the bond and consciousness of collaboration between students; and the Havruta teaching method develops ‘thinking power’. However, the class using Havruta caused pressure and resistance because of its unfamiliar teaching method in which the whole process should be designed and proceeded by students. In order to solve this, it would be necessary to develop various teaching models and effective learning materials for classes using Havruta.
A Case Study of Korean Listening Class Using Blended Learning: For Foreign Undergraduate in Liberal Arts Courses

This study aims to present the actual practice of listening classes by designing a Korean listening class model using blended learning and applying and operating it in the field. First, the blended learning method and the Korean listening area, especially process based listening classes, were combined, and a class model was designed to allow learner-centered self-directed learning away from the existing teacher-centered classes. The class composition was divided into pre-listening, listening, and post-listening stages, personalized learning through online in the first class, inter -learning through face-to-face classes in the second and third class, and learning linked to writing activities after class. In this study, an active blended learning method focused on individual customized learning was applied by directly producing online content. A survey was conducted to investigate the satisfaction level of blended learning classes conducted during the semester for foreign college students. As a result of more than 90% of the respondents saying they were satisfied with the blended learning class, it was found that the learners' satisfaction was very high. It is expected that the results of this study will be able to suggest a new alternative in operating online and offline combined classes in liberal arts Korean subjects for foreign college students, especially listening subjects.
A Study on the Effect of Learning Community Activities on the Core Competencies of College Students

The purpose of this study is to reveal the relationship between learning community activities and core competencies. A pre-post-test of core competencies was conducted on participants in the K University's learning community program, and interviews with participants were conducted to find out the specific significance of the experience of participating in the learning community and core competencies. The results of the study are as follows. First, it was confirmed that all six core competencies of K University improved overall with the participation of the learning community. Second, the degree of improvement in core competency was different according to the nature of the learning community program. Third, it was found that the participation experience of the learning community not only forms individual learning habits but also creates various learning synergies.
A Study on the College Selection Factors of Regional Universities: Focusing on Major Performance Indicators

The purpose of this study is to investigate the college selection factors recognized by prospective freshmen in the college selection process, and to analyze the factors affecting them. Various data related to the educational performance of local four-year universities were collected. The results of the survey on university lecture evaluation, curriculum satisfaction, core competency diagnosis, freshman admission rate, enrollment rate, and employment rate were analyzed through correlation and regression analysis. Some factors showed a positive correlation among freshman recruitment rate, employment rate, core competency diagnosis, curriculum satisfaction, and lecture evaluation. In addition, as a result of regression analysis to determine how much the freshman recruitment rate was predicted, the employment rate was adopted. By analyzing these factors, it was found that increasing the employment rate is important for the survival of local universities.
A Study on the Influence of Student Peer Correction on Writing Classes: Focused on Correction for the Self-introduction Letter

Peer correction between students is a useful teaching method for students who cannot accept the affixation of professors or who do not submit assignments at all due to their strong arguments. This is because it is possible for students who have not submitted their assignments to receive assignments from other students, read and correct them according to the writing evaluation criteria table. Therefore peer correction in the class provides students who have significantly poor writing skills with the opportunity to participate in the class and can be helped with their writing through the experience of reading and evaluating other students’ assignments. Student peer correction of self-introduction statements conducted by the author during the second semester of 2019 and the second semester of 2020 consisted of learner-centered writing class, so that the learner’s class participation and concentration were also improved. Therefore, Student peer correction in university writing education has had a very positive effect.
A Case Study on the Effect of Extra-curricular Activity: Focusing on English Tutoring Program at K Univ.

This study aims to explore the educational effect from convergence of regular curricula and extra-curricular activities. Since the 7th Educational Reform, overall decline in students’ academic abilities has been found. Their Weak-based learning of foreign languages caused freshmen to need more time and chances to learn English with the help of senior students. This case study is on a tutoring program, which is operated in K Univ. in Chungcheong Province. This program is one of the representative extra-curricular activities, providing various opportunities to teach and to be taught for both tutors and tutees, conducted for 8 weeks--2 hours a week. Building sound tutor-tutee relationship is of importance to have meaningful effect on improving academic achievement. To succeed in this program, more attention was paid on tutors with refresher training for establishing its system. The professor in charge gave three-time education for successful tutoring. After this program, questionnaires were given to the both two to survey their satisfaction as well as academic achievement. The result shows that this program contributes to improving self-esteem, self-efficacy, and acquiring interpersonal skills.
The Paradigm Shift in Higher Education from Traditional Learning to Digitalization

The world continues to have an immersive experience that teaches us how the environment is changing the behavior of individuals and organizations. Online learning has been in existence for decades, but it would not have been enough to convince universities to shift to digitalization. The pandemic has pulled the actors in higher education to come out of their comfort zones. They were compelled to reinvent, re-engineer, and innovate so as to be able to maintain the business. In fact, universities could no longer do business as usual, the well-established traditional ways of teaching could no longer function. With the impact of this pandemic, everyone agrees that higher education before COVID would no longer be the same as the one after. This research aims at investigating the primary digital services that universities may offer in simple ways not only to maintain the same level of service, but to improve the learning facilities with better accessibility, flexibility, proximity, and quality. The study considers a typical university as a case by applying the qualitative research method where academics are being interviewed and their views are researched about digital solutions that may be applied with immediate effect to ensure that learning can still take place despite the disruption brought forward by the pandemic. The findings reveal that digitalization is the trustful solution that universities may rely upon to confront the uncertain environment.
An Approach for Summarizing Text Using Sentence Scoring with Key Optimizer

There is an enormous amount of textual content rolled out over the web, which performs automatic text summarization efficiently. Specifically, extracting the multi-keywords from the textual content produces the summary from the source document by reducing the isolating text. In recent research, these summarization approaches and the problems related to this process are easily addressed with the optimization approaches. In existing research, most investigators concentrate on single-objective solutions; however, multi-objective approaches provide solutions to various issues during summarization. This work adopts a Keyword-based Elephant Yard Optimization (KEY) approach that improves the summarization process. In KEY, the analysis of the elephant movement is performed based on the group (cluster) of elephants. The significance of the movement relies on the priority given to the head. Accordingly, the textual contents are optimized based on the clustering priority. The analysis is performed over the available online datasets for text summarization to provide multiple solutions by handling multi-objective problems. Some of the predominant metrics like ROUGE-1 and ROUGE-2 score and kappa coefficient are evaluated to attain superior outcomes.
Modernisation of education: reforms of Peter I and the present day

The article is devoted to the issues of reforming education in transitional periods of historical development. An attempt is made to compare two eras of innovative transformations - the time of Peter the Great's educational reforms (the first quarter of the XVIII century) and the recent period of our history (the 90s of the XX century - present. time). These difficult periods have a lot in common: the radical restructuring of school education, the transition to Western European models in culture, the rejection of traditional, "old" spiritual forms of education and training. Both reforms had different goals and consequences: in the Peter the Great period it was a search for new knowledge and new meanings of education, in the modern era it was an attempt to completely rethink and remake the Soviet education system.The time of Peter's reforms was evaluated by Russian thinkers in different ways, but almost everyone saw them as a process of modernization of society, an attempt to put it on new technological rails. Liberal and Westernizing thinkers evaluated Peter's reforms exclusively in a positive way, considering them a real revolution in the field of education, which led Russia to an unprecedented strengthening and flourishing. A galaxy of conservative thinkers who assessed the transformations of Peter I as a "storm" and a "thunderstorm" that overturned the old "Moscow" education, believed that they were inevitable, but had many negative consequences, in particular, the division of education into spiritual and secular, uncritical borrowing of its content from the Western European tradition, the forcible planting of schools alien to the spirit of the population.Modern innovative transformations, which are largely contradictory in nature, are still waiting for the evaluation by researchers and teachers.
Substraksi Latar Menggunakan Nilai Mean Untuk Klasifikasi Kendaraan Bergerak Berbasis Deep Learning

Moving object detection systems have been widely used in everyday life. Currently, research in the field of background subtraction is still being carried out to achieve maximum accuracy results. This study aims to model the background subtraction of an image using the mean value with the concept of non-overlapping block. Furthermore, the background abstraction results will be used in deep learning-based moving object detection. Specifically, the input image will be divided into several blocks, then the mean value of each block will be calculated to later produce a binary block (binary map). The binary blocks that have been generated will be used as input for background modeling. The background model aims to separate moving objects from the background in the input image. The resulting moving object (object localization) will be sent to the object classification stage using deep learning. The dataset used in this study is CDNet 2014. The results of the study were able to produce a more accurate moving object detection system. Quantitative tests carried out resulted in an accuracy of above 90%.
Seleksi Fitur dan Perbandingan Algoritma Klasifikasi untuk Prediksi Kelulusan Mahasiswa

Students are a major part of the life cycle of a university. The number of students graduating from a university often has a small ratio when compared to the number of students obtained in the same academic year. This small student graduation rate can be caused by several aspects, such as the many student activities accompanied by economic aspects, as well as other aspects. This makes it mandatory for a university to have a model that can take into account whether the student can graduate on time or not. One of the main factors that determine the reputation of a university is student graduation on time. The higher the level of new students at a university, with the same ratio, there must also be students who graduate on time. An increase in the number of student data and academic data occurs if many students do not graduate on time from all registered students. So that it will affect the image and reputation of the university which can later threaten the accreditation value of the university. To overcome this, we need a model that can predict student graduation so that it can be used as policy making later. The purpose of this study is to propose the best classification model by comparing the highest level of accuracy of several classification algorithms including Naïve Bayes, Random Forest, Decision Tree, K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) to predict student graduation. In addition, the feature selection process is also used before the classification process to optimize the model. The use of feature selection in this model with the best features using 12 regular attribute features and 1 attribute as a label. It was found that the classification model using the Random Forest algorithm was chosen, with the highest accuracy value reaching 77.35% better than other algorithms.
Keyword Analysis of Research Trends in ‘Scientometrics’ Journal from 2014 to 2018

Keyword analysis technique has been employed to measure the frequency of author-supplied keywords that represent contents and concepts. To identify patterns, trends and intellectual structure of Scientometrics publications from 2014 to 2018, the study examined 7879 keywords from 1548 articles extracted from the SpringerLink database. Further, these keywords were standardised using vocabulary tools. The period considered for the study is divided into two sectors, i.e. 2014-2016 and 2017-2018 with the intention of rendering dynamic changes in research interest in recent period. The results show that Bibliometric analysis and subsequently Citation analysis have been the most popular research topics. Scientometrics, Network analysis, Patent analysis, and Altmetrics have emerged during the study period. The study shows that published content indicates some well-established research topics.
A Comprehensive Classification of Sentiment Reviews of Twitter Data in the Domain of Climatology using Machine Learning Techniques

Purpose: This study aims at classification of sentiment reviews of Twitter data in the domain of climatology using machine learning techniques. It focuses on the text classification in order to determine the people’s intension about the climatic issues i.e., climate change, climate variability, environmental aspects etc. This paper portrays a comparison of results obtained by applying different classification algorithms like Naive Bayes (NB), Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbour (KNN), Decision Tree Classifier, Neural Network classifier etc. These algorithms are used to classify a sentimental review and people’s emotions associated with climate. Design/Methodology/Approach: Total 2265 climate reviews data have been taken from Twitter’s developers’ account. After that, we pre-processed the total dataset by removing various symbols, HTTP tags, punctuation, etc. The pre-processed text were analysed and represented through Topic modelling, Multi Dimensional Scaling (MDS) and also Visualization of Heatmap. Next, bag of words are evaluated through various algorithms such as Naive Bayes (NB), Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbour (KNN), Decision Tree Classifier and Neural Network classifier. After applying above mentioned classifier, datasets are tested and scores are noted. For the experiment, 70 % of total reviews (i.e.1586) are used for model training and 30% of total reviews (i.e. 680) are used for testing the models. Findings: By performing different algorithms, it shows that Random Forest classifier algorithm works well than other mentioned classifiers and most of the people have positive sentiment towards climate according to Valence Aware Dictionary for Sentiment Reasoning (VADER).
ASSOCIATION RULES OF SOCIAL DETERMINANTS AND DEATH FROM ORAL CANCER IN BRAZIL: A DATA MINING STUDY

Objectives Determining association rules related to social determinants and death from oral cancer in Brazil. Study Design Data mining study from the Information System of Hospital Registry of Cancer for individuals with oral cancer from 1997 to 2017. Multiple preprocessing techniques and standardization of the data were applied, in addition to to attribute engineering methods. Subsequently, the FP-Growth algorithm was applied with selection of rules with minimum acceptable support and confidence, in addition to analysis by specialists in the application area. Results A total of 72,039 individuals were studied. The majority of the population was male, elderly, White, with less education, and drinkers and smokers. Most of the lesions were located on the tongue, diagnosed in advanced stages, and treated with radiotherapy, and 41.6% of the population died. The data mining analysis indicated an association of death with low level of education, absence of race/color information, male sex, and occurrence in hospitals in the southeast. Conclusions The results of this study can assist in decision making and might contribute to the estimation of the risk of death in the population and as a basis for the definition of public policies. Determining association rules related to social determinants and death from oral cancer in Brazil. Data mining study from the Information System of Hospital Registry of Cancer for individuals with oral cancer from 1997 to 2017. Multiple preprocessing techniques and standardization of the data were applied, in addition to to attribute engineering methods. Subsequently, the FP-Growth algorithm was applied with selection of rules with minimum acceptable support and confidence, in addition to analysis by specialists in the application area. A total of 72,039 individuals were studied. The majority of the population was male, elderly, White, with less education, and drinkers and smokers. Most of the lesions were located on the tongue, diagnosed in advanced stages, and treated with radiotherapy, and 41.6% of the population died. The data mining analysis indicated an association of death with low level of education, absence of race/color information, male sex, and occurrence in hospitals in the southeast. The results of this study can assist in decision making and might contribute to the estimation of the risk of death in the population and as a basis for the definition of public policies.
The role of teachers and parents to improve children’s motivational learning in pandemic situation

&lt;span lang="EN-US"&gt;This study analyzed the teachers' and parents' synergy in motivating children to take online learning at home during the COVID-19 pandemic in Indonesia. The research instrument consisted of 13 survey questions distributed via Google Form and succeeded in capturing 139 samples of parents of preschool and elementary school students spread across 17 provinces in Indonesia. Factor analysis and structural equation modeling (SEM) were conducted to analyze the instrument's validity and test hypotheses. The factor analysis results showed that the 13 instruments used showed an excellent correlation to confirm each variable's structure. The SEM analysis results showed that in the online learning system applied to preschool and elementary school children, parents play a significant role in the children's motivation to take online learning at home. Parents were also a positive and significant mediator variable in the teacher's role in increasing children's learning motivation. These results indicated that parents' and teachers' motivating children to carry out online learning at home during the COVID-19 pandemic in Indonesia must be established and continuously improved. The contribution was a reference for those who intend to develop an appropriate online learning system framework for preschool and elementary school children.&lt;/span&gt;
A Study in Non-hierarchical Coherence: Jerry Tabor's lemon; birch

Jerry Tabor (b. 1966) is an explorer in sound. One of his electronic compositions, lemon; birch (2002), is the subject of a brief study included in this collection honoring Michael Budds. This computer synthesized composition is absolutely fresh, both with respect to its concept of form as well as the marvelous sequence of discontinuities from which it is constructed. It poses challenges to all listeners who, if they are willing to come to terms with the original experience it affords, will arrive at a deeper understanding of how we ascribe meaning to our experiences. lemon; birch was written using computer music composition software created in the mid-1970s by the great composer and innovator Iannis Xenakis and was developed at the Center for Mathematical and Automated Music Studies, a computer music center established by Xenakis in Paris in 1972.
The Strangest NFTs [non-fungible tokens]

NFTs (non-fungible tokens) are all the rage, and these forms of ethereal blockchain are ever-increasing in popularity in the tech community. But have you ever wondered what the weirdest NFT is? Well, we&apos;ve found some crackers so you don&apos;t have to.
The advantage resources mining of practical English with internet of things technology

In order to overcome the problems of low efficiency and accuracy of traditional practical English advantage resource mining methods, this paper proposes a new practical English advantage resource mining method integrating internet of things technology. Use the internet of things technology to guide the focused crawler, obtain the search strategy ACO-FC, collect practical English resources, extract the characteristics of practical English resources, calculate the weight corresponding to the characteristics of English resources through the TF-IDF weight calculation method, and use the class centre vector nearest distance classification algorithm to classify practical English resources, so as to complete the mining of advantageous resources of practical English. The experimental results show that compared with the traditional mining methods, the mining efficiency and mining accuracy of the proposed method are higher, and the maximum mining accuracy can reach 98%.
Usability Testing

Prepare the product for testing – this could be a usable prototype, your finished product, or even your competitor’s product.