Reviewerly/exHarmony huggingface dataset:
Exploring a Digital Maturity Metric for Schools

The use of Education Technology (EdTech) in schools can variously be aimed at improving pupil attainment, reducing teacher workload and/or reducing school costs. The relationship between individual EdTech interventions and their outcomes is however far from clear due to the existence of many confounding factors. It is therefore interesting to explore how an approach could be developed to identify schools' preparedness or maturity in using EdTech and to explore how this might relate to a general uplift in pupil attainment. It might reasonably be expected that such general maturity in the use of technology by a school will have specific enhancements in the teaching and learning of technology related subjects such as engineering. This paper explores the concept of developing a hierarchy of a schools' use of technology, referred to as a digital maturity metric. It proposes a framework with just three components (pillars) reflecting separately: the extent of Technology in use; Capability to use it; and Strategy for its development and implementation. The research used survey findings of 654 schools in England at the end of 2020 [1] (during restrictions in England related to the COVID-19 pandemic) to populate a metric. The early indication is that this metric did not provide clear evidence of the relationship between schools' level of digital maturity and pupil attainment in either mathematics or in reading. The dataset however, is restrictively small to be conclusive and the survey was not designed with a digital maturity metric in mind. The approach had some weaknesses, such as relevance of survey questions to forming a digital maturity measure and, for example, pupil achievements in confidence and engagement, could not be credited as positive outcomes in the same way as examination performance. Whilst the current project has been successful in identifying different levels of digital maturity across different schools, further work is proposed, including having a clearer pre-determined definition of ‘digital maturity‘. This will help with the design of any research questions or areas for exploration and would also support the design and implementation of any metric going forward. This includes consideration of appropriate survey questions to ensure all facets of digital maturity are measured.
Exploration of Suncheon Bay Hooded Crane Play Puppet Performance

Purpose: This study creates a dance and puppet play about overcoming the hardships and adversity of a hooded crane, tries to lead local students to look back on the ecological environment while enjoying it, and finds contextual meanings that contribute to the educational community through performances.&#x0D; Methods: As part of an implementation study, scripts, photos, and videos related to the creation and performance of the Hooded Crane Play puppet play, which were conducted in 2019 and 2021, were collected and analyzed.&#x0D; Results: In the case of &lt;Hooded Crane Play Puppet Play&gt;, 'participatory play-based performance by students', 'play based on the story of the Hooded Crane', and 'relationship with the local community' were found.&#x0D; Conclusion: As a result of examining the hooded crane play puppetry and performance from the viewpoint of creative artists/researchers/educators, the elements of student participation-type performances were highlighted as important and could contribute to healthy learning for students.
A Study on the Differences in Perception of Professional Support Environment according to Individual Background Factors of Early Childhood Teachers

Objectives In this study, we tried to analyze and discuss the differences in perception of early childhood teachers' personal background variables(age, academic background, career, type of early childhood education institution, and age) (opportunities for professional learning and development, relationship between teachers and directors, expectations for teachers and infants, and decision-making).&#x0D; Methods A total of 624 early childhood teachers working in kindergartens and daycare centers in the metropolitan and non-metropolitan areas were surveyed to collect data. Using SPSS 21.0, independent sample t-verification, ANOVA, and Scheffé follow-up verification were conducted to analyze differences in recognition and technical statistics such as internal consistency analysis, data normality verification, mean and standard deviation.&#x0D; Results In terms of professional learning and development opportunities, there were significant differences in perception according to the age, career, type of early childhood education institution, and age of the teacher and director. In the perception of expectations for teachers and infants, there was a significant difference according to the age of teachers and the type of early childhood education institution. However, in the case of teacher autonomy and decision-making, no significant difference in perception was found according to individual background variables.&#x0D; Conclusions Based on these findings, it was suggested that it is necessary to establish a professional support environment considering the individual characteristics of early childhood teachers, and that discussions on the professional support environment for early childhood teachers need to be approached by subdividing them into sub-variables as well as higher concepts. In addition, the necessity of activating various in-house activities, seminars, and study meetings in consideration of teacher experience to support the professional development and learning of early childhood teachers was discussed.&#x0D;
Integrando la inteligencia artificial para promover la excelencia educativa en la universidad: Un futuro prometedor

The university faces new challenges in the information society, which requires a fundamental change in its traditional educational approaches. Artificial intelligence tools, such as programs and applications, have the potential to transform higher education significantly. Resources that offer unprecedented benefits by generating new knowledge and improving educational quality, provide students with precise and personalized learning that adapts to their individual needs; At the same time, they integrate the various forms of communication and information and communication technologies for their pedagogical exercise. The purpose of the research is to examine the different contributions of the connection between artificial intelligence and higher education, which represents an important challenge for universities in the 21st century. In this new millennium, it is imperative that academic institutions plan, design, develop and implement digital skills in order to train more competent students, students capable of understanding and adapting to the environment of new digital trends according to their needs; concluding that the university acts as an engine of research, education and ethics, generating advances through artificial intelligence that can contribute to the achievement of the Sustainable Development Goals, while training responsible and ethical professionals in this field (SDG4 -Quality education).
ANALISIS KARAKTERISTIK RESPONDEN DALAM MENGGALI PENGEMBANGAN PARENTS ASISSTED LEARNING MODEL (PALM) TERHADAP KEMAMPUAN LITERASI PESERTA DIDIK

This study aimed to describe the characteristics of the selected respondents in exploring the development of the Parents Assisted Learning Model (PALM) on the literacy skills of upper-class students in public elementary schools in coastal and mountainous areas. The data analysis technique used is the descriptive analysis technique. The selected research sample is the parents of students as the key subjects of PALM implementation. The results showed that the characteristics of the parents of students who support the development of the Parents Assisted Learning Model (PALM) in order to support the literacy movement of students in elementary schools in coastal and mountainous areas are the parents of students with the type of work of farmer/fisherman/trader/honorary.
Creative and Predictive Management of Vocational Education in the Context of Social Partnership Development

This research aims to theoretically substantiate the effectiveness of creative-prospective management in the professional training of contemporary youth in the context of developing social partnerships. Additionally, it aims to describe the functional model of creative-prospective management in professional education within the social partnership framework. The study involved 482 South Ural State Humanitarian-Pedagogical University students, the Russian State Vocational Pedagogical University, and the Shadrinsk State Pedagogical University. Furthermore, 100 graduates currently employed in secondary education institutions participated in the study. The following methodologies were employed in the experiment: the Mehrabian Achieving Tendency Scale (used by employers and social partners to determine the relationships between partners in social partnerships) and the modified Magomed-Eminov (1989, 2002) scale (utilized by employers and social partners to assess the quality of graduates' professional training). A multistage quota sampling technique and a review of the modification questionnaires were used to ensure the experiment's reliability. Respondents participated in the assessment of the effectiveness of professional education managers. The empirical research revealed synergistic connections between an expanded list of specialities and training directions associated with the differentiation of professional education and the employability of graduates in the Ural labour market. It was determined that creative and prospective management is necessary to prepare highly qualified and competitive graduates. This allowed for identifying key criteria for assessing the quality of higher education and their indicators, including the level of overall development and professional qualifications of graduates, as well as the employability of graduates in the labour market. Implementing creative and prospective management can serve as the basis for the dynamic expansion of international educational agglomerations. The development of social partnerships can increase the competitiveness of educational organisations involved in diversification processes within the international educational space. The analysed theoretical and practical positions emphasise the challenges and possible solutions associated with the system of social partnership.
Pedagogical conditions for developing professionalism in future teachers-choreographers in the system of choreographic education

The article studies the complex of conditions ensuring the development of professionalism in future teachers in the system of choreographic education and describes these pedagogical conditions. We have identified the factors that determine the effectiveness of their implementation in the pedagogical process. In the current sociocultural paradigm, the relevance of the article is conditioned by such factors as: the need to improve the quality of choreographic education; the revisal of the whole system of professional training of dance teachers; the lack of a well-founded system of effective pedagogical conditions for the development of professional qualities in students-teachers-choreographers. The purpose of the article is to solve the identified problems in terms of scientific and theoretical substantiation of pedagogical conditions for the development of professionalism in future teachers-choreographers. The personality-oriented approach is chosen as the leading approach in the study of the problem. The results of the study present the proposed and substantiated pedagogical conditions that contribute to the development of professional competencies in students – future teachers-choreographers. The practical value of the results obtained is the possibility of introducing the developed pedagogical conditions into professional training of future choreographers.
Use of Big Book Learning Media in Improving the Reading Ability of Class VII Students at SMPN 1 Tanjung Pura

The aim of this research is to improve the reading ability of class VII students at SMPN 1 Tanjung Pura by using the Big Book learning media. The method used in this research is a qualitative method. The research instruments are observation and interview guidelines. The data source for this research is the results of interviews with respondents. The results of observations in this study show that there are problems relating to reading ability in class VII students at SMPN 1 Tanjung Pura, there are 7 students who still 1) don't read fluently, 2) can't spell and hear correctly, 3) there are still those who don't know letters. perfectly, 4) Combining words, 5) Combining words into sentences 6) Sentences into paragraphs. Therefore, the solution offered to this problem is choosing to use big book learning media to change the monotonous way of learning in class by re-teaching the basics of reading and creating special reading learning media that are easy to understand and restore interest in reading for grade 7 students at SMPN 1 Tanjung. Temple. Using the Big Book learning media can help students to be more active in learning activities and facilitate students in understanding teaching materials, especially in learning to read, so that the learning objectives are also achieved with learning media such as the Big Book.
Heterogeneous Ensemble Feature Selection and Multilevel Ensemble Approach to Machine Learning Phishing Attack Detection

Over the decade, technology has presented human facets with easiest means of accomplishing complex tasks seamlessly, especially in the area of communication. Malicious and vicious links are consciously doctored to resemble the original and sent through emails to millions of users at once at a lower price. Since the emergence of phishing and its cohorts, every solution and means to mitigate the attacks has proven unsuccessful due to the dynamic nature of the attacks. Meanwhile, machine learning (ML) is adopted as the right antidote to phishing detection, with its performance based on diverse steps, especially feature selection. Most studies in the problem domain concentrate more on model optimization than sourcing for a reliable feature selection system and fail to integrate a reliable feature selection along with the classification model. The systems are fed with low-quality data that hampers the performance of such models. The authors noticed the contribution of feature selection to the performance of machine learning models and developed a novel Heterogeneous Ensemble Feature Selection (HEFS) framework for multilevel ensemble machine learning-based phishing detection. In HEFS, three filter-based statistical techniques were exploited to produce a primary subset of phishing features, and the variable selected by each of the techniques was automatically aggregated to produce the baseline features. The selection of the techniques is to overcome each limitation since their ranking principles are different. The experiment revealed that the multilevel ensemble (stacked) on the baseline features outperformed others with an accuracy of 98.8%., including multilevel model on each filter-based method.
RELATIONSHIP BETWEEN LANGUAGE LEARNING STRATEGIES AND ACHIEVEMENT WITH LEARNING MOTIVATION AMONG STUDENTS OF CHINESE AS FOREIGN LANGUAGE PROGRAMME IN MALAYSIA

In Malaysia, teaching Chinese as a foreign language began in 1963 and has grown in popularity. Language Learning Strategies, or the measures students take to increase their learning, are critical to the final success of Chinese as a Foreign Language. Student achievement is determined by the application of language learning methods. Learning motivation, on the other hand, has a significant impact on academic achievement. This study's purpose is to (1) investigate the level of Language Learning Strategy, Learning motivation, and Language Learning achievement. (2) To investigate the relationship between Language Learning Strategy and Language Learning achievement; (3) To investigate the relationship between Language Learning Strategy and Learning motivation; and (4) To investigate the relationship between Learning Motivation and Language Learning Achievement among students of the Chinese as a Foreign Language programme in Malaysia. The collected data were analysed quantitatively using SPSS for descriptive analysis and the relationship between language learning strategies, learning motivation, and achievement. The Pearson correlation analysis showed the highly correlated between language learning strategies, learning motivation, and achievement.
Development and Application of Problem-Based Job Experience Program to Improve Elementary School Students’ Creative and Convergence Career Competency

This study developed a program to improve creative and convergent career capabilities using job experience facility, which is an active career education method, and investigated whether the program affects learners' satisfaction and creative·convergent career competency(creative and convergent thinking skills about the profession and career perception). For the vocational experience program, the ADDIE instructional design model and problem-oriented learning instructional design strategy were applied to develop vocational experience pre and post professional programs for elementary school students. After that, the program was conducted to 3rd and 4th graders, satisfaction was investigated, and creative·convergent career competency evaluation sheets were reorganized and evaluated according to the target. As a result, there was no statistically significant difference in program satisfaction, but there was a significant difference in creative and convergent thinking skills and career perception for jobs, which had a positive effect on satisfaction and creative and convergent career competency.
Robust Korean Table Machine Reading Comprehension across Various Domains

Unlike regular text data, tabular data has structural features that allow it to represent compressed information. This has led to their use in a variety of domains, and machine reading comprehension of tables has become an increasingly important aspect of Machine Reading Comprehension(MRC). However, the structure of tables and the knowledge required for each domain are different, and when a language model is trained for a single domain, the evaluation performance of the model in other domains is likely to be reduced, resulting in poor generalization performance. To overcome this, it is important to build datasets of various domains and apply various techniques rather than simply pre-trained models. In this study, we design a language model that learns cross-domain invariant linguistic features to improve domain generalization performance. We applied adversarial training to improve performance on evaluation datasets in each domain and modify the structure of the model by adding an embedding layer and a transformer layer specialized for tabular data. When applying adversarial learning, we found that the model with a structure that does not add table-specific embeddings improves performance. On the other hand, while adding a table-specific transformer layer and having the added layer receive additional table-specific embeddings as input, shows the best performance on data from all domains.
Transforming Domestic Helper Recruitment and Management with Deep Learning : A Web Application

In our research, we tackle the challenge faced by families in finding reliable household workers like caretakers and gardeners. Many struggles with trust issues when hiring help for their homes. To address this, we've developed a secure web application. Users can connect with background- checked workers, ensuring the safety of their homes. The application consists of modules for different users - Admin, Worker, and User - each serving a specific purpose. What makes our app unique is the integration of Deep learning. The DL verifies uploaded images to ensure they are of human faces, enhancing security. Additionally, it checks the similarity of images of workers to maintain consistency and reliability in their profiles. By implementing these features, our web application aims to provide a trustworthy platform for households seeking reliable help, contributing to a safer and more secure hiring process.
Perbandingan Analisis Sentimen Aplikasi Traveloka dan Tiket.com pada Twitter dengan Metode Support Vector Machine

The emergence of the COVID-19 pandemic in Indonesia resulted in an economic crisis, including in the world of tourism, which caused a decline in the national economy. With the existence of Online Travel Agencies (OTA) such as Traveloka and Tiket.com, it is hoped that they can help improve the tourism sector for the Indonesian economy. As a popular OTA and to see the opinion of the Indonesian people, it can be seen from public opinion in the form of tweets on the Twitter application. The tweets data will be taken and sentiment analysis will be carried out on the OTA Traveloka and Tiket.com applications which will be classified into certain classes based on opinions and modeling will be carried out using the Support Vector Machine (SVM) algorithm method. This research aims to determine the level of accuracy of the SVM algorithm and find out how sentiment analysis compares between Traveloka and Tiket.com. In the sentiment analysis comparison, in terms of price, Traveloka is superior and in terms of service, Tiket.com is superior. After modeling by comparing splitting data and handling imbalanced data using Synthetic Minority Oversampling Technique (SMOTE), the best SVM accuracy results for the Tiket.com price dataset were 68%, for Traveloka prices it was 97%, for Tiket.com services it was 92%, and for Traveloka services it is 89%.
Classification of Human Postures

The classification of human postures is required in various aspects such as the description of human activities, the synthesis of humanoid robots’ movement, and in the research on robots cooperating with humans. The presented research aims to develop the method for posture classification using the data recorded by the RGB-D cameras. The method enables the recognition of the activity represented by the sequence of human postures viewed in the sagittal plane. The competitive neural network (cNN) was used. The method enables the fast recognition of human activity and is useful for ‘learning by observation’ robots.
A Method for Improving Millimeter Wave SAR Image Quality Based on Coherent Speckle Noise Suppression

Speckle noise seriously hinders the application of synthetic aperture radar (SAR) images. Based on the formation mechanism of speckle noise, the principle of filtering algorithms for suppressing SAR speckle noise was analyzed. The filtering algorithm was validated using millimeter wave SAR image data to remove speckle noise in SAR images. The filtering effect was quantitatively analyzed based on the evaluation parameters of noise filtering effect.
Development of a Textile Device Applied to Hand Gesture Recognition

This paper presents a novel chip-free textile tag for gesture recognition applications. The proposed tag consists of a frequency selective surface (FSS) printed on the fabric with conductive ink. The transmission coefficients of the device proposed are controlled by each finger position. This is achieved by associating each finger phalanx with a conductive element. The authors present and discuss a two-finger prototype that was fabricated from fabric commonly used in the textile industry, the jeans. The results obtained demonstrate that the proposed device has a high degree of applicability in gesture recognition applications.