Flip your classroom: Reach every student in every class every day

For a growing number of schools and districts, the hottest thing in education improvement is "flipped learning," the idea that technology, properly exploited, can push a lot of basic learning outside school walls, creating room and time for deeper, more personalized instruction within. Though details vary considerably, a flipped classroom is fundamentally about upending time on task. Instead of holding forth at the front of a classroom—the traditional notion of the "sage on a stage"—teachers convert their lectures to videos, slide shows, or audio lessons that can be watched by students at home or elsewhere via the Internet or other information transfer tools, such as DVDs and thumb drives. With lectures out of the classroom, teachers are free to use time during the school day for more individualized instruction, labs, and projects to promote and motivate deeper learning. In the parlance of flipped-learning advocates, teachers become "guides on the side," shepherding students in charge of their own learning. "The difference I see is that I am not collecting pieces of paper from students to indicate that they have 'done their homework,'" says Dalia Zygas, a 30-year veteran who flipped her chemistry class at West Leyden High School in Northlake, IL.

공학교육에서 Flipped Learning 적용

Mobile internet, cloud computing, big data technologies, and significant breakthroughs in Artificial Intelligence (AI) have all transformed education. In recent years, there has been an emergence of more advanced AI-enabled learning systems, which are gaining traction due to their ability to deliver learning content and adapt to the individual needs of students. Yet, even though these contemporary learning systems are useful educational platforms that meet students’ needs, there is still a low number of implemented systems designed to address the concerns and problems faced by many students. Based on this perspective, a systematic mapping of the literature on AI-enabled adaptive learning systems was performed in this work. A total of 147 studies published between 2014 and 2020 were analysed. The major findings and contributions of this paper include the identification of the types of AI-enabled learning interventions used, a visualisation of the co-occurrences of authors associated with major research themes in AI-enabled learning systems and a review of common analytical methods and related techniques utilised in such learning systems. This mapping can serve as a guide for future studies on how to better design AI-enabled learning systems to solve specific learning problems and improve users’ learning experiences.

AI-enabled adaptive learning systems: A systematic mapping of the literature

The current study explores the students’ behavioral intention to use social media and actual social media use in higher education, specifically the perception of their academic performance and satisfaction. The study is theoretically based on the technology acceptance model (TAM) with evaluation information system success models (ISSM). Theoretically, five independent constructs were identified as contributory to behavioral intention to use social media, and actual social media use towards the students’ satisfaction and performance impact was analyzed. A questionnaire survey based on the technology acceptance model (TAM) and information system success model (ISSM) was utilized as the key method for collecting data and disseminated to 1200 students from four public universities of Malaysia chosen through a random sampling technique. For data analysis, the SPSS and structural equation modeling (SEM-Amos) were used. Outcomes obtained from the students’ behavioral intention to use and actual social media usage indicates a positive and constructive influence on satisfaction and academic performance in higher education. In addition, both male and female students were satisfied with perceived usefulness (β = 0.095, t-value = 3.325, p < 0.001 and β = −0.045, t-value = −2.079, p < 0.001, respectively), perceived ease of use (β = 0.108, t-value = 3.29, p < 0.001 and β = 0.307, t-value = 12.365, p < 0.001, respectively), perceived technology fit (β = 0.14, t-value = 4.769, p < 0.001 and β = 0.277, t-value = 12.358, p < 0.001, respectively), information quality (β = 0.108, t-value = 3.825, p < 0.001 and β = 0.109, t-value = 5.087, p < 0.001, respectively), and system quality (β = 0.232, t-value = 7.573, p < 0.001 and β = 0.176, t-value = 7.429, p < 0.001, respectively). Therefore, we encourage students to use social media for educational purposes and encourage more interactions with peers at higher education institutions. The study’s empirical findings present strong support for the integrative association between the TAM and the ISSM in using online learning platforms to improve students’ academic achievements and satisfaction. This could help decision makers in universities, higher education institutions, and colleges to plan, evaluate, and implement online learning platforms in their institutions.

https://www.mdpi.com/2071-1050/13/14/7770/pdf

The Influence of Information System Success and Technology Acceptance Model on Social Media Factors in Education

Emerging methodologies that apply and integrate science, technology, engineering, art, and math (STEAM) in education have appeared in recent years as a pedagogical alternative providing more holistic and attractive education. The research methodology used in this work is of a bibliometric nature. Specifically, an academic performance analysis and a co-word analysis has been carried out. The term STEAM was analyzed in the Web of Science (WoS) database. The WoS programs Analyze Results, Creation Citation Report, and SciMAT were used. A total of 1116 manuscripts were analyzed. The results show that studies in the field education of STEAM began in 2006 and have continued uninterruptedly up to the present day, although interest generated in the scientific community has been irregular. It can be concluded that STEAM studies have not had an established and robust line of research over time, although it can be observed that the trends in this aspect are focused on the scientific branch of education. In addition, the topics of study on STEAM include points related to gender differences, the influence of STEAM on people of different races, the skills developed by students, and training teachers to implement teaching and learning processes with STEAM.

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STEAM in education: a bibliometric analysis of performance and co-words in Web of Science

COVID-19 has produced a transformation in society that has, in turn, influenced the field of education. The purpose of this study was to analyze the influence of COVID-19 on education using the Web of Science database. A methodology based on bibliometrics was used. Specifically, a scientific mapping of the literature was carried out, as well as a co-word analysis on the state of the question. The analysis included 940 publications. The results show that the institution with the highest volume of production in this field is the University of London. Among the journals, the Journal of Chemical Education stands out. Furthermore, in the analysis of the structural and thematic development of co-words, a high percentage of keyword matching was observed. In 2020, the motor themes were mental-health, organic-chemistry, general-public, first-year-undergraduate, and upper-division-undergraduate, while in 2021, they were autism-spectrum-disorder, adoption, internet, and intervention. It can be concluded that investigation into COVID-19 in the educational field is in its initial process. The research is currently mainly oriented to pedagogical methods, especially e-learning or collaborative learning, although they are not the only trends in this field. Research is also focusing on mental health, students with various disorders, and higher education. In the near future, research on COVID-19 in the field of education will probably be oriented to the application of effective pedagogical methods to train students at various educational stages.

COVID-19 in the Field of Education: State of the Art

Technology and computer learning have acquired a great projection in the field of education. Arduino arises from current technological innovations with the intention of promoting a new approach to learning through machines. The objective of this research is to analyze the evolution of the Arduino concept in the scientific literature. To achieve this aim, a bibliometric methodology based on scientific mapping and an analysis of co-words has been used. The scientific production of Arduino indexed in Web of Science has been analyzed. We have worked with an analysis unit of 346 documents. The results of this research show that the scientific production on Arduino in the field of education starts in 2010 until today. The communications in the congresses to present the results of the investigations developed are the most used means of diffusion. The National University of Distance Education and the Lucian Blaga University of Sibiu are particularly noteworthy. The authors with more scientific production in this field of study are Bogdan, M. and Castro, M. It is significant that the collection of studies on this subject is carried out, above all, by EDULEARN Proceedings and INTED Proceedings. It can be concluded that the field of study on Arduino in the educational field is relatively recent, so that today, the basis for scientific research is still being established. However, the most relevant aspects in this field of study are physic experiments, computational thinking and computer-based learning.

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Arduino Advances in Web of Science. A Scientific Mapping of Literary Production

The social and technological changes that society is undergoing in this century are having a global influence on important aspects such as the economy, health and education. An example of this is the inclusion of artificial intelligence in the teaching–learning processes. The objective of this study was to analyze the importance and the projection that artificial intelligence has acquired in the scientific literature in the Web of Science categories related to the field of education. For this, scientific mapping of the reported documents was carried out. Different bibliometric indicators were analyzed and a word analysis was carried out. We worked with an analysis unit of 379 publications. The results show that scientific production is irregular from its beginnings in 1956 to the present. The language of greatest development is English. The most significant publication area is Education Educational Research, with conference papers as document types. The underlying organization is the Open University UK. It can be concluded that there is an evolution in artificial intelligence (AI) research in the educational field, focusing in the last years on the performance and influence of AI in the educational processes.

https://www.mdpi.com/1999-5903/12/8/124/pdf

Scientific Development of Educational Artificial Intelligence in Web of Science

The handling of a large amount of data to analyze certain behaviors is reaching a great popularity in the decade 2010–2020. This phenomenon has been called Big Data. In the field of education, the analysis of this large amount of data, generated to a greater extent by students, has begun to be introduced in order to improve the teaching–learning process. In this paper, it was proposed as an objective to analyze the scientific production on Big Data in education in the databases Web of Science (WOS), Scopus, ERIC, and PsycINFO. A bibliometric study was carried out on a sample of 1491 scientific documents. Among the results, the increase in publications in 2017 and the configuration of certain journals, countries and authors as references in the subject matter stand out. Finally, potential explanations for the study findings and suggestions for future research are discussed.

/pdf/big-data-in-education-a-bibliometric-review-2120ucj78r.pdf

José-Antonio Marín-Marín

Papers

STEAM in education: a bibliometric analysis of performance and co-words in Web of Science

COVID-19 in the Field of Education: State of the Art

Arduino Advances in Web of Science. A Scientific Mapping of Literary Production

Scientific Development of Educational Artificial Intelligence in Web of Science

Big Data in Education. A Bibliometric Review