Research Trends in Elementary and Secondary School Artificial Intelligence Education Using Topic Modeling and Problems in Technology Education

2021 ◽  
Vol 21 (1) ◽  
pp. 106-124
Author(s):  
Sung-ae Kim
Keyword(s):  
Artificial Intelligence ◽  
Secondary School ◽  
Technology Education ◽  
Topic Modeling ◽  
Research Trends ◽  
Intelligence Education

scholarly journals A Micro Perspective of Research Dynamics Through “Citations of Citations” Topic Analysis

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Xiaoli Chen ◽  
Tao Han
Keyword(s):  
Artificial Intelligence ◽  
Topic Modeling ◽  
Design Methodology ◽  
Topic Model ◽  
Research Impact ◽  
Research Trends ◽  
Topic Analysis ◽  
Forward Chaining ◽  
Practical Implications ◽  
Highly Cited

AbstractPurposeResearch dynamics have long been a research interest. It is a macro perspective tool for discovering temporal research trends of a certain discipline or subject. A micro perspective of research dynamics, however, concerning a single researcher or a highly cited paper in terms of their citations and “citations of citations” (forward chaining) remains unexplored.Design/methodology/approachIn this paper, we use a cross-collection topic model to reveal the research dynamics of topic disappearance topic inheritance, and topic innovation in each generation of forward chaining.FindingsFor highly cited work, scientific influence exists in indirect citations. Topic modeling can reveal how long this influence exists in forward chaining, as well as its influence.Research limitationsThis paper measures scientific influence and indirect scientific influence only if the relevant words or phrases are borrowed or used in direct or indirect citations. Paraphrasing or semantically similar concept may be neglected in this research.Practical implicationsThis paper demonstrates that a scientific influence exists in indirect citations through its analysis of forward chaining. This can serve as an inspiration on how to adequately evaluate research influence.OriginalityThe main contributions of this paper are the following three aspects. First, besides research dynamics of topic inheritance and topic innovation, we model topic disappearance by using a cross-collection topic model. Second, we explore the length and character of the research impact through “citations of citations” content analysis. Finally, we analyze the research dynamics of artificial intelligence researcher Geoffrey Hinton's publications and the topic dynamics of forward chaining.

Research Trends of Consumer Education Using Topic Modeling

2020 ◽  
Vol 16 (2) ◽  
pp. 83-115
Author(s):  
Mira Kim ◽  
◽  
Hye Sun Hwang ◽  
Xu Li
Keyword(s):  
Topic Modeling ◽  
Research Trends ◽  
Consumer Education

scholarly journals Exploring Latent Topics and International Research Trends in Competency-Based Education Using Topic Modeling

2021 ◽  
Vol 11 (6) ◽  
pp. 303
Author(s):  
Seungsu Paek ◽  
Taehun Um ◽  
Namhyoung Kim
Keyword(s):  
Education Reform ◽  
International Research ◽  
Topic Modeling ◽  
Modern Society ◽  
Research Trends ◽  
Competency Development ◽  
Competency Based Education ◽  
Competency Based ◽  
Latent Topics ◽  
International Research Trends

Recently, there has been growing educational interest in competency. Global organizations, such as the United Nations (UN) and Organization for Economic Co-operation and Development (OECD), which are leading the discourse on education reform, are undertaking the lead in spreading awareness regarding competency education. Since 2015, the number of published articles on competency education has been rapidly increasing. This paper aims to provide significant implications for creating a sustainable future of competency education. A topic modeling method was used to empirically analyze latent topics and international research trends in 26,532 articles published on competency-based education (CBE). As a result of the analysis, 15 topics were derived, including “approach to competency development.” In addition, five topics including “learning skills” and “teacher training” were found to be hot topics with the increasing article publication. The rapidly changing modern society is calling for a transformation in education. We hope that the results of this study paves the way for further research exploring new directions for education, such as competency education.

scholarly journals Analysis and Prediction of “AI + Education” Attention Based on Baidu Index—Taking Guizhou Province as an Example

2021 ◽  
Vol 13 (5) ◽  
pp. 120
Author(s):  
Yulin Zhao ◽  
Junke Li ◽  
Jiang-E Wang
Keyword(s):  
Artificial Intelligence ◽  
Southwest China ◽  
Arima Model ◽  
Modern Technology ◽  
Guizhou Province ◽  
Western Region ◽  
Elastic Coefficient ◽  
The Sustainable Development ◽  
Intelligence Education ◽  
Coefficient Method

Studying the attention of “artificial intelligence + education” in ethnic areas is of great significance for China for promoting the integrated development of new educational modes and modern technology in the western region. Guizhou province is an area inhabited by ethnic minorities, located in the heart of Southwest China. The development of its intelligent education has strong enlightenment for the whole country and the region. Therefore, this paper selects the Baidu Index of “artificial intelligence (AI) + education” in Guizhou province from 2013 to 2020, analyzes the spatial–temporal characteristics of its network attention by using the elastic coefficient method, and builds the ARIMA model on this basis to predict future development. The results show that the public’s attention to “AI + education” differs significantly in time and space. Then, according to the prediction results, this paper puts forward relevant suggestions for the country to promote the sustainable development of education in western ethnic areas.

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