• Title/Summary/Keyword: big thinking

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Relationship between Thinking Styles and the Big-Five Personality Traits of scientifically-gifted students. (과학영재들의 사고양식과 5 인성 요인간의 관계)

  • 배미란;한기순;박인호
    • Journal of Gifted/Talented Education
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    • v.13 no.1
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    • pp.43-63
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    • 2003
  • The purpose of this study is to investigate the relationships between the thinking styles and the big five personality traits of gifted students. Two hundred and fifty-five gifted students(169 boys, 97 girls) enrolled in the Science Elite Program responded to the Big Five Personality Inventory and Thinking Styles Inventory. Although significant relationships were identified between particular thinking styles and certain personality traits, it was concluded that it is premature to claim that a personality measure can be used to measures thinking styles. Neuroticism, Agreeableness, in Big Five Personality Inventory and level and form dimensions of Thinking Styles Inventory was found to measure the each construct independently.

Is Big Data Analysis to Be a Methodological Innovation? : The cases of social science (빅데이터 분석은 사회과학 연구에서 방법론적 혁신인가?)

  • SangKhee Lee
    • The Journal of the Convergence on Culture Technology
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    • v.9 no.3
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    • pp.655-662
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    • 2023
  • Big data research plays a role of supplementing existing social science research methods. If the survey and experimental methods are somewhat inaccurate because they mainly rely on recall memories, big data are more accurate because they are real-time records. Social science research so far, which mainly conducts sample research for reasons such as time and cost, but big data research analyzes almost total data. However, it is not easy to repeat and reproduce social research because the social atmosphere can change and the subjects of research are not the same. While social science research has a strong triangular structure of 'theory-method-data', big data analysis shows a weak theory, which is a serious problem. Because, without the theory as a scientific explanation logic, even if the research results are obtained, they cannot be properly interpreted or fully utilized. Therefore, in order for big data research to become a methodological innovation, I proposed big thinking along with researchers' efforts to create new theories(black boxes).

A case study of elementary school mathematics-integrated classes based on AI Big Ideas for fostering AI thinking (인공지능 사고 함양을 위한 인공지능 빅 아이디어 기반 초등학교 수학 융합 수업 사례연구)

  • Chohee Kim;Hyewon Chang
    • The Mathematical Education
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    • v.63 no.2
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    • pp.255-272
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    • 2024
  • This study aims to design mathematics-integrated classes that cultivate artificial intelligence (AI) thinking and to analyze students' AI thinking within these classes. To do this, four classes were designed through the integration of the AI4K12 Initiative's AI Big Ideas with the 2015 revised elementary mathematics curriculum. Implementation of three classes took place with 5th and 6th grade elementary school students. Leveraging the computational thinking taxonomy and the AI thinking components, a comprehensive framework for analyzing of AI thinking was established. Using this framework, analysis of students' AI thinking during these classes was conducted based on classroom discourse and supplementary worksheets. The results of the analysis were peer-reviewed by two researchers. The research findings affirm the potential of mathematics-integrated classes in nurturing students' AI thinking and underscore the viability of AI education for elementary school students. The classes, based on AI Big Ideas, facilitated elementary students' understanding of AI concepts and principles, enhanced their grasp of mathematical content elements, and reinforced mathematical process aspects. Furthermore, through activities that maintain structural consistency with previous problem-solving methods while applying them to new problems, the potential for the transfer of AI thinking was evidenced.

Examining the relationship between educational effectiveness and computational thinking in smart learning environment

  • Han, Oakyoung;Kim, Jaehyoun
    • Journal of Internet Computing and Services
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    • v.19 no.2
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    • pp.57-67
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    • 2018
  • The $4^{th}$ industrial revolution has brought innovation in the educational environment. The purpose of this study is to verify the educational effectiveness of smart learning environment especially with the computational thinking. A big data analysis was performed to confirm that computational thinking is the one to prepare the 4th industrial revolution. To teach computational thinking at university, educational design should be careful. This study verified the relationship between improvement of computational thinking ability and major of students with coding education. There was difference in effectiveness of the coding education depending on the major of students, it means students must be guaranteed to be educated by the differentiated coding education for different major. This study extracted factors of computational thinking through literature review. Thirteen research hypotheses were applied for the statistical analysis in R language. It was proved that expectation of class and improvement of abstraction ability and algorithmic thinking ability had mediation effect to the relationship between knowledge acquisition and problem-solving abilities. Based on this study, effectiveness of education can be improved, and it will lead to produce a lot of distinguished students who are ready for the 4th industrial revolution.

Design Thinking Methodology for Social Innovation using Big Data and Qualitative Research (사회혁신분야에서 근거이론 기반 질적연구와 빅데이터 분석을 활용한 디자인 씽킹 방법론)

  • Park, Sang Hyeok;Oh, Seung Hee;Park, Soon Hwa
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
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    • v.13 no.4
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    • pp.169-181
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    • 2018
  • Under the constantly intensifying global competition environment, many companies are exploring new business opportunities in the field of social innovation using creating shared value. In seeking social innovation, it is a key starting point of social innovation to clarify the problem to be solved and to grasp the cause of the problem. Among the many problem solving methodologies, design thinking is getting the most attention recently in various fields. Design Thinking is a creative problem solving method which is used as a business innovation tool to empathize with human needs and find out the potential desires that the public does not know, and is actively used as a tool for social innovation to solve social problems. However, one of the difficulties experienced by many of the design thinking project participants is that it is difficult to analyze the observed data efficiently. When analyzing data only offline, it takes a long time to analyze a large amount of data, and it has a limit in processing unstructured data. This makes it difficult to find fundamental problems from the data collected through observation while performing design thinking. The purpose of this study is to integrate qualitative data analysis and quantitative data analysis methods in order to make the data analysis collected at the observation stage of the design thinking project for social innovation more scientific to complement the limit of the design thinking process. The integrated methodology presented in this study is expected to contribute to innovation performance through design thinking by providing practical guidelines and implications for design thinking implementers as a valuable tool for social innovation.

Analysis of the Big6 Skills Model and the Modified Big6 Models (Big6 모델 및 수정 모델 분석 연구)

  • Park, Juhyeon
    • Journal of Korean Library and Information Science Society
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    • v.49 no.3
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    • pp.331-359
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    • 2018
  • The purpose of this study is to analyse the Big6 model and the Big6 modification model to find out the characteristics of the Big6 model and to derive implications for applying the Big6 model in the field. For this purpose, the information literacy standards of the AASL and the ACRL were compared with the Big6 model. The Big6 model, influenced by Bloom's taxonomy was analyzed alongside the Big6+3 model, the Big8 model and the modified Big6 model, provided by LG Science Land. As a result, the Big6 model could be used as an information problem-solving model, metacognitive activation strategy, and scaffolding to improve students' information literacy. In addition, it could be used as a model for constructivism, inquiry-based learning, the integration of curriculum, collaborative education, and ICT technology. How teacher-librarians or librarians apply the Big6 model is related to the improvement of critical thinking skills. Teacher-librarians and librarians need to plan situations, subjects, topics, and methods in a systematic and specific way when applying the Big6 model to the information literacy curriculum.

The Effects of the Science Writing Heuristic Approach on the Middle School Students' Achievements (중학생의 성취 수준에 따른 탐구적 과학 글쓰기(Science Writing Heuristic) 수업의 효과)

  • Shin, Soyoung;Choi, Aeran;Park, Jong-Yoon
    • Journal of The Korean Association For Science Education
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    • v.33 no.5
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    • pp.952-962
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    • 2013
  • The purpose of this study was to investigate the effects of the Science Writing Heuristic (SWH) approach on the students' summary writing, logical thinking and achievements for the course. Participants in this study were 132 female students from a girls' middle school. The SWH approach was used for two experimental classes and the typical teacher-centered instructional approach was used for two comparative classes. Summary writing test, logical thinking test (GALT) and achievement test for the course were administered before and after the instruction period. Results of this study indicated that the SWH approach was helpful for students in finding big ideas, understanding science concepts, developing logical thinking abilities and doing well in the course. This study also implied that the SWH approach was effective for the low achieving students.

A Meta-Analysis on the Effects of Software Education on Computational Thinking

  • Kim, Dong-Man;Lee, Tae-Wuk
    • Journal of the Korea Society of Computer and Information
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    • v.23 no.11
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    • pp.239-246
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    • 2018
  • In this paper, we propose to investigate the existing SW education related studies and to collect the total effect size for the improvement of computational thinking(CT) through meta-analysis and to confirm the effect size according to various variables. So, we have objectively identified and generalized the practical effects of SW education on the various variable. The results of the meta-analysis showed that 1) the overall effect of SW educational activities on CT improvement was 'Hedges' g=0.643', so SW educational activity can get a CT improvement of about 24% over other educational activities. 2) EPL is a SW teaching-learning method that greatly improves students' CT. 3) Since SW education can be guaranteed to improve CT among elementary school students and university students, essential SW education for these should be further promoted. And it was found that SW education activity were more effective for CT improvement in small cities & rural areas than big cities, but the cause was not identified, so further study will be required.

Analysis of Students' Interest in Computational Thinking (컴퓨팅적 사고 과목에 대한 수강자의 관심도 분석)

  • Kim, Mi Yeon;Lee, Choong Ho
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2022.05a
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    • pp.343-345
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    • 2022
  • Computational thinking is a subject that must be selected by non-major freshmen at H University, and classes are completed regardless of their major. The class content is conducted using block coding that allows easy access to programming. A survey was conducted before the start of the semester class to find out the students' interest in the subject and to help the students understand it. This study analyzes the student's survey response data so that the students' interest in the subject can be grasped at a glance.

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A Study on the Comparison of Educational Effects between Convergence Majors and Single Majors in R Lecture

  • Ryu, Gui Yeol
    • International Journal of Internet, Broadcasting and Communication
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    • v.12 no.3
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    • pp.18-24
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    • 2020
  • The purpose of this paper is an analysis of the difference between convergence majors and single majors in the convergence core competency and educational performance. We used survey data for the analysis of the convergence core competencies, the results of the midterm and final exams for the education performance. Analysis targets are 10 students in big data business intelligence at Seokyeong University as convergence majors and 11 students in business administration as single majors. The target course was an analysis of economic data provided in the second semester of 2019. And the lecture contents were analysis of big data using R programming. The survey was conducted on December 5, 2019. The convergence core competences were creative thinking, critical thinking, understanding convergence knowledge, problem solving ability, communication skills, cooperation ability, use of convergence tools, consideration, and responsibility. As results of homogeneity tests, we found that there was no significant difference in all competencies, but there were very significant differences in the educational performance evaluated by the midterm and final exams. Therefore we can see willingness to convergence of single majors was no different from that of convergence majors, but had not led to practice. It is desirable to activate and support convergence courses.