• Journal of Korean Home Economics Education Association
• /
• v.8 no.1
• /
• pp.51-57
• /
• 1996

The purpose of this study were to examine the difference of the effect of CAI and students’attitude to Home Economics Class through CAI. 120 girl students of the first year were chosen at B middle school in Daegu. Among them each 30 students were classified into 4 groups； High Intelligence group/Individual learning pattern, High Intelligence group/Small group learning pattern, Low Intelligence group/Individual learning pattern, Low Intelligence group/Small group learning pattern. The task of learning was “management of washing”Unit of the second grade. The data were processed with Cronbach’s ${\alpha}$, t-test, ANOVA by SPSS/PC(sup)+. The research findings are as follows： 1. In the verification of CAI Learning effect according to student group pattern, there is no difference between Individual learning pattern and Small group learning pattern in Achivement and Retention of learning. 2. In the verification of CAI Learning effect according to student intelligence level, there is no difference between High Intelligence group and Low Intelligence group in Achivement and Retention of learning. 3. The result of students’attitude to Home Economics Class verificated is an follows. (1) Individual learning pattern is more attensive than Small group learning pattern, but there is no difference in Intelligence level. (2) Low Intelligence group is more positive than High Intelligence group, and Small group learning pattern is more positive than Individual learning pattern in a view of Home Economics Class after using CAI.

• Journal of Korean Elementary Science Education
• /
• v.40 no.1
• /
• pp.66-80
• /
• 2021

This study is a case study that applies plant classification learning using machine learning to fourth graders in elementary school in online learning situations. In this study, a plant classification learning education program associated with 2015 revision science curriculum was developed by applying the Artificial Intelligence biological classification teaching Learning model. The study participants were 31 fourth graders who agreed to participate voluntarily. Plant classification learning using machine learning was applied six hours for three weeks. The results of this study are as follows. First, as a result of image analysis on artificial intelligence, participants were mainly aware of artificial intelligence as mechanical (27%), human (23%) and household goods (23%). Second, an artificial intelligence recognition survey by semantic discrimination found that artificial intelligence was recognized as smart, good, accurate, new, interesting, necessary, and diverse. Third, there was a difference between men and women in perception and emotion of artificial intelligence, and there was no difference in perception of the ability of artificial intelligence. Fourth, plant classification learning using machine learning in this study influenced changes in artificial intelligence perception. Fifth, plant classification learning using machine learning in this study had a positive effect on reasoning ability.

• Korean Medical Education Review
• /
• v.15 no.1
• /
• pp.46-53
• /
• 2013

The purpose of this study was to analyze the relationship among medical students' learning motivation, characteristics of multiple intelligence, and academic achievement. The participants were 144 medical students. The data were collected by administering learning motivation tests (self-confidence, self-efficacy, level of task, emotion of learning, learning behavior, failure tolerance, task difficulty, and academic self-efficacy), a multiple intelligence test (linguistic intelligence, logical-mathematical intelligence, musical intelligence, bodily-kinesthetic intelligence, spatial intelligence, interpersonal intelligence, intrapersonal intelligence, and naturalistic intelligence), and two semesters of grades. There is a correlation between multiple intelligences and learning motivation. Among academic self-efficacy of academic motivation, the self-control efficacy (0.28) and behavior (0.18) subscales are significantly positively correlated with academic achievement. However, the emotion subscale (-0.18) was significantly negatively correlated. Learning motivation was correlated with two of the eight multiple intelligence profiles: the intrapersonal intelligence (0.18) and bodily-kinesthetic intelligence (-0.19). The structural equation modeling analysis showed that the behavior and self-control efficacy subscales of intrapersonal intelligence had an impact on academic achievement. An analysis according to the academic achievement group showed significant differences in self-control efficacy and emotion subscales with intrapersonal intelligence. A positive relationship can be observed between learning motivation and some characteristics of multiple intelligence of medical school students. In light of the findings, it is worth examining whether we can control medical students' learning motivation through educational programs targeting self-control efficacy and intrapersonal intelligence.

• Journal of Convergence for Information Technology
• /
• v.10 no.1
• /
• pp.120-127
• /
• 2020

The purpose of this study was to examine related variables that improve learning outcomes in collaborative learning. Based on literature reviews, emotional intelligence was used as a variable of personal character, communication ability and collective intelligence were used as variables in learning process, and learning satisfaction, and persistence were used as variables of learning outcomes. Data were collected from 3,475 students at A university, and were analyzed using structural equation modeling. The results of this study are as follows: First, it turned out that emotional intelligence had a significant and positive impact on communication ability, collective intelligence, learning satisfaction, and persistence. Second, communication ability influenced collective intelligence and persistence positively. Third, collective intelligence influenced learning satisfaction and persistence positively. Fourth, learning satisfaction had a significant and positive impact on persistence. These findings offer basic data for collaborative learning by revealing the structural relationships among related variables that improve learning outcomes in collaborative learning of college students.

• 대한공업교육학회지
• /
• v.30 no.2
• /
• pp.72-81
• /
• 2005

The purpose of this study is to do research on correlation between multiple intelligence and e-Learning strategy, which can be used to develop and strengthen e-Learning strategy. The subject was 183 college students of engineering at C university who had ever taken a lesson through e-Learning. Two items are investigated in these college students. One is whether there is a gap and any gender-related difference in multiple intelligence and e-Learning strategy, and the other is what multiple intelligences have an effect on e-Learning strategy. The e-Learning strategy developed by In-Sock Lee and the instrument by Yong-Lin Moon were used. The results of this study are as follows. First, it appeared that there was a difference between gender in musical intelligence and naturalistic intelligence. The average of musical intelligence was higher in female collegian, while the average of naturalistic intelligence was higher in male collegian. But there was no gender-related difference in using e-Learning strategy. Second, it appeared that multiple intelligence gave an explanation of self-direction intelligence up to 31.1%, expression strategy 20.35%, and information processing strategy 16.6%. The results of this study showed that logical-mathematical intelligence affected all of three e-Learning strategies. So, new curriculum which can make use of logical-mathematical intelligence needs to be developed so as to devise efficient e-Learning strategies.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.19 no.1
• /
• pp.153-172
• /
• 2023

We look into the theory of experiential learning, which allows learners to design and organize their own lives, as well as to develop the necessary competencies for students who will be living in intelligent information society. We also investigate the teaching and learning methods, as well as the educational contents of artificial intelligence education, and develop an approach to artificial intelligence education that will develop learners' capabilities. As a result, we have investigated the pedagogical needs for artificial intelligence education in elementary and secondary schools, critically reviewed the discussions on experiential learning-based education for artificial intelligence education in elementary and secondary schools, and proposed a plan. Experiential learning achieves comprehension and knowledge acquisition naturally, as well as subject connection and integration. When preparing for artificial intelligence education, practical methods and procedures for developing capabilities in artificial intelligence education, focusing on in-depth learning, inter-subject linkage and integration, life-related learning, and reflection on the learning process, should be considered unavoidable.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.05a
• /
• pp.177-179
• /
• 2022

In the Google Deepmind Challenge match, Alphago defeated Korea's Sedol Lee (human) with 4 wins and 1 loss in the Go match. Finally, artificial intelligence is going beyond the use of human intelligence. The Korean government's budget for the Digital New Deal is 9 trillion won in 2022, and an additional 301 types of data construction projects for artificial intelligence learning will be secured. From 2023, the industrial paradigm will change with the use and application of learning of artificial intelligence in all fields of industry. This paper conducts research to utilize artificial intelligence algorithms. Focusing on the analysis and judgment of data in artificial intelligence learning, research on the appropriate target and scope of application of algorithms in artificial intelligence machine learning and deep learning learning is conducted. This study will provide basic data for artificial intelligence in the 4th industrial revolution technology and artificial intelligence robot use in the 5th industrial revolution technology.

• Journal of The Korean Society of Integrative Medicine
• /
• v.4 no.1
• /
• pp.65-72
• /
• 2016

Purpose : This study was carried out to find out the relationship between emotion intelligence, learning flow and career stress of nursing students and influence factors for career stress. Methods : This study targeted 197 university students in their freshman-senior year attending College of Nursing located in P Metropolitan City. For collected data, real numbers and percentage, mean and standard deviation and multiple regression analysis were carried out by using PASW 21.0 program and the correlation between emotion intelligence, learning flow and career stress was analyzed with Pearson's correlation coefficients. Results : Emotional self-awareness(M=3.80, SD =.71), clear goals(M=3.39, SD=.90) and school environment stress(M=2.97, SD=.96) were found to be high in the degree of emotion intelligence, learning flow and career stress of the subjects. The relationship between emotion intelligence and learning flow showed a positive correlation(r=.489, p<.01) in the correlation between emotion intelligence, learning flow, career stress and emotion intelligence showed a negative correlation with career stress(r=-.204, p<.01). Emotion intelligence and learning flow show that career stress is predicted significantly (${\beta}$ =-.15, p < .01) and explained a career stress variate as 18%(F = 24.5, p < .01). Conclusion : Emotion intelligence of nursing students was found to be very influential on the degree of learning flow or career stress. Based on the results of this study, replication studies on emotion intelligence and career stress are needed and the development of intervention programs to increase emotion intelligence is needed.

• Journal of The Korean Association of Information Education
• /
• v.26 no.5
• /
• pp.353-360
• /
• 2022

The purpose of this study is to verify the effect of artificial intelligence storytelling education program designed to help learning artificial intelligence based on storytelling, the most important element of human intelligence, on learning flow. To this end, a 16-hour artificial intelligence education program was designed and developed, and applied over 8 weeks to 19 gifted students in 5th and 6th grades of elementary school. Artificial intelligence storytelling education program was developed in the form of teaching and learning course plans for each class and storybooks. Artificial intelligence storytelling education program application results showed significant improvements in average scores in all 9 sub-factors of learning flow, including combination of challenges and abilities, integration of behavior and consciousness, clear goal, concrete feedback, focus on task, sense of control, loss of self-consciousness, Distortion of the sense of time, and self-purpose experience. In other words, it was confirmed that artificial intelligence storytelling education program was effective in improving learning flow.

• Knowledge Management Research
• /
• v.19 no.3
• /
• pp.173-187
• /
• 2018

This study was aimed at suggesting an integrative framework for creating collective intelligence and enhancing group performance after reviewing previous studies including those related to learning organizations, organizational learning, knowledge management, and collective intelligence. In the first, we examined that the similarities and differences between collective intelligence and other similar concepts, such as learning organizations, organizational learning, and knowledge management. Next, an integrative framework for creating collective intelligence and channeling it into strong group performance were suggested. In this process, we reviewed conditions for creating collective intelligence and segmented the major variables as expectancy, valence, and instrumentality, according to Vroom's (1964) expectancy theory. Characteristics of problems and the roles of leaders were respectively considered as valence for inducing collaboration and expectancy for managing probability to achieve goals. Instrumental factors were also adopted from conditions for creating group intelligence suggested from several researchers, such as creativity, openness, willingness for working together, horizontal communication, centralization in decision making, and building effective information and communication technology system and active usage of it. We discussed two potentially disputable matters about the scope and level of collective intelligence and group performance and suggest several theoretical and practical implications in the Discussion.