• Journal of Korean Home Economics Education Association
• /
• v.32 no.4
• /
• pp.19-30
• /
• 2020

Maker education started on the basis of the maker movement in which makers gathered in makerspace share their activities and experiences, and the educational value pursued in maker education is based on the constructivist paradigm. The purpose of this study is to present maker education components to be used in school education, focus on the characteristics and educational values of maker education, and explore ways to use them. To this end, this study explored the theoretical grounds to re-conceptualize maker education, drew statements based on in-depth interview data of teachers conducting maker education classes, and reviewed its validity through experts. Based on these statements, by deriving the components for the use of maker education, the direction of maker education in school education was set, and an example framework that could be used in subject class and creative experiential learning was proposed. Research shows that in maker education, makers cooperate to carry out activities, share ideas with others and try to improve them, and include self-direction such as learning, tinkering, design thinking, sharing and reflection. can see. In addition, maker education emphasizes experiential learning that can solve real problems that students face, rather than confining specific activities to student choices as needed. It emphasizes the learner's course of action rather than the outcome of the activity, tolerates the learner's failure, and emphasizes the role of the teacher as a facilitator to promote re-challenge. In the future, it can be used in various ways in each subject (curriculum expert, teaching/learning expert, elementary and middle school teachers, parents, local educators, etc.) and school activities, and it will contribute to setting future research directions as a basic research for school maker education.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.51 no.3
• /
• pp.70-82
• /
• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.

• The Journal of Society for e-Business Studies
• /
• v.17 no.4
• /
• pp.69-93
• /
• 2012

Various influence-related relationships in Social Network Services (SNS) among users, posts, and user-and-post, can be expressed using links. The current research evaluates the influence of specific users or posts by analyzing the link structure of relevant social network graphs to identify influential users. We applied the concept of mutual interactions proposed for ranking semantic web resources, rather than the voting notion of Page Rank or HITS, to blogosphere, one of the early SNS. Through many experiments with network models, where the performance and validity of each alternative approach can be analyzed, we showed the applicability and strengths of our approach. The weight tuning processes for the links of these network models enabled us to control the experiment errors form the link weight differences and compare the implementation easiness of alternatives. An additional example of how to enter the content scores of commercial or spam posts into the graph-based method is suggested on a small network model as well. This research, as a starting point of the study on identifying influential users in SNS, is distinctive from the previous researches in the following points. First, various influence-related properties that are deemed important but are disregarded, such as scraping, commenting, subscribing to RSS feeds, and trusting friends, can be considered simultaneously. Second, the framework reflects the general phenomenon where objects interacting with more influential objects increase their influence. Third, regarding the extent to which a bloggers causes other bloggers to act after him or her as the most important factor of influence, we treated sequential referencing relationships with a viewpoint from that of PageRank or HITS (Hypertext Induced Topic Selection).

• The Journal of the Convergence on Culture Technology
• /
• v.7 no.4
• /
• pp.369-376
• /
• 2021

The purpose of this study is to develop a coaching model which can enhance teaching ability of lifelong educator. To achieve this purpose, this study verifies and analyzes several documentary records related with diverse teaching capabilities, operation reality and coaching method run by lifelong educator. Furthermore, an in-depth interview about teaching capability was undertaken for field experts who have worked at the institutions of lifelong education for more than 10 years. As a result, the study could develop a coaching model to identify teaching capability of lifelong educator by conducting matrix analysis. First, according to the documentary studies, the paradigm for lifelong education has been shifted to centralize learner's demand with the advent of 4th industrial revolution and it suggests coaching capability which could enhance educator's capability should come first. A lifelong educator should have capabilities including identification of vision and goal, creation of mission declaration, development of coaching skill and procedure, management of crisis and coaching capability as an expert in the lifelong education field. Second, a model which can centralize learners could be developed for lifelong teaching capability by adopting a teaching capability suggested by field experts, According to the experts, it is essential to develop a program model to acquire professional knowledge, communication capability, understanding of adult learner, personal relations capability. If there is a model which can develop such capabilities, it is able to strengthen lifelong teaching capability to focus on learner's demand, mainly adult learners, a major consumer of the field. Third, a coaching model to enhance teaching capability for an educator is to acquire and implement sufficient step-by-step teaching capability which has been suggested from a procedure comprised of entrance, progress, critique and return. This, present study suggests, after the critique, a lifelong educator oneself can newly develop and extend a teaching capability basis on pursuing teaching capability as a lifelong educator through the return process.