• Journal of Digital Convergence
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• v.17 no.8
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• pp.379-384
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• 2019

This study is an online survey user experience study. The purpose of this research is user experience research to make use and development of online surveys. In-depth interviews were conducted with 8 native Koreans who were not experienced with Naver and Google, and were surveyed using Peter Morville's Honeycomb model. In addition, we performed evaluation through tasks and think-aloud. Naver is highly useful, usable, desirable and findable, and Google can confirm its superior accessible and flexible. Research has shown that improvements in usability and ease of functioning are needed by reclassifying and moving menu categories. Online survey user experience that has not been studied previously can predict the direction of usability improvement and can help the user side. We hope that this research will improve the usability of online surveys, and will lead to various related research.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.207-213
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• 2022

MBTI(Myer Briggs Type Indicator) is an effective personality type test to intuitively identify and classify people's tendencies. Accordingly, there are active attempts to apply MBTI to the learning area, but research on creating new learning types using MBTI is insufficient. Therefore, this paper examines the factors that affect learning and implements new learning types MY,STI(MY, Study Type Indicator) by applying them to a machine learning algorithm that has these characteristics. Data were collected by conducting a learning type test made with Google Forms on 144 general people, and supervised learning was used during machine learning. As a result, the accuracies of MY,STI were 0.933, 0.866, 0.844, and 0.733 for each learning method, learning motivation, presence or absence of external stimulus, and learning time criteria, respectively.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.267-273
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• 2024

Using a cloud-based vertex AI platform that can develop an artificial intelligence learning model without coding, this study easily developed an artificial intelligence learning model by the non-professional general public and confirmed its clinical applicability. Nine dental diseases and 2,999 root disease X-ray images released on the Kaggle site were used for the learning data, and learning, verification, and test data images were randomly classified. Image classification and multi-label learning were performed through hyper-parameter tuning work using a learning pipeline in vertex AI's basic learning model workflow. As a result of performing AutoML(Automated Machine Learning), AUC(Area Under Curve) was found to be 0.967, precision was 95.6%, and reproduction rate was 95.2%. It was confirmed that the learned artificial intelligence model was sufficient for clinical diagnosis.

• Journal of the Korean Applied Science and Technology
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• v.40 no.3
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• pp.496-506
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• 2023

The purpose of this study was to identify the relationship between social-emotional isolation, depression, and smartphone addiction of college students who experienced social distancing caused by COVID-19 and to identify the factors influencing smartphone addiction of college students. Total of 220 students from four universities participated in this study, and data collection was conducted by organizing a questionnaire in a Google form. Data were analyzed using the SPSS/WIN 28.0. There were significantly correlation among smartphone addiction of college students, social-emotional isolation (r=.44, p<.001) and depression (r=.51, p<.001). The factors affecting smartphone addiction of college students were gender (β=.176, p=.001), weekend smartphone usage time 7-9 hours (β=.387, p=.001), 10-12 hours (β=.313, p=.006), 12 hours or more (β=.299, p=<.001), depression (β=.302, p<.001), and social-emotional isolation (β=.210, p=.004). The regression model was statistically significant (F=15.81, p<.001). The explanatory power of the model was 43% (adj R²=.43, p<.001). Therefore, in order to prevent smartphone addiction of college students, it is necessary to develop and utilize a mental health promotion program that can reduce social-emotional isolation and depression.