• Journal of The Korean Association For Science Education
• /
• v.37 no.1
• /
• pp.87-101
• /
• 2017

The analysis was carried out with meta-analysis on master's and doctoral dissertations, and academic journals that analyzed the effects of STEAM education between 2012 and 2015. From the total number of 75 dissertations and articles analyzed, 183 different effect sizes were calculated. The analysis was done to find out the kinds of differences that would be created according to the effect size of creativity, problem-solving ability, and researcher, target area, student division research design type, and level of schools. The total effect size of creativity scored 0.776, and demonstrated satisfaction in symmetry of funnel plot, with no publication biases. The fail-safe N scored 780, and since the number is smaller than 8,945, the results of this research has credibility. Furthermore, problem-solving ability shows intermediate level of effect size with a score of 0.584. It also showed satisfaction in symmetry with funnel plot, with no publication bias. With the different research methods of the sub-factors of creativity, fluency scored the highest with 0.929, flexibility with 0.881, originality with 0.838, sophistication with 0.653, abstractness with title 0.705, and resistance to termination, 0.527. This study finds its significance in the demonstration of average effect size of STEAM education through meta-analysis. According to research results, the effects of inclusive education could be determined, yet the specific effect cause or learning principles were difficult to find. It was found that the effects of STEAM education do not rise or fall depending on school age, and demonstrated differences in creativity according to the research methods or the researchers.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.43 no.2 s.308
• /
• pp.19-29
• /
• 2006

As the security of personal information is becoming more important in mobile phones, we are starting to apply iris recognition technology to these devices. In conventional iris recognition, magnified iris images are required. For that, it has been necessary to use large magnified zoom & focus lens camera to capture images, but due to the requirement about low size and cost of mobile phones, the zoom & focus lens are difficult to be used. However, with rapid developments and multimedia convergence trends in mobile phones, more and more companies have built mega-pixel cameras into their mobile phones. These devices make it possible to capture a magnified iris image without zoom & focus lens. Although facial images are captured far away from the user using a mega-pixel camera, the captured iris region possesses sufficient pixel information for iris recognition. However, in this case, the eye region should be detected for accurate iris recognition in facial images. So, we propose a new fast iris detection method, which is appropriate for mobile phones based on corneal specular reflection. To detect specular reflection robustly, we propose the theoretical background of estimating the size and brightness of specular reflection based on eye, camera and illuminator models. In addition, we use the successive On/Off scheme of the illuminator to detect the optical/motion blurring and sunlight effect on input image. Experimental results show that total processing time(detecting iris region) is on average 65ms on a Samsung SCH-S2300 (with 150MHz ARM 9 CPU) mobile phone. The rate of correct iris detection is 99% (about indoor images) and 98.5% (about outdoor images).

• Journal of the Korean Society for information Management
• /
• v.38 no.3
• /
• pp.141-174
• /
• 2021

In this study, by examining the actual usage data of the university library website before and after COVID-19 outbreak, the usage behavior of users was analyzed, and the data before and after the virus outbreak was compared, so that university libraries can provide more efficient information services in a pandemic situation. We would like to suggest ways to improve it. In this study, the user traffic made on the website of University C was 'using Google Analytics', from January 2018 to December 2018 before the oneself of the COVID-19 virus and from January 2020 to 2020 after the outbreak of the virus. A comparative analysis was conducted until December. Web traffic variables were analyzed by classifying them into three characteristics: 'User information', 'Path', and 'Site behavior' based on metrics such as session, user, number of pageviews, number of pages per session time, and bounce rate. To summarize the study results, first, when compared with data from January 1 to January 20 before the oneself of COVID-19, users, new visitors, and sessions all increased compared to the previous year, and the number of sessions per user, number of pageviews, and number of pages per session, which showed an upward trend before the virus outbreak in 2020, increased significantly. Second, as social distancing was upgraded to the second stage, there was also a change in the use of university library websites. In 2020 and 2018, when the number os students was the lowest, the number of page views increased by 100,000 more in 2020 compared to 2018, and the number of pages per session also recorded10.46, which was about 2 more pages compared to 2018. The bounce rate also recorded 14.38 in 2018 and 2019, but decreased by 1 percentage point to 13.05 in 2020, which led to more active use of the website at a time when social distancing was raised.