본 연구의 목적은 예비영유아교사의 행복감, 스마트폰 중독, SNS 중독의 관계에 대하여 알아보는 것이다. 연구 대상은 경기도 소재 4년제 및 3년제 영유아교육 관련학과에 재학 중인 남녀 대학생 220명이었다. 수집된 설문조사 결과는 SPSS 20.0을 사용하여 분석하였다. 연구 결과는 다음과 같다. 첫째, 예비영유아교사의 행복감, 스마트폰 중독, SNS 중독 정도로는 행복감에서 외적행복의 평균이 3.64(SD=.54)로 가장 높았으며 내적행복이 3.32(SD=0.73), 조절행복이 3.08(SD=0.69)로 나타났고, 스마트폰 중독에서는 내성에 대한 평균이 2.10(SD=.73)으로 가장 높았으며, 일상생활장애가 2.10(SD=.73), 금단이 2.09(SD=.71)이었으며, 가상세계지향의 평균이 1.67(SD=.63)로 가장 낮았다. SNS 중독에서는 몰입 및 내성에 대한 평균이 2.26(SD=.57) 로 가장 높았으며, 조절실패 및 일상생활장애 2.14(SD=.56), 부정정서의 회피 2.11(SD=.56)이 었으며, 가상세계지향의 평균이 1.87(SD=.48)로 가장 낮았다. 둘째, 예비영유아교사의 행복감, 스마트폰 중독, SNS 중독의 상관관계에서는 행복감이 높을수록 스마트폰 중독과 SNS 중독은 낮게 나타났고, 내적 행복감이 높을수록 몰입 및 내성, 가상세계지향성 및 금단에 대한 중독성은 낮아진다는 결과가 나왔다.

Contemporary University students are considered the Z generation who were born after 1995. They are more tech savvy than millennials. To target the generation, traditional class management platforms have evolved to smart LMS that is more customized and accessible for smart devices. Global level information search and collaboration can also be implemented using such smart LMS. However, switching from one LMS to another LMS requires great effort from teachers and support from staffs. This study measured the learners’ perception of the system when they were exposed to a new smart-LMS. Blackboard Learn Ultra was used for 15 weeks and at the end of the semester, a questionnaire was administered to the students of these classes. Results indicated that experience with previous LMS discouraged students from adopting Blackboard Learn. Result of TAM modeling indicated that perceived usefulness, compared to perceived ease of use and attitude, was an effective aspect to bring positive acceptance of the system. A qualitative approach and network analysis were also conducted based on students’ responses. Both positive and negative responses were detected. Inconvenience due to mechanical aspects was mentioned. Dissatisfaction compared to previous local LMS use was also mentioned. Mobile application and communication effectiveness were positive aspects. Revised course development and promoting how useful the system may help enhance the acceptance of the new system.

본 연구를 통해 병 재배 느타리버섯 ‘춘추2호’의 정밀 재배를 위한 최적 생육모델 개발하기 위하여 느타리 농가를 대상으로 스마트팜 기술을 적용하여 생육환경을 분석한 결과를 보고하고자 한다. 실험 농가의 균상면적은 114 m 2 , 균상형태는 2열 5단, 냉동기는 10마력, 단열은 샌드위치 판넬 100T, 가습기는 초음파 가습기 2대, 난방은 10KW를 사용하였고, 5,500병을 입병하여 재배하고 있었다. 느타리버섯 재배농가에서 생육환경 데이터를 수집하기 위하여 설치한 환경센서부로 부터 버섯의 생육에 직접적으로 영향을 미치는 온도, 습도, 이산화탄소 농도, 조도 등을 수집 분석하였다. 온도는 균 긁기한 후 입상시 19.5 o C에서 시작하여 버섯이 발생되어 병을 뒤집기 후 5 일차까지 거의 21 o C를 유지하고 자실체가 자라서 수확기에 가까워지면 18 o C에서 14 o C를 유지하면서 버섯을 수확 하였다. 습도는 균 긁기한 후 입상시 거의 100%에 가까 웠고, 버섯 발생 및 생육과정 중에도 습도는 거의 95~100%를 유지하였다. 이산화탄소농도는 입상후 5일까 지는 최고 5,500 ppm까지 증가하였고, 6일차부터는 환기를 통해 단계적으로 농도를 낮추어 수확기에는 1,600 ppm 을 유지하였다. 조도는 입상후 6일차까지는 8 lux의 빛을 조사하였고, 그 이후 주기적으로 4 lux의 빛을 조사하면서 생육을 진행하였다. 농가에 재배하고 있는 ‘춘추2호’의 자실체 특성은 갓 직경은 26.5 mm, 갓 두께는 4.9 mm이며, 대 굵기는 8.9 mm, 대 길이는 68.7 mm였다. 대 경도 는 3.9 g/mm, 갓 경도는 0.9 g/mm였고, 대와 갓의 L값은 78.2와 60.5이였다. 자실체 수량은 166.8 g/850 ml였고, 개체중은 12.8 g/10 unit였다.

In this study, we analyzed the factors affecting the introduction of Smart Factory by domestic SMEs through AHP analysis and tried to provide implications for the introduction of Smart Factory. It was confirmed that the manufacturing and introduction group, the non-manufacturing introduction group, and the already introduced group had the highest weight in the cost reduction in the first hierarchy standard. At this time, it can be seen that the weight for cost reduction is relatively high in the manufacturing introduction group and the introduction group, and the weight for the productivity improvement is relatively high in the non-manufacturing introduction group. It can also be seen that the portion of marketing enhancement does not have a significant impact on smart factory choices. It was confirmed that image enhancement is the highest in the manufacturing introduction group and the non-manufacturing introduction group in the first hierarchy standard, and the marketing has the highest weight in the introduction group. In the two - tiered standard, customer - friendly and proper inventory maintenance weights were relatively high in all the introduced groups, except for the high rankings.

This study is to develop a diagnostic model for the effective introduction of smart factories in the manufacturing industry, to diagnose SMEs that have difficulties in building their own smart factory compared to large enterprise, to identify the current level and to present directions for implementation. IT, AT, and OT experts diagnosed 18 SMEs using the "Smart Factory Capacity Diagnosis Tool" developed for smart factory level assessment of companies. They analyzed the results and assessed the level by smart factory diagnosis categories. Companies' smart factory diagnostic mean score is 322 out of 1000 points, between 1 level (check) and 2 level (monitoring). According to diagnosis category, Factory Field Basic, R&D, Production/Logistics/Quality Control, Supply Chain Management and Reference Information Standardization are high but Strategy, Facility Automation, Equipment Control, Data/Information System and Effect Analysis are low. There was little difference in smart factory level depending on whether IT system was built or not. Also, Companies with large sales amount were not necessarily advantageous to smart factories. This study will help SMEs who are interested in smart factory. In order to build smart factory, it is necessary to analyze the market trends, SW/ICT and establish a smart factory strategy suitable for the company considering the characteristics of industry and business environment.