• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.107-129
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• 2022

It is important to preempt new technology because the technology competition is getting much tougher. Stakeholders conduct exploration activities continuously for new technology preoccupancy at the right time. Gartner's Hype Cycle has significant implications for stakeholders. The Hype Cycle is a expectation graph for new technologies which is combining the technology life cycle (S-curve) with the Hype Level. Stakeholders such as R&D investor, CTO(Chef of Technology Officer) and technical personnel are very interested in Gartner's Hype Cycle for new technologies. Because high expectation for new technologies can bring opportunities to maintain investment by securing the legitimacy of R&D investment. However, contrary to the high interest of the industry, the preceding researches faced with limitations aspect of empirical method and source data(news, academic papers, search traffic, patent etc.). In this study, we focused on two research questions. The first research question was 'Is there a difference in the characteristics of the network structure at each stage of the hype cycle?'. To confirm the first research question, the structural characteristics of each stage were confirmed through the component cohesion size. The second research question is 'Is there a pattern of diffusion at each stage of the hype cycle?'. This research question was to be solved through centralization index and network density. The centralization index is a concept of variance, and a higher centralization index means that a small number of nodes are centered in the network. Concentration of a small number of nodes means a star network structure. In the network structure, the star network structure is a centralized structure and shows better diffusion performance than a decentralized network (circle structure). Because the nodes which are the center of information transfer can judge useful information and deliver it to other nodes the fastest. So we confirmed the out-degree centralization index and in-degree centralization index for each stage. For this purpose, we confirmed the structural features of the community and the expectation diffusion patterns using Social Network Serice(SNS) data in 'Gartner Hype Cycle for Artificial Intelligence, 2021'. Twitter data for 30 technologies (excluding four technologies) listed in 'Gartner Hype Cycle for Artificial Intelligence, 2021' were analyzed. Analysis was performed using R program (4.1.1 ver) and Cyram Netminer. From October 31, 2021 to November 9, 2021, 6,766 tweets were searched through the Twitter API, and converting the relationship user's tweet(Source) and user's retweets (Target). As a result, 4,124 edgelists were analyzed. As a reult of the study, we confirmed the structural features and diffusion patterns through analyze the component cohesion size and degree centralization and density. Through this study, we confirmed that the groups of each stage increased number of components as time passed and the density decreased. Also 'Innovation Trigger' which is a group interested in new technologies as a early adopter in the innovation diffusion theory had high out-degree centralization index and the others had higher in-degree centralization index than out-degree. It can be inferred that 'Innovation Trigger' group has the biggest influence, and the diffusion will gradually slow down from the subsequent groups. In this study, network analysis was conducted using social network service data unlike methods of the precedent researches. This is significant in that it provided an idea to expand the method of analysis when analyzing Gartner's hype cycle in the future. In addition, the fact that the innovation diffusion theory was applied to the Gartner's hype cycle's stage in artificial intelligence can be evaluated positively because the Gartner hype cycle has been repeatedly discussed as a theoretical weakness. Also it is expected that this study will provide a new perspective on decision-making on technology investment to stakeholdes.

• Journal of The Korean Association For Science Education
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• v.38 no.4
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• pp.467-480
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• 2018

This study aims to investigate how flipped learning-based socioscientific issue instruction (FL-SSI instruction) affected middle school students' key competencies and character development. Traditional classrooms are constrained in terms of time and resources for exploring the issues and making decision on SSI. To address these concerns, we designed and implemented an SSI instruction adopting flipped learning. Seventy-three 8th graders participated in an SSI program on four topics for over 12 class periods. Two questionnaires were used as a main data source to measure students' key competencies and character development before and after the SSI instruction. In addition, student responses and shared experience from focus group interviews after the instruction were collected and analyzed. The results indicate that the students significantly improved their key competencies and experienced character development after the SSI instruction. The students presented statistically significant improvement in the key competencies (i.e., collaboration, information and technology, critical thinking and problem-solving, and communication skills) and in two out of three factors in character and values as global citizens (social and moral compassion, and socio-scientific accountability). Interview data supports the quantitative results indicating that SSI instruction with a flipped learning strategy provided students in-depth and rich learning opportunities. The students responded that watching web-based videos prior to class enabled them to deeply understand the issue and actively engage in discussion and debate once class began. Furthermore, the resulting gains in available class time deriving from a flipped learning approach allowed the students to examine the issue from diverse perspectives.

• Journal of Korean Society of Forest Science
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• v.103 no.4
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• pp.593-598
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• 2014

In this study, we developed the carbon emission factors for 4 major species of warm-temperate region in Korea, and tried to provide their carbon emissions and removals estimates using these carbon emission factors. We selected Castanopsis cuspidata, Camellia japonica, Quercus acuta and Quercus glauca as target species and derived their carbon emission factors. The basic wood density that serve as one of the carbon emission factors were 0.583 for Castanopsis cuspidata, 0.657 for Camellia japonica, 0.833 for Quercus acuta and 0.763 for Quercus glauca and their uncertainties ranged from 5.3 to 17.9%. Biomass expansion factors were calculated as well: 1.386 for Castanopsis cuspidata, 2.621 for Camellia japonica, 1.701 for Quercus acuta and 2.123 for Quercus glauca and associated uncertainties varied from 14.7 to 30.5%. Lastly root-shoot ratios for each species were also determined: 0.454 for Castanopsis cuspidata, 0.356 for Camellia japonica, 0.191 for Quercus acuta and 0.299 for Quercus glauca with the uncertainties lying within a range from 19.8 to 35.7%. These three carbon emission factors including basic wood density had the uncertainties of less than 40% recommended by FAO. Therefore the application of country-specific emission factors seemed to provide quite accurate estimates of carbon emissions and removals. The estimation of the carbon stored in the 4 species were also conducted which amounted to $186.10tCO_2/ha$ for Castanopsis cuspidata, $280.63tCO_2/ha$ for Camellia japonica, $344.04tCO_2/ha$ for Quercus acuta and $278.91tCO_2/ha$ for Quercus glauca and their annual carbon removals were $6.65tCO_2/ha/yr$, $6.25tCO_2/ha/yr$, $11.70tCO_2/ha/yr$ and $12.29tCO_2/ha/yr$, respectively. This systematic assessment of forest resources can be a reliable source of information for managing evergreen broadleaved forest in warm temperate regions and thus serve as useful data for effective decision-making to address vegetation zone shifts due to climate change.