• Journal of Korea Technology Innovation Society
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• v.2 no.1
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• pp.1-15
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• 1999

This study aims at integrating the sociological study of technology and the economic study of technological learning. It is argued that the sociological approaches of innovation have some strong points in criticizing technological determinism, but have some weak points in explaining how the knowledge base for innovation is accumulated. On the contrary, the economic approaches of innovation have strong points in explaining technology accumulation, but ignore socio-political process of innovation. This study suggests the model which integrates the socio-political process and technological loaming process.

• Educational Technology International
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• v.22 no.2
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• pp.169-197
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• 2021

Learning agility as high potentials is drawing attention as a competency for leading an uncertain future society. The present study aims to determine the factors of learning agility in higher education context for future society. To address this goal, Major factors related to learning agility were derived through literature review and statistically verified. For statistical analysis, the nationwide data were collected from 1,000 undergraduate students in South Korea by National Youth Policy Institute. The participants asked to answer 29 items of learning agility questionnaires (LAQ). The collected data were analyzed by descriptive statistical analysis, exploratory factor analysis, and confirmatory factor analysis. As a result, learning agility items were verified normality and reliability. Learning agility was identified seven factors; challenging mind, learning responsibility, reflecting experience, intellectual curiosity, systemic thinking, change adaptability, and logical thinking. Also, the structural model fit of the seven factors of learning agility was also confirmed to be good. Based on the findings of the present study, empirical, theoretical, and practical contributions were presented, and suggestions for further research were proposed in detail.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.9-17
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• 2021

In this paper, an autonomous multi-agent path planning using reinforcement learning for monitoring of infrastructures and resources in a computationally distributed system was proposed. Reinforcement-learning-based multi-agent exploratory system in a distributed node enable to evaluate a cumulative reward every action and to provide the optimized knowledge for next available action repeatedly by learning process according to a learning policy. Here, the proposed methods were presented by (a) approach of dynamics-based motion constraints multi-agent path-planning to reduce smaller agent steps toward the given destination(goal), where these agents are able to geographically explore on the environment with initial random-trials versus optimal-trials, (b) approach using agent sub-goal selection to provide more efficient agent exploration(path-planning) to reach the final destination(goal), and (c) approach of reinforcement learning schemes by using the proposed autonomous and asynchronous triggering of agent exploratory phases.

• Educational Technology International
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• v.18 no.2
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• pp.101-129
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• 2017

This study attempted to collect learners' emotion and state motivation, analyze their inner states, and measure state motivation using a non-self-reported survey. Emotions were measured by learning segment in detailed learning situations, and they were used to indicate total state motivation with prediction power. Emotion was also used to explain state motivation by learning segment. The purpose of this study was to overcome the limitations of video-based learning environments by verifying whether the emotions measured during individual learning segments can be used to indicate the learner's state motivation. Sixty-eight students participated in a 90-minute to measure their emotions and state motivation, and emotions showed a statistically significant relationship between total state motivation and motivation by learning segment. Although this result is not clear because this was an exploratory study, it is meaningful that this study showed the possibility that emotions during different learning segments can indicate state motivation.

• Journal of Practical Engineering Education
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• v.14 no.1
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• pp.49-59
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• 2022

This study was conducted to develop a teaching competency scales customized for teaching programs conducted by Center for Teaching & Learning at A University. To achieve this purpose, a preliminary study was set up, which consists of three competency groups (basic competency, practice competency, innovation competency) and 26 learning competency factors through a review of previous studies. In order to verify the reliability and validity of the provisional teaching competency scales, an online survey was conducted on A university teachers in September 2020, The collected questionnaire data were organized and exploratory factor analysis and confirmatory factor analysis were conducted. As a result of exploratory factor analysis, 26 teaching competency was reduced to 17. As a result of the confirmatory factor analysis, the model was found to be good, Also, as a result of analyzing the construct reliability and AVE of the confirmed teaching competency factors, all 17 factors showed a good level of .7 or more. The teaching competency scales developed through this study can be used as basic data for performance evaluation and development of new programs of CTL teaching program.

• The Journal of Engineering Geology
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• v.33 no.4
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• pp.673-687
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• 2023

Exploratory data analysis is the process of observing and understanding data collected from various sources to identify their distributions and correlations through their structures and characterization. This process can be used to identify correlations among conditioning factors and select the most effective factors for analysis. This can help the assessment of landslide susceptibility, because landslides are usually triggered by multiple factors, and the impacts of these factors vary by region. This study compared two stages of exploratory data analysis to examine the impact of the data exploration procedure on the landslide prediction model's performance with respect to factor selection. Deep-learning-based landslide susceptibility analysis used either a combinations of selected factors or all 23 factors. During the data exploration phase, we used a Pearson correlation coefficient heat map and a histogram of random forest feature importance. We then assessed the accuracy of our deep-learning-based analysis of landslide susceptibility using a confusion matrix. Finally, a landslide susceptibility map was generated using the landslide susceptibility index derived from the proposed analysis. The analysis revealed that using all 23 factors resulted in low accuracy (55.90%), but using the 13 factors selected in one step of exploration improved the accuracy to 81.25%. This was further improved to 92.80% using only the nine conditioning factors selected during both steps of the data exploration. Therefore, exploratory data analysis selected the conditioning factors most suitable for landslide susceptibility analysis and thereby improving the performance of the analysis.

• Educational Technology International
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• v.18 no.2
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• pp.131-157
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• 2017

The purposes of the study were to investigate the participation of artists in paper circuit computing learning and to conduct an in-depth study on the formation and development of practical knowledge. To do this, we selected as research participants six artists who participated in the learning program of an art museum, and used various methods such as pre-open questionnaires, participation observation, and individual interviews to collect data. The collected data were analyzed based on community of practice theory. Results showed that the artists participated in the learning based on a desire to use new technology or find a new work production method for interacting with their audiences. In addition, the artists actively formed practical knowledge in the curriculum and tried to apply paper circuit computing to their works. To continuously develop the research, participants formed a study group or set up a practical goal through planned exhibitions. The results of this study can provide implications for practical approaches to, and utilization of, paper circuit computing.

• Educational Technology International
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• v.9 no.2
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• pp.1-15
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• 2008

One of the challenges that online learners face is feeling of isolation and diminishing desire of maintaining active participation during e-learning. Social presence, that is considered to be a vital factor in e-learning, is recently started to receive a support from the field. Although research indicated a significant role of social presence in both learning process and learning outcome, there is no widely accepted measurement scale of social presence. This study, therefore, developed a new scale to measure social presence based on the existing theories and validated it against 723 participants. Nineteen self-report items with three dimensions, co-presence, influence, and cohesiveness, were identified and validated using Exploratory Factor Analysis (EFA) in a preliminary and a follow-up study.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.1
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• pp.36-41
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• 2009

Self organizing map(SOM) is one of the unsupervised methods based on the competitive learning. Many clustering works have been performed using SOM. It has offered the data visualization according to its result. The visualized result has been used for decision process of descriptive data mining as exploratory data analysis. In this paper we propose improvement of SOM using stratified sampling of statistics. The stratification leads to improve the performance of SOM. To verify improvement of our study, we make comparative experiments using the data sets form UCI machine learning repository and simulation data.

• Educational Technology International
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• v.18 no.2
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• pp.215-247
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• 2017

This study aims to conduct a literature review on visual scaffolding. Visual scaffolding, as a support for learning, employs various forms of visual objects which can be either content-independent or content-dependent and the types of which would be abstract-verbal, concrete-verbal, concrete-visual, or abstract visual. The effectiveness of visual scaffolding can be argued in the following three aspects: 1) explicit representation of information and emphasis of critical features in effective and efficient manner, 2) supplement of additional information, 3) structural understanding with decrease in cognitive load. The limitations of the study and the suggestions for future study are discussed.