• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2101-2121
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• 2020

The rapid growth of instructional videos repositories and their widespread use as a tool to support education have raised the need of studies to assess the quality of those educational resources and their impact on the quality of learning process that depends on them. Khan Academy (KA) repository is one of the prominent educational videos' repositories. It is famous and widely used by different types of learners, students and teachers. To better understand its characteristics and the impact of such repositories on education, we gathered a huge amount of KA data using its API and different web scraping techniques, then we analyzed them. This paper reports the first quantitative and descriptive analysis of Khan Academy repository (KA repository) of open video lessons. First, we described the structure of repository. Then, we demonstrated some analyses highlighting content-based growth and evolution. Those descriptive analyses spotted the main important findings in KA repository. Finally, we focused on users' interactions with video lessons. Those interactions consisted of questions and answers posted on videos. We developed interaction profiles for those videos based on the number of users' interactions. We conducted regression analysis and statistical tests to mine the relation between those profiles and some quality related proposed metrics. The results of analysis showed that all interaction profiles are highly affected by video length and reuse rate in different subjects. We believe that our study demonstrated in this paper provides valuable information in understanding the logic and the learning mechanism inside learning repositories, which can have major impacts on the education field in general, and particularly on the informal learning process and the instructional design process. This study can be considered as one of the first quantitative studies to shed the light on Khan Academy as an open educational resources (OER) repository. The results presented in this paper are crucial in understanding KA videos repository, its characteristics and its impact on education.

• Journal of Korea Water Resources Association
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• v.54 no.3
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• pp.157-166
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• 2021

Forecasting dam inflow based on high reliability is required for efficient dam operation. In this study, deep learning technique, which is one of the data-driven methods and has been used in many fields of research, was manipulated to predict the dam inflow. The Long Short-Term Memory deep learning with Sequence-to-Sequence model (LSTM-s2s), which provides high performance in predicting time-series data, was applied for forecasting inflow of Soyang River dam. Various statistical metrics or evaluation indicators, including correlation coefficient (CC), Nash-Sutcliffe efficiency coefficient (NSE), percent bias (PBIAS), and error in peak value (PE), were used to evaluate the predictive performance of the model. The result of this study presented that the LSTM-s2s model showed high accuracy in the prediction of dam inflow and also provided good performance for runoff event based runoff prediction. It was found that the deep learning based approach could be used for efficient dam operation for water resource management during wet and dry seasons.

• Journal of The Korean Association of Information Education
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• v.26 no.1
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• pp.23-34
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• 2022

Teachers should be able to reorganize the curriculum according to the student level, reorganize textbooks freely, and distribute them to students. However, current paper-textbooks are difficult to modify or edit some contents and distribute them to students, also current digital textbooks are grouped into units, so the order or educational resources cannot be reconstructed. In addition, The digital textbooks are difficult to update external links or the latest resources, and to contain various multimedia materials or high-definition realistic content due to capacity limitations. Therefore, this study presented functions: teaching and learning and evaluation functions, resources search and sharing functions, learning records and analysis functions, screen showing and printing functions, so that teachers can provide customized learning by level to students using digital textbooks. Through the expert Delphi survey, detailed functions for each area were divided into teachers and students. We proposed expanding and developing digital textbooks to various subjects, and distributing various teaching and learning models using digital textbooks.

• Journal of Family Resource Management and Policy Review
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• v.20 no.2
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• pp.43-56
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• 2016

Time poverty is a kind of objective and subjective state which a person does not have a enough time to do his/her work or is in the mood to do something in a hurry. The major of family resources management has studied time as a resource to manage for long years. How to manage time has been a major part in education of family resources management. The education itself in nature has focused to inform knowledge and the disciplines of time management, to the students, on the other way, has a rare interest with a each student how to apply them or whether do in practical. Coaching is characterized as a practical learning and mutual communication skills with open questions, which help for a individual student to find his/her own goal related with time poverty or furthermore, whatever he/she wants to achieve in life. If the benefits of the education of family resources management as well as the benefits of practical learning of coaching could be merged in education on time management, the effectiveness of education to resolve time poverty is able to be increased. For the purpose, this study suggests a coaching approach in education of family resources management to resolve time poverty, by some comparisons of family resources management and coaching about time and time management.

• Knowledge Management Research
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• v.11 no.4
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• pp.41-66
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• 2010

This research examines the effect of the relatedness and the gap between Resources and mechanisms on effectiveness of inter-organizational knowledge transfer. According to the literature, there has been a competing theory between two claims; one is that inter-organizational knowledge transfer will be more effective due to the reduction of the transaction cost as the relatedness increases. And the other is that the mutual complementarity of different organizational characteristics will increase synergy. In total, the relatedness and the gap of the Resource and mechanism makes the inverted U-shaped relationship with the inter-organizational knowledge transfer. As the result of empirical analysis about 109 Korean-based Joint Ventures entered country, it shows that the relatedness of parent company's production Resources, learning mechanisms, and coordination mechanisms made the inverted U-shaped relations with the inter-organizational knowledge transfer and the gap of production Resources and adjustment mechanism formed the same relationship. However, the U-shaped relationship has been established in the relatedness of market Resources, but the gap of market Resources and the learning mechanism was not statistically significant. Through this study, I can draw a best conclusion that the inter-organizational knowledge transfer will be more effective when the relatedness and the gap of management resources and mechanisms is in optimal level. However, when it comes to market Resources, it can be inferred that the result could be the opposite because the partner country's market environment would be different.

• IE interfaces
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• v.19 no.4
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• pp.324-332
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• 2006

It is believed that each learner has a preferred method to acquire and manage knowledge according to her/his learning style which influences learning achievement directly. The purpose of this paper is to statistically analyze relationships among individual learning styles, tutoring functions, and learning achievement in an e-learning environment. 524 survey results from participants of enterprise e-learning classes are classified into total group and superior group. T-Test and ANOVA analyses are carried between learning style and learning achievement and between learning style and preferred tutoring functions. The analysis results show that individual learning styles do not contribute to learning achievement while they are strongly related to preferences for some of tutoring functions. These results can be used to identify limitation of current e-learning practice and design better e-learning systems, especially, supporting appropriate tutoring functions for different types of learners.

• Laboraroty Animal Research
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• v.34 no.4
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• pp.317-328
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• 2018

Cognitive impairment responses are important research topics in the study of degenerative brain diseases as well as in understanding of human mental activities. To compare response to scopolamine (SPL)-induced cognitive impairment, we measured altered parameters for learning and memory ability, inflammatory response, oxidative stress, cholinergic dysfunction and neuronal cell damages, in Korl:ICR stock and two commercial breeder stocks (A:ICR and B:ICR) after relevant SPL exposure. In the water maze test, Korl:ICR showed no significant difference in SPL-induced learning and memory impairment compared to the two different ICRs, although escape latency was increased after SPL exposure. Although behavioral assessment using the manual avoidance test revealed reduced latency in all ICR mice after SPL treatment as compared to Vehicle, no differences were observed between the three ICR stocks. To determine cholinergic dysfunction induction by SPL exposure, activity of acetylcholinesterase (AChE) assessed in the three ICR stocks revealed no difference of acetylcholinesterase activity. Furthermore, low levels of superoxide dismutase (SOD) activity and high levels of inflammatory cytokines in SPL-treated group were maintained in all three ICR stocks, although some variations were observed between the SPL-treated groups. Neuronal cell damages induced by SPL showed similar response in all three ICR stocks, as assessed by terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay, Nissl staining analysis and expression analyses of apoptosis-related proteins. Thus, the results of this study provide strong evidence that Korl:ICR is similar to the other two ICR. Stocks in response to learning and memory capacity.

• Journal of Digital Convergence
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• v.8 no.4
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• pp.109-121
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• 2010

E-learning has become an ever-increasing delivery method in school and workplace. Many Web sites provide Internet users with lots of information and resources on study, research, and career development in workplace. First, this study aimed at presenting a process of validating instruments to measure perception of using the Web for learning. Secondly, this paper attempted to find out a list of critical constructs that university students recognize when they access Web sites to get some resources on their fields of study. This study also suggested the features of those constructs. This paper would help improve our understanding of Web usage for schoolwork and research. This result of the paper will facilitate further understanding of constructs associated with Web usage in other areas, thereby enabling researchers, practitioners, and policy makers to draw much attention to e-learning.

• Journal of the Korean Society of Mineral and Energy Resources Engineers
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• v.55 no.6
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• pp.649-659
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• 2018

In this study, after operational data for a landfill gas power plant were collected, the methane gas concentration was predicted using a deep learning method. Concentrations of methane gas, carbon dioxide, hydrogen sulfide, oxygen concentration, as well as data related to the valve opening degree, air temperature and humidity were collected from 23 pipeline bases for 88 matches from January to November 2017. After the deep learning model learned the collected data, methane gas concentration was estimated by applying other data. Our study yielded extremely accurate estimation results for all of the 23 pipeline bases.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.183-183
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• 2021

Deep learning methods and their application have become an essential part of prediction and modeling in water-related research areas, including hydrological processes, climate change, etc. It is known that application of deep learning leads to high availability of data sources in hydrology, which shows its usefulness in analysis of precipitation, runoff, groundwater level, evapotranspiration, and so on. However, there is still a limitation on microclimate analysis and prediction with deep learning methods because of deficiency of gauge-based data and shortcomings of existing technologies. In this study, a real-time rainfall prediction model was developed from a sky image data set with convolutional neural networks (CNNs). These daily image data were collected at Chung-Ang University and Korea University. For high accuracy of the proposed model, it considers data classification, image processing, ratio adjustment of no-rain data. Rainfall prediction data were compared with minutely rainfall data at rain gauge stations close to image sensors. It indicates that the proposed model could offer an interpolation of current rainfall observation system and have large potential to fill an observation gap. Information from small-scaled areas leads to advance in accurate weather forecasting and hydrological modeling at a micro scale.