• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.633-640
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• 2021

This study analyzes the effects of text and image presenting methods in video lectures on students' learning flow, learning satisfaction and learning outcomes. The text presenting methods include forming short sentences of 2 or 3 words or using key words, while image presenting methods include images featuring both detailed and related information as well as images containing only related information. 167 first year students from Xingtai University were selected as experimental participants. Groups of participants were randomly assigned to engage in four types of video. The research results are as follows. First, it was found that learning flow, learning satisfaction and learning outcomes of group presented with video forms of short sentences had higher statistical significance compared to the group experiencing the key word method. Second, learning flow, learning satisfaction and learning outcomes of group presented with video forms of only related information had higher statistical significance compared to the group experiencing the presenting method of both detailed and related information. That is, the mean values of dependent variables for groups of short form text and only related information were highest. In contrast, the mean values of dependent variables for groups of key words and both detailed and related information were the lowest.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.4
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• pp.91-101
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• 2016

This research focused on searching the specialized factors which affect the cyber universities' performance. This research was approached from three perspective of professor, graduation and employment, and brand image to embody the cyber universities' performance. We drew five factors of contents variety, contents quality, practical orientation of contents, lecture presence, and learning management as the Influencing factors of cyber universities' performance. We proved our research model by analyzing 170 questionnaires which were completed by college students who were studying at cyber universities in China. The results of analysis are showed as below: 1) The level of contents variety can affect the cyber universities' performance; higher level gains better outcomes. 2) The level of contents quality can affect the cyber universities' performance; better quality gains better outcomes. 3) The level of practical orientation of contents can affect the cyber universities' performance; the higher the better. 4) The level of lecture presence can affect the cyber universities' performance; the stronger the better. 5) The level of learning management can affect the cyber universities' performance; the higher the better.

• Journal of Gifted/Talented Education
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• v.26 no.3
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• pp.541-559
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• 2016

This study investigated a relationship among facilitating discourse, students' perceived challenge, and learning outcomes(persistent intention and learning achievement) in an online science gifted education program. Two hundreds and forty-two middle school students participated in the study. A survey questionnaire which was consisted of 6 items of facilitating discourse from teaching presence questionnaire(Shea, Swan, & Pickett, 2005) and 5 items of challenge from Student Perceptions of Classroom Quality(Gentry & Owen, 2004) was administered. First, the findings of this study showed that students' perceived facilitating discourse as a part of teaching presence was positively related to students' perceived challenge in an online course. Second, students' perceived facilitating discourse were positively related to persistent intention, but were negatively related to students' achievement. Third, students' perceived challenge was positively related to persistent intention and achievement. Finally, challenge mediated the relationship between students' perceived facilitating discourse and persistent intention, and the relationship between students' perceived facilitating discourse and students' achievement as well. This results suggested that online program should be designed to increase the levels of facilitating discourse.

• Journal of IKEEE
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• v.26 no.3
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• pp.341-348
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• 2022

Wi-Fi Sensing, which uses Wi-Fi technology to sense the surrounding environments, has strong potentials in a variety of sensing applications. Recently several advanced deep learning-based solutions using CSI (Channel State Information) data have achieved high performance, but it is still difficult to use in practice without explicit data collection, which requires expensive adaptation efforts for model retraining. In this study, we propose a Channel State Information Automatic Labeling System (CALS) that automatically collects and labels training CSI data for deep learning-based Wi-Fi sensing systems. The proposed system allows the CSI data collection process to efficiently collect labeled CSI for labeling for supervised learning using computer vision technologies such as object detection algorithms. We built a prototype of CALS to demonstrate its efficiency and collected data to train deep learning models for detecting the presence of a person in an indoor environment, showing to achieve an accuracy of over 90% with the auto-labeled data sets generated by CALS.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.507-519
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• 2023

Multi-temporal optical images have been utilized for time-series monitoring of croplands. However, the presence of clouds imposes limitations on image availability, often requiring a cloud removal procedure. This study assesses the applicability of various machine learning algorithms for effective cloud removal in optical imagery. We conducted comparative experiments by focusing on two key variables that significantly influence the predictive performance of machine learning algorithms: (1) land-cover types of training data and (2) temporal variability of land-cover types. Three machine learning algorithms, including Gaussian process regression (GPR), support vector machine (SVM), and random forest (RF), were employed for the experiments using simulated cloudy images in paddy fields of Gunsan. GPR and SVM exhibited superior prediction accuracy when the training data had the same land-cover types as the cloud region, and GPR showed the best stability with respect to sampling fluctuations. In addition, RF was the least affected by the land-cover types and temporal variations of training data. These results indicate that GPR is recommended when the land-cover type and spectral characteristics of the training data are the same as those of the cloud region. On the other hand, RF should be applied when it is difficult to obtain training data with the same land-cover types as the cloud region. Therefore, the land-cover types in cloud areas should be taken into account for extracting informative training data along with selecting the optimal machine learning algorithm.

• Journal of Distribution Science
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• v.22 no.4
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• pp.1-10
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• 2024

Purpose: This study is aimed to introduce the application of phantom approach with structural equation modelling method for online learning. By integrating these innovative methodologies, the research seeks to advance the understanding of how the phantom approach can effectively complement and augment structural equation modeling techniques. Research design, data and methodology: A theoretical framework of Technology Acceptance Model (TAM) was modified and updated. A questionnaire was developed and used to extract information from 189 instructors who used online learning as their primary medium. The Covariance Based Structural Equation Modelling (CBSEM) was applied to test the direct effects and the phantom approach is used to handle the 2 mediators in the model. Results:social influence, perceived usefulness, and perceived ease of use exerted discernible impacts on instructors' intentionsto engage in online learning. These findings illuminate the intricate dynamics influencing instructor behavior within the realm of online education, underscoring the significance of various factors in shaping their intentions. Conclusions: In additions, the perceived usefulness and perceived ease of use had mediated the effect of social influence and instructor intention using phantom approach. Therefore, one can have concluded that this modified model was also confirmed, thereby reinforcing distribution strategies to online learning and overall education presence.

• Communications of Mathematical Education
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• v.38 no.1
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• pp.27-48
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• 2024

In order to prepare for changes in future society, cross-curricular learning is emphasized, and the need to link cross-curricular learning topics and subjects is increasing. However, there are few studies on how to deal with cross-curricular learning in mathematics education. This study analyzed the contents and methods of cross-curricular learning topics in subject-specific curriculum and mathematics textbooks. As a result of the study, the curriculum can be categorized into four types according to the variety of cross-curricular learning topics applied and the presence or absence of a main cross-curricular learning topic, and the mathematics curriculum belongs to the type where some cross-curricular learning topics are dealt with passively and there is no main topic. On the other hand, the analysis of 10 math textbooks for grades 5 and 6 according to the 2015 revised curriculum showed that, unlike the curriculum, various cross-curricular learning topics were applied in the textbooks, mainly environment and sustainable development education, safety and health education, career education, character education, and economic and financial education. In addition, in mathematics textbooks, cross-curricular learning topics appeared in various types such as materials, questions, explanations, illustrations, and in many cases, they appeared mainly as materials or illustrations. Based on these findings, implications were explored and suggested on how to integrate and apply cross-curricular learning topics in mathematics.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.181-184
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• 1997

Practical applications of learning systems usually involve complex domains exhibiting nonlinear behavior and dilution by noise. Consequently, an intelligent system must be able to adapt to nonlinear processes as well as probabilistic phenomena. An important class of application for a knowledge based systems in prediction: forecasting the future trajectory of a process as well as the consequences of any decision made by e system. This paper examines the robustness of data mining tools under varying levels of noise while predicting nonlinear processes in the form of chaotic behavior. The evaluated models include the perceptron neural network using backpropagation (BPN), the recurrent neural network (RNN) and case based reasoning (CBR). The concepts are crystallized through a case study in predicting a Henon process in the presence of various patterns of noise.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.1
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• pp.53-59
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• 2017

Optical character recognition (OCR) automatically recognizes text in an image. OCR is still a challenging problem in computer vision. A successful solution to OCR has important device applications, such as text-to-speech conversion and automatic document classification. In this work, we analyze character recognition performance using the current state-of-the-art deep-learning structures. One is the AlexNet structure, another is the LeNet structure, and the other one is the SPNet structure. For this, we have built our own dataset that contains digits and upper- and lower-case characters. We experiment in the presence of salt-and-pepper noise or Gaussian noise, and report the performance comparison in terms of recognition error. Experimental results indicate by five-fold cross-validation that the SPNet structure (our approach) outperforms AlexNet and LeNet in recognition error.

• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.367-371
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• 1989

As a menas of designing a robust servo system for electrical motor drive system, an iterative learning control method is proposed by employing the structure of the model algorithmic control. A sufficient condition for the convergency is shown, and via simulation for permanent magnet synchronous motor drive system, it is demonstrated 1hat the method yields a 'good performance even in the presence of the external load distrurbances.