• Proceedings of the ESK Conference
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• 1992.10a
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• pp.113-120
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• 1992

In this paper, a mathematical model of learning curve is proposed to study the finger's reaction time. The model is a logarithmic linear type which represents a learning curve appropriately, and parameters are estimated by the linear. The learning coefficient and percentage of a reaction time can be easily computed in the mathematical model. This quantitative approach provides an important information to be used for the working capability qualification for re-employment as well as for the adaptability estimation of aged workers.

• Journal of Satellite, Information and Communications
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• v.12 no.3
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• pp.69-73
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• 2017

In this paper, we developed a real-time image recognition algorithm based on machine learning and tested the performance of the algorithm. The real-time image recognition algorithm recognizes the input image in real-time based on the machine-learned image data. In order to test the performance of the real-time image recognition algorithm, we applied the real-time image recognition algorithm to the autonomous vehicle and showed the performance of the real-time image recognition algorithm through the application of the autonomous vehicle.

• Journal of The Korean Association of Information Education
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• v.25 no.5
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• pp.813-824
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• 2021

To improve educational performance by analyzing the learning trends of adult learners of Open High Schools, various algorithm models using artificial intelligence were designed and performance was evaluated by applying them to real data. We analyzed Log data of 115 adult learners in the cyber education system of Open High Schools. Most adult learners of Open High Schools learned more than recommended learning time, but at the end of the semester, the actual learning time was significantly reduced compared to the recommended learning time. In the second half of learning, the participation rate of VODs, formation assessments, and learning activities also decreased. Therefore, in order to improve educational performance, learning time should be supported to continue in the second half. In the latter half, we developed an artificial intelligence algorithm models using Tensorflow to predict learning time by data they started taking the course. As a result, when using CNN(Convolutional Neural Network) model to predict single or multiple outputs, the mean-absolute-error is lowest compared to other models.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.137-142
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• 2021

Distance learning universities provide online course content. The main methods of providing class contents are on-demand and live-streaming. This means that students are not restricted by time or space. The advantage is that students can take the course anytime and anywhere. Therefore, unlike commuting students, there is no commuting time to the campus, and there is no natural process required to take classes. However, despite this convenient situation, the attendance rate and graduation rate of distance learning universities tend to be lower than that of commuting universities. Although the course environment is not the only factor, students cannot obtain a bachelor's degree unless they fulfill the graduation requirements. In both commuter and distance learning universities, taking classes is an important factor in earning credits. There are fewer time and space constraints for distance learning students than for commuting students. It is also easy for distance learning students to take classes at their own timing. There should be more ease of learning than for students who commute to school with restrictions. However, it is easier to take a course at a commuter university that conducts face-to-face classes. I thought that the reason for this was that commuting to school was a part of the process of taking classes for commuting students. Commuting to school was thought to increase the willingness and motivation to take classes. Therefore, I thought that the inconvenient constraints might encourage students to take the course. In this research, I focused on the act of commuting to school by students. These situations are also applied to the distance learning environment. The students have physical time constraints. To achieve this goal, I will implement a course restriction method that aims to promote the willingness and attitude of students. Therefore, in this paper, I have implemented a virtual school system called "virtual go to school (VG2S)" that reflects the actual route to school.

• Journal of Fisheries and Marine Sciences Education
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• v.27 no.4
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• pp.901-911
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• 2015

This article starts with a review of the e-self directed learning, future time perspective and decision making, especially in relation to the career myths. In particular, we empirically analyzed the factors affecting the future time perspective and the decision making on the characteristics of career myths(e.g. relatedness of the test myths, the supreme myth and the family myths). Hence the main purpose of this article is to suggest an empirical model explaining how these factors affect e-self directed learning to future time perspective and decision making. Furthermore, we suggested an expanded model about future time perspective, decision making and especially in relation to the career myths. We founded that the e-self directed learning significantly affect the future time perspective and the decision making, also the future time perspective affect the test myths and family myths except the supreme myths and the decision making significantly affect the career myths(i.e., the test myths, the supreme myth, the family myths).

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.2
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• pp.19-28
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• 2019

The purpose of this study is to examine the trends on machine learning and deep learning research in the published journals from the Web of Science Database. To achieve the study purpose, we used the abstracts of 20,664 articles published between 1990 and 2017, which include the word 'machine learning', 'deep learning', and 'artificial neural network' in their titles. Twenty major research topics were identified from topic modeling analysis and they were inclusive of classification accuracy, machine learning, optimization problem, time series model, temperature flow, engine variable, neuron layer, spectrum sample, image feature, strength property, extreme machine learning, control system, energy power, cancer patient, descriptor compound, fault diagnosis, soil map, concentration removal, protein gene, and job problem. The analysis of the time-series linear regression showed that all identified topics in machine learning research were 'hot' ones.

• Educational Technology International
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• v.15 no.1
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• pp.27-48
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• 2014

The purpose of the present research was to examine the relationship between the using goals of computer and self-regulated learning ability on the gender difference. To accomplish this goal, we have analyzed the data of Korea Children and Youth Panel Survey III which is nationally collected from primary school students, currently on the 6th grade in South Korea. 2,219 samples were used in the study excluding missing samples. The participants were 1167 males (49.5%) and 1052 females (50.5%). The mean age was 13.94 years (SD=.25). As results, female students spent more time on using computer than male students did: (1) the male students' time spent on Playing game was significantly larger than that of female students, but (2) on the rest seven using goals of computer including e-Learning/Information retrieval for learning, the female students spent significantly more time than the male students did. Also, in terms of the self-regulated learning ability, using computer for e-Learning/Information retrieval for learning itself gave significantly positive effects on both male and female students' self-regulated learning ability. On the other hand, Playing game gave significantly negative effects on both. Based on the results, some strategies were suggested on the proper use of computer for learning.

• Journal of Information Technology Applications and Management
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• v.12 no.3
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• pp.57-80
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• 2005

This study defined the notion of general idea on m-learning as based upon e-Learning and mobile internet related literature review and identified the m-Learning distinctive features. Also, this study has searched for factors that are expected to influence the use intended for m-Learning from self-regulated learning, which is acknowledged to be a useful method for learning accomplishment in education field, in order to measure the relationship between learners' motivation and use intention. Then it has empirically validated the conceptual model based on Davis' TAM (Technology Acceptance Model) As a result, self-efficacy, self-determination, interest, contents quality, time management, help seeking, and Peer study are factors affecting Perceived usefulness. Also self-efficacy, self-determination, interest, contents qualify, time management, and peer study are factors affecting perceived ease of use. Finally both perceived usefulness and perceived ease of use are significant factors affecting use intention.

• Journal of Information Technology Services
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• v.10 no.1
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• pp.151-165
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• 2011

Recently building up of u-learning oriented teaching and learning system has been expanded rapidly, However domestic junior college's challenging for adapting it might be slower than other educational body's doing, and in that result it might be paid more or be taken longer time to improve their old system effectively. Now, it is very time for them to develop and implement u-learning oriented teaching and learning system quickly. This paper offers and draws the core factors to design ubiquitous teaching and learning model systematically through investigation of worldwide recent technology and R&D, patent, service and standardization tendency related with u-learnig modeling.

• Communications for Statistical Applications and Methods
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• v.28 no.4
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• pp.329-338
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• 2021

Deep learning methods have been developed, used in various fields, and they have shown outstanding performances in many cases. Many studies predicted a daily stock return, a classic example of time-series data, using deep learning methods. We also tried to apply deep learning methods to Korea's stock market data. We used Korea's stock market index (KOSPI) and several individual stocks to forecast daily returns and directions. We compared several deep learning models with other machine learning methods, including random forest and XGBoost. In regression, long short term memory (LSTM) and gated recurrent unit (GRU) models are better than other prediction models. For the classification applications, there is no clear winner. However, even the best deep learning models cannot predict significantly better than the simple base model. We believe that it is challenging to predict daily stock return data even if we use the latest deep learning methods.