• 대한인간공학회:학술대회논문집
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• 대한인간공학회 1992년도 추계학술대회논문집
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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.

• 한국위성정보통신학회논문지
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• 제12권3호
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• pp.69-73
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• 2017

본 논문에서는 기계학습 기반의 실시간 이미지 인식 알고리즘을 개발하고 개발한 알고리즘의 성능을 테스트 하였다. 실시간 이미지 인식 알고리즘은 기계 학습된 이미지 데이터를 바탕으로 실시간으로 입력되는 이미지를 인식한다. 개발한 실시간 이미지 인식 알고리즘의 성능을 테스트하기 위해 자율주행 자동차 분야에 적용해보았고 이를 통해 개발한 실시간 이미지 인식 알고리즘의 성능을 확인해보았다.

• 정보교육학회논문지
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• 제25권5호
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• pp.813-824
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• 2021

A사이버교육시스템 성인학습자의 자기조절학습 관련 학습 추이를 분석하여 교육 성과를 높이기 위해 인공지능을 활용한 알고리즘 모델을 다양하게 설계하고, 그것을 실제 데이터에 적용함으로써 성능을 평가하였다. 이를 위해 A사이버교육시스템에서 115명의 성인학습자의 로그 데이터를 분석하였다. A사이버교육시스템 성인학습자들은 대부분 권장 학습 시간 이상을 학습하였으나, 학기 말에는 권장 학습 시간 대비 실제 학습 시간이 현저하게 감소하였다. VOD 참여율이나 형성평가 참여율, 학습 활동 참여율에서도 학습 후반부에 접어들수록 학습 참여율이 떨어졌다. 따라서 교육 성과를 높이려면 학습 시간이 후반에도 지속될 수 있도록 지원해야 한다 판단하여 후반부에 학습 시간이 떨어지는 학습자를 찾아내기 위해 Tensorflow를 활용한 인공지능 모델을 개발하여 수강 시작 날짜별 학습 시간을 예측하였다. 그 결과, CNN 모델을 활용하여 단일 출력 또는 다중 출력을 예측할 경우 다른 모델에 비해 평균 절대 오차가 가장 낮게 나타났다.

• International Journal of Computer Science & Network Security
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• 제21권12호
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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.

• 수산해양교육연구
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• 제27권4호
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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).

• 디지털산업정보학회논문지
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• 제15권2호
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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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• 제15권1호
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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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• 제12권3호
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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.

• 한국IT서비스학회지
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• 제10권1호
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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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• 제28권4호
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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.