• Review of Korea Contents Association
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• v.16 no.3
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• pp.19-24
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• 2018

• Journal of Korea Game Society
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• v.18 no.5
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• pp.83-92
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• 2018

Many studies in deep learning show results as good as human's decision in various fields. And importance of activation of online-community and SNS grows up in game industry. Even it decides whether a game can be successful or not. The purpose of this study is to construct a system which can read texts and create comments according to schedule in online-community and SNS using deep learning. Using recurrent neural network, we constructed models generating a comment and a schedule of writing comments, and made program choosing a news title and uploading the comment at twitter in calculated time automatically. This study can be applied to activating an online game community, a Q&A service, etc.

• Journal of the Korean School Mathematics Society
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• v.22 no.4
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• pp.395-413
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• 2019

The purpose of this study was to find out how Blended e-Learning affected the teaching and learning of engineering mathematics for engineering students. It has researched the application condition of Blended e-Learning and the students' attitude in the offline classes of students. The subject were 42 students of Junior in the Department of Mechanic Engineering in M-University participated in the study. The lecturer taught the class for the students by fact-to-face teaching at the offline. It was recorded all processes during the class, and the video was loaded at the Learning Management System(LMS). The students studied online by themselves. This study investigated the attitude of students at the offline and the Utilization of Online Data by learners through the mixed class for one semester. The results were as follows. First, Blended e-Learning applied engineering mathematics affected positively for the self-regulated and individualized learning to the students. Second, Blended e-Learning has shown a positive impact on the teaching and learning of engineering mathematics. Finally, it also had a positive effect on the class satisfaction level of students.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.208-220
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• 2015

This study analyze Online class's current condition in Smart era. And suggest better operation model based on Internet Architecture. This study focuses the condition of e-learning operation model in University online class. Especially, 'Time Check Idea' that using for attendance on e-learning class has some side effects. So this study would applied 'Qualitative Check Idea Concept' on e-learning class. Question Based System, QBS is example model. QBS is leading a Learner's participation in e-class by Making Quiz. These quizs are shared with other students and refer to studing contents. Practically operating Qualitative Concept model QBS on university e-class, we can seek for the effectiveness of Qualitative e-learning model QBS.

• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.217-226
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• 2021

Since Covid-19, many educational institutions no longer view online learning as an additional material, but use it as their main learning tool. In this study, we tried to summarize the definition of smart learning and examined how smart learning math classes affect academic achievement, mathematical interest, and attitudes. We manipulate groups that conducted smart learning and groups that conducted face-to-face learning, and compare academic performance, mathematical interest, and attitudes after six weeks of learning. As a result, we found that the smart learning group had a large values in all three factors compared to the face-to-face learning group. We also found moderating effect. Students with lower grades largely improved their academic achievement scores as the difference in attitude changes through smart learning compared to those with higher grades.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.1033-1036
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• 2009

인터넷을 통한 온라인 교육의 활성화로 블랜드형 교육이 이슈로 대두되고 있다. 또한 이러닝 교육을 위한 저작도구 및 하드웨어 장비들이 속속들이 개발되고 활용된다. 이러한 이러닝 교육에 도구 중 전자칠판은 중요 요소이지만 전자칠판과 전자교탁이 분리되어 운용되는 것이 대부분이다. 이로 인한 관리 및 비용 측면에서 단점을 가지고 있다. 본 연구에서는 기존의 전자칠판 및 전자교탁이 따로 분리되어 운용되던 방식을 통합하여 단일형태의 임베디드 교육시스템의 구현을 소개한다. 임베디드 교육시스템은 온라인 교육은 물론 오프라인 교육도 동시에 가능하고, 교수자의 편의를 위하여 USB 기반의 자동 프레젠테이션을 지원하며 학습관리 및 컨턴츠 관리를 위한 소프트웨어를 포함하고 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.849-852
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• 2008

인터넷을 통한 온라인 교육의 활성화로 블랜드형 교육이 이슈로 대두되고 있다. 또한 이러닝 교육을 위한 저작도구 및 하드웨어 장비들이 속속들이 개발되고 활용된다. 이러한 이러닝 교육에 도구 중 전자칠판은 중요 요소이지만 전자칠판과 전자교탁이 분리되어 운용되는 것이 대부분이다. 이로 인한 관리 및 비용 측면에서 단점을 가지고 있다. 본 연구에서는 기존의 전자칠판 및 전자교탁이 따로 분리되어 운용되던 방식을 통합하여 단일형태의 임베디드 교육시스템의 구현을 소개한다. 임베디드 교육시스템은 온라인 교육은 물론 오프라인 교육도 동시에 가능하고, 교수자의 편의를 위하여 USB 기반의 자동 프레젠테이션을 지원하며 학습관리 및 컨턴츠 관리를 위한 소프트웨어를 포함하고 있다.

• The Journal of Korean Academy of Sensory Integration
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• v.15 no.2
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• pp.80-92
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• 2017

Objective : The purpose of this study is to investigate the meaning of students' experience participating in the flipped learning based instruction in sensory integration, and to understand the demand and introspection of participants. Methods : This study used a phenomenological approach of qualitative study. The participants were 10 students in 3rd year of the occupational therapy program in K Univeristy. Data information was collected by one-to-one interview and analyzed through phenomenological research method. Results : Through the interview, 20 units of meaning, 8 central meanings, and 3 themes were drew. The information collected were analyzed into three themes; Learning Experiences in Online and Offline Courses, Request and Introspection of Learners on Flipped Learning. The result showed that online courses brought learners convenience and satisfaction with repeatable learning in every time and space the learner want. However, the learners appealed issues of communication and concentration due to the absence of face-to-face instruction by their instructor. For the offline courses, students showed interest in various practical classwork of sensory integration and changes in their attitude to actively engage in the practical classes. Conclusion : Flipped learning based instruction was effective for the sensory integration classes which require practice in terms of time securement and immersion in practice. The learners requested for adopting flipped learning based instruction to other subjects in occupational therapy curriculum, and introspected that they needed to actively engage in classes through the experience of flipped learning-based classes of sensory integration. The results of this study can be used as a basic resource when flipped learning classes are planned in occupational therapy education.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.11
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• pp.473-478
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• 2022

Evaluation learning based on code testing is becoming a popular solution in programming education via Online judge(OJ). In the recent past, many papers have been published on how to detect plagiarism through source code similarity analysis to support OJ. However, deep learning-based research to support automated tutoring is insufficient. In this paper, we propose Input & Output side FiLM models to predict whether the input code will pass or fail. By applying Feature-wise Linear Modulation(FiLM) technique to GRU, our model can learn combined information of Java byte codes and problem information that it tries to solve. On experimental design, a balanced sampling technique was applied to evenly distribute the data due to the occurrence of asymmetry in data collected by OJ. Among the proposed models, the Input Side FiLM model showed the highest performance of 73.63%. Based on result, it has been shown that students can check whether their codes will pass or fail before receiving the OJ evaluation which could provide basic feedback for improvements.

• The Journal of Bigdata
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• v.8 no.2
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• pp.15-25
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• 2023

Extreme learning machines (ELMs) are a major analytical method in various prediction fields. ELMs can accurately predict even if the data contains noise or is nonlinear by learning the complex patterns of time series data through optimal learning. This study presents the recent trends of machine learning models that are mainly studied as tools for analyzing online time series data, along with the application characteristics using existing algorithms. In order to efficiently learn large-scale online data that is continuously and explosively generated, it is necessary to have a learning technology that can perform well even in properties that can evolve in various ways. Therefore, this study examines a comprehensive overview of the latest machine learning models applied to big data in the field of time series prediction, discusses the general characteristics of the latest models that learn online data, which is one of the major challenges of machine learning for big data, and how efficiently they can learn and use online time series data for prediction, and proposes alternatives.