824,740 research outputs found
์ด๋ฌ๋(e-Learning)์๋น์ค๊ธฐ์ ์ ์คํ๋ถ์๊ณผ ๊ฒฝ์๋ ฅ๊ฐํ๋ฐฉ์์ ๊ดํ ์ฐ๊ตฌ
Abstract
A Study on the Research of the Actual State and Strengthening Competitiveness for e-Learning Service Companies
-Primarily on the Services Companies Dealing with HRD-
Kim, Hyun-Cho
Department of International Trade
The Graduate School of
Korea Maritime University
E-Learning(sometimes electronic learning or eLearning) is defined commonly as new multimedia technologies to improve the educational environment and the quality of learning based on internet or electronic equipment without face-to-face interaction. e-Learning means the educational method using electronic equipment such as new information and communication, wave and broadcasting technologies. It is essential to extensively shift paradigm in part of consciousness, utilization and access of e-Learning to make it newly real growth engines. It might be difficult to grow this industry continuously if it is regarded just level of a tool to cut cost and budget in respect of off-line education. To provide qualified contents, the fostering culture which selects companies with technological edges must be established throughout placing an ordering of project by government, organization and companies .
Throughout examining of this study, e-Learning industry and market base deeper and deeper on corporations and employment insurance refund system. Most of the e-Learning markets form mainly with corporation education, however, the portion of e-Learning investment by government and education-sector is poorer than that of corporations. The investment of education organizations have focused on strength to support facilities. The relevant parties have to make every to ready supply basement complex which can replace off-line contents with various kinds of e-Learning contents. In order to foster e-Learning for next generation knowledge based industry, not only the reform of relevant laws and systems in reference to e-Learning services are required by compulsory introduction of public organizations, copyright protection for relevant e-Learning but also joint efforts for long term development. Most of the e-Learning service companies are still small in capital and organization. On the other hand there are some e-Learning service companies having market share based on support of collosal capital and big companies, small and medium e-Learning service companies which have to be survived in yet small scale market independently need fair competition and improvement of subcontract system.
To share interior advancements between e-Learning service companies, it is required self-purification which is eradication of price dumping by the leading companies, right partnership for associates, improvement of supply and demand for manpower of instruction design and betterment of contents quality, etc. This study shows "business model on customized contents(BMCC) which small and medium e-Learning service companies can obtain independent competition at the same time of going side by side with self-purification. BMCC is thought as efficient business model which can maximize not only satisfaction with e-Learning service companies and educatee corporations but also accord regulation of Ministry of labor throughout an empirical study and analysis of research trend. E-Learning is an alternative to solve the problems of institutional education because of having destructive power to change educational paradigm. It is, therefore, indispensable for government and relevant companies to make every effort. The local e-Learning market has now matured from the point of view of infrastructure such as internet, information technologies, etc., however, it has still so many difficulties to be solved from the side of reforming regulations, various contents, motivating educatee for participating classes.
Abstract ......................................................................................................................... 11
์ 1์ฅ ์๋ก .................................................................................................................... 13
์ 1์ ์ฐ๊ตฌ์ ๋ชฉ์ ๋ฐ ๋ฒ์ ............................................................................................13
์ 2์ ์ ํ์ฐ๊ตฌ์ ๋ด์ฉ๊ณผ ์ฐ๊ตฌ๋ฐฉ๋ฒ ............................................................................... 15
1. ์ฐ๊ตฌ์ ๋ด์ฉ ................................................................................................................ 15
2. ์ฐ๊ตฌ์ ๋ฐฉ๋ฒ ................................................................................................................ 16
์ 2์ฅ ์ง์๊ธฐ๋ฐ์๋น์ค์ ๊ฐ์ ................................................................................... 19
์ 1์ ์ง์๊ธฐ๋ฐ์๋น์ค์ฐ์
์ ๊ฐ์ ................................................................................. 19
1. ์ง์๊ธฐ๋ฐ์ฐ์
์ ์ ์ .................................................................................................. 19
1. ์ง์๊ธฐ๋ฐ ์๋น์ค์ฐ์
๊ณผ ์ ๋ต๊ธฐ์ ............................................................................... 20
์ 2์ ์ง์์๋น์ค ์ก์ฑ์ ๋ต ๋ฐ ์ ์ฑ
์ง์ ..................................................................... 21
์ 3์ ์ง์์๋น์ค ์ ๋ต๊ธฐ์ ๊ฐ๋ฐ R&D ์ง์ ................................................................. 23
์ 4์ ์ง์์๋น์ค ๊ธฐ๋ฐ ๊ตฌ์ถ ์ง์ ................................................................................. 25
์ 5์ ์ด๋ฌ๋์ฐ์
๊ณผ ์ด๋ฌ๋์ ๊ฐ๋
............................................................................... 27
์ 6์ ์ด๋ฌ๋์ฐ์
์ ๋ถ๋ฅ ................................................................................................ 31
1. ์ฝํ
์ธ ์ฐ์
................................................................................................................. 33
2. ์๋ฃจ์
์ฐ์
................................................................................................................. 33
3. ์๋น์ค์ฌ์
................................................................................................................. 34
์ 7์ ์ด๋ฌ๋์ฐ์
๊ด๋ จ ๋ฒ๋ฅ ์ฒด์ .................................................................................... 35
1. ์ด๋ฌ๋(์ ์ํ์ต)์ฐ์
๋ฐ์ ๋ฒ ....................................................................................... 35
2. ์จ๋ผ์ธ๋์งํธ์ฝํ
์ธ ์ฐ์
๋ฐ์ ๋ฒ ................................................................................... 38
3. ๋
ธ๋๋ถ์ ๊ทผ๋ก์์ง์
ํ๋ จ์ด์ง๋ฒ์ ๊ทผ๊ฑฐํ ์ธํฐ๋ทํต์ ํ๋ จ์ ๋ ................................. 39
์ 8์ ๊ทผ๋ก์์ง์
๋ฅ๋ ฅ๊ฐ๋ฐ๋ฒ์ ๋ฐ๋ฅธ ์ง์
๋ฅ๋ ฅ๊ฐ๋ฐํ๋ จ๊ฐ๊ด ..................................... 40
์ 3์ฅ ์ด๋ฌ๋์ฐ์
์ ๊ตญ๋ด์ธ ํํฉ ....................................................................... 44
์ 1์ ๊ตญ๋ด์ด๋ฌ๋์ฐ์
์ ๊ฐ๊ด ........................................................................................ 44
์ 2์ ์ด๋ฌ๋ ์ฌ์
์ ๊ณต๊ธ์์ฅ ๊ท๋ชจ ............................................................................. 45
์ 3์ ์ด๋ฌ๋์ฐ์
์ ์ธ๋ ฅํํฉ ๋ฐ ๊ณ ์ฉ๊ตฌ์กฐ .................................................................. 50
์ 4์ ์ด๋ฌ๋๊ธฐ์
์ ํด์ธ์์ฅ ์ง์ถํํฉ ....................................................................... 51
์ 5์ ์ ๊ท๊ต์ก๊ธฐ๊ด์ ์ด๋ฌ๋ํํฉ ................................................................................ 55
1. ์ ๊ท๊ต์ก๊ธฐ๊ด์ ์ด๋ฌ๋ ๋์
๋ฅ ................................................................................... 55
2. ์ ๊ท๊ต์ก๊ธฐ๊ด์ ์ด๋ฌ๋ ์์ฅ๊ท๋ชจ ............................................................................... 56
3. ์ ๊ท๊ต์ก๊ธฐ๊ด์ ์ด๋ฌ๋ ์ด์๋ฐฉ์ ............................................................................... 57
์ 2์ ๊ธฐ์
์ ์ด๋ฌ๋์์ฅํํฉ ........................................................................................ 60
1. ๊ธฐ์
์ ์ด๋ฌ๋๋์
ํํฉ ............................................................................................... 61
2. ๊ธฐ์
์ ์ด๋ฌ๋ ํ์ฉ๋ ................................................................................................. 63
3. ๊ธฐ์
์ ์ด๋ฌ๋ ์ด์ฉ๋ถ์ผ ............................................................................................. 64
4. ๊ธฐ์
์ ์ด๋ฌ๋ ๋์
ํจ๊ณผ ............................................................................................. 66
์ 3์ ๊ณต๊ณต๊ธฐ๊ด์ ์ด๋ฌ๋์์ฅํํฉ ................................................................................. 69
1. ๊ณต๊ณต๊ธฐ๊ด์ ์ด๋ฌ๋ ๋์
ํํฉ ...................................................................................... 69
2. ์ ๋ถ ๊ณต๊ณต๊ธฐ๊ด์ ์ด๋ฌ๋ ์ฌ์ฉํํฉ ............................................................................. 70
์ 4์ ์ด๋ฌ๋์๋น์ค์
์ฒด ํํฉ ........................................................................................ 74
1. ์ฌ๊ต์ก์ฉ ์ด๋ฌ๋์๋น์ค ์
์ฒด ...................................................................................... 74
2. ๊ธฐ์
๊ต์ก์ฉ ์ด๋ฌ๋์๋น์ค ์
์ฒดํํฉ ............................................................................ 79
์ 5์ ํด์ธ์ด๋ฌ๋์ฐ์
๋ํฅ ๋ฐ ํํฉ ............................................................................. 82
1. ๋ฏธ์ฃผ๊ถ ๊ตญ๊ฐ์ ์ด๋ฌ๋ ์ ์ฑ
๋ํฅ ................................................................................. 85
2. ๋ฏธ๊ตญ์ ์ด๋ฌ๋ ์ ์ฑ
๋ํฅ ............................................................................................. 86
3. ์บ๋๋ค์ ์ด๋ฌ๋ ์ ์ฑ
๋ํฅ .......................................................................................... 87
์ 6์ ์ ๋ฝ์ฐํฉ(EU)์ ์ด๋ฌ๋ ์ ์ฑ
๋ํฅ ...................................................................... 88
1. ์ฃผ์ ์ ๋ฝ๊ถ ๊ตญ๊ฐ์ ์ด๋ฌ๋ ์ถ์งํํฉ ........................................................................ 90
์ 7์ ์์์ ๋ฐ ๊ธฐํ์ง์ญ .............................................................................................. 91
1 ์ผ๋ณธ์ ์ด๋ฌ๋์์ฅํํฉ ................................................................................................ 92
2. ์ค๊ตญ์ ์ด๋ฌ๋์์ฅํํฉ ............................................................................................... 93
3. ๊ธฐํ ์์์๊ถ ๊ตญ๊ฐ์ ์ด๋ฌ๋ ์์ฅํํฉ ..................................................................... 94
์ 4์ฅ ์ค์ฆ๋ถ์ ............................................................................................................. 97
์ 1์ ์ด๋ฌ๋๊ณต๊ธ์์ฅ ํํฉ๋ถ์ .................................................................................... 97
1. ์ฌ์
์ ์ผ๋ฐํํฉ ......................................................................................................... 97
์ 2์ ์ด๋ฌ๋์ฐ์
์ ๊ณ ์ฉํจ๊ณผ๋ถ์.................................................................................. 99
์ 3์ ์๋ต์
์ฒด ๋ฐ ์๋ต์์ ํน์ฑ๋ถ์ .......................................................................105
์ 4์ ์ด๋ฌ๋ ์๋น์ค์ ๋ํ ๊ณ ๊ฐ์ธ์ ....................................................................... 107
์ 5์ ์ด๋ฌ๋ ์๋น์ค์ ๋ฌธ์ ์ ๋ฐ ๊ฐ์ ๋ฐฉ์ ............................................................. 113
์ 5์ฅ ์๊ฒฉํ๋ จ๊ธฐ๋ฐ ๊ธฐ์
์ด๋ฌ๋์๋น์ค์
์ฒด ๊ฒฝ์๋ ฅ๊ฐํ๋ฐฉ์ ......................... 117
์ 1์ ์๊ฒฉํ๋ จ์ ๋์
๋ฐฐ๊ฒฝ .......................................................................................... 117
์ 2์ ์๊ฒฉํ๋ จ๊ธฐ๊ด์ ํํฉ .......................................................................................... 118
์ 3์ ์ด๋ฌ๋์๋น์ค๊ธฐ์
์ ๊ฒฝ์๋ ฅ์ ํ ์์ธ ๋ฐ ๊ฐ์ ๋ฐฉ์ ....................................... 120
์ 4์ ์ด๋ฌ๋์๋น์ค๊ธฐ์
์์ต๋ชจ๋ธ์ ์ ์ ................................................................. 123
1. ๊ณ ์ฉ๋ณดํํ๊ธ์ ์ด์ฉํ ์ด๋ฌ๋์๋น์ค์์ฅ์ ํ๊ฒฝ๋ถ์ .............................................. 123
2. ๊ธฐ์
๋ง์ถคํ์ปจํ
์ธ ๊ฐ๋ฐ์ ํตํ ์์ต๋ชจ๋ธ๊ตฌ์กฐ์ ์ ์ ................................................ 124
์ 6์ฅ ์์ฝ ๋ฐ ๊ฒฐ๋ก ................................................................................................... 128
์ 1์ ์ฐ๊ตฌ์์ฝ ๋ฐ ๊ฒฐ๋ก ............................................................................................. 128
์ 2์ ์ฐ๊ตฌํ๊ณ ๋ฐ ๊ธฐ์ฌ ............................................................................................. 12
Requirements for an Adaptive Multimedia Presentation System with Contextual Supplemental Support Media
Investigations into the requirements for a practical adaptive multimedia presentation system have led the writers to propose the use of a video segmentation process that provides contextual supplementary updates produced by users. Supplements consisting of tailored segments are dynamically inserted into previously stored material in response to questions from users. A proposal for the use of this technique is presented in the context of personalisation within a Virtual Learning Environment. During the investigation, a brief survey of advanced adaptive approaches revealed that adaptation may be enhanced by use of manually generated metadata, automated or semi-automated use of metadata by stored context dependent ontology hierarchies that describe the semantics of the learning domain. The use of neural networks or fuzzy logic filtering is a technique for future investigation. A prototype demonstrator is under construction
Ontology-based Fuzzy Markup Language Agent for Student and Robot Co-Learning
An intelligent robot agent based on domain ontology, machine learning
mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning
is presented in this paper. The machine-human co-learning model is established
to help various students learn the mathematical concepts based on their
learning ability and performance. Meanwhile, the robot acts as a teacher's
assistant to co-learn with children in the class. The FML-based knowledge base
and rule base are embedded in the robot so that the teachers can get feedback
from the robot on whether students make progress or not. Next, we inferred
students' learning performance based on learning content's difficulty and
students' ability, concentration level, as well as teamwork sprit in the class.
Experimental results show that learning with the robot is helpful for
disadvantaged and below-basic children. Moreover, the accuracy of the
intelligent FML-based agent for student learning is increased after machine
learning mechanism.Comment: This paper is submitted to IEEE WCCI 2018 Conference for revie
Active learning in annotating micro-blogs dealing with e-reputation
Elections unleash strong political views on Twitter, but what do people
really think about politics? Opinion and trend mining on micro blogs dealing
with politics has recently attracted researchers in several fields including
Information Retrieval and Machine Learning (ML). Since the performance of ML
and Natural Language Processing (NLP) approaches are limited by the amount and
quality of data available, one promising alternative for some tasks is the
automatic propagation of expert annotations. This paper intends to develop a
so-called active learning process for automatically annotating French language
tweets that deal with the image (i.e., representation, web reputation) of
politicians. Our main focus is on the methodology followed to build an original
annotated dataset expressing opinion from two French politicians over time. We
therefore review state of the art NLP-based ML algorithms to automatically
annotate tweets using a manual initiation step as bootstrap. This paper focuses
on key issues about active learning while building a large annotated data set
from noise. This will be introduced by human annotators, abundance of data and
the label distribution across data and entities. In turn, we show that Twitter
characteristics such as the author's name or hashtags can be considered as the
bearing point to not only improve automatic systems for Opinion Mining (OM) and
Topic Classification but also to reduce noise in human annotations. However, a
later thorough analysis shows that reducing noise might induce the loss of
crucial information.Comment: Journal of Interdisciplinary Methodologies and Issues in Science -
Vol 3 - Contextualisation digitale - 201
Using Grouped Linear Prediction and Accelerated Reinforcement Learning for Online Content Caching
Proactive caching is an effective way to alleviate peak-hour traffic
congestion by prefetching popular contents at the wireless network edge. To
maximize the caching efficiency requires the knowledge of content popularity
profile, which however is often unavailable in advance. In this paper, we first
propose a new linear prediction model, named grouped linear model (GLM) to
estimate the future content requests based on historical data. Unlike many
existing works that assumed the static content popularity profile, our model
can adapt to the temporal variation of the content popularity in practical
systems due to the arrival of new contents and dynamics of user preference.
Based on the predicted content requests, we then propose a reinforcement
learning approach with model-free acceleration (RLMA) for online cache
replacement by taking into account both the cache hits and replacement cost.
This approach accelerates the learning process in non-stationary environment by
generating imaginary samples for Q-value updates. Numerical results based on
real-world traces show that the proposed prediction and learning based online
caching policy outperform all considered existing schemes.Comment: 6 pages, 4 figures, ICC 2018 worksho
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