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    ์ด๋Ÿฌ๋‹(e-Learning)์„œ๋น„์Šค๊ธฐ์—…์˜ ์‹คํƒœ๋ถ„์„๊ณผ ๊ฒฝ์Ÿ๋ ฅ๊ฐ•ํ™”๋ฐฉ์•ˆ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    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 ์ œ1์žฅ ์„œ๋ก  .................................................................................................................... 13 ์ œ1์ ˆ ์—ฐ๊ตฌ์˜ ๋ชฉ์  ๋ฐ ๋ฒ”์œ„ ............................................................................................13 ์ œ2์ ˆ ์„ ํ–‰์—ฐ๊ตฌ์˜ ๋‚ด์šฉ๊ณผ ์—ฐ๊ตฌ๋ฐฉ๋ฒ• ............................................................................... 15 1. ์—ฐ๊ตฌ์˜ ๋‚ด์šฉ ................................................................................................................ 15 2. ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ• ................................................................................................................ 16 ์ œ2์žฅ ์ง€์‹๊ธฐ๋ฐ˜์„œ๋น„์Šค์˜ ๊ฐœ์š” ................................................................................... 19 ์ œ1์ ˆ ์ง€์‹๊ธฐ๋ฐ˜์„œ๋น„์Šค์‚ฐ์—…์˜ ๊ฐœ์š” ................................................................................. 19 1. ์ง€์‹๊ธฐ๋ฐ˜์‚ฐ์—…์˜ ์ •์˜ .................................................................................................. 19 1. ์ง€์‹๊ธฐ๋ฐ˜ ์„œ๋น„์Šค์‚ฐ์—…๊ณผ ์ „๋žต๊ธฐ์ˆ  ............................................................................... 20 ์ œ2์ ˆ ์ง€์‹์„œ๋น„์Šค ์œก์„ฑ์ „๋žต ๋ฐ ์ •์ฑ…์ง€์› ..................................................................... 21 ์ œ3์ ˆ ์ง€์‹์„œ๋น„์Šค ์ „๋žต๊ธฐ์ˆ ๊ฐœ๋ฐœ R&D ์ง€์› ................................................................. 23 ์ œ4์ ˆ ์ง€์‹์„œ๋น„์Šค ๊ธฐ๋ฐ˜ ๊ตฌ์ถ• ์ง€์› ................................................................................. 25 ์ œ5์ ˆ ์ด๋Ÿฌ๋‹์‚ฐ์—…๊ณผ ์ด๋Ÿฌ๋‹์˜ ๊ฐœ๋… ............................................................................... 27 ์ œ6์ ˆ ์ด๋Ÿฌ๋‹์‚ฐ์—…์˜ ๋ถ„๋ฅ˜ ................................................................................................ 31 1. ์ฝ˜ํ…์ธ ์‚ฐ์—… ................................................................................................................. 33 2. ์†”๋ฃจ์…˜์‚ฐ์—… ................................................................................................................. 33 3. ์„œ๋น„์Šค์‚ฌ์—… ................................................................................................................. 34 ์ œ7์ ˆ ์ด๋Ÿฌ๋‹์‚ฐ์—…๊ด€๋ จ ๋ฒ•๋ฅ ์ฒด์ œ .................................................................................... 35 1. ์ด๋Ÿฌ๋‹(์ „์žํ•™์Šต)์‚ฐ์—…๋ฐœ์ „๋ฒ• ....................................................................................... 35 2. ์˜จ๋ผ์ธ๋””์ง€ํ„ธ์ฝ˜ํ…์ธ ์‚ฐ์—…๋ฐœ์ „๋ฒ• ................................................................................... 38 3. ๋…ธ๋™๋ถ€์˜ ๊ทผ๋กœ์ž์ง์—…ํ›ˆ๋ จ์ด‰์ง„๋ฒ•์— ๊ทผ๊ฑฐํ•œ ์ธํ„ฐ๋„ทํ†ต์‹ ํ›ˆ๋ จ์ œ๋„ ................................. 39 ์ œ8์ ˆ ๊ทผ๋กœ์ž์ง์—…๋Šฅ๋ ฅ๊ฐœ๋ฐœ๋ฒ•์— ๋”ฐ๋ฅธ ์ง์—…๋Šฅ๋ ฅ๊ฐœ๋ฐœํ›ˆ๋ จ๊ฐœ๊ด€ ..................................... 40 ์ œ3์žฅ ์ด๋Ÿฌ๋‹์‚ฐ์—…์˜ ๊ตญ๋‚ด์™ธ ํ˜„ํ™ฉ ....................................................................... 44 ์ œ1์ ˆ ๊ตญ๋‚ด์ด๋Ÿฌ๋‹์‚ฐ์—…์˜ ๊ฐœ๊ด€ ........................................................................................ 44 ์ œ2์ ˆ ์ด๋Ÿฌ๋‹ ์‚ฌ์—…์ž ๊ณต๊ธ‰์‹œ์žฅ ๊ทœ๋ชจ ............................................................................. 45 ์ œ3์ ˆ ์ด๋Ÿฌ๋‹์‚ฐ์—…์˜ ์ธ๋ ฅํ˜„ํ™ฉ ๋ฐ ๊ณ ์šฉ๊ตฌ์กฐ .................................................................. 50 ์ œ4์ ˆ ์ด๋Ÿฌ๋‹๊ธฐ์—…์˜ ํ•ด์™ธ์‹œ์žฅ ์ง„์ถœํ˜„ํ™ฉ ....................................................................... 51 ์ œ5์ ˆ ์ •๊ทœ๊ต์œก๊ธฐ๊ด€์˜ ์ด๋Ÿฌ๋‹ํ˜„ํ™ฉ ................................................................................ 55 1. ์ •๊ทœ๊ต์œก๊ธฐ๊ด€์˜ ์ด๋Ÿฌ๋‹ ๋„์ž…๋ฅ  ................................................................................... 55 2. ์ •๊ทœ๊ต์œก๊ธฐ๊ด€์˜ ์ด๋Ÿฌ๋‹ ์‹œ์žฅ๊ทœ๋ชจ ............................................................................... 56 3. ์ •๊ทœ๊ต์œก๊ธฐ๊ด€์˜ ์ด๋Ÿฌ๋‹ ์šด์˜๋ฐฉ์‹ ............................................................................... 57 ์ œ2์ ˆ ๊ธฐ์—…์˜ ์ด๋Ÿฌ๋‹์‹œ์žฅํ˜„ํ™ฉ ........................................................................................ 60 1. ๊ธฐ์—…์˜ ์ด๋Ÿฌ๋‹๋„์ž…ํ˜„ํ™ฉ ............................................................................................... 61 2. ๊ธฐ์—…์˜ ์ด๋Ÿฌ๋‹ ํ™œ์šฉ๋„ ................................................................................................. 63 3. ๊ธฐ์—…์˜ ์ด๋Ÿฌ๋‹ ์ด์šฉ๋ถ„์•ผ ............................................................................................. 64 4. ๊ธฐ์—…์˜ ์ด๋Ÿฌ๋‹ ๋„์ž…ํšจ๊ณผ ............................................................................................. 66 ์ œ3์ ˆ ๊ณต๊ณต๊ธฐ๊ด€์˜ ์ด๋Ÿฌ๋‹์‹œ์žฅํ˜„ํ™ฉ ................................................................................. 69 1. ๊ณต๊ณต๊ธฐ๊ด€์˜ ์ด๋Ÿฌ๋‹ ๋„์ž…ํ˜„ํ™ฉ ...................................................................................... 69 2. ์ •๋ถ€ ๊ณต๊ณต๊ธฐ๊ด€์˜ ์ด๋Ÿฌ๋‹ ์‚ฌ์šฉํ˜„ํ™ฉ ............................................................................. 70 ์ œ4์ ˆ ์ด๋Ÿฌ๋‹์„œ๋น„์Šค์—…์ฒด ํ˜„ํ™ฉ ........................................................................................ 74 1. ์‚ฌ๊ต์œก์šฉ ์ด๋Ÿฌ๋‹์„œ๋น„์Šค ์—…์ฒด ...................................................................................... 74 2. ๊ธฐ์—…๊ต์œก์šฉ ์ด๋Ÿฌ๋‹์„œ๋น„์Šค ์—…์ฒดํ˜„ํ™ฉ ............................................................................ 79 ์ œ5์ ˆ ํ•ด์™ธ์ด๋Ÿฌ๋‹์‚ฐ์—… ๋™ํ–ฅ ๋ฐ ํ˜„ํ™ฉ ............................................................................. 82 1. ๋ฏธ์ฃผ๊ถŒ ๊ตญ๊ฐ€์˜ ์ด๋Ÿฌ๋‹ ์ •์ฑ…๋™ํ–ฅ ................................................................................. 85 2. ๋ฏธ๊ตญ์˜ ์ด๋Ÿฌ๋‹ ์ •์ฑ…๋™ํ–ฅ ............................................................................................. 86 3. ์บ๋‚˜๋‹ค์˜ ์ด๋Ÿฌ๋‹ ์ •์ฑ…๋™ํ–ฅ .......................................................................................... 87 ์ œ6์ ˆ ์œ ๋Ÿฝ์—ฐํ•ฉ(EU)์˜ ์ด๋Ÿฌ๋‹ ์ •์ฑ…๋™ํ–ฅ ...................................................................... 88 1. ์ฃผ์š” ์œ ๋Ÿฝ๊ถŒ ๊ตญ๊ฐ€์˜ ์ด๋Ÿฌ๋‹ ์ถ”์ง„ํ˜„ํ™ฉ ........................................................................ 90 ์ œ7์ ˆ ์•„์‹œ์•„ ๋ฐ ๊ธฐํƒ€์ง€์—ญ .............................................................................................. 91 1 ์ผ๋ณธ์˜ ์ด๋Ÿฌ๋‹์‹œ์žฅํ˜„ํ™ฉ ................................................................................................ 92 2. ์ค‘๊ตญ์˜ ์ด๋Ÿฌ๋‹์‹œ์žฅํ˜„ํ™ฉ ............................................................................................... 93 3. ๊ธฐํƒ€ ์•„์‹œ์•„๊ถŒ ๊ตญ๊ฐ€์˜ ์ด๋Ÿฌ๋‹ ์‹œ์žฅํ˜„ํ™ฉ ..................................................................... 94 ์ œ4์žฅ ์‹ค์ฆ๋ถ„์„ ............................................................................................................. 97 ์ œ1์ ˆ ์ด๋Ÿฌ๋‹๊ณต๊ธ‰์‹œ์žฅ ํ˜„ํ™ฉ๋ถ„์„ .................................................................................... 97 1. ์‚ฌ์—…์ž ์ผ๋ฐ˜ํ˜„ํ™ฉ ......................................................................................................... 97 ์ œ2์ ˆ ์ด๋Ÿฌ๋‹์‚ฐ์—…์˜ ๊ณ ์šฉํšจ๊ณผ๋ถ„์„.................................................................................. 99 ์ œ3์ ˆ ์‘๋‹ต์—…์ฒด ๋ฐ ์‘๋‹ต์ž์˜ ํŠน์„ฑ๋ถ„์„ .......................................................................105 ์ œ4์ ˆ ์ด๋Ÿฌ๋‹ ์„œ๋น„์Šค์— ๋Œ€ํ•œ ๊ณ ๊ฐ์ธ์‹ ....................................................................... 107 ์ œ5์ ˆ ์ด๋Ÿฌ๋‹ ์„œ๋น„์Šค์˜ ๋ฌธ์ œ์  ๋ฐ ๊ฐœ์„ ๋ฐฉ์•ˆ ............................................................. 113 ์ œ5์žฅ ์›๊ฒฉํ›ˆ๋ จ๊ธฐ๋ฐ˜ ๊ธฐ์—…์ด๋Ÿฌ๋‹์„œ๋น„์Šค์—…์ฒด ๊ฒฝ์Ÿ๋ ฅ๊ฐ•ํ™”๋ฐฉ์•ˆ ......................... 117 ์ œ1์ ˆ ์›๊ฒฉํ›ˆ๋ จ์˜ ๋„์ž…๋ฐฐ๊ฒฝ .......................................................................................... 117 ์ œ2์ ˆ ์›๊ฒฉํ›ˆ๋ จ๊ธฐ๊ด€์˜ ํ˜„ํ™ฉ .......................................................................................... 118 ์ œ3์ ˆ ์ด๋Ÿฌ๋‹์„œ๋น„์Šค๊ธฐ์—…์˜ ๊ฒฝ์Ÿ๋ ฅ์ €ํ•˜ ์š”์ธ ๋ฐ ๊ฐœ์„ ๋ฐฉ์•ˆ ....................................... 120 ์ œ4์ ˆ ์ด๋Ÿฌ๋‹์„œ๋น„์Šค๊ธฐ์—… ์ˆ˜์ต๋ชจ๋ธ์˜ ์ œ์‹œ ................................................................. 123 1. ๊ณ ์šฉ๋ณดํ—˜ํ™˜๊ธ‰์„ ์ด์šฉํ•œ ์ด๋Ÿฌ๋‹์„œ๋น„์Šค์‹œ์žฅ์˜ ํ™˜๊ฒฝ๋ถ„์„ .............................................. 123 2. ๊ธฐ์—…๋งž์ถคํ˜•์ปจํ…์ธ  ๊ฐœ๋ฐœ์„ ํ†ตํ•œ ์ˆ˜์ต๋ชจ๋ธ๊ตฌ์กฐ์˜ ์ œ์‹œ ................................................ 124 ์ œ6์žฅ ์š”์•ฝ ๋ฐ ๊ฒฐ๋ก  ................................................................................................... 128 ์ œ1์ ˆ ์—ฐ๊ตฌ์š”์•ฝ ๋ฐ ๊ฒฐ๋ก  ............................................................................................. 128 ์ œ2์ ˆ ์—ฐ๊ตฌํ•œ๊ณ„ ๋ฐ ๊ธฐ์—ฌ ............................................................................................. 12

    Requirements for an Adaptive Multimedia Presentation System with Contextual Supplemental Support Media

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    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

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    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

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    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

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    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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