• 제목/요약/키워드: Resources-based Learning

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Educational-Resources Recommending System for Web Based Learning

  • Ochi, Youji;Yano, Yoneo;Wakita, Riko
    • 한국지능정보시스템학회:학술대회논문집
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    • 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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    • pp.310-315
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    • 2001
  • We are focusing on an approach which handle a general Web as a resource in order to support self-directed learning for a student. Then, we are developing a Web based learning environment "Web-Retracer"for utilizing Web as teaching materials by a user′s Annotation. Although the learner can share the Web resource that the others utilized in this environment, Web resources unsuitable for a student′s needs becomes hindrance about her/his self-directed learning. In this paper, we propose a recommending method of the resource united with a student′s needs on the basis of a student′s learning and Web browsing history. This method analyzed the feature peculiar to a resource, and extracts the resource with which the needs of the feature and a student agreed.

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A Joint Allocation Algorithm of Computing and Communication Resources Based on Reinforcement Learning in MEC System

  • Liu, Qinghua;Li, Qingping
    • Journal of Information Processing Systems
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    • 제17권4호
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    • pp.721-736
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    • 2021
  • For the mobile edge computing (MEC) system supporting dense network, a joint allocation algorithm of computing and communication resources based on reinforcement learning is proposed. The energy consumption of task execution is defined as the maximum energy consumption of each user's task execution in the system. Considering the constraints of task unloading, power allocation, transmission rate and calculation resource allocation, the problem of joint task unloading and resource allocation is modeled as a problem of maximum task execution energy consumption minimization. As a mixed integer nonlinear programming problem, it is difficult to be directly solve by traditional optimization methods. This paper uses reinforcement learning algorithm to solve this problem. Then, the Markov decision-making process and the theoretical basis of reinforcement learning are introduced to provide a theoretical basis for the algorithm simulation experiment. Based on the algorithm of reinforcement learning and joint allocation of communication resources, the joint optimization of data task unloading and power control strategy is carried out for each terminal device, and the local computing model and task unloading model are built. The simulation results show that the total task computation cost of the proposed algorithm is 5%-10% less than that of the two comparison algorithms under the same task input. At the same time, the total task computation cost of the proposed algorithm is more than 5% less than that of the two new comparison algorithms.

효과적 인적자원 개발을 위한 e-Learning의 성공요인 (The Successful Factors of e-Learning for Human Resources Development)

  • 이성
    • 농촌지도와개발
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    • 제8권1호
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    • pp.1-14
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    • 2001
  • e-Learning has brought dramatic changes in education system for many companies in Korea. Many researchers and practitioners believe that e-Learning will be the main educational system for every companies in the world. e-Learning is an alternative education system, which includes computer based learning, web based learning, virtual classroom, and distance learning. e-learning has been expected to impact every educational sectors including Extension services. This study intends to identify and suggest some implications for successful e-Learning implementation of Extension education by investigating the successful factors of enterprises' e-Learning system, where outstanding results have be shown.

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분산 AIoT 환경에서 합성곱신경망 기반 계층적 IoT Edge 자원 할당 및 관리 기법 (Hierarchical IoT Edge Resource Allocation and Management Techniques based on Synthetic Neural Networks in Distributed AIoT Environments)

  • 정윤수
    • 산업과 과학
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    • 제2권3호
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    • pp.8-14
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    • 2023
  • 대다수의 IoT 기기들은 이미 AIoT를 사용하고 있지만, AI 애플리케이션을 구축하기 위해서는 아직 해결해야 할 문제가 많이 남아 있다. 본 연구에서는 IoT 에지 자원을 보다 효과적으로 분산하기 위해 머신러닝 기반의 IoT 에지 자원 관리 기법을 제안한다, 제안 기법은 머신러닝을 이용하여 IoT 에지 자원 동향을 파악함으로써 IoT 자원의 할당을 지속적으로 개선하며, 최적화된 IoT 자원은 머신러닝 컨볼루션을 활용하여 항상 변화하는 IoT 에지 자원을 안정적으로 유지한다, 제안 기법은 각각의 머신러닝 기반 IoT 에지 자원을 이전 패턴의 자원과 함께 해시값으로 저장함으로써 분산된 AIoT 맥락에서 공격 패턴으로 자원을 효과적으로 검증한다. 실험 결과에서는 IoT Edge 리소스의 무결성을 검증하기 위해서 이질적인 계산 하드웨어가 있는 복잡한 환경에서 잘 동작하는지 세 가지 다른 테스트 시나리오에서 에너지 효율성을 평가하였다.

Critical Factors in the Integration of Information and Communication Technologies in Early Childhood Education in Kenya : A Case of Nairobi County

  • Begi, Nyakwara
    • Journal of Information Technology Applications and Management
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    • 제21권3호
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    • pp.79-96
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    • 2014
  • In Kenya during the last one decade, public and private sectors have invested a lot of resources in computer based Information and Communication Technologies (ICT) to improve the quality of education in schools. The main objective has been to integrate ICT in the delivery of curriculum in order to improve the quality of teaching-learning and to produce ICT literate workforce. The computer based technologies are used in management, pedagogy, and communication. This paper presents results from a study that was conducted in Nairobi County in Kenya to determine the key factors in the integration of computer based ICT in teaching-learning in pre-primary and lower primary schools. Results had revealed that the use of computer based ICT in teaching-learning by both pre-primary and lower primary schools was influenced by accessibility of resources, capacity to use the technology, availability of time, and provision of technical support.

Cultural Sensitivity and Design Implications of MOOCs from Korean Learners' Perspectives: Case Studies on edX and Coursera

  • AHN, Mi Lee;YOON, Hwan Sun;CHA, Hyun Jin
    • Educational Technology International
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    • 제16권2호
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    • pp.201-229
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    • 2015
  • Culture is a crucial concept that forms the thinking and behaviors of a group of people, and it influences interactions in learning. Thus, it is also essential to consider cultural sensitivity in online learning technologies and instructional design as education is a set of learning actions based on values and perceptions. MOOCs, the latest online learning platform, are global online learning platforms that provide global learners with free and various learning resources including courses from different world-class institutions. Despite globalization having brought learners closer to sharing similar learning resources, the actual experiences with the resource are expected to vary according to cultures, mainly because learning behavior is a set of outcomes based on cultural differences. Taking this into consideration, this study aims to examine MOOCs from a cultural perspective in order to facilitate global learners, especially Korean learners, to utilize MOOCs with user-friendly services and contents. To achieve this objective, the study first identified and developed an evaluation criteria to examine the cultural sensitivity of MOOCs and conducted case studies on courses from major MOOC providers including edX and Coursera. From the findings, design recommendations of contents and courses on MOOCs were suggested to provide Korean learners with optimal learning experiences.

Applying the ADDIE Instructional Design Model to Multimedia Rich Project-based Learning Experiences in the Korean Classroom

  • LEE, Youngmin
    • Educational Technology International
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    • 제7권1호
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    • pp.81-98
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    • 2006
  • The purpose of this study was to apply the ADDIE instructional design model to develop multimedia rich project-based learning methods for effective instruction in a Korean mechanical engineering high school. This study was conducted as action research based on a high school situation. The study included 40 participants in a class purposively selected from 52 classes at 2080 student high school. Data were collected through observations, surveys and artifacts. Results indicated the multimedia rich project-based learning allowed students to take part in learning activities and there was close cooperation with and among group members to create better products. Also, the flexibility in the project-based learning environment allowed the participants to make decisions about their abilities, resources, and plans. Recommendations and implications for teacher educators as well as in-service and pre-service teachers are also presented.

Adaptive Learning Path Recommendation based on Graph Theory and an Improved Immune Algorithm

  • BIAN, Cun-Ling;WANG, De-Liang;LIU, Shi-Yu;LU, Wei-Gang;DONG, Jun-Yu
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제13권5호
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    • pp.2277-2298
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    • 2019
  • Adaptive learning in e-learning has garnered researchers' interest. In it, learning resources could be recommended automatically to achieve a personalized learning experience. There are various ways to realize it. One of the realistic ways is adaptive learning path recommendation, in which learning resources are provided according to learners' requirements. This paper summarizes existing works and proposes an innovative approach. Firstly, a learner-centred concept map is created using graph theory based on the features of the learners and concepts. Then, the approach generates a linear concept sequence from the concept map using the proposed traversal algorithm. Finally, Learning Objects (LOs), which are the smallest concrete units that make up a learning path, are organized based on the concept sequences. In order to realize this step, we model it as a multi-objective combinatorial optimization problem, and an improved immune algorithm (IIA) is proposed to solve it. In the experimental stage, a series of simulated experiments are conducted on nine datasets with different levels of complexity. The results show that the proposed algorithm increases the computational efficiency and effectiveness. Moreover, an empirical study is carried out to validate the proposed approach from a pedagogical view. Compared with a self-selection based approach and the other evolutionary algorithm based approaches, the proposed approach produces better outcomes in terms of learners' homework, final exam grades and satisfaction.

Sequence to Sequence based LSTM (LSTM-s2s)모형을 이용한 댐유입량 예측에 대한 연구 (Application of sequence to sequence learning based LSTM model (LSTM-s2s) for forecasting dam inflow)

  • 한희찬;최창현;정재원;김형수
    • 한국수자원학회논문집
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    • 제54권3호
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    • pp.157-166
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    • 2021
  • 효율적인 댐 운영을 위해서는 높은 신뢰도를 기반으로 하는 유입량 예측이 요구된다. 본 연구에서는 최근 다양한 분야에서 사용되고 있는 데이터 기반의 예측 방법 중 하나인 딥러닝을 댐 유입량 예측에 활용하였다. 그 중 시계열 자료 예측에 높은 성능을 보이는 Sequence-to-Sequence 구조기반의 Long Short-Term Memory 딥러닝 모형(LSTM-s2s)을 이용하여 소양강 댐의 유입량을 예측하였다. 모형의 예측 성능을 평가하기 위해 상관계수, Nash-Sutcliffe 효율계수, 평균편차비율, 그리고 첨두값 오차를 이용하였다. 그 결과, LSTM-s2s 모형은 댐 유입량 예측에 대한 높은 정확도를 보였으며, 단일 유량 수문곡선 기반의 예측 성능에서도 높은 신뢰도를 보였다. 이를 통해 홍수기와 이수기에 수자원 관리를 위한 효율적인 댐 운영에 딥러닝 모형의 적용 가능성을 확인할 수 있었다.

이러닝 교수 설계자 인적 자원 유통을 위한 휴먼 클라우드 플랫폼 프레임워크 설계 (A Design of Human Cloud Platform Framework for Human Resources Distribution of e-Learning Instructional Designer)

  • 김용
    • 유통과학연구
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    • 제16권7호
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    • pp.67-75
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    • 2018
  • Purpose - In the 21st century, as information technology advances alongside the emergence of the 4th generation, industrial age, industrial environment has become individualized and customized. It is important to hire good quality employees for good service in the industry. The e-learning market is growing every year. Although e-learning companies are finding better quality employees in e-learning, it is not easy to find it. Companies also spend a lot of time and cost to find employee. On the employees side, they want to get a job freely when they want, but they cannot find their job easily. Furthermore, the labor market environment is changing fast. In the 4th generation, industrial age, employers require to find manpower whenever they need and want at little cost. So of their own accord, we have considered the necessity of management of human resources for employees and employers in e-learning. The purpose of this study is to propose a human cloud platform framework for enabling an efficient management of human resources in e-learning industry. Research design, data, and methodology - To pinpoint the items of a human cloud platform framework, the study was initiated according to the following process. First, items of competency relating to e-learning instructional designer was analyzed. Second, based on the items of information from this analysis, selection and validity verification took place with 5 e-learning specialists group. Third, the opinion of experts who were in charge of hiring in e-learning companies were collated with the questionnaire. Lastly, the human cloud platform framework was proposed based on opinion results. Results - The framework was comprised of 7 domains and 27 items in order to develop the human cloud platform for e-learning instructional designer. The analysis results showed that the most highly considered item were 'skill (4.60)' that employee already have the capability. Following this (in order) were 'project type (4.56)', 'work competency (4.56)', and 'strength area of instructional design (4.52)'. Conclusions - The 27 items in the human cloud platform framework were suggested in this study. Following this, we can consider to develop the human cloud platform for finding a job and hiring e-learning instructional designer easily. For successful platform operation, we need to consider reliability between employer and employee. In addition, we need quality assurance system based on operation has public confidence.