• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 춘계학술발표대회
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• pp.385-387
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• 2022

With the tremendous rise in popularity of the Internet and technological advancements, many news keeps generating every day from multiple sources. As a result, the information (News) on the network has been highly increasing. The critical problem is that the volume of articles or news content can be overloaded for the readers. Therefore, the people interested in reading news might find it difficult to decide which content they should choose. Recommendation systems have been known as filtering systems that assist people and give a list of suggestions based on their preferences. This paper studies a personalized news recommendation system to help users find the right, relevant content and suggest news that readers might be interested in. The proposed system aims to build a hybrid system that combines collaborative filtering with content-based filtering to make a system more effective and solve a cold-start problem. Twitter social media data will analyze and build a user's profile. Based on users' tweets, we can know users' interests and recommend personalized news articles that users would share on Twitter.

• 공학교육연구
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• 제20권3호
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• pp.25-31
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• 2017

As the world enters the era of the Fourth Industrial Revolution, which is represented by advanced technology, it not only changes the industrial field but also the education field. In recent years, Smart Learning has enriched learning by using diverse forms and technologies that utilize vast amount of information about learners' individual knowledge through the emergence of realistic and intelligent contents that combine high technology such as artificial intelligence, big data and virtual reality and there is an increasing interest in intelligent adaptive learning, which can customize individual education. Therefore, the purpose of this study is to explore intelligent adaptive learning method through recent smart education environment, beyond traditional writing-based communication education which is highly dependent on the competency of instructors. In this study, we analyzed the various learner information collected in the communication course and constructed a concrete teaching and learning method of intelligent adaptive learning based on the instructor's intended smart contents. The result of this study is expected to be the basis of highly personalized teaching and learning method of digital method in communication education which is emphasized in the fourth industrial revolution era.

• 대한수학교육학회지:학교수학
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• 제2권2호
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• pp.333-355
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• 2000

In this paper, possibility of practical use of cryptarithmetic problems in teaching and learning of mathematics is discussed. There might be seven cases to use them practically like followings: (1) Cryptarithmetic problems might be used for deepening the mathematical knowledges. (2) Cryptarithmetic problems might be used for fostering mathematical thinking abilities. (3) Cryptarithmetic problems might be used for fostering problem solving abilities. (4) Cryptarithmetic problems might be used as open ended problems. (5) Cryptarithmetic problems might be used as materials for personalized learning. (6) Cryptarithmetic problems might be used as materials for cooperative learning. (7) Cryptarithmetic problems might be used as materials for problems posing.

• 스마트미디어저널
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• 제11권5호
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• pp.82-90
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• 2022

Along with the growing popularity of 5G technology, providing flexible and personalized network services suitable for requirements of customers has also become a lucrative venture and business key for network service providers. Therefore, dynamic network provisioning is needed to help network service providers. Moreover, increasing user demand for network services meets specific requirements of users, including location, usage duration, and QoS. In this paper, a routing algorithm, which makes routing decisions using Reinforcement Learning (RL) based on the information about link stability, is proposed and called Link Stability aware Reinforcement Learning (LSRL) routing. To evaluate this algorithm, several mininet-based experiments with various network settings were conducted. As a result, it was observed that the proposed method accepts more requests through the evaluation than the past link annotated shorted path algorithm and it was demonstrated that the proposed approach is an appealing solution for dynamic network provisioning routing.

• 한국콘텐츠학회논문지
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• 제9권6호
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• pp.17-24
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• 2009

웹2.0의 UCC와 개방 개념에 기반한 참여적 설계방법을 도입하여 학습자의 적극적인 참여를 이끌어 자기주도적 학습을 성취할 수 있는 새로운 이러닝 모델을 제시하고자 한다. 학습자는 문단 단위의 마이크로 콘텐츠 생성에 적극적으로 참여하고, 자신의 지적능력, 학습목표, 학습성향 등을 고려하여 다양한 영역의 마이크로콘텐츠를 자신의 학습전략에 맞춰 직접 재구성함으로써 학습자 중심의 학습이 이루어질 수 있도록 한다. 학습자 맞춤형 학습콘텐츠로 재구성하기 위하여 학습자는 학습자들간의 소셜 네트워크를 활용하여 필요한 마이크로콘텐츠를 선택하며 학습자들간의 유대감을 형성하여 높은 학습효과를 기대할 수 있다.

• 공학교육연구
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• 제27권4호
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• pp.3-11
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• 2024

This study classified domestic and international systems by type, presenting their key features and examples, with the aim of outlining future directions for system development and research. AI-based learning assistance systems can be categorized into instructional-learning evaluation types and academic recommendation types, depending on their purpose. Instructional-learning evaluation types measure learners' levels through initial diagnostic assessments, provide customized learning, and offer adaptive feedback visualized based on learners' misconceptions identified through learning data. Academic recommendation types provide personalized academic pathways and a variety of information and functions to assist with overall school life, based on the big data held by schools. Based on these characteristics, future system development should clearly define the development purpose from the planning stage, considering data ethics and stability, and should not only approach from a technological perspective but also sufficiently reflect educational contexts.

• International Journal of Advanced Culture Technology
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• 제12권3호
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• pp.449-465
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• 2024

This study conducted a systematic literature review using online databases to investigate the effective feedback types that enhance the learning experiences of online students. Feedback is a critical component for learner success. With the expansion of online education, the importance of feedback has become more evident due to the reduced interaction between instructors and learners. Instructors must provide high-quality feedback that motivates learners and supports their educational goals. This involves using automated tools appropriate for the environment and effective feedback strategies to deliver personalized feedback. The literature was gathered through an extensive search process, adhering to predetermined inclusion and exclusion criteria, and included a risk assessment of selected studies, drawing from sources such as Google Scholar, Elsevier, and other Scopus-indexed journals. The review adhered to the guidelines set forth by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Specific keywords related to the study's focus, including "Online learning," "Improving learning," "Learner performance," "Feedback type," and "Feedback," guided the database searches. The protocol for selecting systematic reviews on learning enhancement involved screening articles published from 2013 to 2021 based on their titles and abstracts according to established criteria. Analyzing and studying data on learning patterns in non-face-to-face educational environments can improve learners' needs and educational effectiveness. Selecting the right types of feedback, taking into account the learners' levels and educational objectives, is crucial for providing effective feedback. A variety of feedback types are essential for the continuous improvement of learners' learning.

• 디지털산업정보학회논문지
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• 제18권4호
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• pp.67-75
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• 2022

Recently, e-learning has been attracting significant attention due to COVID-19. However, while e-learning has many advantages, it has disadvantages as well. One of the main disadvantages of e-learning is that it is difficult for teachers to continuously and systematically monitor learners. Although services such as personalized e-learning are provided to compensate for the shortcoming, systematic monitoring of learners' concentration is insufficient. This study suggests a method to evaluate the learner's concentration by applying machine learning techniques. In this study, emotion and gaze data were extracted from 184 videos of 92 participants. First, the learners' concentration was labeled by experts. Then, statistical-based status indicators were preprocessed from the data. Random Forests (RF), Support Vector Machines (SVMs), Multilayer Perceptron (MLP), and an ensemble model have been used in the experiment. Long Short-Term Memory (LSTM) has also been used for comparison. As a result, it was possible to predict e-learners' concentration with an accuracy of 90.54%. This study is expected to improve learners' immersion by providing a customized educational curriculum according to the learner's concentration level.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2006년도 춘계학술발표대회
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• pp.303-306
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• 2006

유비쿼터스 컴퓨팅의 핵심은 네트워크 환경에 대한 고 가용성이라 할 수 있다. 이러한 사실은 사용자 컨텍스트(Context)가 반영된 서비스를 제공하기 위한 필수조건이 이미 갖추어져 있다는 것을 시사한다. 지금까지 상황인지(Context-Aware) 서비스를 위한 여러 응용들이 제시되어 왔지만, 동적으로 변화하는, 즉 예측하기 어려운 환경을 충분히 반영할 만큼의 유연성을 제공하지 못했다. 왜냐하면, 응용 태스크 시나리오가 시작단계부터 이미 정해져 있었기 때문이다. 여기에, 본 고는 평생동안 개인화된 태스크를 동적으로 생성, 제공할 수 있는 멀티 에이전트 시스템 구조를 제안하고자 한다. 평생 개인화 태스크(Life Long Personalized Task)는 끊임없이 변화하는 사용자의 행동패턴을 반영할 수 있도록, 동적으로 생성, 제공되는 태스크를 의미한다. 이는 태스크 시나리오가 컴파일 타임에 이미 결정되지 않고, 실행 시간 중에 자동으로 생성된다는 것을 의미한다. 이러한 유연성은 평생학습 엔진(Life Long Learning Engine)을 활용함으로써 가능하다. 이 엔진은 사용자의 행동패턴을 학습하며, 결과적으로 사용자 행동패턴 규칙들을 생성한다.

• International journal of advanced smart convergence
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• 제13권3호
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• pp.150-155
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• 2024

Genomic data plays a transformative role in medicine, biology, and forensic science, offering insights that drive advancements in clinical diagnosis, personalized medicine, and crime scene investigation. Despite its potential, the integration and analysis of diverse genomic datasets remain challenging due to compatibility issues and the specialized nature of existing tools. This paper presents the GenomeSync system, designed to overcome these limitations by utilizing the Hadoop framework for large-scale data handling and integration. GenomeSync enhances data accessibility and analysis through SQL-based search capabilities and machine learning techniques, facilitating the identification of genetic traits and the resolution of forensic cases. By pre-processing DNA profiles from crime scenes, the system calculates similarity scores to identify and aggregate related genomic data, enabling accurate prediction models and personalized treatment recommendations. GenomeSync offers greater flexibility and scalability, supporting complex analytical needs across industries. Its robust cloud-based infrastructure ensures data integrity and high performance, positioning GenomeSync as a crucial tool for reliable, data-driven decision-making in the genomic era.