• Title/Summary/Keyword: 학습지능

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Design of an Infant's App using AI for increasing Learning Effect (학습효과 증대를 위한 인공지능을 이용한 영유아 앱 설계)

  • Oh, Sun Jin
    • The Journal of the Convergence on Culture Technology
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    • v.6 no.4
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    • pp.733-738
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    • 2020
  • It is really hard to find an infant's App, especially for the age under 5, even though there are lots of Apps developed and distributed nowadays. The selection of the proper infant's App is difficult since the infants' App should be useful, safe and helpful for the development of their intelligence. In this research, we design the useful infant's App for the development of their intelligence by applying the AI technology for increasing the learning effect in order to satisfy the characteristics of the infants' needs. A proposed App is the collection of interesting games for infants such as picture puzzle game, coloring shapes game, pasting stickers game, and fake mobile phone feature enables them to play interesting phone game. Furthermore, the proposed App is also designed to collect and analyze the log information generated while they are playing games, share and compare with other infants' log information to increase the learning effect. After then, it figures out and learns their game tendency, intelligibility, workmanship, and apply them to the next game in order to increase their interests and concentration of the game.

Considerations for the Improving Domestic Personal Information Protection Act in accordance with The Life Cycle of Personal Information In Generative Artificial Intelligence Model: Comparative analysis of GDPR and Personal Information Protection Act of Korea (생성형 인공지능 모델의 개인정보 라이프 사이클에 따른 국내 개인정보 보호법 개선 고려 요소: GDPR과 개인정보 보호법의 비교·분석)

  • Jaeyoung Jang
    • Convergence Security Journal
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    • v.24 no.3
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    • pp.81-93
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    • 2024
  • The purpose of this paper is to derive considerations when improving the Personal Information Protection Act based on the personal information protection life cycle of the generative artificial intelligence model as generative artificial intelligence models are introduced and used in Korea a lot. Through the study, the necessity of using open information in the collection stage, using personal information preservation technology in the learning stage, and preparing the basis for the development of protection technology in the holding stage was derived. It also revealed the necessity of managing the generated information in the generation and inference stage, re-learning in the limitation and destruction stage, and preparing a filtering basis. It is expected that the results of this study can be used to revise the Personal Information Protection Act and make policies in the future.

The Relationship Study between the Academic Achievement in ICT Literacy Education and Multiple Intelligences of the Elementary School Students (초등학생의 ICT소양교육 학업 성취도와 다중지능의 관계 연구)

  • Kim, Do-Yun;Lee, Tae-Wuk
    • The Journal of Korean Association of Computer Education
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    • v.7 no.4
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    • pp.103-110
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    • 2004
  • As the importance of learner-centered ICT education is emphasized, school education should move toward the direction of respecting individual learners' capability rather than fitting learners into a fixed frame. Such a view finds its theoretical ground in Gardner's multiple intelligences theory. Thus ICT literacy education in elementary school also needs to consider individual student's abilities and talents based on their multiple intelligences and apply appropriate education programs and teaching methods. In order to provide basic materials for this, the present study examined the correlation between children's academic achievement in ICT literacy education and their multiple intelligences and inquired into which intelligences are factors determining achievement in ICT literacy education. According to the results. logical-mathematical intelligence is in a significant correlation with academic achievement in ICT literacy education. and logical-mathematical intelligence and linguistic intelligence are the most influential factors on academic achievement in ICT literacy education.

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The Core Concepts of Mathematics for AI and An Analysis of Mathematical Contents in the Textbook (수학과 인공지능(AI) 핵심 개념과 <인공지능 수학> 내용 체계 분석)

  • Kim, Changil;Jeon, Youngju
    • Journal of the Korean School Mathematics Society
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    • v.24 no.4
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    • pp.391-405
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    • 2021
  • In this study, 'data collection', 'data expression', 'data analysis, and 'optimization and decision-making' were selected as the core AI concepts to be dealt with in the mathematics for AI education. Based on this, the degree of reflection of AI core concepts was investigated and analyzed compared to the mathematical core concepts and content of each area of the elective course. In addition, the appropriateness of the content of was examined with a focus on core concepts and related learning contents. The results provided some suggestions for answering the following four critical questions. First, How to set the learning path for ? Second, is it necessary to discuss the redefinition of the nature of ? Third, is it appropriate to select core concepts and terms for ? Last, is it appropriate to present the relevant learning contents of the content system of ?

Analysis of achievement predictive factors and predictive AI model development - Focused on blended math classes (학업성취도 예측 요인 분석 및 인공지능 예측 모델 개발 - 블렌디드 수학 수업을 중심으로)

  • Ahn, Doyeon;Lee, Kwang-Ho
    • The Mathematical Education
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    • v.61 no.2
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    • pp.257-271
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    • 2022
  • As information and communication technologies are being developed so rapidly, education research is actively conducted to provide optimal learning for each student using big data and artificial intelligence technology. In this study, using the mathematics learning data of elementary school 5th to 6th graders conducting blended mathematics classes, we tried to find out what factors predict mathematics academic achievement and developed an artificial intelligence model that predicts mathematics academic performance using the results. Math learning propensity, LMS data, and evaluation results of 205 elementary school students had analyzed with a random forest model. Confidence, anxiety, interest, self-management, and confidence in math learning strategy were included as mathematics learning disposition. The progress rate, number of learning times, and learning time of the e-learning site were collected as LMS data. For evaluation data, results of diagnostic test and unit test were used. As a result of the analysis it was found that the mathematics learning strategy was the most important factor in predicting low-achieving students among mathematics learning propensities. The LMS training data had a negligible effect on the prediction. This study suggests that an AI model can predict low-achieving students with learning data generated in a blended math class. In addition, it is expected that the results of the analysis will provide specific information for teachers to evaluate and give feedback to students.

The Design of Student Module in the ITS for learning Electronic Calculator Architecture (전자계산기구조 학습을 위한 ITS 학습자 모듈의 설계)

  • Oh, Pill-Woo;Kim, Do-Yun;KIm, Myeong-Ryeol
    • The Journal of Korean Association of Computer Education
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    • v.8 no.2
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    • pp.33-40
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    • 2005
  • It has been found that the learning method based on conventional CAI(Computer Assisted Instruction) to be inadequate and inefficient as it is designed without considering the individual learning characteristics of the learners. In order to rectify and remedy the problem, the development of an ITS(Intelligent Tutoring System) that is adequately equipped with an artificial intelligence that successfully interprets the individual learning ability characteristics through accumulated individual data is in order. This study attempts to verify the individual acquisition ability and the possible error committed by learners in the process of learning in order to present the elements to be considered for designing a successful student module that enables the effective learning through the 'learner ability grouping' for learning Electronic Calculator Architecture.

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Adaptation Methods for a Probabilistic Fuzzy Rule-based Learning System (확률적 퍼지 룰 기반 학습 시스템의 적응 방법)

  • Lee, Hyeong-Uk;Byeon, Jeung-Nam
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2007.11a
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    • pp.223-226
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    • 2007
  • 지식 발견 (knowledge discovery)의 관점에서, 단기간 동안 취득된 데이터 패턴을 학습하고자 하는 경우 데이터에 비일관적인(inconsistent) 패턴이 포함되어 있다면 확률적 퍼지 룰(probabilistic fuzzy rule) 기반의 지식 표현 방법 및 적절한 학습 알고리즘을 이용하여 효과적으로 다룰 수 있다. 하지만 장기간 동안 지속적으로 얻어진 데이터 패턴을 다루고자 하는 경우, 데이터가 시변(time-varying) 특성을 가지고 있으면 기존에 추출된 지식을 변화된 데이터에 활용하기 어렵게 된다. 때문에 이러한 데이터를 다루는 학습 시스템에는 패턴의 변화에 맞추어 갈 수 있는 지속적인 적응력(adaptivity)이 요구된다. 본 논문에서는 이러한 적응성의 측면을 고려하여 평생 학습(life-long learning)의 관점 에 서 확률적 퍼지 룰 기반의 학습 시스템에 적용될 수 있는 두 가지 형태의 적응 방법에 대해서 설명하도록 한다.

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Enhanced RBF Network by Using Auto-Turning Method of Learning Rate, Momentum and ART2 (학습률 및 모멘텀의 자동 조정 방법과 ART2를 이용한 개선된 RBF네트워크)

  • 주영호;김태경;김광백
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2003.09b
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    • pp.91-94
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    • 2003
  • 본 논문에서는 RBF 네트워크의 중간층과 출력층 사이의 연결강도를 효율적으로 조정하기 위해 퍼지 논리 시스템을 이용하여 학습률과 모멘텀을 동적으로 조정하는 개선된 RBF 네트워크를 제안한다. 입력층과 중간층 사이의 학습 구조로 ART2를 적용하고 중간층과 출력층 사이의 연결 강도 조정 방법으로는 제안된 학습률 자동 조정 방식을 적용한다. 제안된 방법의 학습 성능을 평가하기 위해 기존의 delta-bar-delta 알고리즘, 기존의 ART2 기반의 RBF 네트워크와 비교 분석한 결과, 제안된 방법이 학습 속도와 수렴성에서 개선된 것을 확인하였다.

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Intelligent Self Learning System for Keyboard Instrument using a Smartphone (스마트폰을 이용한 지능형 건반악기 자율학습 시스템)

  • Kim, Young-Geun;Kim, Won-Jung
    • The Journal of the Korea institute of electronic communication sciences
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    • v.9 no.9
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    • pp.999-1004
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    • 2014
  • This intelligent keyboard instrument self learning system developed in this study consists of smartphone based learning application and keyboard-instrument auxiliary module. The keyboard instrument auxiliary module receives playing information of the instrument through smartphone application and bluetooth communication. Then the module shows it through LED display so that the relationship between the keyboard and scale could be easily recognized even for beginners. Also, this system provides saving function and analyzing function of user's performance data, making learning more effective.

DAKS: A Korean Sentence Classification Framework with Efficient Parameter Learning based on Domain Adaptation (DAKS: 도메인 적응 기반 효율적인 매개변수 학습이 가능한 한국어 문장 분류 프레임워크)

  • Jaemin Kim;Dong-Kyu Chae
    • Annual Conference of KIPS
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    • 2023.05a
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    • pp.678-680
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    • 2023
  • 본 논문은 정확하면서도 효율적인 한국어 문장 분류 기법에 대해서 논의한다. 최근 자연어처리 분야에서 사전 학습된 언어 모델(Pre-trained Language Models, PLM)은 미세조정(fine-tuning)을 통해 문장 분류 하위 작업(downstream task)에서 성공적인 결과를 보여주고 있다. 하지만, 이러한 미세조정은 하위 작업이 바뀔 때마다 사전 학습된 언어 모델의 전체 매개변수(model parameters)를 학습해야 한다는 단점을 갖고 있다. 본 논문에서는 이러한 문제를 해결할 수 있도록 도메인 적응기(domain adapter)를 활용한 한국어 문장 분류 프레임워크인 DAKS(Domain Adaptation-based Korean Sentence classification framework)를 제안한다. 해당 프레임워크는 학습되는 매개변수의 규모를 크게 줄임으로써 효율적인 성능을 보였다. 또한 문장 분류를 위한 특징(feature)으로써 한국어 사전학습 모델(KLUE-RoBERTa)의 다양한 은닉 계층 별 은닉 상태(hidden states)를 활용하였을 때 결과를 비교 분석하고 가장 적합한 은닉 계층을 제시한다.