• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제17권1호
• /
• pp.1-15
• /
• 2023

The aim of this study is to identify intelligent learning support functions in Learning Management System (LMS) to support university student learning activities during the transition from face-to-face classes to online learning. To accomplish this, we investigated the perceptions of students on the levels of importance and urgency toward learning support functions of LMS powered with Artificial Intelligent (AI) technology and analyzed the differences in perception according to student characteristics. As a result of this study, the function that students considered to be the most important and felt an urgent need to adopt was to give automated grading and feedback for their writing assignments. The functions with the next highest score in importance and urgency were related to receiving customized feedback and help on task performance processed as well as results in the learning progress. In addition, students view a function to receive customized feedback according to their own learning plan and progress and to receive suggestions for improvement by diagnosing their strengths and weaknesses to be both vitally important and urgently needed. On the other hand, the learning support function of LMS, which was ranked as low importance and urgency, was a function that analyzed the interaction between professors and students and between fellow students. It is expected that the results of this student needs analysis will be helpful in deriving the contents of learning support functions that should be developed as well as providing basic information for prioritizing when applying AI technology to implement learner-centered LMS in the future.

• 대한수학교육학회지:학교수학
• /
• 제10권2호
• /
• pp.155-179
• /
• 2008

7차 교육과정에서 곱셈 문제들은 구구단을 암기하고 적용하여 푸는 기능적인 면에 치중하고 있어 아동들이 세거나 그리는 덧셈적 사고에 머무르고 있다. 정수, 소수, 분수, 비 비율과 같은 수의 확장에서 효율적으로 곱셈과 나눗셈을 사용하여 풀 수 있는 능력과 자신이 풀이한 방법을 정확하게 설명할 수 있는 곱셈적 사고로의 이행을 위한 다양한 연구가 부족하다. 본 논문은 초등학교 5학년을 중심으로 덧셈적 사고에 머무르는 아동의 사고가 보다 높은 수준의 곱셈적 사고로 이행하도록 하기 위한 교수-학습 자료를 개발하고, 적용한 후 그 결과를 분석하였다. 덧셈적 사고와 곱셈적 사고에 대한 새로운 틀을 제시하고 이에 알맞은 자료를 개발함으로써 개발된 자료의 타당성과 곱셈적 사고로의 용이로운 전이가 가능함을 검증할 수 있었다.

• 전자공학회논문지B
• /
• 제31B권4호
• /
• pp.175-182
• /
• 1994

In this study, we present unvoiced consonant recognition system using characteristic parameters of the phoneme of the each syllable. For the recognition, the characteristic parameters on the time domain such as ZCR, total energy of the consonant region and half region energy of the consonant region, and those on the frequency domain such as the frequency spectrum of the transition region are used. The objective unvoiced consonants in this study are /ㄱ/,/ㄷ/,/ㅂ/,/ㅈ/,/ㅋ/,/ㅌ/,/ㅍ/ and /ㅊ/. Each characteristic parameter of two regions extracted from these segmented unvoiced consonants are used for each recognition system of the region, independently, And complementing two outputs of each other system, the final output is to be produced. The recognition system is implemented using MLP which has learning ability. The recognition simulation results for 112 unvoiced consonant samples are that average recognition rates are 96.4$\%$ under 80$\%$ learning rates and 93.7$\%$ under 60$\%$ learning rates.

• 디지털산업정보학회논문지
• /
• 제19권4호
• /
• pp.45-52
• /
• 2023

With the advent of deep learning, Artificial Intelligence (AI) technology has experienced rapid advancements, extending its application across various industrial sectors. However, the focus has shifted from the independent use of AI technology to its dispersion and proliferation through the open AI ecosystem. This shift signifies the transition from a phase of research and development to an era where AI technology is becoming widely accessible to the general public. However, as this dispersion continues, there is an increasing demand for the verification of outcomes derived from AI technologies. Causal AI applies the traditional concept of causal inference to AI, allowing not only the analysis of data correlations but also the derivation of the causes of the results, thereby obtaining the optimal output values. Causal AI technology addresses these limitations by applying the theory of causal inference to machine learning and deep learning to derive the basis of the analysis results. This paper analyzes recent cases of causal AI technology and presents the major tasks and directions of causal AI, extracting patterns between data using the correlation between them and presenting the results of the analysis.

• 한국지능시스템학회논문지
• /
• 제16권2호
• /
• pp.164-171
• /
• 2006

최근에 서비스되기 시작한 디지털 멀티미디어 방송은 다양한 종류의 수많은 컨텐츠를 제공하기 때문에 고객은 때로 자신이 선호하는 컨텐츠를 찾는데 많은 시간을 소비한다. 심지어는 선호 컨텐츠를 찾는 동안 이미 방송이 끝날 수도 있다. 이와 같은 문제를 해결하기 위해서는 고객이 필요로 하는 최소 정보만을 추천하기 위한 방법이 필요하다. 본 논문에서는 고객이 시청한 컨텐츠 선호도 전이 확률을 이용하여 고객이 선호하는 컨텐츠를 미리 예측하여 추천하기 위한 알고리즘과 시스템을 제안한다. 제안하는 시스템은 클라이언트 관리자 에이전트, 모니터링 에이전트, 러닝 에이전트, 그리고 추천 에이전트 모듈로 구성된다. 클라이언트 관리자 에이전트는 다른 모듈과 상호 작용을 하면서 조정자 역할을 한다. 모니터링 에이전트는 컨텐츠에 대한 고객의 선호도를 분석하기 위해 고객이 이용했던 usage history 데이터를 수집하기 위한 에이전트이다. 러닝 에이전트는 고객으로부터 수집된 usage history 데이터를 정제하여 시간 변화에 따른 상태 전이 행렬로 모델링하기 위한 에이전트이다. 추천 에이전트는 고객의 상태 전이 행렬로 구성된 모델링 데이터에 본 논문에서 제안하는 선호도 전이 확률 모델을 이용하여 고객이 바로 다음에 선호하게 될 컨텐츠를 추천하기 위한 에이전트이다. 추천 에이전트 모듈에서 컨텐츠에 대한 고객의 선호도 전이 확률을 이용하는 추천 알고리즘을 제안한다. 제안하는 추천 시스템은 무선 인터넷 표준 플랫폼인 WIPI(Wireless Internet Platform for Interoperability) 플랫폼에서 프로토타입 시스템을 설계, 구현하였으며, 실험결과 제안된 선호도 전이 확률 모델의 추천 정확도가 전형적인 방법에 비해 효과적임을 보인다.

• 한국정보통신학회논문지
• /
• 제11권3호
• /
• pp.592-599
• /
• 2007

무선 기술의 발달은 모바일 기능과의 결합으로 전자적인 학습을 향상시키고, 모바일 학습으로 확산되고 있다. 기본적으로 모바일 학습은 학습자에게 교육 내용 접근을 위한 시간과 공간적인 유용성을 제공한다. 그러나 모바일 디바이스는 작은 디스플레이 장치와 제한된 메모리 공간으로 인해서 학습 내용으로의 접근을 단지 텍스트 기반의 전달로 제한하고 있다. 본 논문은 멀티미디어 오브젝트 동기화를 지원하는 SMIL을 사용하여 모바일 디바이스에서 멀티미디어 컨텐츠 제작에 필요한 태그로만 구성한 MSMIL을 정의하여 파서의 크기를 줄이고, 학습 내용 생성시 매크로 방식을 사용하여 멀티미디어 학습 내용의 데이터 감소, 전송 효율 증대를 위한 멀티미디어 모바일 학습 시스템을 설계 및 구현하였다. 구현 결과, 제작 언어의 간소화와 언어 습득의 용이, 그리고 파서의 크기를 줄임으로써 파싱을 위한 CPU의 자원을 절약할 수 있다.

• 교육시설 논문지
• /
• 제19권2호
• /
• pp.3-12
• /
• 2012

This study is carried out to analyze transition about educational facilities of elementary schools with the change of policy and to find out a developmental direction for qualitative improvement. An analysis of blueprints is done to 34 elementary schools representing periodic characteristics after the 1960's. As a result, various cases were shown in arrangement type, unit space, block planning and plane planning of the schools after a abolition of law about the standardization of educational facilities on 1997, however, they were showing a tendency to simplify after the introduction of BTL on 2005. Spatial composition factors were very various in the schools planned from the middle of 1990's to the early of 2000's. Meanwhile, nearly fifty percent of occupying ratio about learning space in the schools of standard type was gradually decreased by modernization planning on 1990's. However, it was increased a little again after the BTL. In case of living area, the occupying ratio was comparatively high in the schools having characteristics of 1990's and it also tends to increase after the introduction of BTL.

• 로봇학회논문지
• /
• 제15권3호
• /
• pp.269-276
• /
• 2020

As we become an aging society, the number of elderly patients continues to increase. Pressure sores that can easily occur in patients with trauma cause serious socio-economic problems. In general, prevention of bedsores through predicting the patient's posture is being developed. Developed method usually use artificial intelligence techniques to estimate the patient's posture by measured pressure images in the mattress. In this method, it has a problem the reduction of estimation accuracy when posture of patient is changed. Therefore, it is necessary to use the filter of pressure images in the position transition of patient. In this paper, we propose an algorithm to predict the patient's posture, and an algorithm to reduce the ambiguity that can occur in the patient's posture transition section. By obtaining stable data through this algorithm, learning/prediction stability of the neural network can be expected, and prediction performance is improved accordingly. Through experiments, the effectiveness of the algorithm was verified.

• 제어로봇시스템학회논문지
• /
• 제10권9호
• /
• pp.763-770
• /
• 2004

The important issue of emotion recognition from speech is a feature extracting and pattern classification. Features should involve essential information for classifying the emotions. Feature selection is needed to decompose the components of speech and analyze the relation between features and emotions. Specially, a pitch of speech components includes much information for emotion. Accordingly, this paper searches the relation of emotion to features such as the sound loudness, pitch, etc. and classifies the emotions by using the statistic of the collecting data. This paper deals with the method of recognizing emotion from the sound. The most important emotional component of sound is a tone. Also, the inference ability of a brain takes part in the emotion recognition. This paper finds empirically the emotional components from the speech and experiment on the emotion recognition. This paper also proposes the recognition method using these emotional components and the transition probability.

• 전자공학회논문지B
• /
• 제31B권6호
• /
• pp.17-24
• /
• 1994

This paper is a study on Korean word recognition and suggest the method that stabilizes the state-transition in the HMM by applying the `inertia' to the feature vector sequences. In order to reduce the quantized distortion considering probability distribution of input vectors, we used SOFM, an unsupervised learning method, as a vector quantizer, By applying inertia to the feature vector sequences, the overlapping of probability distributions for the response path of each word on the self organizing feature map can be reduced and the state-transition in the Hmm can be Stabilized. In order to evaluate the performance of the method, we carried out experiments for 50 DDD area names. The results showed that applying inertia to the feature vector sequence improved the recognition rate by 7.4% and can make more HMMs available without reducing the recognition rate for the SOFM having the fixed number of neuron.