• ETRI Journal
• /
• v.46 no.1
• /
• pp.48-58
• /
• 2024

This paper presents the development of language tutoring systems for nonnative speakers by leveraging advanced end-to-end automatic speech recognition (ASR) and proficiency evaluation. Given the frequent errors in non-native speech, high-performance spontaneous speech recognition must be applied. Our systems accurately evaluate pronunciation and speaking fluency and provide feedback on errors by relying on precise transcriptions. End-to-end ASR is implemented and enhanced by using diverse non-native speaker speech data for model training. For performance enhancement, we combine semisupervised and transfer learning techniques using labeled and unlabeled speech data. Automatic proficiency evaluation is performed by a model trained to maximize the statistical correlation between the fluency score manually determined by a human expert and a calculated fluency score. We developed an English tutoring system for Korean elementary students called EBS AI Peng-Talk and a Korean tutoring system for foreigners called KSI Korean AI Tutor. Both systems were deployed by South Korean government agencies.

• Journal of Information Management
• /
• v.27 no.3
• /
• pp.41-60
• /
• 1996

Export system using artificial intelligence give the technology for the varied library user instruction. Expert system showing problem solving process give educational effectives. In this paper, expert system are reviewed to discuss the application possibility in education and the model of intelligent tutoring system(ITS) applying artificial intelligence is presented.

• Journal of the Korea Convergence Society
• /
• v.9 no.10
• /
• pp.21-28
• /
• 2018

To help users who are experiencing difficulties finding the right learning course corresponding to their level of proficiency, we developed a recommendation model for personalized learning course for Intelligence Tutoring System(ITS). The Personalized Learning Course Recommendation model for ITS analyzes the learner profile and extracts the keyword by calculating the weight of each word. The similarity of vector between extracted words is measured through the cosine similarity method. Finally, the three courses of top similarity are recommended for learners. To analyze the effects of the recommendation model, we applied the recommendation model to the Women's ability development center. And mean, standard deviation, skewness, and kurtosis values of question items were calculated through the satisfaction survey. The results of the experiment showed high satisfaction levels in accuracy, novelty, self-reference and usefulness, which proved the effectiveness of the recommendation model. This study is meaningful in the sense that it suggested a learner-centered recommendation system based on machine learning, which has not been researched enough both in domestic, foreign domains.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2017.04a
• /
• pp.958-959
• /
• 2017

본 논문은 챗봇과 대화시스템을 이용한 영어 교육 시스템을 기술하는 것을 목표로 한다. 본 논문의 시스템은 학습자의 대화 흐름을 제한하지 않고 주제를 벗어난 자유대화를 허용하며 문법오류에 대한 피드백을 한다. 챗봇과 대화시스템을 이용한 영어 교육 시스템은 대화턴 성공률로 평가되었는데, 평균 대화턴 성공률은 80.86%였으며, 주제별로는 1) 뉴욕시티투어 티켓 구매 71.86%, 2) 음식주문 71.06%, 3) 건강습관 대화 85.41%, 4) 미래화폐에 대한 생각 조사 95.09%였다. 또한 영어 문법 오류 교정도 측정되었는데 문법 오류 정확률은 66.7%, 재현율은 31.9%였다.

• The Mathematical Education
• /
• v.62 no.3
• /
• pp.303-326
• /
• 2023

Mathematics is a discipline with a strong systemic structure, and learning deficits in previous stages have a great influence on the next stages of learning. Therefore, it is necessary to frequently check whether students have learned well and to provide immediate feedback, and for this purpose, intelligent tutoring system(ITS) can be used in math education. For this reason, it is necessary to reveal how the intelligent tutoring system is effective in personalized adaptive learning. The purpose of this study is to investigate the functions and applications of intelligent tutoring system for personalized adaptive learning in mathematics. To achieve this goal, literature reviews and surveys with students were applied to derive implications. Based on the literature reviews, the functions of intelligent tutoring system for personalized adaptive learning were derived. They can be broadly divided into diagnosis and evaluation, analysis and prediction, and feedback and content delivery. The learning and lesson plans were designed by them and it was applied to fifth graders in elementary school for about three months. As a result of this study, intelligent tutoring system was mostly supporting personalized adaptive learning in mathematics in several ways. Also, the researcher suggested that more sophisticated materials and technologies should be developed for effective personalized adaptive learning in mathematics by using intelligent tutoring system.

• Journal of Korea Multimedia Society
• /
• v.22 no.11
• /
• pp.1251-1258
• /
• 2019

Intelligent tutoring system enables users to effectively learn by utilizing various artificial intelligence techniques. For instance, it can recommend a proper curriculum or learning method to individual users based on their learning history. To do this effectively, user's characteristics need to be analyzed and classified based on various aspects such as interest, learning ability, and personality. Even though data labeled by the characteristics are required for more accurate classification, it is not easy to acquire enough amount of labeled data due to the labeling cost. On the other hand, unlabeled data should not need labeling process to make a large number of unlabeled data be collected and utilized. In this paper, we propose a semi-supervised learning method based on feedback variational auto-encoder(FVAE), which uses both labeled data and unlabeled data. FVAE is a variation of variational auto-encoder(VAE), where a multi-layer perceptron is added for giving feedback. Using unlabeled data, we train FVAE and fetch the encoder of FVAE. And then, we extract features from labeled data by using the encoder and train classifiers with the extracted features. In the experiments, we proved that FVAE-based semi-supervised learning was superior to VAE-based method in terms with accuracy and F1 score.

• Journal of Fisheries and Marine Sciences Education
• /
• v.20 no.3
• /
• pp.462-472
• /
• 2008

Education is a field that has tried to make use of the advantages of computers since they were introduced to the world. Intelligent Tutoring System and multimedia have become methods of teaching students of Computer Science, Education, Psychology, and Cognitive Science. Until now, they have been designed and produced only on the basis of a very specific domain and format. However, in the education field, most learners ask for integrated service that is practical, realizable, and sensitive to technological change. Therefore, in this study, we would like to present the technological and formal integration model as an ITS model which acknowledges changes in the fields of technology and education. As a technological integration model, the integration model of traditional Symbolic Artificial Intelligence and Artificial Neural Networks was presented. As a formal integration model, three integration models were presented according to (a) the process of learning diagnosis (b) learners' action behaviors (c) intelligence service respectively.

• The Journal of Korean Association of Computer Education
• /
• v.8 no.2
• /
• pp.33-40
• /
• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2013.05a
• /
• pp.662-665
• /
• 2013

ITS(Intelligent Tutoring System) is a system that provides active and flexible tutoring conditions to learners, having adopted artificial intelligence to overcome the limitations of CAI(Computer Assisted Instruction). However, the existing ITS has a few problems; the system provides the same contents to every learner, not considering main variants of their learning and achievement, characters and levels, and therefore, it does not generate satisfactory results; the system does not offer a properly designed course schedule. Therefore, this thesis proposes ARS(Adaptive Recommendation System), founded on ITS, that provides contents designed based on the characters and levels of learners. To catch the characters of learners, the important variant for successful learning, ARS applies and embodies a module of self-assessment test. Also, it puts weighs according to the areas of learning which is different from the simplified assessment that asks for short and mechanical answers for the purpose of knowing the levels of the learners.

• Journal of Intelligence and Information Systems
• /
• v.6 no.1
• /
• pp.83-96
• /
• 2000

본연구는 웹 상에서 원격 협력 학습을 위한 수준별 협력 학습자 진단 및 스케줄링 에이전트의 설계와 구현에 관한 연구이다. 원격 협력 학습은 동일한 학습내용에 흥미를 갖는 아동이 동시에 학습할 수 있는 환경이 필요하며 학습자의 지식 또한 비슷한 수준이어야 효과적인 협력학습을 할 수 있다. 분산 환경의 이질적인 학습자를 모으기 위해서는 좀 더 자율적이고 지능적인 시스템이 필요하며 학습자에 대한 지식을 표현하는 학습자 모델이 요구된다. 이를 위해 에이전트 시스템이 적절하게 사용될수 있으며 학습자의 수준을 판단하기 위한 진단 에이전트와 협력학습이 가능한 여러명의 학습자들을 알맞은 시간과 서버에 연결하는 스케줄링 에이전트를 웹 기반 지능형 교수 시스템에 접목하였다. 학습자 수준을 진단하는 진단 에이전트는 확신도를 높이기 위해 3-모수 로지스틱 확신공식과 시간 가중치 확신인자 공식을 적용하여 신뢰도를 높였다 또한 협력학습의 스케줄링을 위해 다양한 제약조건들의 최적해를 구하기 위해 제약 만족 문제(CSP)로 스케줄링 에이전트를 모델링하였다 본 연구에서 설계 구현한 협력학습자 진단 및 스케줄링 에이전트의 효율성을 살펴보기 위해 여러명의 학습자를 대상으로 실험하였다. 실험을 통해 각 학습자의 지식 수준 진단과 다수의 학습자가 적절한 협력학습을 하기 위한 스케줄링이 매우 효율적으로 이루어짐을 볼 수 있었다.