• Journal of KIISE:Software and Applications
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• v.32 no.5
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• pp.438-448
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• 2005

This paper proposes an adaptive tutoring system that provides learning materials dynamically according to the learners' teaming character and ability. Our system, in which a learning phase and a test phase are linked together, supports the personalized instruction-learning by providing the teaming materials by level in the learning phase according to the teaming ability estimated in the test phase. We design and implement a tutoring system consisted of an evaluation component and a learning component. An evaluation component uses a computerized adaptive test(CAT) based on item response theory to evaluate learners' ability while a learning component employs fuzzy level set theory so that teaming contents are provided to learners according to learners' level.

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

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.07a
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• pp.415-416
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• 2012

본 논문에서는 컴퓨터 프로그래밍 수업에 피어 튜터링 교수법을 적용하여 단계별 수준별 학습을 수행하고자 할때 튜터의 선발 방법에 따라 튜터링 효과에 어떤 영향을 미치는지를 연구하였다. 두 가지 수업 유형에 각기 다른 튜터 선발 방법을 적용하여 운영해보고 그에 따른 튜티들의 학습효과를 분석해보았다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.255-260
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• 2013

Nowadays, the intelligent education system has been studied using the self-directed learning ability. It can connect to the online virtual university and it is based on web technology that can be accessed anywhere anyplace. In order to implement the intelligent tutoring system, the student's weak and strong subjects must be first determined in real time, it proposed level learning capabilities and security algorithms in this paper. Moreover, in this paper, to implement the intelligent education tutoring system it proposed qr code and student level learning simulation.

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

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.893-901
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• 2006

The major limitation of the traditional Intelligent Tutoring Systems (ITS) is that interface is mainly focused on the cognitive factors. However, the new direction of ITS is shifting form the cognitive perspectives to the motivational perspectives reflecting the individual differences. In this study, the specific design guidelines for motivational interface of ITS are proposed to promote learner's motivation to learn during the interaction with the ITS. First, ITS should be able to reflect individual differences in cognitive abilities, interest and motivation, and ongoing changes of the interestingness and comprehensibility during learning activities. Second, it is essential for ITS to guarantee learner controllability, diverse learning activities, curiosity, self-relevance, and challenge to enhance the level of motivation and situational interest. Third, the game-like properties are also needed to maximize the motivational effect of learning with ITS.

• The KIPS Transactions:PartA
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• v.10A no.4
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• pp.321-330
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• 2003

This paper proposes an agent-based adaptive tutoring system that monitors learning process of learners' and provides learning materials dynamically according to the analyzed learning character. Furthermore, it uses fuzzy concept to evaluate learners' ability and to provide learning materials appropriate to the level of learners'. For this, we design a courseware knowledge structure systematically and then construct a fuzzy level set on the basis of it considering importance of learning targets, difficulty of learning materials and relation degree between learning targets and learning materials. Using agent, monitoring continually the learning process of learners 'inferencing to offer proper hints in case of incorrect answer in learning assesment, composing dynamically learning materials according to the learning feature and the evaluation of assesment, our system implements effectively adaptive instruction system. Moreover, appling the fuzzy concept to the system could naturally consider and ideal with various and uncertain items of learning environment thus could offer more flexible and effective instruction-learning methods.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.7
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• pp.159-169
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• 1994

In this paper, we develop a prototype of ITS(Intelligent Tutoring Systems) system: UNIX TUTOR. It is designed for the purpose of teaching the UNIX beginners the principal concepts of UNIX and the shell commands using the communication between the student and the system. UNIX TUTOR engages the student in a two-way conversation that is mixed-initiative dialogue and attempts to teach the student UNIX via the Socratic method of guided discovery and the Coaching method interchangeably. And the student model is based on both the overlay model and the buggy model together. Thus TUTOR aims at teaching the students effectively whose levels of learning are different using various explanations which are determined by the student model. Because the knowledge representation for UNIX-TUTOR is based on the frame structure and the production rules it is easy to represent the complicated constructs. UNIX TUTOR is implemented on the SPARC station using X/Motif and C for cp command among 10 ones which were selected.

• Journal of The Korean Association of Information Education
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• v.1 no.2
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• pp.16-34
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• 1997

This paper describes a method of building a intelligent learning system consisting of a authoring tool, in the area of language education, and a Intelligent Tutoring System(ITS) to study English. This tool is different from commerical authoring tools, as a tool that has capabilities as a component of an ITS and this ITS is efficient to retrieve the lesson plan from the plan memory than to generate it whenever an instructinoal goal is selected. the results of this research could be used either by a developer of the other area of ITS, or by a human teacher as a curriculum in the actual class.

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