• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.9
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• pp.1372-1377
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• 1990

We constructed an intelligent tutoring system that compose four devices i.e. intelligent interface, acqusition module, tutoring controller and tutorial knowledge. We implemented the intelligent tutoring system using prolog as an authoring language in the ultrix os under VAX-11/750, and we propose goal opriented tutoring system algorithm.

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2006.06a
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• pp.111-117
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• 2006

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.519-525
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• 2006

Advances in information and telecommunication technology increasingly reveal the potential of computer supported education. However, most computer supported learning systems until recently did not pay much attention to different characteristics of individual learners. Intelligent learning environments adaptive to learner's preferences and tasks are desired. Each learner has different preferences and needs, so it is very crucial to provide the different styles of learners with different learning environments that are more preferred and more efficient to them. This paper reports a study of the intelligent learning environment where the learner's preferences are diagnosed using learner models, and then user interfaces are customized in an adaptive manner to accommodate the preferences. In this research, the learning user interfaces were designed based on a learning-style model by Felder & Silverman, so that different learner preferences are revealed through user interactions with the system. Then, a learning style modeling is done from learner behavior patterns using Decision Tree and Neural Network approaches. In this way, an intelligent learning system adaptive to learning styles can be built. Further research efforts are being made to accommodate various other kinds of learner characteristics such as emotion and motivation as well as learning mastery in providing adaptive learning support.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.12
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• pp.6455-6460
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• 2013

Learner modeling forms the foundations for intelligent tutoring systems that provide adaptive and active learning guidance for learning and education quality enhancement. The aim of this study was to develop learner modeling technologies to form the foundation of intelligent tutoring systems. Specific research tasks include learner modeling building techniques, diverse learner state diagnosis methods and educational data mining.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.575-579
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• 2006

Intelligent Tutoring System is composed of tutoring module, student module and expert module. Among the modules, interface module is the most closely related to students and shares the biggest part of a learning system. Interface module is important for students both in e-learning system but also in intelligent tutoring system. But research on the improvement of interface hasn't been actively done. It studied the interface improvement that has very close relation to e-Learning students. It studied the main components of the existing interface and designed multimedia interface to correct the problems of previous researches. It also suggested the method to relate it to the intelligent tutoring system.

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

• Koreanishche Zeitschrift fur Deutsche Sprachwissenschaft
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• v.8
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• pp.273-297
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• 2003

Der Computer kann ein nutzliches, herausforderndes und kreatives Werkzeug und Hilfsmittel beim Sprachenlernen darstellen. Bei der Anwendung auf die Situation des Grammatiklernens sind die analytischen und interaktiven $F\"{a}higkeiten$ des Computers mit gutem Erfolg eingesetzt worden. Eine der attraktivsten Eigenschaften des Computers ist nun seine $F\"{a}higkeit$, mit dem Schiller zu interagieren. Gute Computerprogramme $erm\"{o}glichen$ eine ausgedehnte Interaktion zwischen Computer und $Sch\"{u}ler$. Die Computerprogramme zum Sprachenlemen befassen sich mit Grammtik. Hier habe ich ein Lemprogramm des Intelligent Tutoring Systems $f\"{u}r$ die Grammatik Perfekt programmiert. Das Intelligent Tutoring System ist ein Computerlehrer. Wenn dem $Sch\'{u}ler$ Fehler unterlaufen, gibt ein guter Lehrer nicht nur die richtige Antwort, sondern hilft dem Schiller auch, seinen Fehler zu erkennen, ihn zu analysieren und zur richtigen Antwort zu finden. Das ist eine Funktion des Intelligent Tutoring Systems. Die Programme geben die Fehlermeldung richtig oder falsch. Dem Schuler werden Aufgaben vorgelegt, die sich aus mehreren Schritten und Prozessen zusammensetzen, wobei der Computer als Werkzeug bei der Suche nach einer $L\"{o}sung$ dient In guten $Probleml\"{o}sungsprogrammen$ verfolgt der Computer den $L\"{o}sungsansatz$ des $Sch\"{u}lers$ und analysiert seine Fehler. Wenn der Schuler bei der $\"{U}bung$ die richtige Antwort $ausw\"{a}hlt$, lauft das nachste Lernprogrammn des Computers. Die $\"{U}bungstyp$ kann auf dem Computer angereichert werden, indem $pr\"{a}zise$ und hilfreiche Kommentare $f\"{u}r$ jede $Auswahlm\"{o}glichkeit$ geboten werden.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.123-129
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• 2023

The most effective tutoring method is one-on-one, face-to-face in-person human tutoring. However, due to the limited availability of human tutors, computer-based alternatives have been developed. These software based alternatives are called Intelligent Tutoring Systems (ITS) which are used to tutor students in different domains. Although ITS performance is inferior to that of human teachers, the field is growing and has recently become very popular. User interfaces play key role in usability perspective of ITS. Even though ITS research has advanced, the majority of the work has concentrated on learning sciences while mostly disregarding user interfaces. Because of this, the present ITS includes effective learning modules but a less effective interface design. Usability is one approach to gauge a software's performance, while "ease of use" is one way to assess a software's quality. This paper measures the usability effectiveness of an ITS which is designed to teach Object-Oriented (OO) analysis and design concepts using Unified Modeling Language (UML). Computer Supported Usability Questionnaire (CSUQ) survey was conducted for usability evaluation of UML-ITS. According to participants' responses to the system's usability survey, all responses lie between 1 to 3 scale points which indicate that the participants were satisfied and comfortable with most of the system's interface features.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.421-428
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• 2006

Intelligent Tutoring System(ITS), which offers individualized learning environment that consider many learners' variable, is realized by the effective alternative to take the place of domain expert. Accordingly, research on Learning Companion System(LC) is currently noticing. However, to develop LCS which applies effective interaction, it is necessary to combine several LCs, and personalized knowledge base have to be made first. Therefore, in this paper, we propose the 'Knowledge Base Object Medel', which is based on connectionist' in cognition structure, represents learner's knowledge to self-learnig object, and grows adaptive object by proprietor, verify the validity. This model lays the groundwork for design of personalized knowledge base, offers clue to development of adaptive ITS using knowledge base object.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.5
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• pp.601-605
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• 2010

In this paper, we propose a GBL(Game Based Learning) content of intelligent tutoring capability. The objective of our GBL content is to learn the Karnaugh Map which is generally used to simplify boolean functions. Our GBL content well-motivates learners with interesting game-based scenarios and also, through an intelligent tutoring module, gives learners adaptive feedbacks such as hints and explanations while maintaining learners' contextual immersion. Additionally, we identified significant improvement in terms of learning effectiveness by analyzing the test results of two (experimental and controlled) student groups learning the Karnaugh Map.