• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.14 no.12
• /
• pp.6455-6460
• /
• 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 Korean Society of Precision Engineering Conference
• /
• 2004.10a
• /
• pp.1061-1065
• /
• 2004

The knowledge-based society of the 21st century requires effective education and learning methods in each professional field because the development of human resource determines its competence more than any other factors. It is highly desirable to develop an intelligent tutoring system, which meets ever increasing demands of education and learning. Such a system should be adaptive to each individual learner's demands as well as the continuously changing state of the learning process, thus enabling the effective education. The development of a learning environment based on learner modeling is necessary in order to be adaptive to individual learning variants. An intelligent learning environment is being developed targeting the heritage education, which is able to provide a customized and refined learning guide by storing the content of interactions between the system and the learner, analyzing the correlations in learning situations, and inferring the learning preference from the learner's learning history. This paper proposes a heritage learning system of Bulguksa temple, integrating the ontology-based learner modeling and the learning preference which considers perception styles, input and processing methods, and understanding process of information.

• Proceedings of the IEEK Conference
• /
• 2003.07d
• /
• pp.1681-1684
• /
• 2003

We propose the intelligent tutoring system for the mobile english learning. The proposed system is based on the item response theory to analyze the level of learner. We define the types of item, teaching method and item disposition according to contents modeling. The system estimates the learner level and it gives the learning contents, the evaluation results, and feedback. The system gives those by inference engine which consists of learner's level estimation value, method diagnostic value and disposition diagnostic value.

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

• 한국HCI학회:학술대회논문집
• /
• 2006.02a
• /
• pp.918-925
• /
• 2006

Recent development of teachable agent provides learners with active roles as knowledge constructors and focuses on the individualization. The aim of this adaptive agent is not only to maximize the learner's cognitive functions but also to enhance the interests and motivation to learn. In order to establish the relationships among user characteristics and response patterns and to extract the algorithm among variables, we measured the individual characteristics and analyzed logs of the teachable agent named KORI (KORea university Intelligent agent) through the student modeling. A correlation analysis was conducted to identify the relationships among individual characteristics, user responses, and learning outcomes. Among hundreds of possible relationships between numerous variables in three dimensions, nine key user responses were extracted, which were highly correlated with either individual characteristics and learning outcomes. The results suggest that certain type of learner responses or the combination of the responses would be useful indices to predict the learners' individual characteristics and ongoing learning outcome. This study proposed a new type of dynamic assessment for individual differences and ongoing cognitive/motivational learning outcomes through the computation of responses without measuring them directly. The construction of individualized student model based on the ongoing response pattern of the user that are highly correlated with the individual differences and learning outcome may be the useful methodology to understand the learner's dynamic change during learning.

• Journal of the Korea Society of Computer and Information
• /
• v.7 no.2
• /
• pp.59-67
• /
• 2002

Major Characteristic of the 7th National Curriculum in science is to provide deep and supplementary learning, depending on the level of each learner. In the level-oriented curriculum, coursewares are used to present teaching materials to various levels. In most coursewares, however, they provide their contents at a uniform level and hence it is hard to expect level-oriented learning. This paper presents learner's modeling for developing student's level-oriented dynamic tutoring system for science class , Instructional module of this system made by component unit is able to be reconstructed dynamically. Learning module is constructed using a hybrid model mixed of Overlay and Bug model. Testing module interprets diagnostic errors to be established by given differentiated weight in accordance with item's difficulty and discrimination. Through ITS student modeling, this system presents various problem solving methods reconstructed by learner's level differentiated.

• Korean Journal of Childcare and Education
• /
• v.15 no.4
• /
• pp.21-36
• /
• 2019

Objective: Using data from the Panel Study on Korean Children, this study investigated the influence of teacher'thoughts about the transition from ECEC to primary school in relation to learner-centered teaching methods and children's school adjustment. Methods: We analyzed the longitudinal data of 658 seven-year-olds from the 8th and 9th waves of the panel study of Korean children collected by the Korea Institute of Child Care and Education in 2015 and 2016. The main analysis method was Structural Equation Modeling(SEM). Results: First, theachers' thoughts about the transition from ECEC to primary school was noteworthy. Second, the more concern a theacher' had about transition, the higher their learner-centered teaching method. Third, teacher' concern about transition influenced children's school adjustment. Fourth, a teacher's learner-centered teaching method mediated concern about children's transition and school adjustment in the first year and the second year. Conclusion/Implications: According to the results of this study, 1st grade teachers' concern about the transition from ECEC to primary school has been found to be predictors of children's school adjustment.

• Journal of KIISE:Computing Practices and Letters
• /
• v.15 no.3
• /
• pp.226-230
• /
• 2009

Intelligent Tutoring System (ITS) is a procedure of analyzing collected data for teaming, making a strategy and performing adequate service for learners. To perform suitable service for learners, modeling is the first step to collect data from the process of their learning. The model, however, cannot be authentic if collected data can contain learners' inconsistent behaviors or unpredictable learning inclination. This study focused on how to sort normal and abnormal data by analyzing collected data from learners through visualization. A model has been set up to assort unusual data from collected learner's data by using DOLLS-HI which makes possible to diagnose learner's learning propensity based on housing interior learning contents in the experiment. The created model has been confirmed its improved reliability comparing to previous one.

• Journal of the Korea Convergence Society
• /
• v.11 no.10
• /
• pp.81-88
• /
• 2020

This purpose of this study is to develop content that enables repetitive carving practice of the maxillary right central incisor (MRCI) based on augmented reality (AR). For a step-by-step practice of achieving the tooth shape, after creation of the storyboard from the square box shape in step 1 to the completed MRCI block in step 16, three-dimensional (3D) modeling data reflecting the characteristics of the mesial, distal, lingual, and labial surface of the MRCI were generated. An application was built in which 3D modeling data were output on the screen of the learner's mobile device, and image markers suitable for 3D modeling in steps 1 to 16 of the MRCI model were respectively generated. Using this information, the learner could carve a high-quality MRCI by repeatedly performing the tooth shape carving exercises. With AR, we intend to contribute to improved tooth morphology carving skills by linking the theory and practical techniques for a beginners in dentistry.

• KIPS Transactions on Software and Data Engineering
• /
• v.5 no.9
• /
• pp.411-418
• /
• 2016

Learning analytic has been utilized to helps us to successfully complete the course by using the interaction of the teacher and the learner data generated from the teaching and learning support system. In other words, Learning analytic is a method in order to understand the activities of learners. In the learning analytic, the data model is needed in order to utilize the more useful for teaching and learning activities data. Therefore, in this study, we propose a user centric data model of learning styles and learning objects. This model is expressed by aggregating of user learning style, learning objects, and learning activities. The proposed model is significant that laid the foundation for analyzing the activities of the learners in course units.