• Journal of the Korea Society of Computer and Information
• /
• v.9 no.3
• /
• pp.115-120
• /
• 2004

As a part of working on development of the learning management system, a adaptive learning management system which is able to provide individual learner with different learning contents or paths customized to learner's learning behaviors by expanding SCORM was proposed in this dissertation. In terms of instructional technology interrelated with technology of CBI and ITS, new learning environments and learner preferences were analyzed. A related laboratory system was implemented by packaging a process on how to expand the meta data for contents and a process on how to utilize the web-based learning contents dynamically. In order to evaluate the usability of the implemented system, a sample content was provided to some selected learners and their learning achievement resulted from the new learning environment was analysed. A result of the experiment indicated that the adaptive learning management system proposed in this dissertation could provide every learner with the different content tailored to their individual learning preference and behavior. and it worked also to promote the learning performance of every learner.

• The KIPS Transactions:PartA
• /
• v.10A no.4
• /
• pp.321-330
• /
• 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 S
• /
• v.36S no.2
• /
• pp.28-37
• /
• 1999

Differentially Responsible Adaptive Critic Learning technique is proposed for learning the control technique with multiple control inputs as in robot system using reinforcement learning. The reinforcement learning is a self-learning technique which learns the control skill based on the critic information Learning is a after a long series of control actions. The Adaptive Critic Learning (ACL) is the representative reinforcement learning structure. The ACL maximizes the learning performance using the two learning modules called the action and the critic modules which exploit the external critic value obtained seldomly. Drawback of the ACL is the fact that application of the ACL is limited to the single input system. In the proposed Differentially Responsible Action Dependant Adaptive Critic learning structure, the critic function is constructed as a function of control input elements. The responsibility of the individual control action element is computed based on the partial derivative of the critic function in terms of each control action element. The proposed learning structure has been constructed with the CMAC neural networks and some simulations have been done upon the two dimensional Cart-Role system and robot squatting problem. The simulation results are included.

• The Journal of the Korea Contents Association
• /
• v.14 no.6
• /
• pp.529-538
• /
• 2014

This study was conducted as an need analysis through close and open semi-structured surveys, in order to identify the adaptive elements of the adaptive e-learning system. The study was conducted on students majoring computer education in teacher's college. In terms of the process of the need analysis, Rossett Model was applied. For the research method, responses on the open questionnaire were analyzed. In terms of the analysis method, coding was used to extract the theme of the content, and through the constant comparison method, categorizing took place. As the element that offers adaption in the adaptive learning system, it escapes from the existing learning style, and recognized the importance of providing adaptability for different elements such as the learner's level, learning objectives, and learning contents. Especially, An instructional model was identified as an important element that helps reach rationality as well as efficiently conduct the learning objectives.

• Journal of Institute of Control, Robotics and Systems
• /
• v.3 no.4
• /
• pp.363-372
• /
• 1997

In this paper, the design and the implementation of the adaptive learning rate neural network controller for an articulate robot, which is being developed (or) has been developed in our Automatic Control Laboratory, are mainly discussed. The controller reduces software computational load via distributed processing method using multiple CPU's, and simplifies hardware structures by the time-division control with TMS32OC31 DSP chip. Proposed neural network controller with adaptive learning rate structure using expert's heuristics can improve learning speed. The proposed controller verifies its superiority by comparing response characteristics of conventional controller with those of the proposed controller that are obtained from the experiments for the 5 axis vertical articulated robot. We, also, present the generalization property of proposed controller for unlearned trajectory and the change of load through experimental data.

• Proceedings of the Optical Society of Korea Conference
• /
• 1991.06a
• /
• pp.35-40
• /
• 1991

In this paper, a new adaptive learning algorithm, Training by Adaptive Gain (TAG) for optical implementation of large-sized neural networks has been developed and its electro-optical implementation for 2-dimensional input and output neurons has been demostrated. The 4-dimensional global fixed interconnections and 2-dimensional adaptive gain-controls are implemented by multi-facet computer generated holograms and LCTV spatial light modulators, respectively. When the input signals pass through optical system to the output classifying layer, the TAG adaptive learning algorithm is implemented by a personal computer. The system classifies three 5$\times$5 input patterns correctly.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.3
• /
• pp.534-540
• /
• 2008

The adaptive learning circuit is designed on the basis of modeling of MFSFET (Metal-Ferroelectric-Semiconductor FET) and the numerical results is analyzed. The output frequency of the adaptive learning circuit is inversely proportional to the source-drain resistance of MFSFET and the capacitance of the circuit. The saturated drain current with input pulse number is analogous to the ferroelectric polarization reversal. It indicates that the ferroelectric polarization plays an important role in the drain current control of MFSFET. The output frequency modulation of the adaptive learning circuit is investigated by analyzing the source-drain resistance of MFSFET as functions of input pulse numbers in the adaptive learning circuit and the dimensionality factor of the ferroelectric thin film. From the results, adaptive learning characteristics which means a gradual frequency change of output pulse with the progress of input pulse, are confirmed. Consequently it is shown that our circuit can be used effectively in the neuron synapses of neural networks.

• Journal of Information Technology Services
• /
• v.20 no.4
• /
• pp.129-140
• /
• 2021

Recently, interest in AI tutors is rising as a way to bridge the educational gap in school settings. However, research confirming the effectiveness of AI tutors is lacking. The purpose of this study is to explore how effective learner characteristics and recommended item learning activities are in predicting learner's academic performance in an adaptive online learning environment. This study proposed the hypothesis that learner characteristics (prior knowledge, midterm evaluation) and recommended item learning activities (learning time, correct answer check, incorrect answer correction, satisfaction, correct answer rate) predict academic achievement. In order to verify the hypothesis, the data of 362 learners were analyzed by collecting data from the learning management system (LMS) from the perspective of learning analytics. For data analysis, regression analysis was performed using the regsubset function provided by the leaps package of the R program. The results of analyses showed that prior knowledge, midterm evaluation, correct answer confirmation, incorrect answer correction, and satisfaction had a positive effect on academic performance, but learning time had a negative effect on academic performance. On the other hand, the percentage of correct answers did not have a significant effect on academic performance. The results of this study suggest that recommended item learning activities, which mean behavioral indicators of interaction with AI tutors, are important in the learning process stage to increase academic performance in an adaptive online learning environment.

• International Journal of Precision Engineering and Manufacturing
• /
• v.7 no.1
• /
• pp.62-66
• /
• 2006

The learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented an iterative precision of linear decentralized learning control based on p-integrated learning method for the vertical dynamic multiple systems. This paper develops an indirect decentralized learning control based on adaptive control method. The original motivation of the learning control field was learning in robots doing repetitive tasks such as an assembly line works. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. Some techniques will show up in the numerical simulation for vertical dynamic robot. The methods of learning system are shown for the iterative precision of each link.

• The Journal of Korean Association of Computer Education
• /
• v.9 no.1
• /
• pp.29-39
• /
• 2006

In this paper, we propose an adaptive tutoring system, which analyzes learning process, subject materials, and test items of students, diagnoses learning problems of them, and then gives proper advice accordingly. In the system, learning materials are constructed using concept map, on which the relationships among learning concepts are represented. Concept map can be used for several purposes in instruction process. Our work considers that new learning knowledge is dependent on what is already known. That is, it means that precedent concepts should be thoroughly learned for students to comprehend new concepts. After grasping the learning state of students for precedent concepts to be required before learning new concepts, our system provides proper learning materials for want of them, diagnoses the concepts which students have trouble to understand in the learning process, and provides suggestions for it.