• The Journal of Korean Association of Computer Education
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• v.5 no.4
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• pp.1-8
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• 2002

With the development of Internet technology, the construction and spread of network environment increased educational adaption and utilization based on World Wide Web. Learners are educated in the competitive, individual, or collaborative learning structure. Among them, competitive and individual educational methods are criticized for bringing about excessive competition and a lack of cooperation. As a new way of educational method, the interest for the collaborative learning structure was increased. In this perspective, we design and implement a web-based collaborative learning system which is adapted the merit and model of collaboration learning and show that the proposed system improves learning achievement and motivation by experimental study on student groups.

• Journal of The Korean Association For Science Education
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• v.19 no.2
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• pp.266-274
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• 1999

This study investigated the effects of cooperative and individual computer-assisted instructions upon middle school students' science conceptions, achievement, perception of learning environment, and motivation. The cooperative, individual, and traditional learning groups were selected from a middle school, and taught about the motion of molecule for 5 class hours. Data analyses indicated that the students with cooperative computer-assisted instruction scored significantly higher than those with traditional instruction in the tests of conceptual understanding, perception of learning environment and motivation. Better understanding of the cooperative learning group was also found in a retention test of conceptions. In addition, there were significant interactions between the instruction and the level of prior achievement in the tests of retention of conceptions and motivation. Educational implications are discussed.

• Journal of Korea Multimedia Society
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• v.12 no.5
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• pp.757-766
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• 2009

This study presents an adaptive hypermedia learning system which can provide individual learning environment using a learner behavioral model. This system proposes a LBML which can manage learners' learning behavioral information by tracking down such information real-time. The system consists of a collecting system of learning behavioral information and an adaptive learning support system. The collecting system of learning behavioral information uses Web 2.0 technologies and collects learners' learning behavioral information real-time based on a SCORM CMI data model. The collected information is stored as LBML instances of individual learners based on a LBML schema. With the adaptive learning support system, a rule-based learning supporting module and an interactive learning supporting module are developed by analysing LBML instances.

• The Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.377-384
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• 2022

Whether voluntary or involuntary in the field of education, learner participation is a basic premise for all teaching-learning. It is true that behaviorism and cognitive educational psychology have helped the development of teaching-learning theory so far but the reality is that it has not been of great help to provide learner-centered education according to the learner's learning type. We have professional theological knowledge and insight in theological college and having the knowledge to diagnose and solve difficulties and problems in the pastoral field and it is an increasingly difficult reality to educate students to have spiritual leadership that can lead the future society. We know that each student should understand the characteristics of each student and teach according to their learning type but the reason why it is difficult to implement is that each learner has different competencies, conditions, and cultural backgrounds and has particularly diverse learning types. in this respect, in order to increase the learning effect of individuals, individual learning considering the learning type of students is effective.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3099-3120
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• 2023

The present fast-moving era brings a serious stress issue that affects elders and youngsters. Everyone has undergone stress factors at least once in their lifetime. Stress is more among youngsters as they are new to the working environment. whereas the stress factors for elders affect the individual and overall performance in an organization. Electroencephalogram (EEG) based stress level classification is one of the widely used methodologies for stress detection. However, the signal processing methods evolved so far have limitations as most of the stress classification models compute the stress level in a predefined environment to detect individual stress factors. Specifically, machine learning based stress classification models requires additional algorithm for feature extraction which increases the computation cost. Also due to the limited feature learning characteristics of machine learning algorithms, the classification performance reduces and inaccurate sometimes. It is evident from numerous research works that deep learning models outperforms machine learning techniques. Thus, to classify all the emotions based on stress level in this research work a hybrid deep learning algorithm is presented. Compared to conventional deep learning models, hybrid models outperforms in feature handing. Better feature extraction and selection can be made through deep learning models. Adding machine learning classifiers in deep learning architecture will enhance the classification performances. Thus, a hybrid convolutional neural network model was presented which extracts the features using CNN and classifies them through machine learning support vector machine. Simulation analysis of benchmark datasets demonstrates the proposed model performances. Finally, existing methods are comparatively analyzed to demonstrate the better performance of the proposed model as a result of the proposed hybrid combination.

• Journal of The Korean Association of Information Education
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• v.17 no.4
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• pp.383-391
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• 2013

This study developed an adaptive instruction system for individual learning. We applied Gregorc's learning style to support the adaption. This proposed Instruction system provides instruction content based on a more effective instruction method that takes into consideration the learner's individual learning style. We applied Gregorc's learning style to create a clear treatment of differing learning styles. Proposed adaptive instruction system was applied to college students studying computer learning. A t-test indicated that our proposed adaptive instruction system resulted in positive effects in an overall style comparison. In addition, our Abstract-Sequential learning style indicated the most effective results while the Abstract-Random learning style indicated no difference between the experimental and comparative groups in each style analysis for Gregorc's learning style.

• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.78-84
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• 2008

The traditional intelligent teachable system has mainly focused on knowledge and cognition. It has overlooked motivational aspects of learners. Motivation is an important factor in learning making learners to have interests in a given task and persist it. Although the systems include cognitive as well as motivational factors, the effects of ITS on interest are not equivalent depending on individual characteristics. This study is to investigate how influence learners' response patterns to their interests and also examined effects of individual characteristics on interest in teachable agent (TA). In this experiment, we used KORI which is a new type of ITS that learner teach computer agent based on the instructional method of learning by teaching'. In the beginning of experiments, metacognition, achievement goal orientation and self-efficacy were measured as individual characteristics. Then, participants were asked to use KORI at home during 10 days. After using KORI the level of interest were measured. The result showed that metacognition was positively related with interest, whereas performance goal orientation and mastery goal orientation were negatively related to interest. It suggests t hat different individual characteristics should be considered to promote learners' intrinsic motivation in TA.

• International Journal of Internet, Broadcasting and Communication
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• v.4 no.1
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• pp.8-12
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• 2012

The purpose of this study is to review the possible effect of the learning paradigm shift from traditional method to ubiquitous learning. What are the societal issues that need to be address in order to design a new pedagogical platform trending from e-learning to m-learning and now the u-learning? That without the proper study of how learning environment may affect the learning process of an individual will lead to poor quality of education. This new era of learning environment offer a big opportunity for "anytime, anywhere" learning. Thus, Lifelong learning is at hand of everyone. Maximizing the benefit of new trend will be a great help and addressing the limitations will lead to quality education.

• The Journal of the Korea Contents Association
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• v.17 no.7
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• pp.101-112
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• 2017

The purpose of this study is to find out what kind of experience and effect the learner - centered team - based learning (tbl)method has on pre - nursing learner's nursing English course. Participants were 12 preliminary nursing learner, it was analyzed through learning result recording, study observation, learning satisfaction, learner's report, peer evaluation. And described the meaning of the learning experience, individual and team scores were analyzed using frequency analysis, paired-t test. The results showed that the score of each team was higher than the score of individual in both. The tbl experience has been a form of intimacy with colleagues, a motivation for learning, self-study, easy to learn the medical terminology felt through repeated learning, to improve their score by having them secondary group test and they remembered it as a pleasant learning time.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.240-243
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• 2021

Recently, there have been research results of applying Big data and AI technologies to the evaluation and individual learning for education. It is information technology innovations that collect dynamic and complex data, including student personal records, physiological data, learning logs and activities, learning outcomes and outcomes from social media, MOOCs, intelligent tutoring systems, LMSs, sensors, and mobile devices. In addition, e-learning was generated a large amount of learning data in the COVID-19 environment. It is expected that learning analysis and AI technology will be applied to extract meaningful patterns and discover knowledge from this data. On the learner's perspective, it is necessary to identify student learning and emotional behavior patterns and profiles, improve evaluation and evaluation methods, predict individual student learning outcomes or dropout, and research on adaptive systems for personalized support. This study aims to contribute to research in the field of education by researching and classifying machine learning technologies used in anomaly detection and recommendation systems for educational data.