• 대한공업교육학회지
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• v.34 no.2
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• pp.205-229
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• 2009

The purpose of this study is to verify the effect of forming groups according to the brain hemisphere preference on the cooperative problem solving learning achievement in the middle school technology. The subjects of this study were 95 second grade boy students of a middle school in Daejeon and the measurement instrument of the left and right hemisphere preference is the Brain preference Indicator(BPI) which had been developed by Torrance et al(1977) and was adjusted by Ko, Younghee(1991). The academic achievement was analyzed on cognitive, psychomotor and affective domains. Derived results from this research are stated below: First, making groups according that the brain preference is more similar was more effective than making groups according to the high familiarity and the similarity of performance in the academic achievement of psychomotor and affective domains. Second, making groups according that the brain preference is more similar was more effective than making groups according that the brain preference is more diffrent for the academic achievement of affective domains on the cooperative problem solving learning in technology. Third, the academic achievement score of the right hemisphere preference group is higher than the score of the population in three domains. Also, the academic achievement score of the right hemisphere preference group is higher than the score of the left hemisphere preference group.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.1
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• pp.30-41
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• 2011

This study performs the subjective evaluations of LED illumination conditions according to three types of color temperatures (7,000[K], 5,000[K], 3,000[K]) after employing LED illumination system in the classroom, Since the objective of this study is to develop an artificial lighting conditions like day light comfortable to students in the classroom, the learning effect based on three types of LED illumination conditions are analyzed. Three factors (learning intention, learning environment, learning motivation) are extracted by ANOVA and there are preference differences of LED illumination conditions between learning intention and learning environment factors. Especially, preference differences of LED illumination conditions are existed about calculation, reading and fatigue reduction. The test results of this study can be applied to obtaining high achievement of learning based on the lighting conditions.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.5
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• pp.1-11
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• 2022

With the development of deep learning technology, various research and development are underway to estimate preference rankings through learning, and it is used in various fields such as web search, gene classification, recommendation system, and image search. Approximation algorithms are used to estimate deep learning-based preference ranking, which builds more than k comparison sets on all comparison targets to ensure proper accuracy, and how to build comparison sets affects learning. In this paper, we propose a k-disjoint comparison set generation algorithm and a k-chain comparison set generation algorithm, a novel algorithm for generating paired comparison sets for crowd-sourcing-based deep learning affinity measurements. In particular, the experiment confirmed that the k-chaining algorithm, like the conventional circular generation algorithm, also has a random nature that can support stable preference evaluation while ensuring connectivity between data.

• Journal of Korean society of Dental Hygiene
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• v.19 no.6
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• pp.1047-1058
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• 2019

Objectives: The aim of this study is to investigate extracurricular program needs according to the learning styles of dental hygiene students, and to develop and organize non-subject programs that strengthen student competencies. Methods: The subjects in this study were dental hygiene students from three colleges located in Chungbuk, Chungnam, and Ulsan, respectively. The survey tools were composed of learning style, a non-subject field, and non-subject teaching and learning methods. Lastly, 313 data points were analyzed. Results: Learning styles of subjects were as follows: assimilators, divergers, convergers, and accommodators, at 44.6%, 33.0%, 16.0%, and 6.4%, respectively. Preference of the non-subject field, according to learning style, showed that accommodators were higher than divergers on startup, and the difference was found to be statistically significant (p<0.05). Preference of non-subject teaching and learning methods, according to learning style, shows that both divergers and convergers prefer special lectures, while assimilators prefer tours, and convergers prefer experience/exercise. The results had achieved statistical significance (p<0.05). Conclusions: This study shows that dental hygiene students had different learning styles, and their learning methods varied depending on learning style. Therefore, a method should be identified to develop and run non-subject programs suitable for each learning style.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.1061-1065
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• 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.

• Journal of The Korean Association For Science Education
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• v.35 no.1
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• pp.65-72
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• 2015

The purpose of this study is to investigate the relationships between moral sensitivity for topics related to science and preference for science, and belief about learning science. 129 middle school students were involved in this study and completed questionnaires to measure moral sensitivity for topics related to science, preference for science, and belief about learning science. Students' responses were analysed to show the distribution of variables and the correlation between variables by gender and grade. As a result, moral sensitivity was not affected by respondents' grades and genders, but was affected by different topics. Preference for science was not affected by respondents' grades and genders, while belief about learning science was not affected by respondents' genders but affected by lower grade. There were correlations between students' moral sensitivity and preference in case of female students and higher grades, as well as relationship between moral sensitivity and belief about learning science. This result infers that students who have higher moral sensitivity can prefer science and show more positive belief about learning science. Also, it can implicate that affective domain including interest or belief can play an important role in the context of science education focusing on moral aspect or ethics, and that teachers should be aware of personal differences in case of teaching moral aspect of science.

• Journal of Gifted/Talented Education
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• v.22 no.1
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• pp.39-59
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• 2012

This study has two purposes: The first is to compare gifted with non-gifted about learning styles and examine differences in the preference of learning styles between group characteristics depending on gender. The second is to examine differences between gifted and non-gifted about the preference of learning styles. The participants were 152 students from the middle schools in A City. 76 students of them belonged to the gifted group and the rest were non-gifted group. LSDI Learning Styles Diagnostic Inventory has been employed as measurement tools. Besides descriptive statistics, ANOVA, ${\chi}^2$ analysis were used to measure items. The results from data analysis are as follows. First, there was difference in learning styles between gifted and non-gifted. Second, there was not difference in the preference of learning styles between groups depending on the gender. Finally, this study discussed the results and their implication, the direction of future research in understanding and interpreting of learning styles for their practical usages.

• Journal of Technologic Dentistry
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• v.46 no.3
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• pp.126-132
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• 2024

Purpose: This study aims to examine the impact of instructor-learner interaction on online class satisfaction, perceived academic achievement, and online class preference. Methods: From December 20, 2023, to February 10, 2024, this study surveyed students in the medical and public health departments of K University and D University located in Gangwon-do and Daejeon, respectively. Results: In the online class environment, instructor-learner interaction showed a significant positive correlation with online class satisfaction, academic achievement, and online class preference. On re-examination using regression analysis, it was found that among the subfactors of instructor-learner interaction, instructional support and instructor presence had a significant impact. Conclusion: The findings suggest that in an online learning environment, instructors must make efforts to help learners identify what they need to learn by repeatedly asking whether they understand the learning content and providing appropriate feedback.

• The Journal of the Korea Contents Association
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• v.8 no.2
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• pp.1-8
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• 2008

The information system in which users can utilize to control and to get the filtered information efficiently has appeared. Content-based filtering can reflect content information, and it provides recommendation by comparing the feature information about item and the profile of preference. This has the shortcoming of the varying accuracy of prediction depending on teaming method. This paper suggests the discovery of preference through learning the profile for the content-based filtering. This study improves the accuracy of recommendation through learning the profile according to granting the preference of 6 levels to estimated value in order to solve the problem. Finally, to evaluate the performance of the proposed method, this study applies to MovieLens dataset, and it is compared with the performance of previous studies.

• Journal of Information Technology Applications and Management
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• v.29 no.6
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• pp.81-93
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• 2022

As deep learning technology in natural language and visual processing has rapidly developed, collaborative filtering-based recommendation systems using deep learning technology are being actively introduced in the recommendation field. In this study, OCF-GAN, a hybrid collaborative filtering model using GAN, was proposed to solve the one-class and cold-start problems, and its usefulness was verified through performance evaluation. OCF-GAN based on conditional GAN consists of a generator that generates a pattern similar to the actual user preference pattern and a discriminator that tries to distinguish the actual preference pattern from the generated preference pattern. When the training is completed, user preference vectors are generated based on the actual distribution of preferred items. In addition, the cold-start problem was solved by using a hybrid collaborative filtering recommendation method that additionally utilizes user and item profiles. As a result of the performance evaluation, it was found that the performance of the OCF-GAN with additional information was superior in all indicators of the Top 5 and Top 20 recommendations compared to the existing GAN-based recommender. This phenomenon was more clearly revealed in experiments with cold-start users and items.