• Journal of The Korean Association For Science Education
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• v.36 no.1
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• pp.159-166
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• 2016

This study analyzed perceptions and difficulties in applying Flipped Learning with an interview method grounded on understanding and experience of Flipped Learning in science class. The interviewees selected were six teachers working in Seoul and Gyeonggi Province. Three teachers having over two years of experience in operating Flipped Learning in science were selected, one each from elementary, middle, and high school and another three teachers who had just started to operate Flipped Learning this year were chosen, one each from elementary, middle, and high school. According to the result of a 3-step interview conducted with the participating teachers, they indicated very high satisfaction with the implementation of Flipped Learning in elementary, middle, and high school science classes. They responded that Flipped Learning was particularly more helpful for inquiry activity. The teachers, however, felt burdened by the class preparation and had difficulty in organizing the class. To explore ways to get rid of the difficulties in Flipped Learning, this author discussed the textbook system where one could draw various implications for improving the current class.

• Journal of Korea Game Society
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• v.11 no.5
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• pp.3-11
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• 2011

The learning by computer games, is in the spotlight as an effective education method for the game generation. The current design methods for educational games is to design all needed game elements and learning elements which should be included in. So to design an educational game for motivation and learning effects, is generally difficult. In this paper, we propose the G-learning model IGLM which is interworked with gameplay and learning play by motivation mechanism, in order to solve the difficult problem. The comparative analysis of the proposed model IGLM on the design difficulty with the current design methods for educational games or G-learning, shows the proposed method can be a solution of the design difficult problem and is the best on the difficulty of, the freedom of, and the scope of designing the component parts of educational games or G-learning.

• Journal of the Korea Society of Computer and Information
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• v.18 no.2
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• pp.175-182
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• 2013

The purpose of this study was to investigate the factors that influence elementary school learners' participation in cyber home study, and to suggest possible solutions to problem of the participation rates. To identify the factors, we analyzed the literature and developed a questionnaire consisting of 8 possible factors and 33 items. Data gathered from 429 elementary school learners were analyzed in a logistic regression model, which was utilized to determine the probability of the learner's participation in cyber home study because of any of the identified variables. We studied the following variables: problem from learning motivation & attitude, burden from computer, difficulty of new learning method, problem from instructional design, difficulty of interaction, problem from screen design, and problem from learning environment. The results indicated that two factors were important: burden from computer, difficulty of new learning method. That is to say, having a low level of burden of computer and difficulty of new learning method helps improve the rate of elementary school learners' participation in cyber home study.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1277-1283
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• 2010

Many e-learning systems adopts SCORM as an e-learning standard. E-learning systems evaluate and apply the learning difficulty in order to improve the studying efficiency. But, it is hard to change the learning difficulty and provide the customized learning contents during studying in SCORM. Because it is difficult to provide the customized contents to learners by changing the learners' level at runtime and to control optional learning. In this paper, we designed and implemented SCORM-based Contents Organization System on learners' level(SCOS) for complementing the SCORM's weakness. SCOS re-evaluates a learner's level whenever the learner's level is changed during learning, and provides customized contents on the learner's level.

• Smart Structures and Systems
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• v.29 no.1
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• pp.195-206
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• 2022

Crack detection is essential for inspection of existing structures and crack segmentation based on deep learning is a significant solution. However, datasets are usually one of the key issues. When building a new dataset for deep learning, laborious and time-consuming annotation of a large number of crack images is an obstacle. The aim of this study is to develop an approach that can automatically select a small portion of the most informative crack images from a large pool in order to annotate them, not to label all crack images. An active learning method with difficulty learning mechanism for crack segmentation tasks is proposed. Experiments are carried out on a crack image dataset of a steel box girder, which contains 500 images of 320×320 size for training, 100 for validation, and 190 for testing. In active learning experiments, the 500 images for training are acted as unlabeled image. The acquisition function in our method is compared with traditional acquisition functions, i.e., Query-By-Committee (QBC), Entropy, and Core-set. Further, comparisons are made on four common segmentation networks: U-Net, DeepLabV3, Feature Pyramid Network (FPN), and PSPNet. The results show that when training occurs with 200 (40%) of the most informative crack images that are selected by our method, the four segmentation networks can achieve 92%-95% of the obtained performance when training takes place with 500 (100%) crack images. The acquisition function in our method shows more accurate measurements of informativeness for unlabeled crack images compared to the four traditional acquisition functions at most active learning stages. Our method can select the most informative images for annotation from many unlabeled crack images automatically and accurately. Additionally, the dataset built after selecting 40% of all crack images can support crack segmentation networks that perform more than 92% when all the images are used.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.851-863
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• 2023

The mandatory use of BIM(Building Information Model) in larger Korean public construction projects necessitates participants to have a comprehensive understanding of the relevant procedures and technologies, especially during the bidding stage. However, most small and medium-sized construction and engineering companies possess limited BIM proficiency and understanding. This hampers their ability to recognize bidding requirements and make informed decisions. To address this challenge, our study introduces a method to gauge the complexity of BIM requirements in bidding documents. This is achieved by integrating a morphological analyzer, which encompasses BIM bidding terminology, with a deep learning model. We investigated the effects of the parameters in our proposed deep learning model and examined its predictive validity. The results revealed an F1-score of 0.83 for the test data, indicating that the model's predictions align closely with the actual BIM performance challenges.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.43-54
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• 2024

Recently, there has been a lot of research on utilizing Language Models, and Large Language Models have achieved innovative results in various tasks. However, the practical application faces limitations due to the constrained resources and costs required to utilize Large Language Models. Consequently, there has been recent attention towards methods to effectively utilize models within given resources. Curriculum Learning, a methodology that categorizes training data according to difficulty and learns sequentially, has been attracting attention, but it has the limitation that the method of measuring difficulty is complex or not universal. Therefore, in this study, we propose a methodology based on data heterogeneity-based Curriculum Learning that measures the difficulty of data using reliable prior information and facilitates easy utilization across various tasks. To evaluate the performance of the proposed methodology, experiments were conducted using 5,000 specialized documents in the field of information communication technology and 4,917 documents in the field of healthcare. The results confirm that the proposed methodology outperforms traditional fine-tuning in terms of classification accuracy in both LoRA fine-tuning and full fine-tuning.

• 한국정보교육학회:학술대회논문집
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• 2011.01a
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• pp.23-28
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• 2011

It is possible to solve many structural problems such as the occurrence of a lot of errors involving misspelling which may occur during the learning of existing programming language, difficulty in grammatical expression and understanding, and difficulty in the expression of algorithm if the program learning tool of the department of computer education in a college of education is utilized as an educational robot. This study developed a learning model (curriculum and a textbook) so that students who major in computer education may easily learn programming by using the NXT software of a Mindstorms robot.

• Journal of information and communication convergence engineering
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• v.2 no.2
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• pp.80-83
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• 2004

The core purpose of the system proposed in this paper is to help learners pursue proactive and self-oriented education by allowing learners to proactively configure their own content, that is, learners no longer have to be restricted by prescribed sequence of lectures. Although a variety of standardization and Learning Management System (LMS) were produced to develop and effectively manage web contents in response to active diffusion of internet application, practical changes to assist online learners are not yet to be found. In this paper, I would like to introduce a LMS that can support self-leading education by providing various types of learners at Virtual University with delicately organized educational contents for maximum efficiency. The system allows a learner to select a lecture or a chapter which has been presorted to meet his educational needs and intellectual ability. In general, most LMSs cannot meet every individual's educational needs because they structure their programs by letting learners simply choose from a list of available lectures at prescribed level or difficulty. However the Self-Leading LMS eliminates such boundaries by allowing learners to choose contents and difficulty within the limit set by their own educational competence.

• Journal of Korean Elementary Science Education
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• v.24 no.5
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• pp.531-538
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• 2005

The primary purpose of the study was to investigate the degree of difficulty and the causes of the difficulties in science instruction reported by elementary teachers and students. The secondary purpose was to suggest the effective methods to both of them to improve the efficiency of science instruction. For this study, the survey was carried out on 322 students and 156 teachers in the 3rd, 4th, and 5th grades. The questionnaires consisted of interest, difficulty and its factors. The results of this investigation are summarized as follows; 1. Teachers had relatively higher interest than students in science instruction content(textbook). Furthermore, the units that teachers showed interest differed from those that students had. This means that there is a significant difference of interest in instruction process between teachers and students. 2. Teachers felt much higher difficulty than students in the content of science instruction. This shows that the degree of interest might have little relation with that of difficulty, and that teaching of science seemed more difficult than teaming. Moreover, there was a difference of difficulty in the units between the teachers and students. The result shows a distinctive difference of difficulty in 8 units of the 3rd grade, 9 units n the 4th grade, and 12 units in the 5th grade (p<.05, p<.001, p<.005, respectively). It means that the difficulty depended on the units. 3. Students responded that they are responsible for difficulty factors of teaming, otherwise teachers thought that students are responsible for it, when the factors were divided into student factor, teacher factor, and curriculum factor. 4. Students demanded many experiments in the lab as well as easy and interesting instructions in order to reduce the degree of difficulty and to improve the condition of science instructions. However, teachers demanded the expansion of experimental equipments and materials as well as the improvement of the lab condition.