• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2814-2832
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• 2014

As information society matures to an even higher level and as information technology becomes a necessity to our everyday lives, the needs to develop, support and satisfy personal and social needs without the limitation of time, space, and location have become a vital point to everyday lives. Smartphone users are increasing at a staggering rate but the research on mobile-Learning model and the implementation of m-Learning scenario are still behind the needs of the users. Therefore, this paper focuses on the design of 'repeatable and short-spanned m-Learning model' to meet the needs of the learners who are on the go and on the move with their smartphones. Smartphone users frequently reach out for their phones but compare to the frequencies, the actual span of time they spend per use are relatively and surprisingly short. One way to understand this phenomenon is that the users tend to immediately replace their smartphones with laptops or desktops whenever they are available. A leaning model was needed to reflect this short and frequent use, a use that is solely based on the smartphone environment. This proposed learning model first defines this particular setting and implements the model to real smartphone users over an 8 week period. To understand whether different learning backgrounds can influence this model, different schools with online and offline learning channels participated in the experiment. User survey was conducted after the experiment to get a better understanding of the smartphone users. Pretest and posttest were conducted before and after the experiment and the data were validated and analyzed using SPSS version 18.0 for PC. Preliminary descriptive statistics, multiple regression and cross validation was conducted for the analysis. The results showed that the proposed English Listening and Comprehension Mobile Digital Textbook (ELCMDT) had a positive effect on the learners in general and was more effective for learners who were already experienced with online learning.

• Journal of Biomedical Engineering Research
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• v.43 no.3
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• pp.131-135
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• 2022

Due to COVID-19, Correct method of wearing mask is important to prevent COVID-19 and the other respiratory tract infections. And the deep learning technology in the image processing has been developed. The purpose of this study is to create the type of mask wearing dataset for deep learning models and select the deep learning model to detect the wearing mask correctly. The Image dataset is the 2,296 images acquired using a web crawler. Deep learning classification models provided by tensorflow are used to validate the dataset. And Object detection deep learning model YOLOs are used to select the detection deep learning model to detect the wearing mask correctly. In this process, this paper proposes to validate the type of mask wearing datasets and YOLOv5 is the effective model to detect the type of mask wearing. The experimental results show that reliable dataset is acquired and the YOLOv5 model effectively recognize type of mask wearing.

• Journal of Digital Contents Society
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• v.13 no.4
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• pp.517-524
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• 2012

This paper deals with the design of teaching learning model for programming language education. Various courses related to programming language education have opened at the university having many academic majors. In the meantime, a variety of programming languages have been developed, many integrated development environments of programming language have also been developed for users to make a program easily. But it is difficult for many novice learners to learn programming language still, likewise it is difficult for many teachers to teach the introduction course of programming language. In this paper, we have designed a teaching learning model based on scholastic achievements and blended learning for programming language education. The teaching learning model designed in this study was applied to a course opened in the second semester of 2011. According to the course evaluation result, the teaching learning model for programming language has shown to be an effective for novice learner.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.1
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• pp.31-51
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• 2024

The purpose of this study is to propose a design model for school library based instruction under EduTech. EduTech-based education expands learning boundaries and requires new instructional environments and learning experiences for learner-centered deeper learning. Accordingly, this study modified the ADDIE model based on the system theory and presented a four-stage instructional design model (draft) consisting of 'analysis stage, preliminary learning and development stage, learning management stage, and team teaching evaluation stage.' This model reflects elements of flipped learning, the backward design model, and inquiry-based learning to develop of customized student materials and inquiry activities. In addition, the scope of learning was expanded to include prior learning, face-to-face learning, and additional learning to increase the diversity of collaboration and opportunities to utilize school library materials. Also, Several ways for school library based instruction within EduTec were proposed in terms of teacher librarians' expertise, school library space, budget, standard curriculum development, and comprehensive support system for reading education.

• Korean Journal of Childcare and Education
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• v.7 no.2
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• pp.115-135
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• 2011

The purpose of this study was to developing a cooperative learning model utilize storytelling technique to the Preliminary childhood teachers whose learning was in order to improve ability and collaboration of making relationships a teams members by the cooperative learning which was performed on the basis of the confidence after understanding themselves and others. To achieve the purpose, the study was performed by dividing into two stages. First, the draft of storytelling based cooperative learning model was proposed by performing a literature survey and a case study. Second, the draft model was applied to the actual work. And the draft was modified and developed to the final model on the basis of the draft model's strenth and implemented to 39 students who were the sophomore of child care education department and enrolled the profession class of at B college P city for 8 weeks. From the implementation result of the model, it was obtained that there was the positive reaction on applying storytelling technique to the beginning stage of cooperative learning. And adding of 'Re-constitution role sharing team' step in the original steps was suggested. In the end, this model was modified and issued based on the research result. The researchers proposed a cooperative learning model storytelling based for Preliminary childhood teachers which consists 6 phases : (1) understanding cooperative learning (2) Building the team and role sharing team (3) theme setting and theme structuralization (4) Re-constitution role sharing team (5) announcement of the results and evaluating (6) reflection of general.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.262-273
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• 2024

We provide a detailed analysis of the data processing and model training process for vulnerability classification using Transformer-based language models, especially sentence text-to-text transformers (ST5)-XXL and XLNet. The main purpose of this study is to compare the performance of the two models, identify the strengths and weaknesses of each, and determine the optimal learning rate to increase the efficiency and stability of model training. We performed data preprocessing, constructed and trained models, and evaluated performance based on data sets with various characteristics. We confirmed that the XLNet model showed excellent performance at learning rates of 1e-05 and 1e-04 and had a significantly lower loss value than the ST5-XXL model. This indicates that XLNet is more efficient for learning. Additionally, we confirmed in our study that learning rate has a significant impact on model performance. The results of the study highlight the usefulness of ST5-XXL and XLNet models in the task of classifying security vulnerabilities and highlight the importance of setting an appropriate learning rate. Future research should include more comprehensive analyzes using diverse data sets and additional models.

• Journal of the Earthquake Engineering Society of Korea
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• v.28 no.2
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• pp.113-119
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• 2024

Existing reinforced concrete buildings with seismically deficient column details affect the overall behavior depending on the failure type of column. This study aims to develop and validate a machine learning-based prediction model for the column failure modes (shear, flexure-shear, and flexure failure modes). For this purpose, artificial neural network (ANN), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) models were used, considering previously collected experimental data. Using four machine learning methodologies, we developed a classification learning model that can predict the column failure modes in terms of the input variables using concrete compressive strength, steel yield strength, axial load ratio, height-to-dept aspect ratio, longitudinal reinforcement ratio, and transverse reinforcement ratio. The performance of each machine learning model was compared and verified by calculating accuracy, precision, recall, F1-Score, and ROC. Based on the performance measurements of the classification model, the RF model represents the highest average value of the classification model performance measurements among the considered learning methods, and it can conservatively predict the shear failure mode. Thus, the RF model can rapidly predict the column failure modes with simple column details.

• Journal of Digital Convergence
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• v.11 no.6
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• pp.199-204
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• 2013

Speech recognition system is shall be composed model of learning from the inaccurate input speech. Similar phoneme models to recognize, because it leads to the recognition rate decreases. Therefore, in this paper, we propose a method of speech recognition optimal learning model configuration using the Bhattacharyya algorithm. Based on feature of the phonemes, HMM feature extraction method was used for the phonemes in the training data. Similar learning model was recognized as a model of exact learning using the Bhattacharyya algorithm. Optimal learning model configuration using the Bhattacharyya algorithm. Recognition performance was evaluated. In this paper, the result of applying the proposed system showed a recognition rate of 98.7% in the speech recognition.

• Journal of The Korean Association For Science Education
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• v.15 no.3
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• pp.284-290
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• 1995

This study examined the effects of the learning cycle model by learner's characteristics such as I.Q., cognitive levels, inquiry skins, cognitive style, activity, reflectiveness. To see the effects of the learning cycle model, nonequivalent control group pretest-posttest multiple treatment designs was used in the study. 99 middle school second-graders(female) were divided into two groups. One group was selected as the experimental group (n=50), the other served at the comparison group(n=49). During the eight-month period, the students in the experimental group were instructed according to the learning cycle model, while the students in the comparison group were instructed according to the traditional instruction methods. Achievement data from science achievement test were analyzed by an ANOVA technique. The results of the study are as follows : 1. Science knowledge achievement. For the lower level students of activity, the learning cycle model is superior to the traditional approaches in science knowledge achievement. 2. Science inquiry skills. For the upper level students of I.Q., cognitive levels, inquiry skills, cognitive style and reflectiveness, the learning cycle model is superior to the traditional approaches in science inquiry skills. 3. Attitudes toward science. For the lower level students of I.Q., cognitive levels, inquiry skills, cognitive style, activity and reflectiveness, the learning cycle model is superior to the traditional approaches in attitudes toward science.

• Journal of The Korean Digital Architecture Interior Association
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• v.12 no.3
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• pp.67-79
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• 2012

The purpose of this study is to suggest the interior architecture design studio through the pedagogical method of educational technology for college students who lack self-directed learning. The pedagogical method has been organized to make a student-centered class based on the operation of existing architectural design studios. This teaching and learning method emphasizes the role of teachers as facilitators to help students lacking in self-directed learning in the design process, the BIM visualization to give students an expression of design project and the critics to give students an experience of working circumstances. The results of this study can be summarized as follows. First, This pedagogical model can improve the self-directed learning of students, accomplish the design process well through teamwork, and provide problem based learning (PBL) to settle obstacles that come up during the project. Second, through this model, students can improve their field design capacity by instructor, design feedback and criticism. Finally, This model can suggest new pedagogical methods for interior architectural design studios and management of student-centered studios.