• Cross-Cultural Studies
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• v.25
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• pp.549-596
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• 2011

This paper aims to introduce some useful multimedia freewares, and also support Chinese instructor with discussing how to operate them for instructional design of multimedia language learning class. For this aims, this paper consists of three parts: First, instructional design. This part is focused to what kind of instructional model to be based on, for example, Dick & Carey model, ADDIE model, ASSURE model etc. This part introduces these models, and modifies ADDIE and ASSURE model to D.D.A.I.E.S and S.S.A.U.R.E.S as 'A(nalysis)' in these model may apply to the next 'D(evelopment)' on ADDIE, 'S(elect Methods, Media and Materials)' on ASSURE in the practical Chinese class. Second, Programme: What to use. This part is focused to what kind of free software we can use. In the web site online, there are huge free softwares so we usually hesitate to select and also don't know how to operate even though selected one of them. This part, accordingly, introduces ten of useful freewares and compares each other in terms of usefulness for Chinese instructors. Third, Programme: How to use. It is of no use just to know what to use but not to know how to operate, so this part describes how to use freewares like a kind of manual in detail as far as possible. In conclusion, we hope more Chinese instructors to learn and use more useful freewares for designing the better multimedia Chinese class by this paper.

• Communications of Mathematical Education
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• v.29 no.3
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• pp.465-489
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• 2015

The purpose of this study is to suggest implications for the development and revision of future teaching materials for mathematically gifted students by using network text analysis of secondary mathematics materials. Subjects of the analysis were learning goals of 110 teaching materials in a gifted education center in science attached to a university from 2002 to 2014. In analysing the frequency of the texts that appeared in the learning goals, key words were selected. A co-occurrence matrix of the key words was established, and a basic information of network, centrality, centralization, component, and k-core were deducted. For the analysis, KrKwic, KrTitle, and NetMiner4.0 programs were used, respectively. The results of this study were as follows. First, there was a pivot of the network formed with core hubs including 'diversity', 'understanding' 'concept' 'method', 'application', 'connection' 'problem solving', 'basic', 'real life', and 'thinking ability' in the whole network from 2002 to 2014. In addition, knowledge aspects were well reflected in teaching materials based on the centralization analysis. Second, network text analysis based on the three periods of the Mater Plan for the promotion of gifted education was conducted. As a result, a network was built up with 'understanding', and there were strong ties among 'question', 'answer', and 'problem solving' regardless of the periods. On the contrary, the centrality analysis showed that 'communication', 'discovery', and 'proof' only appeared in the first, second, and third period of Master Plan, respectively. Therefore, the results of this study suggest that affective aspects and activities with high cognitive process should be accompanied, and learning goals' mannerism and ahistoricism be prevented in developing and revising teaching materials.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.14
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• pp.5607-5609
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• 2014

Introduction: Although continuing education is necessary for practicing nurses, it is very difficult to organize traditional classes because of large numbers of nurses and working shifts. Considering the increasing development of mobile electronic learning, we carried out a study to compare effects of the traditional face to face method with mobile learning delivered as text messages by cell phone. Materials and Methods: Sixty female nurses working in our hospital were randomly divided into class and short message service (SMS) groups. Lessons concerning breast cancer screening were prepared as 54 messages and sent in 17 days for the SMS group, while the class group participated in a class held by a university lecturer of breast and cancer surgery. Pre- and post-tests were undertaken for both groups at the same time; a retention test also was performed one month later. For statistical analysis, the paired T test and the independent sample T test were used with SPSS software version 16; p<0.05 was considered significant. Results: Mean age and mean work experience of participants in class and SMS groups was $35.8{\pm}7.2$, $9.8{\pm}6.7$, $35.4{\pm}7.3$, and $11.5{\pm}8.5$, respectively. There was a significant increase in mean score post-tests (compared with pretests) in both groups (p<0.05). Although a better improvement in scores of retention tests was demonstrated in the SMS group, the mean subtraction value of the post- and pretests as well as retention- and pretests showed no significant difference between the 2 groups (p=0.3 and p =0.2, respectively). Conclusions: Our study shows that teaching via SMS may probably replace traditional face to face teaching for continuing education in working nurses. Larger studies are suggested to confirm this.

• Journal of Digital Convergence
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• v.19 no.4
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• pp.25-31
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• 2021

Moving toward an aged society, traffic accidents involving elderly drivers have also attracted broader public attention. A rapid increase of senior involvement in crashes calls for developing appropriate crash-severity prediction models specific to senior drivers. In that regard, this study leverages machine learning (ML) algorithms so as to predict the severity of vehicle-pedestrian collisions induced by elderly drivers. Specifically, four ML algorithms (i.e., Logistic model, K-nearest Neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM)) have been developed and compared. Our results show that Logistic model and SVM have outperformed their rivals in terms of the overall prediction accuracy, while precision measure exhibits in favor of RF. We also clarify that driver education and technology development would be effective countermeasures against severity risks of senior driver-induced collisions. These allow us to support informed decision making for policymakers to enhance public safety.

• Communications of Mathematical Education
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• v.34 no.4
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• pp.421-437
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• 2020

Problem/Project based learning(PBL) is a student-centered teaching method in which students collaboratively solve problems and reflect their experiences. According to the results of PBL study and the experiences of the authors in PBL instruction, this paper introduced the necessities, output and significance of learning process PBL evaluation method and sums up our PBL evaluation process. The issue of appropriate and fair evaluation has been raised in untact (non-contact) university mathematics education due to the novel coronavirus (COVID-19) of the year 2020. To this end, when we had the course on for the summer semester held at S University in the summer of 2020. To ensure the fairness in evaluation and to improve the quality of our college math education, the PBL evaluation method was fully adapted. As a result, most of the students who took the lecture have learned a wide range of related knowledge without a single exception, and students agreed it is an ideal, fair, rational, and effective evaluation method applicable to other online courses in the era of untact education. This case was summarized in detail and introduced in this paper.

• Journal of Korea Entertainment Industry Association
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• v.15 no.4
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• pp.279-286
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• 2021

In this study, we looked at the appearance of Edutech, which is being put into the educational field after Corona 19, with the advent of the 4th industrial revolution. In the era of the 4th industrial revolution, the infrastructure, data, and service of Smart Stick that actively utilized ICT became the main pillars of smart education. In particular, smart education is being implemented through e-learning, smart learning, and edutech, and on this basis, it has become possible through the expansion and use of the Internet and computers, the dissemination of smart devices, and a software foundation using big data. Based on this, it was confirmed that Edutech is being implemented through the establishment of a quarantine safety net, a learning safety net, and a care safety net for individual learners and safe life based on artificial intelligence. Lastly, in order for edutech education using big data to become a discourse for everyone, it is necessary to consider artificial intelligence and ethics in the use and application of edutech.

• Genomics & Informatics
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• v.17 no.2
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• pp.19.1-19.10
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• 2019

In this paper, we investigate cross-platform interoperability for natural language processing (NLP) and, in particular, annotation of textual resources, with an eye toward identifying the design elements of annotation models and processes that are particularly problematic for, or amenable to, enabling seamless communication across different platforms. The study is conducted in the context of a specific annotation methodology, namely machine-assisted interactive annotation (also known as human-in-the-loop annotation). This methodology requires the ability to freely combine resources from different document repositories, access a wide array of NLP tools that automatically annotate corpora for various linguistic phenomena, and use a sophisticated annotation editor that enables interactive manual annotation coupled with on-the-fly machine learning. We consider three independently developed platforms, each of which utilizes a different model for representing annotations over text, and each of which performs a different role in the process.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.154-154
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• 2019

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.53.4-53.4
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• 2019

We reconstruct the underlying dark matter (DM) density distribution of the local universe within 20Mpc/h cubic box by using the galaxy position and peculiar velocity. About 1,000 subboxes in the Illustris-TNG cosmological simulation are used to train the relation between DM density distribution and galaxy properties by using UNet-like convolutional neural network (CNN). The estimated DM density distributions have a good agreement with their truth values in terms of pixel-to-pixel correlation, the probability distribution of DM density, and matter power spectrum. We apply the trained CNN architecture to the galaxy properties from the Cosmicflows-3 catalogue to reconstruct the DM density distribution of the local universe. The reconstructed DM density distribution can be used to understand the evolution and fate of our local environment.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.1-23
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• 2018

From the 21st century, various high-quality services have come up with the growth of the internet or 'Information and Communication Technologies'. Especially, the scale of E-commerce industry in which Amazon and E-bay are standing out is exploding in a large way. As E-commerce grows, Customers could get what they want to buy easily while comparing various products because more products have been registered at online shopping malls. However, a problem has arisen with the growth of E-commerce. As too many products have been registered, it has become difficult for customers to search what they really need in the flood of products. When customers search for desired products with a generalized keyword, too many products have come out as a result. On the contrary, few products have been searched if customers type in details of products because concrete product-attributes have been registered rarely. In this situation, recognizing texts in images automatically with a machine can be a solution. Because bulk of product details are written in catalogs as image format, most of product information are not searched with text inputs in the current text-based searching system. It means if information in images can be converted to text format, customers can search products with product-details, which make them shop more conveniently. There are various existing OCR(Optical Character Recognition) programs which can recognize texts in images. But existing OCR programs are hard to be applied to catalog because they have problems in recognizing texts in certain circumstances, like texts are not big enough or fonts are not consistent. Therefore, this research suggests the way to recognize keywords in catalog with the Deep Learning algorithm which is state of the art in image-recognition area from 2010s. Single Shot Multibox Detector(SSD), which is a credited model for object-detection performance, can be used with structures re-designed to take into account the difference of text from object. But there is an issue that SSD model needs a lot of labeled-train data to be trained, because of the characteristic of deep learning algorithms, that it should be trained by supervised-learning. To collect data, we can try labelling location and classification information to texts in catalog manually. But if data are collected manually, many problems would come up. Some keywords would be missed because human can make mistakes while labelling train data. And it becomes too time-consuming to collect train data considering the scale of data needed or costly if a lot of workers are hired to shorten the time. Furthermore, if some specific keywords are needed to be trained, searching images that have the words would be difficult, as well. To solve the data issue, this research developed a program which create train data automatically. This program can make images which have various keywords and pictures like catalog and save location-information of keywords at the same time. With this program, not only data can be collected efficiently, but also the performance of SSD model becomes better. The SSD model recorded 81.99% of recognition rate with 20,000 data created by the program. Moreover, this research had an efficiency test of SSD model according to data differences to analyze what feature of data exert influence upon the performance of recognizing texts in images. As a result, it is figured out that the number of labeled keywords, the addition of overlapped keyword label, the existence of keywords that is not labeled, the spaces among keywords and the differences of background images are related to the performance of SSD model. This test can lead performance improvement of SSD model or other text-recognizing machine based on deep learning algorithm with high-quality data. SSD model which is re-designed to recognize texts in images and the program developed for creating train data are expected to contribute to improvement of searching system in E-commerce. Suppliers can put less time to register keywords for products and customers can search products with product-details which is written on the catalog.