• Research in Mathematical Education
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• v.25 no.4
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• pp.267-284
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• 2022

In response to the current educational situation of students' heavy workload, the author constructs the precision-targeted classroom based on Precision Teaching (PT), Network Pharmacology, and Treatment Based on Syndrome Differentiation. The precision-targeted classroom can solve the current problems of PT and the phenomenon of the heavy academic burden on students, achieve the reduction of the burden and increase the efficiency of education. The precision-targeted classroom includes five key points: targeted goals, childlike thinking, precise intervention, intelligent homework, and stereoscopic evaluation, and the implementation process of the precision-targeted classroom is built from three aspects: before, during and after class. In addition, the author applied it to the actual mathematics classroom to test its teaching effect, and the experimental results showed that: the precision-targeted classroom significantly improved students' academic performance and thinking level; considerably improved students' classroom learning status, and facilitated teaching personalization and realized homework quantity control and quality improvement.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.134-134
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• 2021

Precipitation is a crucial component of water cycle and play a key role in hydrological processes. Traditionally, gauge-based precipitation is the main method to achieve high accuracy of rainfall estimation, but its distribution is sparsely in mountainous areas. Recently, satellite-based precipitation products (SPPs) provide grid-based precipitation with spatio-temporal variability, but SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution quite coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation using Automatic weather system (AWS) in Korea and multiple SPPs(i.e. CHIRPSv2, CMORPH, GSMaP, TRMMv7) during the period of 2003-2017. And this study used a machine learning based Random Forest (RF) model for generating new merging precipitation. In addition, several statistical linear merging methods are used to compare with the results of the RF model. In order to investigate the efficiency of RF, observed data from 64 observed Automated Synoptic Observation System (ASOS) were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the random forest model showed higher accuracy than each satellite rainfall product and spatio-temporal variability was better reflected than other statistical merging methods. Therefore, a random forest-based ensemble satellite precipitation product can be efficiently used for hydrological simulations in ungauged basins such as the Mekong River.

• Educational Technology International
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• v.14 no.1
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• pp.75-108
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• 2013

The purpose of this study is to find a way to improve digital textbooks for self-regulated learning by applying cognitive scaffolding designs to an elementary math digital textbook and examining the effectiveness of the system. Hence this study was conducted in two steps. First, a framework for scaffolding design was devised by examining the problems and difficulties students encounter when using a mathematics digital textbook. Second, after the digital textbook was revised by applying the scaffolding design frameworks, the effectiveness of the scaffolding framework was examined by comparing students' achievement levels in an experimental group and that of students in a control group. Seventy fifth-graders participated in this study. Students were divided into two groups: an experimental group and a control group. The students in the experimental group studied with the revised version of the digital textbook and the students in the control group studied with the original version of the digital textbook. The students received a pretest before the experiment. After the experiment, they took an achievement test and completed a usability questionnaire. The data were analyzed by ANCOVA with the SPSS Windows version. The results revealed that the students who used the revised program (to which design strategies for scaffolding were applied) showed higher levels of achievement than those who used the original version. In addition, students in the experimental group generally showed higher scores on the usability survey, which consisted of four sub-categories such as 'effectiveness', 'efficiency', 'satisfaction', and 'learnability'. There was a statistically significant effect on 'efficiency'. These results implied that scaffolding strategies were effective for mathematics learning through the use of an elementary digital textbook.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.79-91
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• 2024

Large warehouses are building automation systems to increase efficiency. However, small warehouses, military bases, and local stores are unable to introduce automated logistics systems due to lack of space and budget, and are handling tasks manually, failing to improve efficiency. To solve this problem, this study designed small loading and unloading equipment that can be mounted on transportation vehicles. The equipment can be controlled remotely and is automatically controlled from the point where pallets loaded with cargo are visible using real-time video from an attached camera. Cargo recognition and control command generation for automatic control are achieved through a newly designed deep learning model. This model is designed to be optimized for loading and unloading equipment and mission environments based on the YOLOv3 structure. The trained model recognized 10 types of palettes with different shapes and colors with an average accuracy of 100% and estimated the state with an accuracy of 99.47%. In addition, control commands were created to insert forks into pallets without failure in 14 scenarios assuming actual loading and unloading situations.

• Journal of Korea Multimedia Society
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• v.23 no.9
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• pp.1139-1146
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• 2020

Recently, as e-learning technology advances, interaction and data exchange between lecturers and learners have become very important. In addition, accuracy of data delivery and efficiency of system implementation should be ensured. Considering these aspects, SignalR is the most suitable communication method for constructing an audience response system in e-learning. Existing audience response systems require separate wireless devices and have problems with system compatibility. SignalR, on the other hand, is capable of operating in all environments including PC programs, web, Android, and iOS, and has an advantage of being easy to develop applications. As such, SignalR is widely used in chatting functions for small scale, real-time communication system, and it has never been used to implement an audience response system. Thus, for the first time in this paper, an audience response system using SignalR was proposed and an experiment was conducted on whether it was applicable at the e-learning education field. Therefore, from the results fo an experiment, a variety of e-learning environments can be built through the audience response system using SignalR proposed in this paper.

• Journal of the Korean School Mathematics Society
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• v.10 no.3
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• pp.323-340
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• 2007

This study set out to analyze the learning types that most students were engaged in after school, to review the efficiency of private education through academic institutions or tutoring, and to examine the directions in the after-school learning in math under the current system. It also aimed to analyze the impacts of those after-school learning activities on school classes and to suggest some plans to help public education get back on the track. In the study the after-school learning activities in the math subject were categorized into taking classes at academic institutions, tutoring, and autonomous learning. The grades of the subject students were compared and analyzed for three semesters to find the directions right for the school classes.

• International Journal of Contents
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• v.15 no.3
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• pp.32-38
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• 2019

We usually suffer from difficulties in treating or managing Big Data generated from various digital media and/or sensors using traditional mining techniques. Additionally, there are many problems relative to the lack of memory and the burden of the learning curve, etc. in an increasing capacity of large volumes of text when new data are continuously accumulated because we ineffectively analyze total data including data previously analyzed and collected. In this paper, we propose a general-purpose classifier and its structure to solve these problems. We depart from the current feature-reduction methods and introduce a new scheme that only adopts changed elements when new features are partially accumulated in this free-style learning environment. The incremental learning module built from a gradually progressive formation learns only changed parts of data without any re-processing of current accumulations while traditional methods re-learn total data for every adding or changing of data. Additionally, users can freely merge new data with previous data throughout the resource management procedure whenever re-learning is needed. At the end of this paper, we confirm a good performance of this method in data processing based on the Big Data environment throughout an analysis because of its learning efficiency. Also, comparing this algorithm with those of NB and SVM, we can achieve an accuracy of approximately 95% in all three models. We expect that our method will be a viable substitute for high performance and accuracy relative to large computing systems for Big Data analysis using a PC cluster environment.

• The Journal of the Korea Contents Association
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• v.20 no.12
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• pp.49-57
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• 2020

This study predicts the ratio of added value, which represents the competitiveness of export industries in South Korea, using various machine learning techniques. To enhance the accuracy and stability of prediction, forecast combination technique was applied to predicted values of machine learning techniques. In particular, this study improved the efficiency of the prediction process by selecting key variables out of many variables using recursive feature elimination method and applying them to machine learning techniques. As a result, it was found that the predicted value by the forecast combination method was closer to the actual value than the predicted values of the machine learning techniques. In addition, the forecast combination method showed stable prediction results unlike volatile predicted values by machine learning techniques.

• Educational Technology International
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• v.11 no.1
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• pp.47-68
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• 2010

The purpose of this study was to analyze majors trends and issues of e-Learning curriculum for human resource development in the corporate context. The e-Learning curriculum was chosen as the subject of research consists of 2,710 lectures that were given from 2007 to July 2009 for the recent three years by providing at Ministry of Labor and Korea Research Institute for Vocational Education & Training. In order to investigate trends and issues, it was employed theme analysis which is one of the types of document analysis that approach a qualitative research methodology. As a result of this research, 7 major trends and issues in e-Learning curriculum for HRD in the field of corporate education were drawn; ① Strengthening expertise through learning of job related professional knowledge, ② Cultivation of common & essential knowledge for a job to increase work performance efficiency ③ Organizational management strategy for improving performance, ④ Organizational management and operational strategy for actively responding to environmental changes, ⑤ Leadership as a strategy for cultivating core personnel and field-centered practical leadership. ⑥ Creating a happy workplace through the work-life balance, ⑦ Strengthening global communication skill. Based on these analysis, practicals and theoretical implications of e-Learning professionals and HR researchers for HRD were suggested.

• Journal of information and communication convergence engineering
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• v.21 no.4
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• pp.300-305
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• 2023

Audience reactions in response to remote virtual performances must be compressed before being transmitted to the server. The server, which aggregates these data for group insights, requires a distribution code for the transfer. Recently, distributed learning algorithms such as federated learning have gained attention as alternatives that satisfy both the information security and efficiency requirements. In distributed learning, no individual user has access to complete information, and the objective is to achieve a learning effect similar to that achieved with the entire information. It is therefore important to distribute interdependent information among users and subsequently aggregate this information following training. In this paper, we present a new extension technique for minimal code that allows a new minimal code with a different length and Hamming weight to be generated through the product of any vector and a given minimal code. Thus, the proposed technique can generate minimal codes with previously unknown parameters. We also present a scenario wherein these combined methods can be applied.