• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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• pp.125-133
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• 2017

Recently, as the programming education has become essential in school, discussion of how to teach programming has been important. This study performed a meta-analysis of the effect size depending on the teaching and learning method for the programming education. 78 research data selected from 45 papers were analyzed from cognitive and affective aspects according to dependent variables. The analysis from the cognitive aspect showed that there was no statistically significant difference in the effect size depending on whether or not the teaching and learning method was specified in the research paper. Meta-analysis of the research data where the teaching and learning method was designated displayed significances in CPS, PBL and Storytelling. Unlike the cognitive aspect, the analysis from the affective aspect showed that the effect size of the research data without the specified teaching and learning method was larger than those with specified teaching and learning method with a statistical significance. Meta-analysis of the data according to the teaching and learning method displayed no statistical significance. Based upon these research results, this study suggested implications for the effective programming education.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.711-714
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• 2024

In the dynamic landscape of modern machine learning, Federated Learning (FL) has emerged as a compelling paradigm designed to enhance privacy by enabling participants to collaboratively train models without sharing their private data. Specifically, Distillation-based Federated Learning, like Federated Learning with Model Distillation (FedMD), Federated Gradient Encryption and Model Sharing (FedGEMS), and Differentially Secure Federated Learning (DS-FL), has arisen as a novel approach aimed at addressing Non-IID data challenges by leveraging Federated Learning. These methods refine the standard FL framework by distilling insights from public dataset predictions, securing data transmissions through gradient encryption, and applying differential privacy to mask individual contributions. Despite these innovations, our survey identifies persistent vulnerabilities, particularly concerning the susceptibility to logit inversion attacks where malicious actors could reconstruct private data from shared public predictions. This exploration reveals that even advanced Distillation-based Federated Learning systems harbor significant privacy risks, challenging the prevailing assumptions about their security and underscoring the need for continued advancements in secure Federated Learning methodologies.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.37-45
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• 2020

The development process of deep learning is an iterative task that requires a lot of manual work. Among the steps in the development process, pre-processing of learning data is a very costly task, and is a step that significantly affects the learning results. In the early days of AI's algorithm research, learning data in the form of public DB provided mainly by data scientists were used. The learning data collected in the real environment is mostly the operational data of the sensors and inevitably contains various noises. Accordingly, various data cleaning frameworks and methods for removing noises have been studied. In this paper, we proposed a method for detecting and removing noises from time-series data, such as sensor data, that can occur in the IoT environment. In this method, the linear regression method is used so that the system repeatedly finds noises and provides data that can replace them to clean the learning data. In order to verify the effectiveness of the proposed method, a simulation method was proposed, and a method of determining factors for obtaining optimal cleaning results was proposed.

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.1
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• pp.75-84
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• 2011

The e-learning contents offered in the current educational system does not appropriately reflect the needs of actual users in the planning and development phases. Considering this problem, this study sets the following four topics as its research: Stability of e-learning; Obstacles of the applications of e-learning; e-learning contents that users wants to be offered besides lectures; and methods of e-learning, and based on these goals, it aims at determining the 'needs of the users for the promotion of e-learning. As the target of the study, a survey was conducted with 200 students who have experienced taking e-learning classes in four universities located in Eastern Seoul, which have introduced an e-learning system. The data collected from the survey went through data coding and data cleaning processes and were analyzed by year, major, and department using SAS 9 statistics package program. The result of this study showed that developing and offering services of e-learning contents that are customized to students based on their majors and year can become an effective plan for promoting e-learning.

• Journal of Fisheries and Marine Sciences Education
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• v.26 no.1
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• pp.156-165
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• 2014

The purpose of this study was to find research trends of smart learning. For this, we identified the research's characteristics such as the subject or keyword of research, method, data collection, and statistical analysis method. The 2,865 articles published from 1995 to 2013 were gathered from five Korean academic journals related to smart learning. Among them, research keyword, areas, research method, data collection method, and statistical analysis method were analyzed on 596 papers. The findings of this study were as follows: (a) Smart learning papers such keyword likes u-learning, m-learning, and smart-learning were emerging after 2006. Smart learning papers with ICT related topics were highly increased after 2000, but they were decreased after 2006. Smart learning papers with e-learning related keywords were steadily increased after 2000 through 2013. (b) The research field of deign had the highest portion in smart learning research, but managing had the lowest portion. (c) Development was mainly used as a research method. Both questionnaire and experiment were mainly used for collecting data methods. T-test and frequency analysis were mainly used as statistical analysis methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.5
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• pp.89-95
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• 2020

As the e-learning market expands, interest in customized education for learners based on artificial intelligence is increasing. Customized education for learners requires essential components such as a large amount of data and learning contents for learner analysis, and it requires time and cost efforts to collect such data. In this paper, to enable efficient learner-tailored learning even in small learning groups, unstructured learner data was analyzed using python modules, and a learning algorithm was presented based on this. Through the analysis of the unstructured learning data presented in this paper, it is possible to quantify and measure the unstructured data related to learning, and the accuracy of more than 80% was confirmed when analyzing keywords for providing customized education for learners.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.69-75
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• 2021

Recently, deep learning, which is the most used in the field of artificial intelligence, has a structure that is gradually becoming larger and more complex. As the deep learning model grows, a large amount of data is required to learn it, but there are cases in which it is difficult to integrate and learn the data because the data is distributed among several owners and security issues. In that situation we conducted parallel learning for each users that own data and then studied how to integrate it. For this, distributed learning was performed for each owner assuming the security situation as V-environment and H-environment, and the results of distributed learning were integrated using Average, Max, and AbsMax. As a result of applying this to the mnist-fashion data, it was confirmed that there was no significant difference from the results obtained by integrating the data in the V-environment in terms of accuracy. In the H-environment, although there was a difference, meaningful results were obtained.

• The Journal of Korean Academic Society of Nursing Education
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• v.27 no.4
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• pp.381-391
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• 2021

Purpose: This study aimed to identify the influence of learning presence and self-directed learning ability on nursing students' learning satisfaction according to the online learning method. Methods: The participants of this study were 167 nursing students attending three universities in different cities. The data were collected from July 16 to July 23, 2021, via an online self-reported questionnaire. Using SPSS WIN 27.0, data were analyzed by descriptive statistics, Pearson's correlation coefficient and a multiple regression analysis. Results: The most effective online learning method experienced by nursing students was asynchronous online learning according to 58.2% of the respondents, while 30.3% of the respondents answered synchronous online learning. The main merit of asynchronous online learning was that it was possible to listen repeatedly (61.7%) to lectures, and the top advantage of synchronous online learning was that the location of the class was free (53.3%). In asynchronous online learning, the factors that significantly affected nursing students' learning satisfaction were cognitive presence (𝛽=.60, p<.001) and emotional presence (𝛽=.25, p<.001). These variables accounted for 56% of their learning satisfaction (F=54.12, p<.001). Similarly, cognitive presence (𝛽=.64, p<.001) and emotional presence (𝛽=.21, p=.001) in synchronous online learning, were the factors cited for significantly affecting learning satisfaction. The explanatory power was 62% (F=69.19, p<.001). Conclusions: In conclusion, it was found that cognitive and social presence from the learning presence factors in both asynchronous and synchronous online learning influence and enhance nursing students' learning satisfaction. Therefore, these results provide important data for future online class design in nursing education.

• Journal of Korean Academy of Nursing Administration
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• v.16 no.3
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• pp.267-275
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• 2010

Purpose: This study conducted to identify the effect of learning organization construction and learning orientation on organizational effectiveness among hospital nurses. Method: Data was collected from convenient sample of 296 nurses who worked for the major hospitals in Seoul, Gyeonggi-do and Gangwoen-do. The self-reported questionnaire was used to assess the general characteristics, the level of the learning organization construction, learning orientation and organizational effectiveness. The data were analyzed using descriptive statistics, pearson's correlation coefficient and multiple regression. Result: The mean score of learning organization construction was 3.61(${\pm}.32$), learning orientation got 3.26(${\pm}.39$), and organizational effectiveness obtained 3.38(${\pm}.42$). The learning organization construction affects of organizational effectiveness by 44.18% and learning orientation by 37.43%. Conclusion: This finding indicates that learning organization construction and learning orientation affects the nurses' organizational effectiveness in hospital.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.412-419
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• 2022

The amount of digital text data is growing exponentially, and many machine learning solutions are being used to monitor and manage this data. Artificial intelligence and machine learning are used in many areas of our daily lives, but the underlying processes and concepts are not easy for most people to understand. At a time when many experts are needed to run a machine learning solution, no-code machine learning tools are a good solution. No-code machine learning tools is a platform that enables machine learning functions to be performed without engineers or developers. The latest No-Code machine learning tools run in your browser, so you don't need to install any additional software, and the simple GUI interface makes them easy to use. Using these platforms can save you a lot of money and time because there is less skill and less code to write. No-Code machine learning tools make it easy to understand artificial intelligence and machine learning. In this paper, we examine No-Code machine learning tools and compare their features.