• Educational Technology International
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• v.23 no.2
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• pp.129-155
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• 2022

This study sought to design and develop an interactive video player (IVP) capable of promoting student engagement through the use of online video content. We designed features built upon interactive, constructive, active, passive (ICAP), and crowd learning frameworks. In the development stage of this study, we integrated numerous interactive features into the IVP intended to help learners shift from passive to interactive learning activities. We then explored the effectiveness and usability of the developed IVP by conducting an experiment in which we evaluated students' exam scores after using either our IVP or a conventional video player. There were 158 college students who participated in the study; 76 students in the treatment group used the IVP and 82 students in the control group used a conventional video player. Results indicate that the participants in the experiment group demonstrated better achievement than the participants in the control group. We further discuss the implications of this study based on an additional survey that was administered to disclose how usable the participants perceived the IVP to be.

• Annual Conference of KIPS
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• 2020.05a
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• pp.228-230
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• 2020

The emergence of collaborative learning to the public is to tackle the user's privacy issue in centralized learning by bringing the AI models to the data source or client device for training Collaborative learning employs computing and storage resources on the client's device. Thus, it is privacy preserved by design. In harmony, blockchain is also prominent since it does not require an intermediary to process a transaction. However, these approaches are not yet fully ripe to be implemented in the real world, especially for the complex system (several challenges need to be addressed). In this work, we present the performance of collaborative learning and potential use case of blockchain. Further, we discuss privacy issues in the system.

• International Journal of Computer Science & Network Security
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• v.24 no.10
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• pp.81-90
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• 2024

Lung cancer is a complex and frightening disease that typically results in death in both men and women. Therefore, it is more crucial to thoroughly and swiftly evaluate the malignant nodules. Recent years have seen the development of numerous strategies for diagnosing lung cancer, most of which use CT imaging. These techniques include supervisory and non-supervisory procedures. This study revealed that computed tomography scans are more suitable for obtaining reliable results. Lung cancer cannot be accurately predicted using unsupervised approaches. As a result, supervisory techniques are crucial in lung cancer prediction. Convolutional neural networks (CNNs) based on deep learning techniques has been used in this paper. Convolutional neural networks (CNN)-based deep learning procedures have produced results that are more precise than those produced by traditional machine learning procedures. A number of statistical measures, including accuracy, precision, and f1, have been computed.

• Journal of Information Processing Systems
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• v.18 no.2
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• pp.197-207
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• 2022

The applications and user interfaces (UIs) of smart mobile devices are constantly diversifying. For example, deep learning can be an innovative solution to classify widgets in screen images for increasing convenience. To this end, the present research leverages captured images and the ReDraw dataset to write deep learning datasets for image classification purposes. First, as the validation for datasets using ResNet50 and EfficientNet, the experiments show that the dataset composed in this study is helpful for classification according to a widget's functionality. An implementation for widget detection and classification on RetinaNet and EfficientNet is then executed. Finally, the research suggests the Widg-C and Widg-D datasets-a deep learning dataset for identifying the widgets of smart devices-and implementing them for use with representative convolutional neural network models.

• Journal of Digital Convergence
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• v.16 no.3
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• pp.27-35
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• 2018

The purpose of this study is to design smart learning based extracurricular program of the university and to analyze the effect of core competencies. This program was formed by 10 teams with S and K University in Seoul, and run a daily camp format to use smart devices and apps. As a result, the participating students, communication skill, self-directed and creativity increased significantly. In addition, the educational effects of this program was positive changes in the areas of 'communication', 'self-directed', 'cooperative learning', 'problem solving' in the focus group interview. And the students responded that the use of smart devices and apps help to immerse in the program and increase their interest. The purpose of this study is to suggest new models and implications for the extracurricular program. In the future, we hope to develop the various smart learning based extracurricular programs for enhancing the competencies.

• Research in Mathematical Education
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• v.9 no.1 s.21
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• pp.25-45
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• 2005

Background Phenomenography suggests that more variation is associated with wider ways of experiencing phenomena. In the discipline of mathematics, broadening the 'lived space' of mathematics learning might enhance students' ability to solve mathematics problems Aims The aim of the present study is to: 1. enhance secondary school students' capabilities for dealing with mathematical problems; and 2. examine if students' conception of mathematics can thereby be broadened. Sample 410 Secondary 1 students from ten schools participated in the study and the reference group consisted of 275 Secondary 1 students. Methods The students were provided with non-routine problems in their normal mathematics classes for one academic year. Their attitudes toward mathematics, their conceptions of mathematics, and their problem-solving performance were measured both at the beginning and at the end of the year. Results and conclusions Hierarchical regression analyses revealed that the problem-solving performance of students receiving non-routine problems improved more than that of other students, but the effect depended on the level of use of the non-routine problems and the academic standards of the students. Thus, use of non-routine mathematical problems that appropriately fits students' ability levels can induce changes in their lived space of mathematics learning and broaden their conceptions of mathematics and of mathematics learning.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.797-802
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• 2020

Over the past few years, smart devices, including smartphones, have been continuously required by users based on portability. The performance is improving. Ubiquitous computing environment and sensor network are also improved. Due to various network connection technologies, mobile terminals are widely used. Smart terminals need technology to make energy monitoring more detailed for more stable operation during use. The smart terminal which is light in small size generates the power shortage problem due to the various multimedia task among the terminal operation. Various estimation hardwares have been developed to prevent such situation in advance and to operate stable terminals. However, the method and performance of estimating the remaining amount are not relatively good. In this paper, we propose a method for estimating the remaining amount of smart terminals. The Capacity Estimation of lithium ion cells for stable operation was estimated based on machine learning. Learning the characteristics of lithium ion cells in use, not the existing hardware estimation method, through a map learning algorithm using machine learning technique The optimized results are estimated and applied.

• The Korean Journal of Applied Statistics
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• v.34 no.2
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• pp.167-175
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• 2021

Semi-supervised learning uses both labeled data and unlabeled data. Recently consistency regularization is very popular in semi-supervised learning. Unsupervised data augmentation (UDA) that uses unlabeled data augmentation is also based on the consistency regularization. The Kullback-Leibler divergence is used for the loss of unlabeled data and cross-entropy for the loss of labeled data through UDA learning. UDA uses techniques such as training signal annealing (TSA) and confidence-based masking to promote performance. In this study, we propose to use Jensen-Shannon divergence instead of Kullback-Leibler divergence, reverse-TSA and not to use confidence-based masking for performance improvement. Through experiment, we show that the proposed technique yields better performance than those of UDA.

• Journal of Creative Information Culture
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• v.4 no.2
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• pp.95-101
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• 2018

As a result of introducing robot assisted learning which utilizes social robots or telepresence robots in language learning or special education, research on technology acceptance model for robot-assisted learning is also being conducted. The unified theory of acceptance and use of technology (UTAUT) model of intelligent robot has been studied, but of tele-operated robot is insufficient. The purpose of this paper is to estimate the UTAUT model by pre-service teachers who experienced telepresence robot-assisted learning that can be done in future school. It is found that the estimated UTAUT model consists of more concise factors than social robots, and the importance of perceived enjoyment is higher. In other words, the pre-service teachers showed significant acceptance of tele-operated robots with enhanced enjoyment composed of its mobility, communication, and touchable appearance of the face and body.

• Educational Technology International
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• v.16 no.2
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• pp.231-247
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• 2015

The purpose of this study was to identify the factors of ICT literacy in the primary school students in South Korea and to examine the gender and city size difference on the factor of ICT literacy. To accomplish this goal, we have analyzed the data of Korea Youth Competency Measurement and International Comparative Study I: ICCS 2016 which is nationally collected from the primary school students, currently on the 5 ~ 6th grades in South Korea. 1,188 samples were used in the study excluding missing samples. The participants were 584 5^th grad and 604 6^th grad students, 620 males (52.2%) and 568 females (47.8%). The mean age was 13.49 years (SD=.52). The result of the study reveals the four factors of ICT literacy through cross-validating exploratory factor analysis and confirmative factor analysis; pleasure of using ICT, perceived usefulness of using ICT, learning ability with using ICT, and operating ability of ICT. This study found that the leaner differ in gender on learning ability with using ICT and pleasure of using ICT. The female students were significantly larger than male students on learning ability with using ICT. However, the male students were significantly larger than male students on pleasure of using ICT. This study found that the leaner differ in city size on the factors of ICT literacy excluding pleasure of using ICT. The students living in the big size city were significantly larger than the students living in the middle and small. That is, over all, female students were more learning with ICT, male students were more interesting about ICT, and the students living in the big size city were more ICT use for learning. Based on the results, some strategies were suggested on the proper use of the factors of ICT in education.