• International journal of advanced smart convergence
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• v.8 no.2
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• pp.170-182
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• 2019

In this paper, we use scratch to design and develop non-formal learning experiences that are linked with contents of secondary science textbook to educational programs. The goal of this paper is to develop a convenient and interesting program for non-formal learning in a learning environment using various smart device. Theoretical approaches to mobile education, such as smartphones, and smart education support policies continue to lead to various research efforts. Although most of the smart education systems developed for students who have difficulty in academic performance are utilized, they are limited to general students. To solve the problem, the learning environment was implanted by combining the scratch, which is an educational programming that can be easily written. The science education program proposed in this paper shows the result of process of programming using ICT device using scratch programming. In the evaluation stage, we were able to display the creations and evaluate each other, so that we could refine them more by sharing the completed ideas.

• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.1048-1057
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• 2013

This study aims to evaluate the effect on the academic achievement and satisfaction of the learner's prior knowledge level and segmenting time of video lectures in an learning environment using smartphones. Depending on the level of prior knowledge, learners were divided into two groups of the upper 35% and the lower 35%. Each group was offered video lectures by a 5-min, 10-min, 15-min, and 20-min length. As a result, a high level of prior knowledge only had a positive effect on the academic achievement. With respect to the segmentation time of video lectures, 10-min, 15-min lectures were effective to the academic achievement and 15-min, 20-min lectures influenced positively the learners' satisfaction. Moreover, the interaction between the level of learners' prior knowledge and the segmentation time of video lectures only had an impact on their academic achievement. The results of the simple effect analysis conducted to examine the effect of interaction carefully show that 15-min, 20-min video lectures are more effective for the upper 35% in prior knowledge and 10-min ones are better for its' lower 35%. In a nutshell, these findings suggest that the high-prior knowledge groups could be provided with a longer video lectures, and furthermore, 5-min video lectures are not adequate in a mobile learning environment with smartphones.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.560-561
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• 2018

The spread of smartphones is increasing the proportion of mobile shopping in the online shopping market. Most mobile shopping services are delivered through applications. However, personalization services are very important for user data collection and analysis. Therefore, in this paper, we implemented the product barcode recognition function and machine learning-based product image recognition function using smartphones camera to collect user data in mobile shopping environment. The implemented function and push notification services enabled the collection and analysis of user data and personalization services for online shopping platform applications.

• Journal of Internet Computing and Services
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• v.12 no.4
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• pp.135-143
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• 2011

The smartphone allows learners to be involved in learning environments in which students actively study from anywhere and at anytime. Because learners can keep engaged in the environment where they can access to the internet, they can efficiently study in transit using various features and functions of smartphone. Smart learning is a unique learning based on mobility and functions of mobile digital devices including searching and sharing information and using various applications. For the effective use of smartphones in e-learning systems, the contents and learning management systems should be designed to meet effective teaching and learning principles, such as interactivity and collaborations. In smart learning, learning contents for effective learning need to be integrated with typical functions of smartphones and to develop small pieces of learning contents according to learning topics. In the case of learning management systems, it should reflect understanding of learners' environment using a PA agent program and provide personalized learning services.

• Journal of the Korean Society for Library and Information Science
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• v.54 no.4
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• pp.53-82
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• 2020

With the increase in smartphone ownership and Internet usage using smartphones, the information environment is shifting from the existing PC to the smart mobile. The current undergraduate students are called Generation Z who prefer smartphones to PCs and video contents to texts. This study attempted to understand their usage behaviors of electronic resources in an academic library in a smart mobile environment. This study conducted a usage statistics analysis with 61,433 usage records of e-books, audiobooks, and e-learning contents and 1,595 records of users in the A academic library during 3 years from 2016 to 2018. The scope of the data includes the date of use, the subject, the year of publication, the channel of use, and each user's gender, affiliation, status, admission date, and graduation date. This study investigated not only the general characteristics of electronic resource use, but also the usage behaviors according to the user's demographic characteristics. Based on the findings, this study suggested practical service plans that are applicable in the near future and reflect changing circumstances.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.455-457
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• 2021

App is used on mobile devices such as smartphones and also has malicious code, which can be divided into normal and malicious depending on the presence or absence of hacking codes. Because there are many kind of malware, it is difficult to detect directly, we propose a method to detect malicious app using AI. Most of the existing methods are to detect malicious app by extracting features from malicious app. However, the number of types have increased exponentially, making it impossible to detect malicious code. Therefore, we would like to propose two more methods besides detecting malicious app by extracting features from most existing malicious app. The first method is to learn normal app to extract normal's features, as opposed to the existing method of learning malicious app and find abnormalities (malicious app). The second one is an 'ensemble technique' that combines the existing method with the first proposal. These two methods need to be studied so that they can be used in future mobile environment.

• 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.

• Smart Media Journal
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• v.12 no.3
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• pp.49-59
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• 2023

Optical Camera Communication (OCC), known as the next-generation wireless communication technology, is currently under extensive research. The performance of OCC technology is affected by the communication environment, and various strategies are being studied to improve it. Among them, the most prominent method is applying convolutional neural networks (CNN) to the receiver of OCC using deep learning technology. However, in most studies, CNN is simply used to detect the transmitter. In this paper, we experiment with applying the convolutional neural network not only for transmitter detection but also for the Rx demodulation system. We hypothesize that, since the data images of the OCC system are relatively simple to classify compared to other image datasets, high accuracy results will appear in most CNN models. To prove this hypothesis, we designed and implemented an OCC system to collect data and applied it to 12 different CNN models for experimentation. The experimental results showed that not only high-performance CNN models with many parameters but also lightweight CNN models achieved an accuracy of over 99%. Through this, we confirmed the feasibility of applying the OCC system in real-time on mobile devices such as smartphones.