• Journal of the Korean earth science society
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• v.34 no.7
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• pp.727-737
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• 2013

The purpose of this study was to investigate the middle school students' perception and their learning satisfaction in SMART learning based science instruction. Three types of modules on the solar system and lunar phases unit at the middle school level were developed and lessons on each module were taught to 207 student participants. All participants were provided with tabletPC(iPad2) with iOS5 installed, and using astronomy app Solar Walk, mirroring function, QR code, and Google Presentation, the lessons were carried out both in classroom and at home. The instrument for assessing students' perception on the SMART learning-based instruction was developed based on 4 factors including Self-directed, Motivation, Adaptiveness, and Technology Embedded, with a Likert scale from 1-5 on 20 items. The learning satisfaction survey instrument was originally from Keller's work (1987), and its test items were adapted and modified. To reveal the perception and learning satisfaction about SMART learning-based science lessons, the participants were comparatively analyzed by gender and science achievement levels. Results indicated that male students showed positive perception for the SMART learning-based instruction. Group with higher science achievement scores showed more positive perception of the SMART learning-based instruction in terms of Self-directed and Motivation factor. Also, the learning satisfaction of male students was higher than female students and group with higher academic ability more satisfied with the SMART learning-based instruction than the low group. The results provide implications for future development of programs and help set a direction of increasing the use of a SMART learning-based science in school.

• Journal of applied mathematics & informatics
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• v.38 no.3_4
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• pp.255-260
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• 2020

Today most students have a smartphone. Students learning numerical analysis can practice coding using their smartphone in the classroom without going to the computer lab. The tool NAPHP[1] makes it easy to practice coding using PHP in an internet web browser without installing a coding app on your smartphone. This paper introduces a debugging tool for the NAPHP.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.1-17
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• 2012

Personalized smart devices such as smartphones and smart pads are widely used. Unlike traditional feature phones, theses smart devices allow users to choose a variety of functions, which support not only daily experiences but also business operations. Actually, there exist a huge number of applications accessible by smart device users in online and mobile application markets. Users can choose apps that fit their own tastes and needs, which is impossible for conventional phone users. With the increase in app demand, the tastes and needs of app users are becoming more diverse. To meet these requirements, numerous apps with diverse functions are being released on the market, which leads to fierce competition. Unlike offline markets, online markets have a limitation in that purchasing decisions should be made without experiencing the items. Therefore, online customers rely more on item-related information that can be seen on the item page in which online markets commonly provide details about each item. Customers can feel confident about the quality of an item through the online information and decide whether to purchase it. The same is true of online app markets. To win the sales competition against other apps that perform similar functions, app developers need to focus on writing app descriptions to attract the attention of customers. If we can measure the effect of app descriptions on sales without regard to the app's price and quality, app descriptions that facilitate the sale of apps can be identified. This study intends to provide such a quantitative result for app developers who want to promote the sales of their apps. For this purpose, we collected app details including the descriptions written in Korean from one of the largest app markets in Korea, and then extracted keywords from the descriptions. Next, the impact of the keywords on sales performance was measured through our econometric model. Through this analysis, we were able to analyze the impact of each keyword itself, apart from that of the design or quality. The keywords, comprised of the attribute and evaluation of each app, are extracted by a morpheme analyzer. Our model with the keywords as its input variables was established to analyze their impact on sales performance. A regression analysis was conducted for each category in which apps are included. This analysis was required because we found the keywords, which are emphasized in app descriptions, different category-by-category. The analysis conducted not only for free apps but also for paid apps showed which keywords have more impact on sales performance for each type of app. In the analysis of paid apps in the education category, keywords such as 'search+easy' and 'words+abundant' showed higher effectiveness. In the same category, free apps whose keywords emphasize the quality of apps showed higher sales performance. One interesting fact is that keywords describing not only the app but also the need for the app have asignificant impact. Language learning apps, regardless of whether they are sold free or paid, showed higher sales performance by including the keywords 'foreign language study+important'. This result shows that motivation for the purchase affected sales. While item reviews are widely researched in online markets, item descriptions are not very actively studied. In the case of the mobile app markets, newly introduced apps may not have many item reviews because of the low quantity sold. In such cases, item descriptions can be regarded more important when customers make a decision about purchasing items. This study is the first trial to quantitatively analyze the relationship between an item description and its impact on sales performance. The results show that our research framework successfully provides a list of the most effective sales key terms with the estimates of their effectiveness. Although this study is performed for a specified type of item (i.e., mobile apps), our model can be applied to almost all of the items traded in online markets.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.170-171
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• 2022

Recently, artificial intelligence has been widely used in various fields. Traditionally, students have studied in cramming methods rather than self-directed learning through schools and numerous extracurricular activities. In order to alleviate the problem of injection-type education, students can be expected to improve their self-directed learning skills by considering the level of students through the artificial intelligence English word app. In this paper, we will propose ways to utilize artificial intelligence for efficient learning.

• Journal of Convergence for Information Technology
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• v.7 no.1
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• pp.61-66
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• 2017

As the fourth industrial revolution becomes more popular and advanced services such as artificial intelligence and Internet of Things technology are widely commercialized, awareness of the importance of software is spreading. Recently, software education has been taught not only in elementary school and college but also in college. Also, there is a growing interest in computational thinking needed to solve problems through computing methodology and model. The purpose of this study is to design an app inventor course for non-majors software education based on computational thinking. As a result of the study, six detailed competencies of computational thinking were derived, and six detailed competencies were mapped to the app inventor learning elements. In addition, based on the computational thinking modeling, I designed an app inventor class for students who participated in IT curriculum of university liberal arts curriculum.

• The Journal of Korean Institute for Practical Engineering Education
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• v.5 no.1
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• pp.52-57
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• 2013

Because of rapid spread of the PC, many users have been enjoying a variety of content as PC. Especially in recent years, young people has increased dramatically PC usage. Young people get more easily information using a PC. Especially they relieve their stress through online games and feel another fun of virtual reality. It is obviously a good effect that they contact IT culture with rapidly developed. But young people's perspective with world is narrow because of doing more indoor activities than outdoor. Therefore we built Spot experience voucher system using smart phone application. We hope that many young people act outdoor activities. And Our product offer hybrid device by developing HTML5-based app. Thus this app will give interest of spot-experience to young-people. So If young people use this app, they can have many experience and see diverse aspects.

• Journal of Digital Convergence
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• v.16 no.10
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• pp.59-67
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• 2018

There are not that many m-health related services limited to the elderly. Many of the elderly who are at risk of dementia are unfamiliar to smart devices, so it is required to design an user-customized App. Therefore, I design and embody a mobile voice alert integrated app, which enables voice input to increase the accessibility of the elderly, so as to prevent diseases caused by declined cognitive function such as dementia. I conducted interviews and questionnaire after having the students use the app in Lifelong Education Center in H region of Gyeongbuk, and the analysis result has showed the high satisfaction. It is expected that it will be able to play a key role for M-Health service for the elderly since it is possible to prevent dementia through the voice health care alert app. I would like to learn deep learning in the future to predict the life patterns and the possibility of dementia of the elderly.

• Journal of The Korean Association of Information Education
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• v.17 no.4
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• pp.409-419
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• 2013

The aims of this paper were to review the usable possibility of the educational App Inventor Program in the field of programming education and to develop programming teaching materials that can overcome limitations of the established programming instruction. The study showed that the learners' motivations were strengthened through smart device programs. Containing the elements of STEAM, the teaching materials were developed for the logical and systematic learning that deals with elementary students' real-life situations, and that helps children follow the procedures of software development. By introducing the Waterfall Model to the process of programming, students are able to follow the software developers' thinking process. In addition, beyond the simplistic programming language and simply acquiring related knowledge, the App Inventor programming was designed to enhance students' higher-order thinking skills such as creativity, problem solving ability, collaborative thinking, and so forth.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.317-323
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• 2021

Social media is increasingly becoming a part of our daily life for communicating each other. There are various tools and applications for communication and therefore, identity theft is a common issue among users of such application. A new style of identity theft occurs when cybercriminals break into WhatsApp account, pretend as real friends and demand money or blackmail emotionally. In order to prevent from such issues, data mining can be used for text classification (TC) in analysis authorship attribution (AA) to recognize original sender of the message. Arabic is one of the most spoken languages around the world with different variants. In this research, we built a machine learning model for mining and analyzing the Arabic messages to identify the author of the messages in Saudi dialect. Many points would be addressed regarding authorship attribution mining and analysis: collect Arabic messages in the Saudi dialect, filtration of the messages' tokens. The classification would use a cross-validation technique and different machine-learning algorithms (Naïve Baye, Support Vector Machine). Results of average accuracy for Naïve Baye and Support Vector Machine have been presented and suggestions for future work have been presented.

• Journal of Internet of Things and Convergence
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• v.6 no.2
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• pp.1-10
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• 2020

The mobile application based on the Android platform is simple to decompile, making it possible to create malicious applications similar to normal ones, and can easily distribute the created malicious apps through the Android third party app store. In this case, the Android malicious application in the smartphone causes several problems such as leakage of personal information in the device, transmission of premium SMS, and leakage of location information and call records. Therefore, it is necessary to select a optimal model that provides the best performance among the machine learning techniques that have published recently, and provide a technique to automatically identify malicious Android apps. Therefore, in this paper, after adopting the feature engineering to Android apps on official test set, a total of four performance evaluation experiments were conducted to select the machine learning model that provides the optimal performance for Android malicious app detection.