• The Journal of Information Systems
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• v.27 no.2
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• pp.1-28
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• 2018

Purpose More than 7.6 million mobile apps could be approved on both Apple iTunes Store and Google Play. For managing those existed Apps, Apple Inc. established twenty-four primary categories, as well as Google Play had thirty-three primary categories. However, all of their categorizations have appeared more and more problems in managing and classifying numerous apps, such as app miscategorized, cross-attribution problems, lack of categorization keywords index, etc. The purpose of this study focused on introducing individual information cognitive processing as the classification criteria to update the current categorization on Apple iTunes Store. Meanwhile, we tried to observe the effectiveness of the new criteria from a classification process on Apple iTunes Store. Design/Methodology/Approach A research approach with four research stages were performed and a series of mixed methods was developed to identify the feasibility of adopting individual information cognitive processing as categorization criteria. By using machine-learning techniques with Term Frequency-Inverse Document Frequency and Singular Value Decomposition, keyword lists were extracted. By using the prior research results related to car app's categorization, we developed individual information cognitive processing. Further keywords extracting process from the extracted keyword lists was performed. Findings By TF-IDF and SVD, keyword lists from more than five thousand apps were extracted. Furthermore, we developed individual information cognitive processing that included a categorization teaching process and learning process. Three top three keywords for each category were extracted. By comparing the extracted results with prior studies, the inter-rater reliability for two different methods shows significant reliable, which proved the individual information cognitive processing to be reliable as criteria of categorization on Apple iTunes Store. The updating suggestions for Apple iTunes Store were discussed in this paper and the results of this paper may be useful for app store hosts to improve the current categorizations on app stores as well as increasing the efficiency of app discovering and locating process for both app developers and users.

• Smart Media Journal
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• v.13 no.6
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• pp.35-43
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• 2024

During the post-pandemic period, the interest in foreign language learning surged, leading to increased usage of language-learning apps. With the rising demand for these apps, analyzing app reviews becomes essential, as they provide valuable insights into user experiences and suggestions for improvement. This research focuses on extracting insights into users' opinions, sentiments, and overall satisfaction from reviews of HelloTalk, one of the most renowned language-learning apps. We employed topic modeling and emotion analysis approaches to analyze reviews collected from the Google Play Store. Several experiments were conducted to evaluate the performance of sentiment classification models with different settings. In addition, we identified dominant emotions and topics within the app reviews using feature importance analysis. The experimental results show that the Random Forest model with topics and emotions outperforms other approaches in accuracy, recall, and F1 score. The findings reveal that topics emphasizing language learning and community interactions, as well as the use of language learning tools and the learning experience, are prominent. Moreover, the emotions of 'admiration' and 'annoyance' emerge as significant factors across all models. This research highlights that incorporating emotion scores into the model and utilizing a broader range of emotion labels enhances model performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.404-406
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• 2021

In this paper, we propose an app application for classifying Korean food images and providing information related to Korean food. App Application consists of Flask server, Database (Mysql), and Python deep learning modules. Using the VGG16 model, 150 images of Korean foods are classified. If there is an internet environment, anyone can easily get information about Korean food anytime, anywhere with a single photo.

• Journal of Industrial Convergence
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• v.20 no.10
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• pp.157-167
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• 2022

The purpose of this study is to verify the educational effectiveness of interaction design in mobile APP by observing the impact of interaction design for elementary school education on enhancing learning immersion and continuous use intention, and propose an interaction design scheme based on elementary school education APP. The research methods are literature research and questionnaire survey. Specifically, through the literature research method, the concepts and prior studies on the concept, reviews the continuous use intention and previous research of interaction design. Then, conducts a questionnaire survey on elementary school students in South Korea and China to understand the interaction design, learning immersion, and continuous use intention, and analyzes the relationship between variables.The research result of this study is to observe the influence of interaction design elements within interaction on learning immersion and continuous use intention with elementary school students who are users of elementary school education application as the objects. The results show that interaction design within interaction has a positive impact on improving learning immersion and continuous use intention. It can be thought that this is because in mathematics/science education, it is easy to understand theoretical concepts or explanations, and stories and images will be continued at each stage to help students learn without being bored.In conclusion, this study can confirm that interactive inline design has a positive effect of enabling learners to engage in learning and continue to use.

• The Journal of the Korea Contents Association
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• v.19 no.2
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• pp.88-98
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• 2019

As the 2015 revised curriculum is being implemented from 2018, efforts are being made to cultivate scientific literacy among students in the field of science. Scientific experiments help students to develop their interest in Science and their scientific attitudes. Learning through experimentation rather than learning scientific facts increases learners' understanding, and can be remembered longer. Therefore, experiments in Science subject are very important. However, in middle schools, scientific experiments are not performed due to the lack of time, budget and experimental material. In this research, we analyze middle school science textbooks, conduct questionnaires for students of science pre-service teachers, select the most important science experiments, and develop a mobile App to simulate and experience scientific experiments with the App. The proposed App is developed in a game format using quest-based learning methods to gain learning enhancement. It is also made using Unity. In this paper, after developing the app, we propose the direction of STEAM contents development through analyzing the difference from existing apps and the feedback from users.

• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.65-72
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• 2020

In this paper, we developed a web-based DApp system based on a private blockchain by applying machine learning techniques to automatically identify Android malicious apps that are continuously increasing rapidly. The optimal machine learning model that provides 96.2587% accuracy for Android malicious app identification was selected to the authorized experimental data, and automatic identification results for Android malicious apps were recorded/managed in the Hyperledger Fabric blockchain system. In addition, a web-based DApp system was developed so that users who have been granted the proper authority can use the blockchain system. Therefore, it is possible to further improve the security in the Android mobile app usage environment through the development of the machine learning-based Android malicious app identification block chain DApp system presented. In the future, it is expected to be able to develop enhanced security services that combine machine learning and blockchain for general-purpose data.

• The Korean Journal of Physiology and Pharmacology
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• v.24 no.1
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• pp.39-46
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• 2020

Alzheimer's disease (AD) is the most common neurodegenerative disorder causing dementia worldwide, and is mainly characterized by aggregated β-amyloid (Aβ). Increasing evidence has shown that plant extracts have the potential to delay AD development. The plant sterol β-Sitosterol has a potential role in inhibiting the production of platelet Aβ, suggesting that it may be useful for AD prevention. In the present study, we aimed to investigate the effect and mechanism of β-Sitosterol on deficits in learning and memory in amyloid protein precursor/presenilin 1 (APP/PS1) double transgenic mice. APP/PS1 mice were treated with β-Sitosterol for four weeks, from the age of seven months. Brain Aβ metabolism was evaluated using ELISA and Western blotting. We found that β-Sitosterol treatment can improve spatial learning and recognition memory ability, and reduce plaque load in APP/PS1 mice. β-Sitosterol treatment helped reverse dendritic spine loss in APP/PS1 mice and reversed the decreased hippocampal neuron miniature excitatory postsynaptic current frequency. Our research helps to explain and support the neuroprotective effect of β-Sitosterol, which may offer a novel pharmaceutical agent for the treatment of AD. Taken together, these findings suggest that β-Sitosterol ameliorates memory and learning impairment in APP/PS1 mice and possibly decreases Aβ deposition.

• Journal of Information Technology Services
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• v.17 no.2
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• pp.111-128
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• 2018

About 3.9 million apps and 24 primary categories can be approved on Apple iTunes Store. Making accurate categorization can potentially receive many benefits for developers, app stores, and users, such as improving discoverability and receiving long-term revenue. However, current categorization problems may cause usage inefficiency and confusion, especially for cross-attribution, etc. This study focused on evaluating the reliability of app categorization on Apple iTunes Store by using several rounds of inter-rater reliability statistics, locating categorization problems based on Machine Learning, and making more accurate suggestions about representative functionality stems for each primary category. A mixed methods research was performed and total 4905 popular apps were observed. The original categorization was proved to be substantial reliable but need further improvement. The representative functionality stems for each category were identified. This paper may provide some fusion research experience and methodological suggestions in categorization research field and improve app store's categorization in discoverability.

• Journal of the Korea Society of Computer and Information
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• v.22 no.1
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• pp.123-134
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• 2017

The ability to use information technology has become increasingly important as technological advances continue to sweep through the computing world, and education for improving computational thinking has become globally instituted. In South Korea, informatics subjects have been modified in the 2015 curriculum and are now compulsory in primary and secondary education. However, despite substantial financial investment and numerous studies promoting informatics education, there continues to be a serious lack of pre-service teachers capable of teaching computational thinking. This study investigated pre-service teacher programming education using App Inventor, their perceptions of App Inventor, and how use of the program affected teacher problem-solving abilities and self-efficacy. In the pre-test, the control group and experimental group showed no statistically significant difference; however, the post-test revealed that the two groups showed statistically significant differences in problem-solving skills and self-efficacy. The participants initially showed interest in using App Inventor; however, after practice-teaching and project-based learning, the participants demonstrated a growing negativity toward the program when they made errors and the functional limits of App Inventor became apparent. Although most participants stated that they would not use App Inventor in their classes, the positive statistically significant differences in problem-solving skills and self-efficacy indicate that this study could be utilized as a basis for building a teaching-learning program using App Inventor and creating an educational plan for teaching computational thinking.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.311-320
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• 2021

At present, the secondary school teacher employment examination(SSTEE) is the only gateway to become a national and public secondary teacher in Korea, and after the revision from the 2014 academic year, all the questions of the exam have been converted to supply-type test items, requiring more definitive, accurate, and solid answers. Compared to the selection-type test items that measure recognition memory, the supply-type questions, testing recall memory, require constant memorization and retrieval practices to furnish answers; however, there is not enough learning tools available to support the practices. At this juncture, this study invented a mobile app, called ONE PASS, for the SSTEE. By unpacking the functional mechanisms of the brain, the basis of cognitive processing, this ONE PASS app offers a set of tools that feature brain-based learning principles, such as a personalized study planner, motivation measurement scales, mind mapping, brainstorming, and sample questions from previous tests. This study is expected to contribute to the research on the development of learning contents for applications, and at the same time, it hopes to be of some help for candidates in their exam preparation process.