• The Mathematical Education
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• v.60 no.1
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• pp.1-19
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• 2021

This study investigates students' conceptions of fractions from a measurement approach while providing a technological environment designed to support students' understanding of the relationships between quantities and adjustable units. 13 third-graders participated in this study and they were involved in a series of measurement tasks through task-based interviews. The tasks were devised to investigate the relationship between units and quantity through manipulations. Screencasting videos were collected including verbal explanations and manipulations. Drawing upon the theory of semiotic mediation, students' constructed concepts during interviews were coded as mathematical words and visual mediators to identify conceptual profiles using a fine-grained analysis. Two students changed their strategies to solve the tasks were selected as a representative case of the two profiles: from guessing to recursive partitioning; from using random units to making a relation to the given unit. Dragging mathematical objects plays a critical role to mediate and formulate fraction understandings such as unitizing and partitioning. In addition, static and dynamic representations influence the development of unit concepts in measurement situations. The findings will contribute to the field's understanding of how students come to understand the concept of fraction as measure and the role of technology, which result in a theory-driven, empirically-tested set of tasks that can be used to introduce fractions as an alternative way.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.35-44
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• 2020

In this study, we analyze how stock trend determination affects trend prediction accuracy. In stock markets, successful investment requires accurate stock price trend prediction. Therefore, a volume of research has been conducted to improve the trend prediction accuracy. For example, information extracted from SNS (social networking service) and news articles by text mining algorithms is used to enhance the prediction accuracy. Moreover, various machine learning algorithms have been utilized. However, stock trend determination has not been properly analyzed, and conventionally used methods have been employed repeatedly. For this reason, we formulate the trend determination as a moving average-based procedure and analyze its impact on stock trend prediction accuracy. The analysis reveals that trend determination makes prediction accuracy vary as much as 47% and that prediction accuracy is proportional to and inversely proportional to reference window size and target window size, respectively.

• Journal of The Korean Association of Information Education
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• v.24 no.6
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• pp.653-662
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• 2020

In this study, the content and level of the elements of Computational Thinking in the Software Education area of elementary practical textbooks were analyzed, and also the computing ratio for each textbook learning activity was analyzed. The elements of Computational Thinking were defined based on the components and definitions of Computational Thinking skills suggested by the Ministry of Education. The contents of Software Education area in practical arts textbooks published by six publishers were analyzed. As a result of analyzing the elements of Computational Thinking for each textbook according to the achievement criteria, there was a difference in whether sub-elements of Computational Thinking were included for each textbook. Second, as a result of analyzing the level of Computing of learning content, the proportion of textbooks presenting Abstract activities connected to Computing was relatively low. When the curriculum is reorganized or the textbook is revised in the future, it is necessary to complement the elements of Computational Thinking in a balanced way, and to include general Abstraction activities and Abstraction activities that can lead to Automation.

• Journal of The Korean Association of Information Education
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• v.25 no.2
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• pp.265-277
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• 2021

With the development of technology, the concept of digital literacy is expanding from the focus of function and tool utilization to the extent of communication and participation in social and cultural contexts. In particular, learners' digital literacy capabilities are more important and necessary in remote learning environments such as Corona19. In this study, we studied how to improve digital literacy education through analysis and inspection of digital literacy shown in elementary school textbooks. To this end, we analyze it through compliance consisting of 8 fundamental pieces of knowledge, 20 sub technical skills, and 10 keys of competencies that constitute digital literacy. As a result of the study, the contents of digital literacy education in elementary school textbooks were presented centered on functions and tools, and as they were biased in certain areas, they were found to be lacking in systematicity and connectivity. Therefore, it proposed the composition of the curriculum system of digital literacy through the independence of elementary informatics curriculum, the specific composition of curriculum, and the strengthening of pre-service teachers' practical skills.

• Journal of the Korea Society of Computer and Information
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• v.27 no.1
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• pp.1-8
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• 2022

In this paper, we propose a fire detection system based on CCTV images using an object tracking technology with YOLOv4 model capable of real-time object detection and a DeepSORT algorithm. The fire detection model was learned from 10800 pieces of learning data and verified through 1,000 separate test sets. Subsequently, the fire detection rate in a single image and fire detection maintenance performance in the image were increased by tracking the detected fire area through the DeepSORT algorithm. It is verified that a fire detection rate for one frame in video data or single image could be detected in real time within 0.1 second. In this paper, our AI fire detection system is more stable and faster than the existing fire accident detection system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.510-518
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• 2022

The diagnostic evaluation conducted at the national level is very important to detect underachievers in school early. This study used an artificial intelligence method to find the characteristics of underachievers that affect learning development for middle school students. In this study an artificial intelligence model was constructed and analyzed to determine whether the Busan Education Longitudinal Data in 2020 by entering data from the first year of middle school in 2019. A predictive model was developed to predict basic middle school Korean, English, and mathematics education with machine learning algorithms, and it was confirmed that the accuracy was 78%, 82%, and 83%, respectively, in the prediction for the next school year. In addition, by drawing an achievement prediction decision tree for each middle school subject we are analyzing the process of prediction. Finally, we examined what characteristics affect achievement prediction.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.58-73
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• 2022

The purpose of this study is to construct a predictable estimation model that reflects the relationship between the variables of mobile app performance and to verify how app reviews affect app performance. In study 1 and 2, the relationship between app performance indicators was derived using correlation analysis and random forest regression estimation of machine learning, and app performance estimation modeling was performed. In study 3, sentiment scores for app reviews were by using sentiment analysis of text mining, and it was found that app review sentiment scores have an effect one lag ahead of the number of daily installations of apps when using multivariate time series analysis. By analyzing the dissatisfaction and needs raised by app performance indicators and reviews of apps, companies can improve their apps in a timely manner and derive the timing and direction of marketing promotions.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.101-108
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• 2022

We propose an intrusion detection model that detects denial-of-service(DoS) and distributed reflection denial-of-service(DRDoS) attacks, based on the empirical data of each internet of things(IoT) device by training system and network metrics that can be commonly collected from various IoT devices. First, we collect 37 system and network metrics from each IoT device considering IoT attack scenarios; further, we train them using six types of machine learning models to identify the most effective machine learning models as well as important metrics in detecting and distinguishing IoT attacks. Our experimental results show that the Random Forest model has the best performance with accuracy of over 96%, followed by the K-Nearest Neighbor model and Decision Tree model. Of the 37 metrics, we identified five types of CPU, memory, and network metrics that best imply the characteristics of the attacks in all the experimental scenarios. Furthermore, we found out that packets with higher transmission speeds than larger size packets represent the characteristics of DoS and DRDoS attacks more clearly in IoT networks.

• Journal of Christian Education in Korea
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• v.69
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• pp.105-137
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• 2022

This paper is to study the characteristics of outdoor wilderness education and the impacts of outdoor wilderness experience on the participants in higher education. The first part of this paper addresses the common components of outdoor wilderness programs: adventure or self-discovery in disequilibrium, small groups for accountability in a temporary community, problem solving processes for decision making in real situations, solo time for integration in solitude, and leadership styles and role of the trip leaders. These elements of outdoor wilderness programs help the participants to achieve their goals according to its mission. The second part of this paper divides outdoor wilderness programs into three categories according to the objectives and outcomes of outdoor wilderness education: orientation programs for incoming students, personal leadership development programs, and professional training programs. The impacts of outdoor wilderness experiences on the participants of different programs in higher education were reviewed. Then guidelines for spiritual formation prorgams were proposed for Christian educators who are involved in wilderness programs in higher education to develop their practical wilderness experiences into holistic development programs according to its mission and goals.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.219-228
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• 2022

The purpose of this study is to explore what obstacles adult undergraduate experience in adapting to college life. To this end, in-depth interviews were conducted with 32 adult undergraduate attending colleges in Daegu and Gyeongbuk. For the study, Colaizzi's phenomenological research method was used and analyzed. As a result of the analysis, eight factors such as relation obstacle, bachelor's and curriculum operation obstacle, social recognition obstacle, study ability obstacle, college environment obstacle, economic obstacle, personal disposition obstacle, and temporal obstacle were found. Through the above research results, it was found that the college environment, which is operated mainly by general college students, is expressed as various types of obstacle for adult undergraduate who work and study at various ages and experiences. Based on the derived obstacle factors, it is expected that a follow-up study will be conducted to develop a measurement tool that can empirically explore the obstacle of adult undergraduate to adapt to college life.