• Journal of Digital Convergence
• /
• v.16 no.5
• /
• pp.239-245
• /
• 2018

So far, various researches have been conducted on evaluation of e-learning software, but subjective evaluation criteria are formed according to the classification presented from the viewpoint of the researcher rather than systematized form according to related standards. In addition, standards for software evaluation are continuously being supplemented for practical use, so it is urgent to establish evaluation bases by establishing evaluation criteria. Therefore, in order to establish the quality evaluation standard of e-learning software, this study analyzes the quality requirements of e-learning software based on the usability system among the quality characteristics of ISO/IEC 25000 series. This evaluation standard is distinguished by the fact that the evaluation standard of e-learning software that reflects the latest trend of related standardization has been established and practical utilization has been improved. It can be used effectively for quality evaluation and certification of e-learning software in the future.

• Korean Journal of Artificial Intelligence
• /
• v.11 no.1
• /
• pp.17-24
• /
• 2023

With the continuous growth in the amount of data collected and analyzed, deep learning has become increasingly popular for extracting meaningful insights from various fields. However, hardware limitations pose a challenge for achieving meaningful results with limited data. To address this challenge, this paper proposes an algorithm that leverages the characteristics of convolutional neural networks (CNNs) to reduce the size of image datasets by 20% through smoothing and shrinking the size of images using color elements. The proposed algorithm reduces the learning time and, as a result, the computational load on hardware. The experiments conducted in this study show that the proposed method achieves effective learning with similar or slightly higher accuracy than the original dataset while reducing computational and time costs. This color-centric dataset construction method using image smoothing techniques can lead to more efficient learning on CNNs. This method can be applied in various applications, such as image classification and recognition, and can contribute to more efficient and cost-effective deep learning. This paper presents a promising approach to reducing the computational load and time costs associated with deep learning and provides meaningful results with limited data, enabling them to apply deep learning to a broader range of applications.

• Journal of The Korean Association of Information Education
• /
• v.21 no.6
• /
• pp.665-673
• /
• 2017

This research was performed for learning motivation and learning satisfaction of flipped learning on software education in university class. In order to compare and get the result, this study used 2 groups of experimental group(flipped learning) and comparison group(traditional face to face learning). Consequently an experimental group got more strong learning motivation and learning satisfaction than traditional learning group on software education in non-major class of university. It showed at the same time in factors of learning motivation like concentration, importance of subject, self confidence. and on factors pf learning satisfaction like problem solving, reaction, understanding, interest and relation with lecturer, This study showed that flipped learning method is more effective than face to face traditional learning method for creative or problem solving subject like software education.

• Asia pacific journal of information systems
• /
• v.30 no.3
• /
• pp.457-496
• /
• 2020

This study aims to extensively analyze the performance of various Machine Learning (ML) techniques for predicting version to version change-proneness of source code Java files. 17 object-oriented metrics have been utilized in this work for predicting change-prone files using 31 ML techniques and the framework proposed has been implemented on various consecutive releases of two Java-based software projects available as plug-ins. 10-fold and inter-release validation methods have been employed to validate the models and statistical tests provide supplementary information regarding the reliability and significance of the results. The results of experiments conducted in this article indicate that the ML techniques perform differently under the different validation settings. The results also confirm the proficiency of the selected ML techniques in lieu of developing change-proneness prediction models which could aid the software engineers in the initial stages of software development for classifying change-prone Java files of a software, in turn aiding in the trend estimation of change-proneness over future versions.

• Journal of the Korea Society of Computer and Information
• /
• v.18 no.1
• /
• pp.185-192
• /
• 2013

The development of hardware, popularization of 3D graphics software could get to easily use 3d graphics tool in the school. And learning difficulties of a shape section increased through more being enforced a shape section of an elementary school. Thus we try to improve learning effectiveness in a shape section using Sketech Up software. To do this, we analyzed existing studies, classified 3D graphics software, provided the selection criteria of vector graphics software. And we explained how to select 3D graphics software. We selected and reorganized the shape contents to use Sketch Up, which make and rotate 3D figures, understand aspects of a shape. And we inserted the content about piling 3D figures in the beginning state of the curriculum. we composed 10 periods and practiced our reorganized curriculum to the teaching and learning using Sketch Up. And we conducted before & after survey to check out t-verified. And we acquired meaningful results statistically. Thus applying Sketch Up to the shape learning can be analyzed effectively.

• Journal of the Semiconductor & Display Technology
• /
• v.20 no.1
• /
• pp.94-98
• /
• 2021

As the world's population grows, the food industry becomes increasingly important. Among them, agriculture is an industry that produces stocks of people all over the world, which is very important food industry. Despite the growing importance of agriculture, however, a large number of crops are lost every year due to pests and malnutrition. So, we propose a plant anomaly detection system for managing crops incorporating deep learning and drones with various possibilities. In this paper, we develop a system that analyzes images taken by drones and GPS of the drone's movement path and visually displays them on a map. Our system detects plant anomalies with 97% accuracy. The system is expected to enable efficient crop management at low cost.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.19 no.3
• /
• pp.177-192
• /
• 2023

The employment rate of university graduates has been decreasing significantly recently. With the advent of the Fourth Industrial Revolution, the demand for software employment has increased. It is necessary to analyze the factors for software employment of university graduates. This paper proposes explainable software employment model of university graduates using machine learning and explainable AI. The Graduates Occupational Mobility Survey(GOMS) provided by the Korea Employment Information Service is used. The employment model uses boosting machine learning. Then, performance evaluation is performed with four algorithms of boosting model. Moreover, it explains the factors affecting the employment using SHAP. The results indicates that the top 3 factors are major, employment goal setting semester, and vocational education and training.

• Korean Journal of English Language and Linguistics
• /
• v.3 no.3
• /
• pp.453-497
• /
• 2003

The purpose of this study is to investigate social aspects of young EFL learners' interaction at the computer. Data were taken from the interactions of three pairs of fourth-grade primary school children who worked together on English learning software. Their interactions at the computer were videotaped and then all the talk produced by the students and the utterances emitted from the computer were transcribed. As for the analytical tools, the notion of ‘contextualization cues’ (Gumperz, 1982) and the concept of ‘positioning’ (Davies & Harre,1990) were employed. The analysis reveals that the roles of the students were not tied to a certain position, but rather dynamically changed during the course of interactive work according to the situation at hand. The dynamic changes in their positions were realized through various means; their capability in solving problems, their taking responsibility or assigning it to each other, or cooperation. There were also instances of peer teaching and motivated learning. In addition, the students showed autonomy in their learning activity. These findings suggest that both students in a dyad had their own place in performing task activities, contributing to solving problems and getting benefits from peer interaction. Furthermore, students' working together on English learning software may provide an environment which can promote cooperative attitude and responsibility for learning and enhance motivation and autonomy in their learning process.

• Journal of Information Processing Systems
• /
• v.13 no.4
• /
• pp.778-804
• /
• 2017

Software today has become an inseparable part of our life. In order to achieve the ever demanding needs of customers, it has to rapidly evolve and include a number of changes. In this paper, our aim is to study the relationship of object oriented metrics with change proneness attribute of a class. Prediction models based on this study can help us in identifying change prone classes of a software. We can then focus our efforts on these change prone classes during testing to yield a better quality software. Previously, researchers have used statistical methods for predicting change prone classes. But machine learning methods are rarely used for identification of change prone classes. In our study, we evaluate and compare the performances of ten machine learning methods with the statistical method. This evaluation is based on two open source software systems developed in Java language. We also validated the developed prediction models using other software data set in the same domain (3D modelling). The performance of the predicted models was evaluated using receiver operating characteristic analysis. The results indicate that the machine learning methods are at par with the statistical method for prediction of change prone classes. Another analysis showed that the models constructed for a software can also be used to predict change prone nature of classes of another software in the same domain. This study would help developers in performing effective regression testing at low cost and effort. It will also help the developers to design an effective model that results in less change prone classes, hence better maintenance.

• The Journal of Korean Association of Computer Education
• /
• v.19 no.6
• /
• pp.15-23
• /
• 2016

Recently the era of software integration which is expressed in words of the fourth industrial revolution has begun. Thus the need of the software education for the non-major preservice teachers who are cultivating future talent has been increasing and it is necessary to foster a positive attitude toward software education of non-major preservice teachers. The purpose of this study is to verify the causality between the computer learning attitude factors of non-major preservice teachers in the software education class. To analyze the causality, we performed correlational analysis and regression analysis between the exterior factors of attention, self-learning, application of learning and the other interior factors of computer learning attitude. As a result, the significant factor of attention was interests, and the significant factor of self-learning was superiority, and the significant factors of the application of learning were the sense of purpose and the motive of accomplishment.