• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.4
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• pp.283-292
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• 2020

Artificial intelligence, which is one of the representative images of the 4th industrial revolution, has been highly recognized since 2016. This paper analyzed domestic paper trends for 'Artificial Intelligence', 'Machine Learning', and 'Deep Learning' among the domestic papers provided by the Korea Academic Education and Information Service. There are approximately 10,000 searched papers, and word count analysis, topic modeling and semantic network is used to analyze paper's trends. As a result of analyzing the extracted papers, compared to 2015, in 2016, it increased 600% in the field of artificial intelligence, 176% in machine learning, and 316% in the field of deep learning. In machine learning, a support vector machine model has been studied, and in deep learning, convolutional neural networks using TensorFlow are widely used in deep learning. This paper can provide help in setting future research directions in the fields of 'artificial intelligence', 'machine learning', and 'deep learning'.

• The Journal of Korean Association of Computer Education
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• v.22 no.1
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• pp.31-46
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• 2019

The purpose of this study is to analyze the self-directed learning types of software education learners and to characterize them according to each type. To do this, 429 middle school students participating in online software education at K university were surveyed and a latent class analysis to analyze self-directed learning types was conducted. As a result, the self-directed learning types of the software education learners were classified into 'highest level of self-directed learning type (class 1)', 'self learning style recognition type (class 2)', 'self learning style preference type (class 3)', and 'lack of self-directed learning type(class 4)'. Also, the level of software learning achievement according to self-directed learning type of software education learners was found to be the highest at 'highest level of self-directed learning type (class 1)' and lowest at 'self learning style preference type (class 3)'. Based on these results, we suggested the strategic implications for software education.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.1-15
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• 2023

The aim of this study is to identify intelligent learning support functions in Learning Management System (LMS) to support university student learning activities during the transition from face-to-face classes to online learning. To accomplish this, we investigated the perceptions of students on the levels of importance and urgency toward learning support functions of LMS powered with Artificial Intelligent (AI) technology and analyzed the differences in perception according to student characteristics. As a result of this study, the function that students considered to be the most important and felt an urgent need to adopt was to give automated grading and feedback for their writing assignments. The functions with the next highest score in importance and urgency were related to receiving customized feedback and help on task performance processed as well as results in the learning progress. In addition, students view a function to receive customized feedback according to their own learning plan and progress and to receive suggestions for improvement by diagnosing their strengths and weaknesses to be both vitally important and urgently needed. On the other hand, the learning support function of LMS, which was ranked as low importance and urgency, was a function that analyzed the interaction between professors and students and between fellow students. It is expected that the results of this student needs analysis will be helpful in deriving the contents of learning support functions that should be developed as well as providing basic information for prioritizing when applying AI technology to implement learner-centered LMS in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3099-3120
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• 2023

The present fast-moving era brings a serious stress issue that affects elders and youngsters. Everyone has undergone stress factors at least once in their lifetime. Stress is more among youngsters as they are new to the working environment. whereas the stress factors for elders affect the individual and overall performance in an organization. Electroencephalogram (EEG) based stress level classification is one of the widely used methodologies for stress detection. However, the signal processing methods evolved so far have limitations as most of the stress classification models compute the stress level in a predefined environment to detect individual stress factors. Specifically, machine learning based stress classification models requires additional algorithm for feature extraction which increases the computation cost. Also due to the limited feature learning characteristics of machine learning algorithms, the classification performance reduces and inaccurate sometimes. It is evident from numerous research works that deep learning models outperforms machine learning techniques. Thus, to classify all the emotions based on stress level in this research work a hybrid deep learning algorithm is presented. Compared to conventional deep learning models, hybrid models outperforms in feature handing. Better feature extraction and selection can be made through deep learning models. Adding machine learning classifiers in deep learning architecture will enhance the classification performances. Thus, a hybrid convolutional neural network model was presented which extracts the features using CNN and classifies them through machine learning support vector machine. Simulation analysis of benchmark datasets demonstrates the proposed model performances. Finally, existing methods are comparatively analyzed to demonstrate the better performance of the proposed model as a result of the proposed hybrid combination.

• Journal of Agricultural Extension & Community Development
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• v.22 no.4
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• pp.455-464
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• 2015

The purposes of this study are to explain and identify the frame of structural relations of learning orientation, self-efficacy, learning transfer and job performance of farmers who participated in the strong and small farms education. This is an experimental research with the data collected from 495 farmers who have taken the farm education. Based on the collected data, the study conducted a structural equation modeling(SEM) to confirm the validity and analyze the structural relations of the suggested model. Using measured and latent variables drew from the analyses, the study set a structural equation model and tested the model by analysis of the structural equation modeling with AMOS 18.0. The results found from the empirical analysis can be summarized as follows. 1) Learning orientation and self-efficacy positively influenced job performance through learning transfer. 2) The hypothesis that learning orientation would have direct impact on job performance was not supported. 3) The strong and small farms education is useful to expand learning transfer and to enhance job performance. So, government policy support has to reinforce learning support on farmers in order to achieve high performance of learning and job management through farm educations.

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.1
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• pp.75-84
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• 2011

The e-learning contents offered in the current educational system does not appropriately reflect the needs of actual users in the planning and development phases. Considering this problem, this study sets the following four topics as its research: Stability of e-learning; Obstacles of the applications of e-learning; e-learning contents that users wants to be offered besides lectures; and methods of e-learning, and based on these goals, it aims at determining the 'needs of the users for the promotion of e-learning. As the target of the study, a survey was conducted with 200 students who have experienced taking e-learning classes in four universities located in Eastern Seoul, which have introduced an e-learning system. The data collected from the survey went through data coding and data cleaning processes and were analyzed by year, major, and department using SAS 9 statistics package program. The result of this study showed that developing and offering services of e-learning contents that are customized to students based on their majors and year can become an effective plan for promoting e-learning.

• Journal of Korean Library and Information Science Society
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• v.53 no.3
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• pp.263-285
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• 2022

This study presented the factor analysis on constructing the new factors affecting the middle school students' online learning behaviors from the questionnaires employed among middle school students. A total of 204 students participated and the data were collected in South Korea. The sample of middle school ninth-grade students was selected and used through purposive sampling. Findings from the factor analysis provided evidence for the eight-factor solution for the 35-items accounting for 66.15% of the shared variance. A wide range of factors has been considered to identify students' online learning behaviors. The appropriate experience and use of e-learning in the middle school period is also important as it will be a critical stepstone for future education. This research provides information that has been taken into account for advancing online learning to enhance the quality of e-learning systems for middle school students. The study results provided eight new factors affecting the middle school students' online learning behaviors; that is 1) communication using social media as a learning tool, 2) intention to share information using ICT, 3) addiction of technology, 4) adoption of technology, 5) seeking information using ICT, 6) use of social media learning, 7) information search using ICT, and 8) immersion of technology. This study confirmed that middle school students prefer communication using social media as a learning tool, and value intention to share information using ICT for the most part. The data obtained based on factor analysis can highlight the online learning behaviors towards a mixture of social media learning and ICT to ensure a new educational platform for the future of e-learning. This research expects to be useful for both middle schools of online learning to better understand students' online learning behaviors and design online learning environments and information professionals to better assist students who particularly need digital literacy.

• Journal of Practical Engineering Education
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• v.16 no.2
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• pp.159-165
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• 2024

This study investigates the comprehensive system for collecting intelligent learning activity data tailored to learner-centered personalized education. We compared and analyzed the characteristics of xAPI, Caliper analytics, and cmi5, which are learning activity data collection standards, and established a system that allows not only standardized data but also non-standardized learning activity data to be stored as big data for artificial intelligence learning analysis. As a result, the system was structured into five stages: defining data types, standardizing learning data using xAPI, storing big data, conducting learning analysis (statistical and AI-based), and providing learner-tailored services. The aim was to establish a foundation for analyzing learning data using artificial intelligence technology. In future research, we will divide the entire system into three stages, implement and execute it, and correct and supplement any shortcomings in the design.

• International Journal of Advanced Culture Technology
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• v.8 no.2
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• pp.297-304
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• 2020

Recently, open source automatic machine learning solutions have been applied in many fields. To apply open source automated machine learning to real world problems, you need to write code with expertise in machine learning. Writing code without machine learning knowledge is challenging. To solve this problem, the automatic machine learning solutions provided by startups are made easy to use with a clean user interface. In this paper, we review automatic machine learning solutions of startups.

• The Journal of Korean Association of Computer Education
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• v.15 no.1
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• pp.43-53
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• 2012

The spread of smartphones, many educational applications have been developed. However, the existing educational applications, learning the simple and uniform, due to the way the needs of learners are given feedback after learning did not reflect the present problem is how. In this paper vocabulary learning applications to implement the existing application using the learners' needs, survey analysis, and analysis results to reflect the learner's motivation to continue the learners to self-built learning objectives according to the stages of learning tailored to the training schedule set up and made a systematic feedback learning vocabulary was designed and implemented applications.