• Proceedings of the Korean Society of Computer Information Conference
• /
• 2021.01a
• /
• pp.345-347
• /
• 2021

본 연구는 최근 Horizon report 2020에서 소개하는 고등교육 분야에 영향을 줄 트렌드와 기술 사례들을 수집하여 고등교육의 미래 동향과 학습 분석적 측면에서 분석하였다. 고등 교육이 교수·학습을 중심으로 영향을 줄 트렌드로는 기술적 트렌드, 고등교육 트렌드, 경제적 트렌드 등이 있으며 기술 사례로는 적응형 학습, 인공지능/머신기능 기술의 교육적 활용, 학업 성취도 분석을 위한 학습 분석, 확장 현실 기술 등이 있다. 이 중 학습 분석 기술은 학습자의 학업 성취도를 높이기 위한 방법으로 사용되는 유용한 기술이기도 하며 고등교육에 영향을 줄 가장 핵심 트렌드이기도 하다. 그러나 현실 학습에 적용하는데는 데이터 격차, 품질 문제, 개인 정보보호에 대한 문제 등 윤리적 문제를 함께 고려해야한다. 본 연구를 기반으로 한 향후 학습 분석 시스템을 개발하고자 한다.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.16 no.2
• /
• pp.105-115
• /
• 2020

This study examines the influence of the betweenness centrality on tertius gaudens orientation, relationship commitment, and individual learning performance within the university environment. The betweenness centrality explored the antecedent factor of tertius gaudens orientation. The relationship commitment explored the consequence factor of tertius gaudens orientation, and the learning performance explored the consequence factor of the relationship commitment. This survey was carried out by university students. Data were obtained from 74 respondents who have been studying tourism data analytics at one of the leading universities, in Seoul, Korea. In order to validate the research model, social network analysis tool, UCINET 6.689, and a structural equation modeling tool, SmartPLS 3.3.2, were used. The empirical result showed that all antecedent factors (betweenness centrality position, tertius gaudens orientation, and relationship commitment) of the learning performance were significant. In conclusion, this study discusses the research findings and implications. Then the limitations and future directions of the study were suggested.

• Asia pacific journal of information systems
• /
• v.34 no.1
• /
• pp.209-225
• /
• 2024

This research aims to use machine learning technology in human resource management to predict employees' work-life balance. The study utilized a dataset from IBM Watson Analytics in the IBM Community for the machine learning analysis. Multinomial dependent variables concerning workers' work-life balance were examined, categorized into continuous and categorical types using the Generalized Linear Model. The complexity of assessing variable roles and their varied impact based on the type of model used was highlighted. The study's outcomes are academically and practically relevant, showcasing how machine learning can offer further understanding of psychological variables like work-life balance through analyzing employee profiles.

• Journal of Convergence for Information Technology
• /
• v.8 no.1
• /
• pp.201-206
• /
• 2018

Recently, as technologies for realizing artificial intelligence have become more common, machine learning is widely used. Machine learning provides insight into collecting large amounts of data, batch processing, and taking final action, but the effects of the work are not immediately integrated into the learning process. In this paper proposed an adaptive learning model to improve the performance of real-time stream analysis as a big business issue. Adaptive learning generates the ensemble by adapting to the complexity of the data set, and the algorithm uses the data needed to determine the optimal data point to sample. In an experiment for six standard data sets, the adaptive learning model outperformed the simple machine learning model for classification at the learning time and accuracy. In particular, the support vector machine showed excellent performance at the end of all ensembles. Adaptive learning is expected to be applicable to a wide range of problems that need to be adaptively updated in the inference of changes in various parameters over time.

• Journal of Multimedia Information System
• /
• v.5 no.3
• /
• pp.195-200
• /
• 2018

Recently, as the sensor and big data analysis technology have been developed, there have been a lot of researches that analyze the purchase-related data such as the trajectory information and the stay time. Such purchase-related data is usefully used for the purchase pattern prediction and the purchase time prediction. Because it is difficult to find periodic patterns in large-scale human data, it is necessary to look at actual data sets, find various feature patterns, and then apply a machine learning algorithm appropriate to the pattern and purpose. Although existing papers have been used to analyze data using various machine learning methods, there is a lack of statistical analysis such as finding feature patterns before applying the machine learning algorithm. Therefore, we analyze the purchasing data of Songjeong Maeil Market, which is a data gathering place, and finds some characteristic patterns through statistical data analysis. Based on the results of 1, we derive meaningful conclusions by applying the machine learning algorithm and present future research directions. Through the data analysis, it was confirmed that the number of visits was different according to the regional characteristics around Songjeong Maeil Market, and the distribution of time spent by consumers could be grasped.

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

• Asia pacific journal of information systems
• /
• v.30 no.4
• /
• pp.879-918
• /
• 2020

One of the challenges faced by Talent Acquisition teams today pertains to the acquisition of human resources by matching job descriptions and skillsets desired. It is more so in the case of competitive sectors like the Indian IT sector. There can be various channels for Talent Acquisition and accordingly, the cost and benefits might vary. However, the consequences of a mismatch have an impact on the quality of deliverables, high recruitment expenses and loss of revenue for the organization. With increased and diverse sources of data that are available to organizations today, there is ample opportunity to apply analytics for informed decision making in this field. This paper reveals useful insights that help streamline the Talent Acquisition process in the Indian IT Industry. The paper adopts a data-centric approach to examine the critical determinants for efficient and effective Talent Acquisition process in IT organizations. Selected supervised machine learning algorithms are applied for the analysis of the dataset. The study is likely to help organizations in reassessing their talent acquisition strategy with respect to key parameters like expected cost to company (CTC), candidate sourcing channels and optimal joining period.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.10
• /
• pp.117-124
• /
• 2021

In this paper, we propose CANVAS (Creative ANalytics enVironment And System), an analytics system of the National Research Data Platform (DataON). CANVAS is a personalized analytics cloud service for researchers who need computing resources and tools for research data analysis. CANVAS is designed in consideration of scalability based on micro-services architecture and was built on top of open-source software such as eGovernment Standard framework (Spring framework), Kubernetes, and JupyterLab. The built system provides personalized analytics environments to multiple users, enabling high-speed and large-capacity analysis by utilizing high-performance cloud infrastructure (CPU/GPU). More specifically, modeling and processing data is possible in JupyterLab or GUI workflow environment. Since CANVAS shares data with DataON, the research data registered by users or downloaded data can be directly processed in the CANVAS. As a result, CANVAS enhances the convenience of data analysis for users in DataON and contributes to the sharing and utilization of research data.

• The Journal of Korean Association of Computer Education
• /
• v.17 no.1
• /
• pp.1-12
• /
• 2014

Since students' behaviors during e-learning are automatically stored in LMS(Learning Management System), the LMS log data convey the valuable information of students' engagement. The purpose of this study is to develop a prediction model of e-learning course dropout by utilizing LMS log data. Log data of 578 college students who registered e-learning courses in a traditional university were used for the logistic regression analysis. The results showed that attendance and study time were significant to predict dropout, and the model classified between dropouts and completers of e-learning courses with 96% accuracy. Furthermore, the feasibility of early detection of dropouts by utilizing the model were discussed.

• The Journal of the Korea Contents Association
• /
• v.15 no.9
• /
• pp.652-661
• /
• 2015

The purpose of this study is to investigate the effects of EngageGram which is a motivator of e-learning participation on learners' online participation according to the types of social comparison motive. Research participation was 144 undergraduate students (male: 106, female: 38) who took the course entitled "Creative Thinking." Social comparison motive of learners were investigated by two methods: social comparison motive scales and learners' opinions on EngageGram. As results, there was no statically corelation between the types of social comparison motive by using scales and online participation, however, there was statically differences on e-learning participation according to the types of social comparison motives by analyzing learners' opinions. Learners mostly have self-enhancement motive in a learning context so they are motivated to participate actively by EngageGram. This study provide useful implication in the research area of learning analytics.