• Journal of the Korean Operations Research and Management Science Society
• /
• v.31 no.3
• /
• pp.127-143
• /
• 2006

While e-learners' satisfaction would be determined by qualify of e-learning environment including learning management systems, learning contents and interactions, the influence of quality on satisfaction can be changed by e-learners' self-regulated efficacy The objective of this study is to show difference of the relationship between qualify and satisfaction In e-learning by self-regulated efficacy. For this purpose, we propose a research model which consists of five quality factors in e-learning as explaining variables, satisfaction as a result variable and self-regulated efficacy as a control variable. For empirical test of this model, the sample is collected from e-learning classes in a college and divided into two groups by self-regulated efficacy in order to analyze the effects of control variable. By multi-group analysis, we show two groups are different from each other in the relationship between quality and satisfaction of e-learning environment.

• Journal of Platform Technology
• /
• v.11 no.3
• /
• pp.45-55
• /
• 2023

This paper presents a model that supports decision-makers in the Korean film industry to maximize the success of online movies. To achieve this, we collected historical box office movies and clustered them into types to propose a model predicting each type's online box office performance. We considered various features to identify factors contributing to movie success and reduced feature dimensionality for computational efficiency. We systematically classified the movies into types and predicted each type's online box office performance while analyzing the contributing factors. We used automated machine learning (AutoML) techniques to automatically propose and select machine learning algorithms optimized for the problem, allowing for easy experimentation and selection of multiple algorithms. This approach is expected to provide a foundation for informed decision-making and contribute to better performance in the film industry.

• International journal of advanced smart convergence
• /
• v.10 no.1
• /
• pp.56-61
• /
• 2021

Neighbor path selection is to pre-select alternate routes in case geographically correlated failures occur simultaneously on the communication network. Conventional heuristic-based algorithms no longer improve solutions because they cannot sufficiently utilize historical failure information. We present a novel solution model for neighbor path selection by using machine learning technique. Our proposed machine learning neighbor path selection (ML-NPS) model is composed of five modules- random graph generation, data set creation, machine learning modeling, neighbor path prediction, and path information acquisition. It is implemented by Python with Keras on Tensorflow and executed on the tiny computer, Raspberry PI 4B. Performance evaluations via numerical simulation show that the neighbor path communication success probability of our model is better than that of the conventional heuristic by 26% on the average.

• Proceedings of the KAIS Fall Conference
• /
• 2005.05a
• /
• pp.224-226
• /
• 2005

e-learning에 대한 관심과 활용은 매우 활발히 진행되고 있으나, e-learning 성공의 핵심요소인 학습자 충성도에 영향을 미치는 요인에 대한 연구는 매우 부족한 실정이다. 본 연구의 목적은 기존 연구를 바탕으로 e-learning 구성 요인을 재분류하고, e-learning에 영향을 미치는 요인을 실증적으로 검증할 수 있는 e-learning 학습자 충성도 모델을 제시하는 것이다.

• Journal of The Korean Association of Information Education
• /
• v.5 no.1
• /
• pp.145-156
• /
• 2001

The purpose of this study is to set up the situated learning model for computer education and to investigate which method has better effect on the students' computer skill and learning attitude among the situated learning model and the traditional learning model. The result of this study is as follows. In order to investigate the effect of the students' learning attitude, students had been tested on six factors: the Understanding, the Interest, the Achievement, the Concentration, the Applicability, and the Spontaneity. As for the Understanding, the traditional learning model has better effect on students than the situated learning model. But the situated learning model was much superior in the other factors to the traditional learning model. Next, it had been examined how much students improved their computer skills under the situated learning model and under the traditional learning model. The study showed that the traditional learning model resulted in a little bit higher scores than the situated learning model. However, it was a great success to find out that the situated learning model is superior in the students' learning attitude to the traditional learning model.

• Educational Technology International
• /
• v.15 no.2
• /
• pp.71-88
• /
• 2014

A variety of studies to predict students' performance have been conducted since educational data such as web-log files traced from Learning Management System (LMS) are increasingly used to analyze students' learning behaviors. However, it is still challenging to predict students' learning achievement in blended learning environment where online and offline learning are combined. In higher education, diverse cases of blended learning can be formed from simple use of LMS for administrative purposes to full usages of functions in LMS for online distance learning class. As a result, a generalized model to predict students' academic success does not fulfill diverse cases of blended learning. This study compares two blended learning classes with each prediction model. The first blended class which involves online discussion-based learning revealed a linear regression model, which explained 70% of the variance in total score through six variables including total log-in time, log-in frequencies, log-in regularities, visits on boards, visits on repositories, and the number of postings. However, the second case, a lecture-based class providing regular basis online lecture notes in Moodle show weaker results from the same linear regression model mainly due to non-linearity of variables. To investigate the non-linear relations between online activities and total score, RF (Random Forest) was utilized. The results indicate that there are different set of important variables for the two distinctive types of blended learning cases. Results suggest that the prediction models and data-mining technique should be based on the considerations of diverse pedagogical characteristics of blended learning classes.

• International conference on construction engineering and project management
• /
• 2007.03a
• /
• pp.749-758
• /
• 2007

Learning has become an important aspect for any organization to stay relevant and competitive in the corporate world of survival. In construction industry, the international construction joint ventures (ICJVs) provide an excellent platform with opportunity of learning among partners seeking to develop new area of competency and improve their overall competitiveness for their next project endeavor. This paper discusses the development of a conceptual model of effective learning in ICJVs using four major stages of development in a typical joint venture (JV) 's process. The study identified that there are three key constructs that contribute to effective learning comprising learning conditions in the JV's pre-inception stage, success factors of JV for learning in the forming & organizing stage, and learning actions in the implementation & adjustment stage. The effective learning outcomes are measured by the characteristics of learning organization during the JV's completion & evaluation stage. Details and issues of each stage and the methodology of research will be presented and discussed.

• International Journal of Computer Science & Network Security
• /
• v.23 no.5
• /
• pp.73-88
• /
• 2023

Brain tumors can also be an abnormal collection or accumulation of cells in the brain that can be life-threatening due to their ability to invade and metastasize to nearby tissues. Accurate diagnosis is critical to the success of treatment planning, and resonant imaging is the primary diagnostic imaging method used to diagnose brain tumors and their extent. Deep learning methods for computer vision applications have shown significant improvements in recent years, primarily due to the undeniable fact that there is a large amount of data on the market to teach models. Therefore, improvements within the model architecture perform better approximations in the monitored configuration. Tumor classification using these deep learning techniques has made great strides by providing reliable, annotated open data sets. Reduce computational effort and learn specific spatial and temporal relationships. This white paper describes transfer models such as the MobileNet model, VGG19 model, InceptionResNetV2 model, Inception model, and DenseNet201 model. The model uses three different optimizers, Adam, SGD, and RMSprop. Finally, the pre-trained MobileNet with RMSprop optimizer is the best model in this paper, with 0.995 accuracies, 0.99 sensitivity, and 1.00 specificity, while at the same time having the lowest computational cost.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.4
• /
• pp.347-353
• /
• 2022

In the restaurant industry, start-ups are active due to high demand from consumers and low entry barriers. However, the restaurant industry has a high closure rate, and in the case of franchises, there is a large deviation in sales within the same brand. Thus, research is needed to prevent the closure of food franchises. Therefore, this study examines the factors affecting franchise sales and uses machine learning techniques to predict the success and failure of franchises. Various factors that affect franchise sales are extracted by using Point of Sale (PoS) data of food franchise and public data in Gangnam-gu, Seoul. And for more valid variable selection, multicollinearity is removed by using Variance Inflation Factor (VIF). Finally, classification models are used to predict the success and failure of food franchise stores. Through this method, we propose success and failure prediction model for food franchise stores with the accuracy of 0.92.

• The Journal of the Korea Contents Association
• /
• v.22 no.10
• /
• pp.765-777
• /
• 2022

The purpose of this study is to present a digital transformation-based learning model that can be used in universities based on learning digital transformation in order f to be competitive in a rapidly changing environment. Literature review, case study, and focus group interview were conducted and the implications for the learning model from these are as follows. Universities that stand out in related fields are actively using learning analysis to implement dashboards, develop predictive models, and support adaptive learning based on big data, They also have actively introduced advanced edutech to classes. In addition, problems and difficulties faced by other universities and K University when implementing digital transformation were also confirmed. Based on these findings, a digital transformation-based learning model of K University was developed. This model consists of four dimensions: diagnosis, recommendation, learning, and success. It allows students to proceed with learning by diagnosing and recommending various learning processes necessary for individual success, and systematically managing learning outcomes. Finally, academic and practical implications about the research results were discussed.