• Human Ecology Research
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• v.53 no.2
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• pp.219-228
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• 2015

This study evaluated the content of Korean vocabulary learning applications with a focus on tablet PC applications. We analyzed 51 Korean vocabulary learning applications. The instruments in this study were developed based on Yoo et al. (2012)' Vocabulary Learning Game Application Evaluation Criteria and Hyun et al. (2013)' Educational Application Evaluation Criteria. Data were analyzed using a t-test and one-way analysis of variance. The main results are as follows. First, each criteria's score was fairly good; the ease of use had the highest scores and the amusement had the lowest scores. Second, there was a significant difference in the interaction by vocabulary teaching approach. Applications based on a whole language-teaching method had higher scores than applications based on a phonics instructional teaching method inducing more operation and with immediate feedback. Third, there was significant difference in the sum of score and each criteria of developmental appropriateness, educational values, amusement, function and interaction by type of learning. Applications of combining type had higher scores in every criteria except for ease of use than applications of description type. These findings provide a preliminary evidence that the systematic Korean vocabulary learning application facilitates preschoolers' vocabulary learning.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.4
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• pp.145-156
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• 2015

Incorporation of individual learning and forgetting behaviors within worker-task assignment models produces a mixed integer nonlinear program (MINLP) problem, which is difficult to solve as a NP hard due to its nonlinearity in the objective function. Previous studies commonly assume homogeneity among workers in workforce scheduling that takes account of learning and forgetting characteristics. This paper expands previous researches by considering heterogeneous individual learning/forgetting, and investigates the impact of worker heterogeneity in initial expertise, steady-state productivity, learning and forgetting on system performance to assist manager's decision-making in worker-task assignments without tackling complex MINLP models. In order to understand the performance implications of workforce heterogeneity, this paper examines analytically how heterogeneity in each of the four parameters of the exponential learning and forgetting (L/F) model affects system performance in three cases : consecutive assignments with no break, n breaks of s-length each, and total b break-periods occurred over T periods. The study presents the direction of change in worker performance under different assignment schedules as the variance in initial expertise, steady-state productivity, learning or forgetting increases. Thus, it implies whether having more heterogenous workforce in terms of each of four parameters in the L/F model is desired or not in different schedules from the perspective of system productivity measurement.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.4_2
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• pp.645-653
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• 2022

Recently, deep reinforcement learning has been applied to a variety of industries, such as games, robotics, autonomous vehicles, and data cooling systems. An algorithm called reinforcement learning allows for automated asset allocation without the requirement for ongoing monitoring. It is free to choose its own policies. The purpose of this paper is to carry out an empirical analysis of the performance of asset allocation strategies. Among the strategies considered were the conventional Mean- Variance Optimization (MVO) and the Proximal Policy Optimization (PPO). According to the findings, the PPO outperformed both its benchmark index and the MVO. This paper demonstrates how dynamic asset allocation can benefit from the development of a reinforcement learning algorithm.

• Computers and Concrete
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• v.32 no.6
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• pp.607-613
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• 2023

The objective of this work is to determine the compressive strength of geopolymer concrete utilizing four distinct machine learning approaches. These techniques are known as gradient boosting machine (GBM), generalized linear model (GLM), extremely randomized trees (XRT), and deep learning (DL). Experimentation is performed to collect the data that is then utilized for training the models. Compressive strength is the response variable, whereas curing days, curing temperature, silica fume, and nanosilica concentration are the different input parameters that are taken into consideration. Several kinds of errors, including root mean square error (RMSE), coefficient of correlation (CC), variance account for (VAF), RMSE to observation's standard deviation ratio (RSR), and Nash-Sutcliffe effectiveness (NSE), were computed to determine the effectiveness of each algorithm. It was observed that, among all the models that were investigated, the GBM is the surrogate model that can predict the compressive strength of the geopolymer concrete with the highest degree of precision.

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

• Korean Management Science Review
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• v.26 no.2
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• pp.71-89
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• 2009

The purpose of this research is to identify the factors affecting user acceptance of e-learning site. To more precisely explain an individual's behavior of accepting e-learning site, Perceived Interactivity is divided into four components; User Control, Responsiveness, Personalization and Connectedness. This research investigates the causal relationship among four components and basic factors of TAM. This research uses structural equation modeling (SEM) to confirm the validity and analyzes the causal relationship of the suggested model. The results indicates strong support for the validity of proposed model with 54.8% of the variance in behavioral intention to e-learning site. The result finds that all the basic casuality of TAM are significant and most components of Perceived Interactivity are significant. However the path Connectedness to Perceived Ease of Use and User Control to Perceived Playfulness is not significant. Among components of Perceived Interactivity, Personalization is the strongest antecedent of TAM. Perceived Usefulness is the strongest antecedent of behavioral intention of e-learning site.

• Journal of Korean Critical Care Nursing
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• v.7 no.2
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• pp.1-13
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• 2014

Purpose: The purpose of this study was to determine the difference in reported discharge learning needs between nurses and liver transplantation (LT) patients. Methods: The participants of this study were 40 patients discharged after LT at P University Hospital in Y City and 42 nurses in intensive care units and the ward. The data were collected for two months from December 1, 2012, to January 31, 2013, and were analyzed using descriptive statistics, Student's t-test and analysis of variance (ANOVA). Results: Patients earning a low income (p=.041), having no experience of hospitalization after LT (p=.023), and receiving information about LT from nurses (p=.003) indicated higher discharge learning needs. Among the items evaluated regarding discharge learning needs, "rejection symptoms or signs" were regarded to be more important by nurses than LT patients (p=.038). However, "management of other diseases after LT" (p=.003), "risk of recurrence" (p=.001), "food choices" (p<.001), "obesity prevention" (p=.020), "amount of exercise" (p=.007), and "ways to receive financial help"(p=.033), were thought to be more important by LT patients than nurses. Conclusion: There exist differences between LT patients and nurses with respect to their perceptions of LT discharge learning needs. Therefore, an individualized education program reflecting patients' conditions and learning needs rather than providing information uniformly needs to be developed.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.319-330
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• 2019

Forecasting the electricity consumption through analyzing the past electricity consumption a advantageous for energy planing and policy. Machine learning is widely used as a method to predict electricity consumption. Among them, ensemble learning is a method to avoid the overfitting of models and reduce variance to improve prediction accuracy. However, ensemble learning applied to daily data shows the disadvantages of predicting a center value without showing a peak due to the characteristics of ensemble learning. In this study, we overcome the shortcomings of ensemble learning by considering the temperature trend. We compare nine models and propose a model using random forest with the linear trend of temperature.

• Journal of Technologic Dentistry
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• v.42 no.4
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• pp.402-408
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• 2020

Purpose: To understand the students' level of recognition of online classes in the Department of Dental Technology and to provide the basic data for designing online classes based on the dental technology course. Methods: A survey was conducted among the students of the dental technology department. The collected data was analyzed with the SPSS ver. 25.0 program. To ensure a reliable verification, the α=0.05 significance level was used. The t-test and analysis of variance were also performed. Results: The students' level of recognition of online classes in the Department of Dental Technology is shown in the rate of recognition for video-based classes for both the theory and experiments. Students displayed high positivity with the video-based learning as it is repeated learning that is not affected by the limitations of time. In addition, video-based learning is highly beneficial in terms of convenience, satisfaction, and achievement for learning. Conclusion: Based on the results, video-based learning is a highly positive learning type for students. It was also recommended that the Department of Dental Technology should offer a post-COVID-19 online class to include the blended methods of a face-to-face class and video-based learning.

• The Journal of Korean Society for School & Community Health Education
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• v.23 no.2
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• pp.39-50
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• 2022

Objectives: This study aimed to confirm the association between self-determination and self-directed learning among Korean nursing students, as well as the mediating effect of academic self-efficacy. Methods: Data from 139 nurse students were surveyed in this descriptive cross-sectional study. They were collected from Oct 1 to 30, 2020, using self-report questionnaires. The collected data were analyzed using descriptive statistics, independent t-test, one-way analysis of variance, Scheffé test, Pearson's correlation coefficient analysis, and mediated model for PROCESS macro using the SPSS/WIN 24.0. Results: Self-directed learning was positively associated with self-determination (r=.56, p<.001) and academic self-efficacy (r=.63, p<.001). Furthermore, academic self-efficacy had a mediating effect on the relationship between self-determination and self-directed learning (B=0.21, 95% CI=0.12~0.32). Conclusion: The impact of self-determination on the self-directed learning among nursing students was mediated by academic self-efficacy. Therefore, these results provide important data for future self-directed learning in nursing education.