• Journal of Engineering Education Research
• /
• v.27 no.1
• /
• pp.63-70
• /
• 2024

The capstone design classes in the College of Engineering are often team project-oriented, and teamwork skills are reported to positively impact problem-solving abilities. While team project courses have various advantages, they also come with challenges such as social loafing and issues related to free riders, which consistently hinder the positive effects of team project courses. To prevent these issues, there is a need to provide a clear evaluation-reward system for team members' contributions. In this study, we examined the reliability of peer evaluation scores for team members' contributions and reviewed methods to incorporate them into team project grades. The review results indicated that peer evaluation scores are deemed to have considerable reliability from a qualitative perspective. However, due to the relatively small team size in team project courses (3 to 6 members per team), using the arithmetic mean of peer evaluation scores is statistically challenging. As a complementary approach, this study proposes limiting the reflection ratio of peer evaluation scores and applying a more macroscopic processing method, not the arithmetic mean, to incorporate peer evaluation scores for team contributions into grades.

• Journal of Information Processing Systems
• /
• v.18 no.5
• /
• pp.650-664
• /
• 2022

Mobile applications can be easily downloaded and installed via markets. However, malware and malicious applications containing unwanted advertisements exist in these application markets. Therefore, smartphone users install applications with reference to the application review to avoid such malicious applications. An application review typically comprises contents for evaluation; however, a false review with a specific purpose can be included. Such false reviews are known as fake reviews, and they can be generated using artificial intelligence (AI)-based text-generating models. Recently, AI-based text-generating models have been developed rapidly and demonstrate high-quality generated texts. Herein, we analyze the features of fake reviews generated from Generative Pre-Training-2 (GPT-2), an AI-based text-generating model and create a model to detect those fake reviews. First, we collect a real human-written application review from Kaggle. Subsequently, we identify features of the fake review using natural language processing and statistical analysis. Next, we generate fake review detection models using five types of machine-learning models trained using identified features. In terms of the performances of the fake review detection models, we achieved average F1-scores of 0.738, 0.723, and 0.730 for the fake review, real review, and overall classifications, respectively.

• Journal of Korean Society of Occupational and Environmental Hygiene
• /
• v.29 no.2
• /
• pp.217-225
• /
• 2019

Objectives: The aim of this study is to evaluate the quality management systems of domestic asbestos survey and monitoring service providers and the relationships with the number of licenses or designations and sales performances. Methods: Data on quality management systems were collected by assessors who were assigned by the Korea Occupational Safety and Health Agency(KOSHA) during a pilot evaluation program for designated asbestos survey and monitoring service providers in 2016 using evaluation criteria developed by KOSHA. Basic characteristics, evaluated scores, and sales performance were gathered and statistically analyzed. Results: The median and arithmetic mean of the total scores were 0.64 and 0.66. Evaluation fields that scored highly with the highest percentages were sales performance, installation and availability of equipment, compliance with the mandatory minimum number of airborne samples, laboratory independence, and results of proficiency analytical testing, in that order. Evaluation fields that received low marks with the highest percentages were the training of personnel, blank field samples, calibration of flow rates, preliminary check and visual inspection of the work area prior to the clearance test, and review and approval of final reports, in that order. Comparison of normalized scores between service providers registered for asbestos and other tasks and those designated for only asbestos showed significant differences in their evaluated scores. Sales performance did not show a positive correlation with evaluated scores. Conclusions: The quality management systems of domestic asbestos survey and monitoring service providers were poor. High scores were recorded mostly in evaluation fields related to regulatory requirements. Low scores were recorded mostly in evaluation fields related to documentation and recordkeeping. Considering the low influence of quality on sales performance, the government needs to evaluate the quality management of asbestos survey and monitoring service providers and provide the results to public in order to address their low levels of quality management.

• The Journal of the Korea Contents Association
• /
• v.20 no.11
• /
• pp.228-236
• /
• 2020

As of March 2020, this study divided the applications for young children installed on Android-based smart-phones in Korea into top and bottom groups according to review scores, and selected 30 applications each, conducted content analysis and application evaluation, and looked at differences between groups. Through this, by providing objective information on the smart-phone application for young children, it is intended to help parents and early childhood education professionals select high-quality applications, and to present ideas and directions for developing applications suitable for development to application developers. As a result of application content analysis, only data presentation type, simulation type, and game type were found in all the top and bottom groups as for the application type. There was a difference in order. In the case of app purchase cost, the top group in the review score was evenly distributed from the low price to the high price of 100,000 won or more, while the bottom group had few high-priced applications. On the other hand, as a result of application evaluation, a significant difference was found in the entire evaluation score, including all functional elements and all content elements, between the top and bottom groups of the review score. In the case of detailed sub-factors, significant differences were shown in all factors except 'technicality' of functional elements.

• International Commerce and Information Review
• /
• v.17 no.2
• /
• pp.69-93
• /
• 2015

Developing countries including poor countries cannot accumulate enough domestic saving and government budget for their industrialization. They need to finance the capital for development from abroad sources; foreign direct investment (FDI) and official development assistance (ODA). The developing countries can improve their business climate for more ODA. This paper examines whether ODA improve the business climate of developing countries. In this paper, the business climate are measured by the starting business scores and the scores of credit and protecting investor in Doing Business project of World Bank. According to the empirical result, ODA has significant effect on the starting business scores for low and lower middle income countries, but insignificant effect for upper middle countries. In the case of the scores of credit and protecting investor, ODA has significant effect only for lower middle income countries.

• Journal of Korean Society for Atmospheric Environment
• /
• v.20 no.E1
• /
• pp.29-33
• /
• 2004

In environmental studies, decisions are often made on the analytical data indicating certain contaminants as being 'detected' or 'non-detectible.' Since detection limits are analytical method specific, one has to first review the concepts and definitions associated with analytical method systems and specifications. In this study, the experimental analytical values for a series of low level standards (for an ionic species) were used as an example to estimate two different method detection limits (MDL). The scores of EPA's MDL and Pallesen's MDL determined by real analytical scores are 0.0575 and 0.0561 mg/L, respectively for our nitrate data. These scores determined by two different MDL models are roughly similar, while there are apparent differences between two methods with respect to statistical and systematical procedure. However, determination of MDL for one's laboratory provides some practical applications which helps to assure one's regulating authorities that one's measured scores are accurate.

• Women's Health Nursing
• /
• v.29 no.3
• /
• pp.190-199
• /
• 2023

Purpose: This study aimed to describe the characteristics of mobile applications (apps) related to pregnancy in South Korea and evaluate their quality. Methods: We conducted a systematic search for pregnancy-related apps available in Korea in two app stores as of April 29, 2022. The quality of apps was assessed using the Korean translation of the Mobile Application Rating Scale for objective quality with four subdomains (engagement, function, aesthetics, and information) and four items for subjective quality. Results: In total, 163 apps were selected and reviewed. Both the objective and subjective quality of the apps were found to be desirable, with scores exceeding 3 out of 5 (range, 34-82). All subdomain scores in the objective quality assessment were also desirable. Among the four objective quality subdomains, aesthetics received the highest scores, followed by information, function, and engagement. In terms of subjective quality, the scores for a comprehensive overall evaluation, continuous use, and recommendation exceeded 3 out of 5, with the exception of payment. Only a small number of apps (n=4, 2.9%) were backed by a reliable authority, such as an expert review. Significant differences were observed in the objective quality of apps across different content categories (F=3.86, p=.003). Conclusion: Most pregnancy-related apps had desirable levels of objective and subjective quality. However, app content experts seldom provide reviews. It is crucial for nurses to recommend apps to expectant mothers that offer dependable content, regularly updated with the latest information.

• The Journal of Information Systems
• /
• v.31 no.2
• /
• pp.159-177
• /
• 2022

Purpose Online reviews are critical for sales of online shopping platforms because they provide useful information to consumers. As the eCommerce market grows rapidly, the role of online reviews is becoming more important. The purpose of this study is to analyze how online reviews written by domestic consumers affect product sales by classifying the types of products. Design/methodology/approach This study analyzed how the effects of review characteristics(reviewer reputation, reviewer exposure, review length, time, rating, image, and emotional score) on the usefulness of online reviews differ depending on the product types. Subsequently, how the impact of review attributes (review usefulness, number of reviews, ratings, and emotional scores) on product sales differs according to each product type was compared. Based on the FCB Grid model, the product type was classified into high involvement-rational, high involvement-emotional, low involvement -rational, and low involvement-emotional product types. Findings According to the analysis result, the characteristics of reviews useful to consumers were different for each product type, and the review attributes affecting product sales were also different for each product type. This study confirmed that it revealed that product characteristics are major consideration in evaluating the review usefulness and the factors affecting product sales.

• Health Policy and Management
• /
• v.21 no.4
• /
• pp.527-540
• /
• 2011

Some patients tend to visit tertiary hospitals instead of non-tertiary hospitals for minor illnesses, which is a chronic problem within the Korean health care delivery system. In order to reduce the number of patients with minor severity diseases unnecessarily utilizing the tertiary medical services in Korea, the Ministry of Health and Welfare raised the outpatient co-insurance rate for the tertiary hospitals in July, 2009. Another increase in the prescription drug co-insurance rate by the general and tertiary hospitals is scheduled to take place in the second half of 2011. An increase in copayments may discourage the utilization rate of medical services among the underprivileged or patients who require complicated procedures. This study aims to analyze the diabetic patients' utilization rates of tertiary hospitals according to the Comorbidity score. Diabetic patients' data was gathered from the Health Insurance Claims Records in the Health Insurance Review & Assessment Service between 2007-2009. Comorbidity scores are measured by the Charlson Comorbidity Index and the Elixhauser Index. Chi-square and logistic regressions were performed to compare the utilization rates of both insulin-dependents (n=94,026) and non-insulin-dependents (n=1,424,736) in tertiary hospitals. The higher Comorbidity outcomes in the insulin-dependent diabetic patients who didn't visit tertiary hospitals compared to those who did, was expected. However, after adjusting the gender, age, location, first visits and complications, the groups that scored >=1 on the comorbidity scale utilized the tertiary hospitals more than the O score group. Non-insulin-diabetic patients with higher Comorbidity scores visited tertiary hospitals more than patients who received lower grades. This study found that patients suffering from severe diabetes tend to frequently visit the tertiary hospitals in Korea. This result implied that it is important for Korea to improve the quality of its primary health care as well as to consider a co-insurance rate increase.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.219-239
• /
• 2019

As the importance of providing customized services to individuals becomes important, researches on personalized recommendation systems are constantly being carried out. Collaborative filtering is one of the most popular systems in academia and industry. However, there exists limitation in a sense that recommendations were mostly based on quantitative information such as users' ratings, which made the accuracy be lowered. To solve these problems, many studies have been actively attempted to improve the performance of the recommendation system by using other information besides the quantitative information. Good examples are the usages of the sentiment analysis on customer review text data. Nevertheless, the existing research has not directly combined the results of the sentiment analysis and quantitative rating scores in the recommendation system. Therefore, this study aims to reflect the sentiments shown in the reviews into the rating scores. In other words, we propose a new algorithm that can directly convert the user 's own review into the empirically quantitative information and reflect it directly to the recommendation system. To do this, we needed to quantify users' reviews, which were originally qualitative information. In this study, sentiment score was calculated through sentiment analysis technique of text mining. The data was targeted for movie review. Based on the data, a domain specific sentiment dictionary is constructed for the movie reviews. Regression analysis was used as a method to construct sentiment dictionary. Each positive / negative dictionary was constructed using Lasso regression, Ridge regression, and ElasticNet methods. Based on this constructed sentiment dictionary, the accuracy was verified through confusion matrix. The accuracy of the Lasso based dictionary was 70%, the accuracy of the Ridge based dictionary was 79%, and that of the ElasticNet (${\alpha}=0.3$) was 83%. Therefore, in this study, the sentiment score of the review is calculated based on the dictionary of the ElasticNet method. It was combined with a rating to create a new rating. In this paper, we show that the collaborative filtering that reflects sentiment scores of user review is superior to the traditional method that only considers the existing rating. In order to show that the proposed algorithm is based on memory-based user collaboration filtering, item-based collaborative filtering and model based matrix factorization SVD, and SVD ++. Based on the above algorithm, the mean absolute error (MAE) and the root mean square error (RMSE) are calculated to evaluate the recommendation system with a score that combines sentiment scores with a system that only considers scores. When the evaluation index was MAE, it was improved by 0.059 for UBCF, 0.0862 for IBCF, 0.1012 for SVD and 0.188 for SVD ++. When the evaluation index is RMSE, UBCF is 0.0431, IBCF is 0.0882, SVD is 0.1103, and SVD ++ is 0.1756. As a result, it can be seen that the prediction performance of the evaluation point reflecting the sentiment score proposed in this paper is superior to that of the conventional evaluation method. In other words, in this paper, it is confirmed that the collaborative filtering that reflects the sentiment score of the user review shows superior accuracy as compared with the conventional type of collaborative filtering that only considers the quantitative score. We then attempted paired t-test validation to ensure that the proposed model was a better approach and concluded that the proposed model is better. In this study, to overcome limitations of previous researches that judge user's sentiment only by quantitative rating score, the review was numerically calculated and a user's opinion was more refined and considered into the recommendation system to improve the accuracy. The findings of this study have managerial implications to recommendation system developers who need to consider both quantitative information and qualitative information it is expect. The way of constructing the combined system in this paper might be directly used by the developers.