• Korean Journal of Clinical Pharmacy
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• v.27 no.2
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• pp.55-62
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• 2017

Experiential education is a core curriculum of pharmacy education. In experiential education, formative feedback is an integral component of learning and teaching process. Feedback is defined as information provided by a preceptor regarding student's performance based on direct observation. With effective feedback, students can have opportunities to reinforce or correct behaviors and to acquire knowledge or skills. Students highly value and appreciate feedback. They rank provision of effective feedback as one of the most important qualities of preceptors. Preceptors, however, lack an understanding of feedback or practical skills necessary for providing effective feedback. As a result in reality, the feedback provided to students can be differentially effective in improving students' learning. This article describes a theoretical understanding of feedback including definition and value, as well as types of feedback. In addition, practical aspects in providing feedback, such as contents, timing, techniques, and models, are addressed. By understanding the value of feedback and mastering various feedback skills, preceptors will promote students' learning and enhance educational outcomes of experiential education.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.77-84
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• 2022

In this paper, we propose a novel unsupervised feature selection method. Conventional unsupervised feature selection method defines virtual label and uses a regression analysis that projects the given data to this label. However, since virtual labels are generated from data, they can be formed similarly in the space. Thus, in the conventional method, the features can be selected in only restricted space. To solve this problem, in this paper, features are selected using orthogonal projections and low-rank approximations. To solve this problem, in this paper, a virtual label is projected to orthogonal space and the given data set is also projected to this space. Through this process, effective features can be selected. In addition, projection matrix is restricted low-rank to allow more effective features to be selected in low-dimensional space. To achieve these objectives, a cost function is designed and an efficient optimization method is proposed. Experimental results for six data sets demonstrate that the proposed method outperforms existing conventional unsupervised feature selection methods in most cases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2180-2197
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• 2019

With the proliferation of the Android malicious applications, malware becomes more capable of hiding or confusing its malicious intent through the use of code obfuscation, which has significantly weaken the effectiveness of the conventional defense mechanisms. Therefore, in order to effectively detect unknown malicious applications on the Android platform, we propose DroidVecDeep, an Android malware detection method using deep learning technique. First, we extract various features and rank them using Mean Decrease Impurity. Second, we transform the features into compact vectors based on word2vec. Finally, we train the classifier based on deep learning model. A comprehensive experimental study on a real sample collection was performed to compare various malware detection approaches. Experimental results demonstrate that the proposed method outperforms other Android malware detection techniques.

• Journal of KIISE:Databases
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• v.36 no.4
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• pp.247-253
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• 2009

Rank learning and processing have gained much attention in the IR and data mining communities for the last decade. While other data mining techniques such as classification and regression have been actively researched to interoperate with RDBMS by using the tightly coupled or loose coupling approaches, ranking has been researched independently without integrating into RDBMS. This paper proposes a tightly coupled integration of the Ranking SVM into MySQL in order to perform the rank learning task efficiently within the RDBMS. We implemented new SQL commands for learning ranking functions and predicting ranking scores. We evaluated our tightly coupled integration of Ranking SVM by comparing it to a loose coupling implementation. The experiment results show that our approach has a performance improvement of $10{\sim}40%$ in the training phase and 60% in the prediction phase.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.519-532
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• 2016

Purpose: This study aimed to evaluate the effect of an HIV/AIDS education program for professional graduate medical school students using a teaching-learning methods. Methods: The design of this study was a nonequivalent control group pretest-posttest experiment. A total of 116 professional graduate medical school students in South Korea were included. They were randomly assigned to either a discussion-centered teaching-learning method group (n=60) or a lecture-centered teaching-learning method group (n=56). Data were collected between August and December 2015 and were analyzed using descriptive statistics, ${\chi}^2$-test, one-tailed independent t-test, one-tailed Mann-Whitney U-test, one-tailed Wilcoxon signed-rank test, and one-tailed paired t-test using SPSS WIN 19.0 program. Results: There was no statistically significant difference between the two groups with respect to HIV/AIDS knowledge, attitudes, and education satisfaction. However, the scores of knowledge and attitudes were statistically significantly increased after the education than before the education in both groups. Conclusion: To effectively improve the knowledge and attitude of HIV/AIDS, it is necessary to select an appropriate teaching-learning method for the target subjects and objectives of HIV/AIDS education.

• The Journal of Information Systems
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• v.29 no.3
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• pp.35-52
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• 2020

Purpose Local governments in each region actively hold local festivals for the purpose of promoting the region and revitalizing the local economy. Existing studies related to local festivals have been actively conducted in tourism and related academic fields. Empirical studies to understand the effects of latent variables on local festivals and studies to analyze the regional economic impacts of festivals occupy a large proportion. Despite of practical need, since few researches have been conducted to predict the number of visitors, one of the criteria for evaluating the performance of local festivals, this study developed a model for predicting the number of visitors through various observed variables using a machine learning algorithm and derived its implications. Design/methodology/approach For a total of 593 festivals held in 2018, 6 variables related to the region considering population size, administrative division, and accessibility, and 15 variables related to the festival such as the degree of publicity and word of mouth, invitation singer, weather and budget were set for the training data in machine learning algorithm. Since the number of visitors is a continuous numerical data, random forest, Adaboost, and linear regression that can perform regression analysis among the machine learning algorithms were used. Findings This study confirmed that a prediction of the number of visitors to local festivals is possible using a machine learning algorithm, and the possibility of using machine learning in research in the tourism and related academic fields, including the study of local festivals, was captured. From a practical point of view, the model developed in this study is used to predict the number of visitors to the festival to be held in the future, so that the festival can be evaluated in advance and the demand for related facilities, etc. can be utilized. In addition, the RReliefF rank result can be used. Considering this, it will be possible to improve the existing local festivals or refer to the planning of a new festival.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.1
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• pp.70-76
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• 2010

In this paper, we propose a personalized Chinese character education system using RankBoost algorithm on a smartphone. In a typical Chinese character education scenario, a trainee is supplied with a finite number of Chinese characters as an input set in the beginning. And, as the training session repeats, the trainee will notice her/his difficult characters in the set which she/he hardly answers. Those characters reflect their personalized degrees of difficulty. Our proposed system constructs these personalized degrees of difficulty using RankBoost algorithm. In the beginning, the algorithm start with the set of Chinese characters, of which each is associated with the same weight values. As the training sessions are repeated, the algorithm increase the weights of Chinese characters that the trainee mistakes, thereby eventually constructs the personalized difficulty degrees of Chinese characters. The proposed algorithm maximizes the educational effects by having the trainee exposed to difficult characters more than easy ones.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.19 no.3
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• pp.334-342
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• 2012

Purpose: This descriptive study was done to identify the academic achievement, self-directed learning (SDL), and critical thinking disposition (CTD) of nursing students according to their learning styles. Method: The participants were 240 nursing students. Data were collected using structured questionnaires which included Kolb's Learning Style Inventory, Academic Achievement in Fundamental Nursing and Health Assessment, Self Directed Learning Readiness Scale, and California Critical Thinking Disposition Inventory. Data were analyzed using ${\chi}^2$ test, ANOVA, Pearson' correlation coefficients, and Spearman rank correlation coefficient. Results: One third of respondents were shown to be Convergers in their learning style (33.3%). The Academic Achievement of students who were Convergers was significantly higher than those who were Divergers or Accommodators (F=5.95, p=.001). The SDL and CTD of students who were Convergers were significantly higher than Divergers and Assimilators (F=9.67, p<.001 and F=8.42, p<.001). No significant correlations were found between Academic Achievement and SDL or CTD, but a statistically significant positive correlation was found between SDL and CTD (r=.68, p<.001). Conclusion: The findings of this study indicate that learning style influences academic achievement, SDL and CTD.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.5
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• pp.853-862
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• 2003

With the data base subject of first grade paper test of information handling technician, We proposed special method of evaluating learning ability directivity to judge that student can understand the contents of each chapter exactly or not, using assigned function and fuzzy deduction in this thesis. Using fuzzy logic, the proposed method of evaluating learning ability is dividing the presenting frequency of setting questions for examination about the subject of database into three rank and we can define this as the important. We applied the fuzzy assigned rate about the number of times of studying through the important of studying and the fuzzy assigned rate about formative evaluation to each of nine fuzzy deduction theories and than evaluated comprehension rate of learning. With the fuzzy grade about learning comprehension of each chapter and assigned rate about the score of generalized evaluation; We applied these two thing to the deduction rule of fuzzy and made it as defuzzifier and finally evaluated learning. We made that the result of eventual evaluating learning is very useful for learners to diagnosis learned contents by themselves and also it can be great material to judge that learners can get the goal of learning or not synthetically.

• The Journal of the Korea Contents Association
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• v.20 no.1
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• pp.192-201
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• 2020

This study commercialization of 5G will enable transformation of university education. This study identifies five attributes (device type, learning place, learning content, learning field and expense payment) and corresponding levels to study the impact of 5G in the future of university education. The attributes and the levels are then combined into few 5G education service alternatives for respondents to rank. 102 students ranked the alternatives based on their preferences and intent to use. Results indicate that the intent to use 5G-based education service was high with 86% and the most important factor was expense payment (37%), followed by learning field (26%), learning content (24%), device type (8%) and learning place (5%). Specifically, students preferred smart device, practical and experiential content, ubiquitous (no limitation of space and time) learning, practical education and free rate when adopting 5G-based education service. These will provide implications to accelerate adoption of and exploitation of 5G for innovating university education.