• The Journal of Korean Society for School & Community Health Education
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• v.4
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• pp.117-139
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• 2003

The purpose of this research lies in presenting logical viability for the measures that curtail access to lewd Internet contents by middle school students amidst the reality in which lewd contents are circulated freely through the Internet, a medium that the middle school students find most easy to access. In order to establish right form of awareness towards sex, this research identified the ways they access the lewd Internet contents, their reaction after the exposure to those contents, their knowledge of sex, their concerns regarding sex and their accessibility to sexual activities in order to conduct a comparative analysis on the relationship between lewd Internet contents and their awareness of sex. First, realities of accessing lewd Internet contents and reactions according to the demographics of middle school students There isa significant difference in the experience of accessing lewd Internet contents in terms of gender. Mostly, male students tend to access the contents more. As for the way they access the lewd Internet contents, both male and female students replied that they access through spam mail of lewd nature. Thus, measures to address this problem are needed urgently. As to when they first accessed the lewd contents, most of the research subjects replied that they accessed either in elementary school period or in the early middle school period. This shows that most of the students got exposed to lewd contents even before they could establish positive, correct awareness of sex. Thus, there is a risk that they may formulate wrong kind of sexual awareness. Accordingly, it is necessary to develop measures through focused sex education. Students are divided into two groups according to the time they spend on the contents averagely: those who spend over one hour and those who spend less than an hour on the lewd contents. If the students spending longer hours are not to be checked and properly guided, it may lead to increasing cases of sexual delinquencies due to their wrongly formed awareness of sex. When the question of existence(non-existence) of guardian was addressed, students with both parents tend to access the lewd Internet contents in a more diverse manner and tend to access more compared to those students from single parent or no-parent families. Accordingly, guardians need to pay attention to how their children are using the Internet. Second, awareness of sex depending on the middle school students' demographics In case of sexual knowledge, middle school students shows relatively high level of knowledge. In particular, female students are found more knowledgeable than male students, and the students in upper years are more knowledgeable as well. As a result, this research recommends that the students in lower years should be guided with more basc and detailed information, while those in upper years need to be taught to form and express their own thoughts and attitudes and to build up independence on this matter. In case of worries about sex, both male and female students don't worry too much about it. However, male students are more concerned about sex than female students in a more diverse ways. As for the differences by academic year, concerns for sex increase, as students get older. Accordingly, sex education that helps establish sound perception of the opposite sex and that focuses on the etiquettes that one must adhere to at the presence of the opposite sex need to be conducted against middle school students. In case of accessibility to sex, male students manifest higher tendency than female students. As for the differences by academic year, those in the first and second years show higher accessibility than those in their third year. In general, younger students tend to be more open-minded toward sex. Accordingly, students in lower academic years need to undergo basic knowledge oriented sex education, whereas those in upper academic years need to undergo discussion centered sex education where they subject to questions regarding their attitude and opinion. Third, relationship between the reactions after the exposure to the lewd Internet contents and their awareness of sex As the frequency of contact with lewd Internet contents increases, awareness of sex increases as well. Thus, the lewd Internet contents contribute to the increase in students' concern for sex as well as increase in accessibility to sex, which eventually become barriers to students' establishment of a healthy perception of sex. Reactions to sex after the exposure to lewd Internet contents and awareness of sex indicate a significant correlation. However, negative correlation is manifested with knowledge of sex. Thus, it is possible to know that the greater access to lewd Internet contents does not necessarily translate into increase in knowledge of sex. However, the study showed there is a correlation between concerns for sex and the level of accessibility to sex. In more detail, the more reactions to the contents they show, the more concerns for sex they have and the more positively they take acceptability to sex. Moreover, it is necessary to develop necessary measures since textbooks today do not include measures needed to address the lewd Internet contents. Given the above findings, it is necessary to continue to complement structural measures in order to prevent easy access of lewd Internet contents by middle school students. Moreover, it is necessary to be considerate of the students so that they themselves can form a healthy Internet culture and grow up within positive framework for the sex education.

• Journal of Korean Society of Forest Science
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• v.109 no.4
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• pp.371-381
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• 2020

The Ministry of Environment (ME) is planning to adopt in 2020 the IUCN regional Red List for "Guidelines for listing and delisting rare & endangered species and management of endangered Species System". The ME designated 377 species of vascular plants on the regional Red List. In a previous study it had been suggested that 103 species from this list are candidates for the regional Red List. With respect to a possible Red List, we assessed 59 species (after excluding 34 additional NA species and ten endemic species). These assessments indicated that 16 species are at the "threatened" level. Of those, one species is Critically Endangered, ten are Endangered, and five are Vulnerable. A further four species are classified as Near Threatened, 30 as Of Least Concern, and nine as Data Deficient. We found that most of the assessments proposed by the Ministry of Environment were not supported by scientific data, including quantitative geographic data (over 70%) in Criteria B. In order to determine the endangered species belonging to the orchid family, it is necessary to obtain records of illegal activities or data on overcollection. The current problem with the Ministry of Environment Red List has been the lack of management of scientific data on species showing a trend in decreasing population in the mid- to long-term; thus, there is a lack of critical resources for policy-makers. The ME legally designated categories and assessment, and the lack of expertise in failing to comply with the legal law by itself. The key to presenting an accurate overview of the state of Korean flora is to fill the information gaps with respect to significant geographical and taxonomical biases in the quality and quantity of data. By regularly updating the qualified data, we will be able to track the changes in the conservation status of the flora and inform the necessary conservation policies.

• Korean Journal of Forensic Psychology
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• v.13 no.1
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• pp.1-22
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• 2022

This study tested the effect of the verdict category of lay-participation trial in Korea on the legal decision of layperson and the role of representation of 'innocent' in the process. Representation of 'innocent' refers to a psychological threshold for deciding someone's innocence (no fault or sin) in a general sense. The functions as a threshold for a legal decision of 'beyond a reasonable doubt (BRD)' and the individual threshold (IT), regarded as a standard for judgment of guilt established by law and an estimate of an individual's threshold, respectively, were compared. This study used a 2×2 complete factorial design in which the verdict category (guilty/innocent vs. guilty/not guilty) and the defendant's likelihood of guilt (low vs. high) were manipulated. Data from 137 lay-people who voluntarily participated in the online experiment was analyzed. The experiment's procedure was in the order of measuring 'representation of innocent' and the likelihood of guilt of an accused, presenting one of four trial vignettes, and obtaining legal decisions (verdict confidence and estimation of the likelihood of guilt for the defendant). As a result, it was found that the verdict category did not significantly affect the legal decision of layperson. However, the guilty verdict rate of the 'guilty/innocent' condition tended to be higher than those of the 'guilty/not guilty' condition. The layperson's representation of 'innocent' and the verdict category had an interaction effect on the difference between BRD and IT (threshold change) at the significance level of .1. In the 'guilty/innocent' condition, the threshold change varying with layperson's representation of 'innocent' was larger than in the 'guilty/not guilty' condition. In comparing the function of BRD and IT, IT significantly predicted the lay person's legal decision at the significance level of .1 by interacting with the likelihood of guilt for the defendant. Therefore, it could be said that IT was a better threshold estimator than BRD. The implication of this study is that it provided experimental evidence for the effect of the verdict category of lay-participation trial in Korea, which is a problem often raised among lawyers, and suggested logical reasoning and empirical grounds for the psychological mechanism of the possible effect.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.263-286
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• 2022

This study acquires NBA statistical information for a total of 32 years from 1990 to 2022 using web crawling, observes variables of interest through exploratory data analysis, and generates related derived variables. Unused variables were removed through a purification process on the input data, and correlation analysis, t-test, and ANOVA were performed on the remaining variables. For the variable of interest, the difference in the mean between the groups that advanced to the playoffs and did not advance to the playoffs was tested, and then to compensate for this, the average difference between the three groups (higher/middle/lower) based on ranking was reconfirmed. Of the input data, only this year's season data was used as a test set, and 5-fold cross-validation was performed by dividing the training set and the validation set for model training. The overfitting problem was solved by comparing the cross-validation result and the final analysis result using the test set to confirm that there was no difference in the performance matrix. Because the quality level of the raw data is high and the statistical assumptions are satisfied, most of the models showed good results despite the small data set. This study not only predicts NBA game results or classifies whether or not to advance to the playoffs using machine learning, but also examines whether the variables of interest are included in the major variables with high importance by understanding the importance of input attribute. Through the visualization of SHAP value, it was possible to overcome the limitation that could not be interpreted only with the result of feature importance, and to compensate for the lack of consistency in the importance calculation in the process of entering/removing variables. It was found that a number of variables related to three points and errors classified as subjects of interest in this study were included in the major variables affecting advancing to the playoffs in the NBA. Although this study is similar in that it includes topics such as match results, playoffs, and championship predictions, which have been dealt with in the existing sports data analysis field, and comparatively analyzed several machine learning models for analysis, there is a difference in that the interest features are set in advance and statistically verified, so that it is compared with the machine learning analysis result. Also, it was differentiated from existing studies by presenting explanatory visualization results using SHAP, one of the XAI models.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.443-472
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• 2014

To survive in the global competitive environment, enterprise should be able to solve various problems and find the optimal solution effectively. The big-data is being perceived as a tool for solving enterprise problems effectively and improve competitiveness with its' various problem solving and advanced predictive capabilities. Due to its remarkable performance, the implementation of big data systems has been increased through many enterprises around the world. Currently the big-data is called the 'crude oil' of the 21st century and is expected to provide competitive superiority. The reason why the big data is in the limelight is because while the conventional IT technology has been falling behind much in its possibility level, the big data has gone beyond the technological possibility and has the advantage of being utilized to create new values such as business optimization and new business creation through analysis of big data. Since the big data has been introduced too hastily without considering the strategic value deduction and achievement obtained through the big data, however, there are difficulties in the strategic value deduction and data utilization that can be gained through big data. According to the survey result of 1,800 IT professionals from 18 countries world wide, the percentage of the corporation where the big data is being utilized well was only 28%, and many of them responded that they are having difficulties in strategic value deduction and operation through big data. The strategic value should be deducted and environment phases like corporate internal and external related regulations and systems should be considered in order to introduce big data, but these factors were not well being reflected. The cause of the failure turned out to be that the big data was introduced by way of the IT trend and surrounding environment, but it was introduced hastily in the situation where the introduction condition was not well arranged. The strategic value which can be obtained through big data should be clearly comprehended and systematic environment analysis is very important about applicability in order to introduce successful big data, but since the corporations are considering only partial achievements and technological phases that can be obtained through big data, the successful introduction is not being made. Previous study shows that most of big data researches are focused on big data concept, cases, and practical suggestions without empirical study. The purpose of this study is provide the theoretically and practically useful implementation framework and strategies of big data systems with conducting comprehensive literature review, finding influencing factors for successful big data systems implementation, and analysing empirical models. To do this, the elements which can affect the introduction intention of big data were deducted by reviewing the information system's successful factors, strategic value perception factors, considering factors for the information system introduction environment and big data related literature in order to comprehend the effect factors when the corporations introduce big data and structured questionnaire was developed. After that, the questionnaire and the statistical analysis were performed with the people in charge of the big data inside the corporations as objects. According to the statistical analysis, it was shown that the strategic value perception factor and the inside-industry environmental factors affected positively the introduction intention of big data. The theoretical, practical and political implications deducted from the study result is as follows. The frist theoretical implication is that this study has proposed theoretically effect factors which affect the introduction intention of big data by reviewing the strategic value perception and environmental factors and big data related precedent studies and proposed the variables and measurement items which were analyzed empirically and verified. This study has meaning in that it has measured the influence of each variable on the introduction intention by verifying the relationship between the independent variables and the dependent variables through structural equation model. Second, this study has defined the independent variable(strategic value perception, environment), dependent variable(introduction intention) and regulatory variable(type of business and corporate size) about big data introduction intention and has arranged theoretical base in studying big data related field empirically afterwards by developing measurement items which has obtained credibility and validity. Third, by verifying the strategic value perception factors and the significance about environmental factors proposed in the conventional precedent studies, this study will be able to give aid to the afterwards empirical study about effect factors on big data introduction. The operational implications are as follows. First, this study has arranged the empirical study base about big data field by investigating the cause and effect relationship about the influence of the strategic value perception factor and environmental factor on the introduction intention and proposing the measurement items which has obtained the justice, credibility and validity etc. Second, this study has proposed the study result that the strategic value perception factor affects positively the big data introduction intention and it has meaning in that the importance of the strategic value perception has been presented. Third, the study has proposed that the corporation which introduces big data should consider the big data introduction through precise analysis about industry's internal environment. Fourth, this study has proposed the point that the size and type of business of the corresponding corporation should be considered in introducing the big data by presenting the difference of the effect factors of big data introduction depending on the size and type of business of the corporation. The political implications are as follows. First, variety of utilization of big data is needed. The strategic value that big data has can be accessed in various ways in the product, service field, productivity field, decision making field etc and can be utilized in all the business fields based on that, but the parts that main domestic corporations are considering are limited to some parts of the products and service fields. Accordingly, in introducing big data, reviewing the phase about utilization in detail and design the big data system in a form which can maximize the utilization rate will be necessary. Second, the study is proposing the burden of the cost of the system introduction, difficulty in utilization in the system and lack of credibility in the supply corporations etc in the big data introduction phase by corporations. Since the world IT corporations are predominating the big data market, the big data introduction of domestic corporations can not but to be dependent on the foreign corporations. When considering that fact, that our country does not have global IT corporations even though it is world powerful IT country, the big data can be thought to be the chance to rear world level corporations. Accordingly, the government shall need to rear star corporations through active political support. Third, the corporations' internal and external professional manpower for the big data introduction and operation lacks. Big data is a system where how valuable data can be deducted utilizing data is more important than the system construction itself. For this, talent who are equipped with academic knowledge and experience in various fields like IT, statistics, strategy and management etc and manpower training should be implemented through systematic education for these talents. This study has arranged theoretical base for empirical studies about big data related fields by comprehending the main variables which affect the big data introduction intention and verifying them and is expected to be able to propose useful guidelines for the corporations and policy developers who are considering big data implementationby analyzing empirically that theoretical base.