• The Journal of the Convergence on Culture Technology
• /
• v.10 no.4
• /
• pp.285-294
• /
• 2024

This study aims to enhance learners' creative and integrative thinking through the use of a practical music education program, facilitating high-quality artistic activities and the integration of various disciplines. To achieve this, a practical music education program incorporating the PDIE model was designed, and the content validity of the developed program was verified. Through this process, We have researched and described methodologies for multidisciplinary research that can be applied in practical music education. This paper focuses on the fourth session of the study, which deals with the creative and integrative education of practical music and mathematics. The mathematical theory of interest in this research is the Fibonacci sequence, fundamental to the golden ratio in art. The goal is to enable balanced and high-quality creative activities through learning and applying the Fibonacci sequence. Additionally, to verify the validity and effectiveness of the instructional plan, including the one used in the 15-week course, we have detailed the participants involved in the content validation, the procedures of the research, the research tools used, and the methods for collecting and analyzing various data. Through this, We have confirmed the potential of creative and integrative education in higher practical music education and sought to develop educational methodologies for cultivating various creative talents in subsequent research.

• Journal of Korean Home Economics Education Association
• /
• v.36 no.1
• /
• pp.71-94
• /
• 2024

This study aimed to develop, implement, and evaluate a practical, problem-based global citizenship education (GCED) program in the first grade of elementary school to suggest ways for practical global citizenship education in the next curriculum. This study proceeded following the ADDIE teaching design model. Through literature research, the relevance of global citizenship education to the 2022 national curriculum and the 2022 practical arts curriculum was analyzed, and based on the results, the learning elements of global citizenship education were extracted from the 2022 integrated subjects curriculum to develop the program. The program consisted of four teaching and learning plans for a total of eight sessions based on four practical problems, and was revised and supplemented through validation by a group of experts. The developed teaching and learning curriculum was implemented in all eight sessions over a period of four weeks with 16 first grade students at S Elementary School in Seoul. As a result of the program implementation, the mean of students' global citizenship awareness increased by 3.88 points (±3.91) from pre to post, and the qualitative evaluation of teachers' reflection journals and students' portfolios showed that the program influenced students to realize the importance of taking action to create a better and more sustainable world. Therefore, the practical problem-based global citizenship education program for first graders was found to be effective in fostering global citizenship awareness.

• Knowledge Management Research
• /
• v.25 no.3
• /
• pp.145-171
• /
• 2024

Literacy initially referred to the ability to read and understand written documents and processed information. With the advancement of digital technology, the scope of literacy expanded to include the access and use of digital information, evolving into the concept of digital literacy. The application and purpose of digital literacy vary across different fields, leading to the use of various terminologies. This study focuses on generative artificial intelligence (AI), which is gaining increasing importance in the AI era, to assess users' literacy levels. The research aimed to extend the concept of literacy proposed in previous studies and develop a tool suitable for Korean users. Through exploratory factor analysis, we identified that generative AI literacy consists of four factors: AI utilization ability, critical evaluation, ethical use, and creative application. Subsequently, confirmatory factor analysis validated the statistical appropriateness of the model structure composed of these four factors. Additionally, correlation analyses between the newly developed literacy tool and existing AI literacy scales and AI service evaluation tools revealed significant relationships, confirming the validity of the tool. Finally, the implications, limitations, and directions for future research are discussed.

• Korean journal of applied entomology
• /
• v.51 no.3
• /
• pp.235-243
• /
• 2012

Population dynamics of the American serpentine leafminer, Liriomyza trifolii (Burgess), were observed and modeled in order to compare the effects of air and tomato leaf temperatures inside a greenhouse using DYMEX model builder and simulator (pre-programed module based simulation programs developed by CSIRO, Australia). The DYMEX model simulator consisted of a series of modules with the parameters of temperature dependent development and oviposition models of L. trifolii were incorporated from pre-published data. Leaf surface temperatures of cherry tomato leaves (cv. 'Koko') were monitored according to three tomato plant positions (top, > 1.8 m above the ground level; middle, 0.9 - 1.2 m; bottom, 0.3 - 0.5 m) using an infrared temperature gun. Air temperature was monitored at the same three positions using a self-contained temperature logger. Data sets for the observed air temperature and average leaf surface temperatures were collected (top and bottom surfaces), and incorporated into the DYMEX simulator in order to compare the effects of air and leaf surface temperature on the population dynamics of L. trifolii. The initial population consisted of 50 eggs, which were laid by five female L. trifolii in early June. The number of L. trifolii larvae was counted by visual inspection of the tomato plants in order to verify the performance of DYMEX simulation. The egg, pupa, and adult stage of L. trifolii could not be counted due to its infeasible of visual inspection. A significant positive correlation between the observed and the predicted numbers of larvae was found when the leaf surface temperatures were incorporated into the DYMEX simulation (r = 0.97, p < 0.01), but no significant positive correlation was observed with air temperatures(r = 0.40, p = 0.18). This study demonstrated that the population dynamics of L. trifolii was affected greatly by the leaf temperatures, though to little discernible degree by the air temperatures, and thus the leaf surface temperature should be for a consideration in the management of L. trifolii within cherry tomato greenhouses.

• Asia pacific journal of information systems
• /
• v.21 no.4
• /
• pp.45-62
• /
• 2011

The notion of information systems (IS) continuance has recently emerged as one of the most important research issues in the field of IS. A great deal of research has been conducted thus far on the basis of theories adapted from various disciplines including consumer behaviors and social psychology, in addition to theories regarding information technology (IT) acceptance. This previous body of knowledge provides a robust research framework that can already account for the determination of IS continuance; however, this research points to other, thus-far-unelucidated determinant factors such as habit, which were not included in traditional IT acceptance frameworks, and also re-emphasizes the importance of emotion-related constructs such as satisfaction in addition to conscious intention with rational beliefs such as usefulness. Experiences should also be considered one of the most important factors determining the characteristics of information system (IS) continuance and the features distinct from those determining IS acceptance, because more experienced users may have more opportunities for IS use, which would allow them more frequent use than would be available to less experienced or non-experienced users. Interestingly, experience has dual features that may contradictorily influence IS use. On one hand, attitudes predicated on direct experience have been shown to predict behavior better than attitudes from indirect experience or without experience; as more information is available, direct experience may render IS use a more salient behavior, and may also make IS use more accessible via memory. Therefore, experience may serve to intensify the relationship between IS use and conscious intention with evaluations, On the other hand, experience may culminate in the formation of habits: greater experience may also imply more frequent performance of the behavior, which may lead to the formation of habits, Hence, like experience, users' activation of an IS may be more dependent on habit-that is, unconscious automatic use without deliberation regarding the IS-and less dependent on conscious intentions, Furthermore, experiences can provide basic information necessary for satisfaction with the use of a specific IS, thus spurring the formation of both conscious intentions and unconscious habits, Whereas IT adoption Is a one-time decision, IS continuance may be a series of users' decisions and evaluations based on satisfaction with IS use. Moreover. habits also cannot be formed without satisfaction, even when a behavior is carried out repeatedly. Thus, experiences also play a critical role in satisfaction, as satisfaction is the consequence of direct experiences of actual behaviors. In particular, emotional experiences such as enjoyment can become as influential on IS use as are utilitarian experiences such as usefulness; this is especially true in light of the modern increase in membership-based hedonic systems - including online games, web-based social network services (SNS), blogs, and portals-all of which attempt to provide users with self-fulfilling value. Therefore, in order to understand more clearly the role of experiences in IS continuance, analysis must be conducted under a research framework that includes intentions, habits, and satisfaction, as experience may not only have duration-based moderating effects on the relationship between both intention and habit and the activation of IS use, but may also have content-based positive effects on satisfaction. This is consistent with the basic assumptions regarding the determining factors in IS continuance as suggested by Oritz de Guinea and Markus: consciousness, emotion, and habit. The principal objective of this study was to explore and assess the effects of experiences in IS continuance, with special consideration given to conscious intentions and unconscious habits, as well as satisfaction. IN service of this goal, along with a review of the relevant literature regarding the effects of experiences and habit on continuous IS use, this study suggested a research model that represents the roles of experience: its moderating role in the relationships of IS continuance with both conscious intention and unconscious habit, and its antecedent role in the development of satisfaction. For the validation of this research model. Korean university student users of 'Cyworld', one of the most influential social network services in South Korea, were surveyed, and the data were analyzed via partial least square (PLS) analysis to assess the implications of this study. In result most hypotheses in our research model were statistically supported with the exception of one. Although one hypothesis was not supported, the study's findings provide us with some important implications. First the role of experience in IS continuance differs from its role in IS acceptance. Second, the use of IS was explained by the dynamic balance between habit and intention. Third, the importance of satisfaction was confirmed from the perspective of IS continuance with experience.

• Korean Journal of Plant Resources
• /
• v.30 no.1
• /
• pp.38-49
• /
• 2017

The objective of this research was to develop Near-Infrared Reflectance Spectroscopy (NIRS) model for amylose and protein contents analysis of large accessions of rice germplasm. A total of 511 accessions of rice germplasm were obtained from National Agrobiodiversity Center to make calibration equation. The accessions were measured by NIRS for both brown and milled brown rice which was additionally assayed by iodine and Kjeldahl method for amylose and crude protein contents. The range of amylose and protein content in milled brown rice were 6.15-32.25% and 4.72-14.81%, respectively. The correlation coefficient ($R^2$), standard error of calibration (SEC) and slope of brown rice were 0.906, 1.741, 0.995 in amylose and 0.941, 0.276, 1.011 in protein, respectively, whereas $R^2$, SEC and slope of milled brown rice values were 0.956, 1.159, 1.001 in amylose and 0.982, 0.164, 1.003 in protein, respectively. Validation results of this NIRS equation showed a high coefficient determination in prediction for amylose (0.962) and protein (0.986), and also low standard error in prediction (SEP) for amylose (2.349) and protein (0.415). These results suggest that NIRS equation model should be practically applied for determination of amylose and crude protein contents in large accessions of rice germplasm.

• Asia pacific journal of information systems
• /
• v.19 no.4
• /
• pp.77-100
• /
• 2009

Information Systems (IS) is an essential tool for any organizations. The last decade has seen an increasing body of knowledge on IS usage. Yet, IS often fails because of its misuse or non-use. In general, decisions regarding the selection of a system, which involve the evaluation of many IS vendors and an enormous initial investment, are made not through the consensus of employees but through the top-down decision making by top managers. In situations where the selected system does not satisfy the needs of the employees, the forced use of the selected IS will only result in their resistance to it. Many organizations have been either integrating dispersed legacy systems such as archipelago or adopting a new ERP (Enterprise Resource Planning) system to enhance employee efficiency. This study examines user resistance prior to the adoption of the selected IS or ERP system. As such, this study identifies the importance of managing organizational resistance that may appear in the pre-adoption context of an integrated IS or ERP system, explores key factors influencing user resistance, and investigates how prior experience with other integrated IS or ERP systems may change the relationship between the affecting factors and user resistance. This study focuses on organizational members' resistance and the affecting factors in the pre-adoption context of an integrated IS or ERP system rather than in the context of an ERP adoption itself or ERP post-adoption. Based on prior literature, this study proposes a research model that considers six key variables, including perceived benefit, system complexity, fitness with existing tasks, attitude toward change, the psychological reactance trait, and perceived IT competence. They are considered as independent variables affecting user resistance toward an integrated IS or ERP system. This study also introduces the concept of prior experience (i.e., whether a user has prior experience with an integrated IS or ERP system) as a moderating variable to examine the impact of perceived benefit and attitude toward change in user resistance. As such, we propose eight hypotheses with respect to the model. For the empirical validation of the hypotheses, we developed relevant instruments for each research variable based on prior literature and surveyed 95 professional researchers and the administrative staff of the Korea Photonics Technology Institute (KOPTI). We examined the organizational characteristics of KOPTI, the reasons behind their adoption of an ERP system, process changes caused by the introduction of the system, and employees' resistance/attitude toward the system at the time of the introduction. The results of the multiple regression analysis suggest that, among the six variables, perceived benefit, complexity, attitude toward change, and the psychological reactance trait significantly influence user resistance. These results further suggest that top management should manage the psychological states of their employees in order to minimize their resistance to the forced IS, even in the new system pre-adoption context. In addition, the moderating variable-prior experience was found to change the strength of the relationship between attitude toward change and system resistance. That is, the effect of attitude toward change in user resistance was significantly stronger in those with prior experience than those with no prior experience. This result implies that those with prior experience should be identified and provided with some type of attitude training or change management programs to minimize their resistance to the adoption of a system. This study contributes to the IS field by providing practical implications for IS practitioners. This study identifies system resistance stimuli of users, focusing on the pre-adoption context in a forced ERP system environment. We have empirically validated the proposed research model by examining several significant factors affecting user resistance against the adoption of an ERP system. In particular, we find a clear and significant role of the moderating variable, prior ERP usage experience, in the relationship between the affecting factors and user resistance. The results of the study suggest the importance of appropriately managing the factors that affect user resistance in organizations that plan to introduce a new ERP system or integrate legacy systems. Moreover, this study offers to practitioners several specific strategies (in particular, the categorization of users by their prior usage experience) for alleviating the resistant behaviors of users in the process of the ERP adoption before a system becomes available to them. Despite the valuable contributions of this study, there are also some limitations which will be discussed in this paper to make the study more complete and consistent.

• Korean Journal of Food Science and Technology
• /
• v.52 no.6
• /
• pp.565-572
• /
• 2020

The 3-monochloropropane-1,2-diol (3-MCPD) is a contaminant that occurs in foodstuffs in its free form as well as in its bound form. The objective of this study was to evaluate the effects of emulsifier, frying temperature, and the amounts of salt and oil on the formation of bound 3-MCPD in a fried snack model. Emulsifier affected the formation of bound 3-MCPD; furthermore, it was observed that the largest amount of bound 3-MCPD was detected in the fried snack model when glycerin esters of fatty acids were used as emulsifiers. Frying temperature also affected the formation of bound 3-MCPD, which increased significantly as the frying temperature increased from 145 to 190℃. In addition, salt affected the formation of bound 3-MCPD. As the amount of salt increased, the amount of bound 3-MCPD also increased significantly. Moreover, it was observed that the amount of oil did not affect the formation of bound 3-MCPD. These results will aid in the reduction of bound 3-MCPD in fried snacks.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.26 no.2
• /
• pp.129-152
• /
• 2024

In the application of deep learning object detection via CCTV in tunnels, a large number of false positive detections occur due to the poor environmental conditions of tunnels, such as low illumination and severe perspective effect. This problem directly impacts the reliability of the tunnel CCTV-based accident detection system reliant on object detection performance. Hence, it is necessary to reduce the number of false positive detections while also enhancing the number of true positive detections. Based on a deep learning object detection model, this paper proposes a false positive data training method that not only reduces false positives but also improves true positive detection performance through retraining of false positive data. This paper's false positive data training method is based on the following steps: initial training of a training dataset - inference of a validation dataset - correction of false positive data and dataset composition - addition to the training dataset and retraining. In this paper, experiments were conducted to verify the performance of this method. First, the optimal hyperparameters of the deep learning object detection model to be applied in this experiment were determined through previous experiments. Then, in this experiment, training image format was determined, and experiments were conducted sequentially to check the long-term performance improvement through retraining of repeated false detection datasets. As a result, in the first experiment, it was found that the inclusion of the background in the inferred image was more advantageous for object detection performance than the removal of the background excluding the object. In the second experiment, it was found that retraining by accumulating false positives from each level of retraining was more advantageous than retraining independently for each level of retraining in terms of continuous improvement of object detection performance. After retraining the false positive data with the method determined in the two experiments, the car object class showed excellent inference performance with an AP value of 0.95 or higher after the first retraining, and by the fifth retraining, the inference performance was improved by about 1.06 times compared to the initial inference. And the person object class continued to improve its inference performance as retraining progressed, and by the 18th retraining, it showed that it could self-improve its inference performance by more than 2.3 times compared to the initial inference.

• Journal of Intelligence and Information Systems
• /
• v.22 no.3
• /
• pp.143-163
• /
• 2016

The demographics of Internet users are the most basic and important sources for target marketing or personalized advertisements on the digital marketing channels which include email, mobile, and social media. However, it gradually has become difficult to collect the demographics of Internet users because their activities are anonymous in many cases. Although the marketing department is able to get the demographics using online or offline surveys, these approaches are very expensive, long processes, and likely to include false statements. Clickstream data is the recording an Internet user leaves behind while visiting websites. As the user clicks anywhere in the webpage, the activity is logged in semi-structured website log files. Such data allows us to see what pages users visited, how long they stayed there, how often they visited, when they usually visited, which site they prefer, what keywords they used to find the site, whether they purchased any, and so forth. For such a reason, some researchers tried to guess the demographics of Internet users by using their clickstream data. They derived various independent variables likely to be correlated to the demographics. The variables include search keyword, frequency and intensity for time, day and month, variety of websites visited, text information for web pages visited, etc. The demographic attributes to predict are also diverse according to the paper, and cover gender, age, job, location, income, education, marital status, presence of children. A variety of data mining methods, such as LSA, SVM, decision tree, neural network, logistic regression, and k-nearest neighbors, were used for prediction model building. However, this research has not yet identified which data mining method is appropriate to predict each demographic variable. Moreover, it is required to review independent variables studied so far and combine them as needed, and evaluate them for building the best prediction model. The objective of this study is to choose clickstream attributes mostly likely to be correlated to the demographics from the results of previous research, and then to identify which data mining method is fitting to predict each demographic attribute. Among the demographic attributes, this paper focus on predicting gender, age, marital status, residence, and job. And from the results of previous research, 64 clickstream attributes are applied to predict the demographic attributes. The overall process of predictive model building is compose of 4 steps. In the first step, we create user profiles which include 64 clickstream attributes and 5 demographic attributes. The second step performs the dimension reduction of clickstream variables to solve the curse of dimensionality and overfitting problem. We utilize three approaches which are based on decision tree, PCA, and cluster analysis. We build alternative predictive models for each demographic variable in the third step. SVM, neural network, and logistic regression are used for modeling. The last step evaluates the alternative models in view of model accuracy and selects the best model. For the experiments, we used clickstream data which represents 5 demographics and 16,962,705 online activities for 5,000 Internet users. IBM SPSS Modeler 17.0 was used for our prediction process, and the 5-fold cross validation was conducted to enhance the reliability of our experiments. As the experimental results, we can verify that there are a specific data mining method well-suited for each demographic variable. For example, age prediction is best performed when using the decision tree based dimension reduction and neural network whereas the prediction of gender and marital status is the most accurate by applying SVM without dimension reduction. We conclude that the online behaviors of the Internet users, captured from the clickstream data analysis, could be well used to predict their demographics, thereby being utilized to the digital marketing.