• Safety and Health at Work
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• v.5 no.4
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• pp.216-221
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• 2014

Background: Mental health complaints are quite common in health care employees and can have adverse effects on work functioning. The aim of this study was to evaluate an e-mental health (EMH) approach to workers' health surveillance (WHS) for nurses and allied health professionals. Using the waiting-list group of a previous randomized controlled trial with high dropout and low compliance to the intervention, we studied the pre- and posteffects of the EMH approach in a larger group of participants. Methods: We applied a pretest-posttest study design. The WHS consisted of online screening on impaired work functioning and mental health followed by online automatically generated personalized feedback, online tailored advice, and access to self-help EMH interventions. The effects on work functioning, stress, and work-related fatigue after 3 months were analyzed using paired t tests and effect sizes. Results: One hundred and twenty-eight nurses and allied health professionals participated at pretest as well as posttest. Significant improvements were found on work functioning (p = 0.01) and work-related fatigue (p < 0.01). Work functioning had relevantly improved in 30% of participants. A small meaningful effect on stress was found (Cohen d = .23) in the participants who had logged onto an EMH intervention (20%, n = 26). Conclusion: The EMH approach to WHS improves the work functioning and mental health of nurses and allied health professionals. However, because we found small effects and participation in the offered EMH interventions was low, there is ample room for improvement.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.39-48
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• 2021

This study aimed to analyze the level of professors' understanding and perception of adaptive learning and proposed how college can implement successful adaptive learning in college classes. For research purposes, online survey was conducted by 162 professors of A university in capital region. As a result, professors seemed to feel pressure to provide students personalized feedback and gave concerned that students don't study enough in advance before participating in class. It was also found that professors realized that they have low level of understanding about adaptive learning, while they revealed intention to make use of adaptive learning in their class. They also answered that adaptive learning system is the most helpful support for encouraging professors to apply adaptive learning in real class. We proposed what is required to encourage professor to implement adaptive learning in their class.

• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.738-750
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• 2022

In this study, we reviewed and discussed whether auditory stimuli with short length can evoke emotion-related neurological responses. The findings implicate that if personalized sound tracks are provided to XR users based on machine learning or probability network models, user experiences in XR environment can be enhanced. We also investigated that the arousal-relaxed factor evoked by short auditory sound can make distinct patterns in functional connectivity characterized from background EEG signals. We found that coherence in the right hemisphere increases in sound-evoked arousal state, and vice versa in relaxed state. Our findings can be practically utilized in developing XR sound bio-feedback system which can provide preference sound to users for highly immersive XR experiences.

• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.528-533
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• 2023

Airlines tried to introduce training programs in connection with practical work in order to provide more effective education and training. To this end, airlines have been conducting evidence-based training(EBT) to strengthen the practical capabilities of aviation personnel and enhance safety culture. Airlines can systematically evaluate the capabilities and practical capabilities of aviation personnel by analyzing operational data and case studies for effective EBT model development. In addition, EBT models can be constructed by applying technical methods such as crew resource management (CRM) and a holistic approach that includes human factors. Due to the introduction of EBT, airlines will establish diagnostic and feedback systems for pilots' practical work, provide personalized education, and establish an education and training system that verifies the effectiveness of education through educational outcomes.

• Journal of Nutrition and Health
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• v.56 no.4
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• pp.419-438
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• 2023

Purpose: This study examines the effectiveness of personalized goal setting and smartphone-based nutrition counseling among adults in their 20s and 30s. Methods: Nutrition counseling was conducted for a total of 30 adults through a 1:1 chat room of a mobile instant messenger, once a week for 8 weeks. The first week of counseling included a preliminary online questionnaire survey and a dietary intake survey. Based on the results of the preliminary survey, 2 dietary goals were set in the second week and the participants were asked to record their achievements on a daily checklist. From the third week onwards, counselors sent feedback messages based on the checklist and provided information on dietary guidelines in a card news format every week. Post-counseling questionnaires and dietary intake surveys were conducted in the seventh week. Changes in dietary habits during the counseling were reviewed in the eighth week, followed by a questionnaire survey on the evaluation of the counseling process. Results: The nutrition quotient (NQ) scores and self-efficacy scores were significantly higher after nutrition counseling. The NQ scores of consumption frequencies of fruits, milk and dairy products, nuts, fast food, Ramyeon, sweet and greasy baked products, sugarsweetened beverages, the number of vegetable dishes at meals, and breakfast frequency were significantly higher after nutrition counseling. The intake of protein, vitamin A, thiamin, riboflavin, folate, calcium, and iron, and the index of nutritional quality of vitamin A, riboflavin, folate, calcium, and iron were higher after nutrition education. The participants were satisfied with the nutrition counseling program and the provided nutrition information. Conclusion: Personalized goal setting and smartphone-based nutrition counseling were found to be effective in improving the quality of diet and self-efficacy in young adults. Similar results were obtained in both the underweight/normal weight and the overweight/obese groups.

• 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.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.179-192
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• 2014

Keeping in line with the recent changes in the information technology environment, the online learning environment that supports multiple users' participation such as MOOC (Massive Open Online Courses) has become important. One of the largest professional associations in Information Technology, IEEE Computer Society, announced that "Supporting New Learning Styles" is a crucial trend in 2014. Popular MOOC services, CourseRa and edX, have continued to build active learning environment with a large number of lectures accessible anywhere using smart devices, and have been used by an increasing number of users. In addition, collaborative web services (e.g., blogs and Wikipedia) also support the creation of various user-uploaded learning materials, resulting in a vast amount of new lectures and learning materials being created every day in the online space. However, it is difficult for an online educational system to keep a learner' motivation as learning occurs remotely, with limited capability to share knowledge among the learners. Thus, it is essential to understand which materials are needed for each learner and how to motivate learners to actively participate in online learning system. To overcome these issues, leveraging the constructivism theory and collective intelligence, we have developed a social bookmarking system called WeStudy, which supports learning material sharing among the users and provides personalized learning material recommendations. Constructivism theory argues that knowledge is being constructed while learners interact with the world. Collective intelligence can be separated into two types: (1) collaborative collective intelligence, which can be built on the basis of direct collaboration among the participants (e.g., Wikipedia), and (2) integrative collective intelligence, which produces new forms of knowledge by combining independent and distributed information through highly advanced technologies and algorithms (e.g., Google PageRank, Recommender systems). Recommender system, one of the examples of integrative collective intelligence, is to utilize online activities of the users and recommend what users may be interested in. Our system included both collaborative collective intelligence functions and integrative collective intelligence functions. We analyzed well-known Web services based on collective intelligence such as Wikipedia, Slideshare, and Videolectures to identify main design factors that support collective intelligence. Based on this analysis, in addition to sharing online resources through social bookmarking, we selected three essential functions for our system: 1) multimodal visualization of learning materials through two forms (e.g., list and graph), 2) personalized recommendation of learning materials, and 3) explicit designation of learners of their interest. After developing web-based WeStudy system, we conducted usability testing through the heuristic evaluation method that included seven heuristic indices: features and functionality, cognitive page, navigation, search and filtering, control and feedback, forms, context and text. We recruited 10 experts who majored in Human Computer Interaction and worked in the same field, and requested both quantitative and qualitative evaluation of the system. The evaluation results show that, relative to the other functions evaluated, the list/graph page produced higher scores on all indices except for contexts & text. In case of contexts & text, learning material page produced the best score, compared with the other functions. In general, the explicit designation of learners of their interests, one of the distinctive functions, received lower scores on all usability indices because of its unfamiliar functionality to the users. In summary, the evaluation results show that our system has achieved high usability with good performance with some minor issues, which need to be fully addressed before the public release of the system to large-scale users. The study findings provide practical guidelines for the design and development of various systems that utilize collective intelligence.

• Korean Journal of Community Nutrition
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• v.22 no.3
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• pp.207-217
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• 2017

Objectives: The aim of this study was to examine self-management status, nutritional knowledge, barrier factors in dietary management and needs of nutritional management program for women with Gestational Diabetes Mellitus (GDM). Methods: A total of 100 women with GDM were recruited from secondary and tertiary hospitals in Seoul. The questionnaire composed of general characteristics, status of self-management, dietary habits, nutrition knowledge, barrier factors in dietary management, needs for nutrition information contents and nutritional management programs. Data were collected by a self-administered questionnaire. All data were statistically analyzed using student's t-test and chi-square test using SAS 9.3. Results: About 35% of the subjects reported that they practiced medical nutrition and exercise therapy for GDM control. The main sources of nutrition information were 'internet (50.0%)' and 'expert advice (45.0%)'. More than 70% of the subjects experienced nutrition education. The mean score of nutrition knowledge was 7.5 point out of 10, and only about half of the subjects were reported to be correctly aware of some questions such as 'the cause of ketosis', 'the goal of nutrition management for GDM', 'the importance of sugar restriction on breakfast'. The major obstructive factors in dietary management were 'eating more than planned when dining out', 'finding the appropriate menu when dining out'. The preferred nutrition information contents in developing management program were 'nutritional information of food', 'recommended food by major nutrients', 'the relationship between blood glucose and food', 'tips on menu selection at eating out'. The subjects reported that they need management program such as 'example of menu by calorie prescription', 'recommended weight gain guide', 'meal recording and dietary assessment', 'expert recommendation', 'sharing know-how'. Conclusions: Based on the results of this study, it is necessary to develop a program that provide personalized information by identifying the individual characteristics of the subjects and expert feedback function through various information and nutrition information contents that can be used in real life.

• Journal of Gifted/Talented Education
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• v.21 no.2
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• pp.485-510
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• 2011

This study aimed to analyze the appropriateness of chemistry teaching-learning programs for the gifted in science in middle school gifted classes and to propose improvements. For this study, 5 chemistry teaching-learning 4-6 hour programs developed for science gifted classes by Korea Education Development Institute (KEDI) and 3 chemistry teaching-learning programs developed for science gifted classes by three middle schools in K province were selected. A standard model for gifted education programs was used as tool for analyzing the program targets, program contents, teaching-learning methods, and assessment items. The results showed that all chemistry teaching-learning programs for the gifted in science presented well attainable objectives in the program targets. However, most program targets did not offer differentiated objectives from the general education. Program contents of KEDI stresses intensified education, and also presented a high ratio of sub-elements of creativity, which can enhance gifted creativity. On the other hand, program contents developed by three middle schools focused on acceleration in advancement, and presented low ratio of creativity sub-elements, which could be insufficient in enhancing gifted creativity. Differentiated and personalized, integrated science and interscience, updated research contents were hardly found in programs developed by KEDI and three middle schools. However, teaching-learning methods were composed to fit the learning objectives in the teaching process and the procedures, and were made to self-directed learning. There were no assessment for the feedback after class. Therefore, teaching-learning programs for the gifted in science should be developed further in order to fulfill the objectives of gifted education and gifted characteristics. Also, it is necessary to construct infrastructure to carry out the developed teaching-learning programs.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.