• Journal of Digital Convergence
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• v.15 no.10
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• pp.269-279
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• 2017

The purpose of this study was to analyze differences in learning ability, team satisfaction, and learning preference depending on teaming method and the key sub-variables involved in communication ability,, problem solving ability, self-directed learning ability changes before and after team-based learning, and then to apply a team-based learning method to nursing curriculum. From October to December, 2016, 96 first-year nursing students and 108 second-year nursing students of the K University in G city took TBL classes and their observation values before and after TBL classes was analysed with SPSS and Medcalc programs. The results of this study showed that team-based learning was effective in improving communication ability, problem solving ability, self-directed learning ability, and preference to team-based learning was high in teams composed of academic achievement. It is expected that team-based learning can be settled in the curriculum by emphasizing that students learn problem-solving and communication abilities through self-learning and team dynamics before the class, and that it also is a learning method that improves professionalism and individual development. More researches are needed to focus on various factors such as the methodological composition of team-based learning and the preferences of individual student characteristics and learning methods.

• Journal of Korea Society of Digital Industry and Information Management
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• v.20 no.2
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• pp.109-123
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• 2024

The purpose of this study is to identify strategic insights for cyber universities to secure a competitive advantage based on market analysis grounded in customer needs and motivations. As a research method, we surveyed and analyzed college students using conjoint analysis, identified the importance of cyber university components, estimated the utility of each detailed level, and identified the configuration of cyber universities most preferred by potential customers. In the study results, the importance of attributes that appeared by analyzing all respondents was in the order of 'expected ourcoms after graduation', 'department characteristic', 'cyber university name', and 'learning management style'. Cluster analysis was performed, divided into two groups, and conjoint analysis was performed. For Cluster 1, the importance values of the components were 'expected outcomes after graduation,' 'learning management style,' 'cyber university name,' and 'department characteristics,' in that order. For Cluster 2, the importance values were 'expected outcomes after graduation,' 'department characteristics,' 'cyber university name,' and 'learning management style,' in that order. As an application of the research, As an application of the study, it is suggested that analyzing the preferences of potential customers in the entire group is not accurate; therefore, segmenting the groups for analysis and strategy formulation can be useful.

• Asian Journal of Innovation and Policy
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• v.7 no.1
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• pp.55-78
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• 2018

This paper investigates the linkage between the mode of transformation of entrepreneurial learning into outcomes and the subsequent impact of these learning outcomes in enhancing the survival of high-tech startups in India. The study uses data from 45 high-tech startups headquartered across different locations in India for the purpose of analysis. Survival Analysis of the data is conducted to determine which mode of learning transformation and what type of en trepreneurial decision making preference have a significant influence on the survival of Indian high-tech startups and to what extent do they impact their survival. The results indicate that entrepreneur's prior startup experience, explorative mode of learning transformation, causal decision making of the entrepreneur and availability of funding for the startup as the key factors that reduce the time to survival of Indian high-tech startups. They also provide key insights on how these factors impact the startup survival in this region.

• Science of Emotion and Sensibility
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• v.25 no.3
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• pp.107-116
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• 2022

Purpose: Researchers have shown that aesthetic judgments of artworks depend on contexts, such as the authenticity of an artwork (Newman & Bloom, 2011) and an artwork's location of display (Kirk et al., 2009; Silveira et al., 2015). The present study aims to examine whether contextual information related to the creator, such as whether an artwork was created by a human or artificial intelligence (AI), influences viewers' preference judgments of an artwork. Methods: Images of Impressionist landscape paintings were selected as human-made artworks. AI-made artwork stimuli were created using Google's Deep Dream Generator by mimicking the Impressionist style via deep learning algorithms. Participants performed a preference rating task on each of the 108 artwork stimuli accompanied by one of the two creator labels. After this task, an art experience questionnaire (AEQ) was given to participants to examine whether individual differences in art experience influence their preference judgments. Results: Setting AEQ scores as a covariate in a two-way ANCOVA analysis, the stimuli with the human-made context were preferred over the stimuli with the AI-made context. Regarding the types of stimuli, the viewers preferred AI-made stimuli to human-made stimuli. There was no interaction effect between the two factors. Conclusion: These results suggest that preferences for visual artworks are influenced by the contextual information of the creator when the individual differences in art experience are controlled.

• The Journal of the Korea Contents Association
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• v.9 no.7
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• pp.402-410
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• 2009

A lot of learning systems are applying self-directed learning to increase learner's learning effect. But most of this methods are hardly applied to fit the construction of learning contents considering learner's characteristics or it was processing the learning course without learner's choice. In this research, we proposed the recommendation method that can support the learning contents as calculate learner's preference contents based on learning history information when learner design the learning course. In the result, we chose test learner group and was able to know to generally increase average score of each learner after test between existing method and proposal one.

• Journal of Broadcast Engineering
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• v.9 no.4 s.25
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• pp.305-321
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• 2004

With the advent of the digital broadcasting, the audiences can access a large number of TV programs and their information through the multiple channels on various media devices. The access to a large number of TV programs can support a user for many chances with which he/she can sort and select the best one of them. However, the information overload on the user inevitably requires much effort with a lot of patience for finding his/her favorite programs. Therefore, it is useful to provide the persona1ized broadcasting service which assists the user to automatically find his/her favorite programs. As the growing requirements of the TV personalization, we introduce our automatic user preference learning algorithm which 1) analyzes a user's usage history on TV program contents: 2) extracts the user's watching pattern depending on a specific time and day and shows our automatic TV program recommendation system using MPEG-7 MDS (Multimedia Description Scheme: ISO/IEC 15938-5) and 3) automatically calculates the user's preference. For our experimental results, we have used TV audiences' watching history with the ages, genders and viewing times obtained from AC Nielson Korea. From our experimental results, we observed that our proposed algorithm of the automatic user preference learning algorithm based on the Bayesian network can effectively learn the user's preferences accordingly during the course of TV watching periods.

• Journal of Korean Elementary Science Education
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• v.39 no.1
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• pp.117-132
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• 2020

The purpose of this study is to investigate elementary school teachers' awareness of Artificial Intelligence (AI) and find out how to apply it in elementary science education. The survey was conducted online and involved 95 teachers working in the metropolitan area. The results of this study are as follows. First, teachers need to learn about the general characteristics of AI and how to apply it to education. Second, science classes had the highest preference for AI among elementary school subjects. Third, the preference for AI application by elementary science field was 68.4% for earth and space, 54.7% for exercise and energy, 32.6% for matter, 27.4% for life. Fourth, AI-based Science Education (AISE) teaching- learning strategies were developed based on AI characteristics and the changing perspective of elementary science education, AISE's teaching-learning strategies are five: 'automation', 'individualization', 'diversification', 'cooperation' and 'creativity' and teachers can use them in teaching design, class practice and evaluation stages. Finally, the creative problem-solving Doing Thinking Making Sharing (DTMS) model was devised to implement the creativity strategy in AISE. This model consists of four-steps teaching courses: Doing, Thinking, Making and Sharing based on the empirical learning theory. In the future, follow-up research is needed to verify the effectiveness of this model by applying it to elementary science education.

• The KIPS Transactions:PartB
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• v.18B no.3
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• pp.165-172
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• 2011

In this paper, we propose Ant-Q learning Algorithm[1], which uses the habits of biological ants, to find a new way to solve Stable Marriage Problem(SMP)[3] presented by Gale-Shapley[2]. The issue of SMP is to find optimum matching for a stable marriage based on their preference lists (PL). The problem of Gale-Shapley algorithm is to get a stable matching for only male (or female). We propose other way to satisfy various requirements for SMP. ACS(Ant colony system) is an swarm intelligence method to find optimal solution by using phermone of ants. We try to improve ACS technique by adding Q learning[9] concept. This Ant-Q method can solve SMP problem for various requirements. The experiment results shows the proposed method is good for the problem.

• The Mathematical Education
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• v.44 no.4 s.111
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• pp.565-586
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• 2005

The purpose of this paper is to analyze the selection difference of mathematical problem solving strategy by mathematical learning style, that is, the intellectual, emotional, and physiological factors of students, to allow teachers to instruct the mathematical problem solving strategy most pertinent to the student personality, and ultimately to contribute to enhance mathematical problem solving ability of the students. The conclusion of the study is the followings: (1) Students who studies with autonomous, steady, or understanding-centered effort was able to solve problems with more strategies respectively than the students who did not; (2) Student who studies autonomously or reconfirms one's learning was able to select more proper strategy and to explain the strategy respectively than the students who did not; and (3) The differences of the preference to the strategy are variable, and more than half of the students were likely to select frequently the strategy 'to use a formula or a principle' regardless of the learning style.

• Journal of Platform Technology
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• v.11 no.6
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• pp.21-27
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• 2023

In this paper, we introduce a reinforcement learning-based algorithm for personalized tourist path recommendations. The algorithm employs a reinforcement learning agent to explore tourist regions and identify optimal paths that are expected to enhance tourism experiences. The concept of tourism experience is defined through points of interest (POI) located along tourist paths within the tourist area. These metrics are quantified through aggregated evaluation scores derived from reviews submitted by past visitors. In the experimental setup, the foundational learning model used to find tour paths is the Deep Q-Network (DQN). Despite the limited availability of historical tourist behavior data, the agent adeptly learns travel paths by incorporating preference scores of tourist POIs and spatial information of the travel area.