• Korean journal of medical education
• /
• v.30 no.4
• /
• pp.295-308
• /
• 2018

Purpose: Although medical curricula are now better structured for integration of biomedical sciences and clinical training, most teaching and learning activities still follow the older teacher-centric discipline-specific formats. A newer pedagogical approach, known as Collaborative Learning Cases (CLCs), was adopted in the medical school to facilitate integration and collaborative learning. Before incorporating CLCs into the curriculum of year 1 students, two pilot runs using the action research method was carried out to improve the design of CLCs. Methods: We employed the four-phase Kemmis and McTaggart's action research spiral in two cycles to improve the design of CLCs. A class of 300 first-year medical students (for both cycles), 11 tutors (first cycle), and 16 tutors (second cycle) were involved in this research. Data was collected using the 5-points Likert scale survey, open-ended questionnaire, and observation. Results: From the data collected, we learned that more effort was required to train the tutors to understand the principles of CLCs and their role in the CLCs sessions. Although action research enables the faculty to improve the design of CLCs, finding the right technology tools to support collaboration and enhance learning during the CLCs remains a challenge. Conclusion: The two cycles of action research was effective in helping us design a better learning environment during the CLCs by clarifying tutors' roles, improving group and time management, and meaningful use of technology.

• Journal of KIISE:Software and Applications
• /
• v.37 no.9
• /
• pp.659-668
• /
• 2010

Collaborative filtering systems grasp behaviors for a new user and need new information for the user in order to recommend interesting items to the user. For the purpose of acquiring the information the collaborative filtering systems learn behaviors for users based on the previous data and can obtain new information from the results. In this paper, we propose an inductive inference method to obtain new information for users and rank items by using the new information in the proposed method. The proposed method clusters users into groups by learning users through NMF among inductive machine learning methods and selects the group features from the groups by using chi-square. Then, the method classifies a new user into a group by using the bayesian probability model as one of inductive inference methods based on the rating values for the new user and the features of groups. Finally, the method decides the ranks of items by applying the Rocchio algorithm to items with the missing values.

• The Journal of Korean Academic Society of Nursing Education
• /
• v.16 no.1
• /
• pp.33-42
• /
• 2010

Purpose: The purpose of this study was to ascertain the degree of students' educational satisfaction according to their Problem-based learning strategy. Method: The subjects were 277 nursing students in C College. A questionnaire modified by researchers was used and analyzed by the SPSS WIN 12.0 program. Result: This study showed that there's a positive relationship between the level of students' educational satisfaction and their learning strategies, including collaborative, self-directed, self-expression and time management strategies. Those who were in the second year and those who have considered temporary absence from school and/or change of academic courses used the least learning strategies and showed the lowest level of educational satisfaction. The top three learning strategies influencing educational satisfaction were time management, collaborative strategies and self-directed strategies respectively. Self-expression strategy was not statistically significant as an influencing factor on educational satisfaction. Conclusion: The more learning strategies that are used, the higher the level of educational satisfaction as a whole. Further studies on how to increase student's educational satisfaction and a way to advance in learning strategies are recommended.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.5
• /
• pp.1223-1237
• /
• 2024

Autonomous vehicles use onboard sensors to sense the surrounding environment. In complex autonomous driving scenarios, the detection and recognition capabilities are constrained, which may result in serious accidents. An efficient way to enhance the detection and recognition capabilities is establishing collaborations with the neighbor vehicles. However, the collaborations introduce additional challenges in terms of the data heterogeneity, communication cost, and data privacy. In this paper, a novel personalized federated learning framework is proposed for addressing the challenges and enabling efficient collaborations in autonomous driving environment. For obtaining a global model, vehicles perform local training and transmit logits to a central unit instead of the entire model, and thus the communication cost is minimized, and the data privacy is protected. Then, the inference similarity is derived for capturing the characteristics of data heterogeneity. The vehicles are divided into clusters based on the inference similarity and a weighted aggregation is performed within a cluster. Finally, the vehicles download the corresponding aggregated global model and train a personalized model which is personalized for the cluster that has similar data distribution, so that accuracy is not affected by heterogeneous data. Experimental results demonstrate significant advantages of our proposed method in improving the efficiency of collaborative perception and reducing communication cost.

• The Research Journal of the Costume Culture
• /
• v.32 no.3
• /
• pp.334-344
• /
• 2024

This study aimed to develop a new convergence course applying project-based learning (PBL) and collaborative teaching methods and identify its educational effects. The course development proceeded as follows: First, three instructors collaborated to define course goals, plan objectives, content, and methods, and create a syllabus for a PBL-based fashion studio course. Roles were divided to maximize expertise: one instructor focused on fashion design, another on three-dimensional cutting, and the third on flat cutting, and digital techniques. Second, the classes were conducted and feedback on student progress was shared, enhancing class quality and engagement. Third, teaching effectiveness was assessed through learner evaluation questionnaires, reflection journals, and performance assessments. Lastly, based on the results from these evaluations, positive aspects of the course were reviewed, and ways to modify it and enhance course quality for continuous improvement were explored. The results showed high satisfaction with the learning effects on major competencies, indicating that students not only effectively learned major skills but also improved their communication and teamwork. The students perceived the teaching methods positively allowing them to be more active in class. Instructors noted that the course produced higher-quality design and production outcomes compared to previous courses. Overall, the course applying PBL and collaborative teaching methods was found to improve educational quality and effectiveness, making it a valuable approach for learner-centered education.

• Journal of the Korea Convergence Society
• /
• v.8 no.1
• /
• pp.155-162
• /
• 2017

This paper suggests efficient learning improvement method of computer use instruction based on flipped learning. Traditional computer use classes were difficult to practice and collaborative with sufficient lectures. However, we used KOCW (Korea Open Courseware) as a footsteps in the class using the flipped learning method and learned in advance before entering the classroom. In the classroom, we conducted collaborative hands on class based on mutual discussion. After the instruction, we measured learning motivation and satisfaction by gender, grade, and major using the motivation test tool. The results showed that degree of attention awareness, perception of class relevance and perception of learning satisfaction were analyzed as 'very satisfied' and 'satisfied' more than 90%.

• Journal of Digital Convergence
• /
• v.18 no.6
• /
• pp.45-52
• /
• 2020

This study ranked the degree to which flipped learning contributes to each learning area and, in contrast, to quantitatively examine how effectively these learning methods are used in flipped learning, had four-year university computer majors receive flipped learning. Existing flipped learning experiments have proven effectiveness, while there are also negative effects on effectiveness, which has led to a lot of debate. Effective experiments and classes therefore require more research and an accurate understanding of flipped learning. Analysis of the 123 samples recruited shows that flipped learning contributes to learning is in order of self-directing, collaboration, watching videos, and learning by teachers. Regression analysis of the degree to which learning method affects flipped learning effectiveness resulted in order of self-directed learning, lecture videos, and collaborative learning. This shows that flipped learning not only has the greatest influence on self-directed learning, but also self-directed learning has the greatest influence on flipped learning. It can also see that a collaborative learning and the role of video to prior learning tool is important. Through this study, we hope to understand flipped learning correctly and set learning methods and achievement goals. It is necessary to analyze the interaction between flipped learning and subdivided classroom activities.

• Hwankyungkyoyuk
• /
• v.19 no.2 s.30
• /
• pp.108-121
• /
• 2006

Nowadays environmental education is getting important. So, it is necessary to teach for students to realize the protection environment. Self-direct homepage was developed for 'Ecology & environment' environmental education. This homepage was made for sharing searched data and can be interactive each other on the internet. Therefore, in this study, environmental teaming was planned and practiced for high school 'Ecology & environment' class by e-PBL. Self-directed teaming, collaborative teaming and performance assessment are emphasized in the 7th educational curriculum. The PBL is efficient learning model for them. This study designed for a teaching and teaming method and strategies using PBL based upon the theories and practices. This study will also develop an e-learning. As a result, it is indicated that the teaching and learning method using PBL has the positive effects on learning that the development of self-directed learning and collaboration teaming Is observed by reflect journal and presentation of students. e-PBL is a teaming model for learning-centered that adapted many school and subject. Therefore e-PBL makes full use of be 'Ecology & environment' class and environmental education.

• Korean Journal of Child Studies
• /
• v.28 no.5
• /
• pp.233-251
• /
• 2007

This study explored the contents of practical knowledge about educational planning in early childhood curriculum as constructed by kindergarten teachers at early career stages and then developed a collaborative model of educational planning. Subjects were 6 teachers at early career stages. Using the ethnographic method, data were collected by in-depth interviews. Research outcomes were : (1) teachers specifically worked on 'difficulties in adapting to the teaching job', 'age of children that the teacher cares for', 'integration of theory and practice', and 'variety of actual teaching situations.' (2) A model for collaborative educational planning was constructed on the basis of review of the literature on teachers' knowledge, educational planning for early childhood curriculum, and learning of community.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.1
• /
• pp.109-134
• /
• 2018

Collaborative Filtering (CF) is widely used in recommendation field, which can be divided into rating-based CF and learning-to-rank based CF. Although many methods have been proposed based on these two kinds of CF, there still be room for improvement. Firstly, the data sparsity problem still remains a big challenge for CF algorithms. Secondly, the malicious rating given by some illegal users may affect the recommendation accuracy. Existing CF algorithms seldom took both of the two observations into consideration. In this paper, we propose a recommendation method based on listwise learning-to-rank by incorporating users' social information. By taking both ratings and order of items into consideration, the Plackett-Luce model is presented to find more accurate similar users. In order to alleviate the data sparsity problem, the improved matrix factorization model by integrating the influence of similar users is proposed to predict the rating. On the basis of exploring the trust relationship between users according to their social information, a listwise learning-to-rank algorithm is proposed to learn an optimal ranking model, which can output the recommendation list more consistent with the user preference. Comprehensive experiments conducted on two public real-world datasets show that our approach not only achieves high recommendation accuracy in relatively short runtime, but also is able to reduce the impact of malicious ratings.