• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.6
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• pp.125-130
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• 2010

Cooperative learning is a successful teaching strategy in which small teams, each with students of different levels of ability, use a variety of learning activities to improve their understanding of a subject. Each member of a team is responsible not only for learning what is taught but also for helping teammates learn, thus creating an atmosphere of achievement. In this study, we have propose an english, math education program to the children of elementary school and cooperative learning program technique was applied to implement the program. By cooperative learning program, learners will be performed at the same time learning cooperatively. Finally, we have implement a prototype of cooperative learning program and take a usability test with elementary school children. A complementary team to score and mixed was found to be most effective.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.287-296
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• 2003

There has been much research focused on collaborative filtering technique in Recommender System. However, these studies have shown the First-Rater Problem and the Sparsity Problem. The main purpose of this Paper is to solve these Problems. In this Paper, we suggest the user's predicting preference method using Bayesian estimated value and the associative user clustering for the recalculation of preference. In addition to this method, to complement a shortcoming, which doesn't regard the attribution of item, we use Representative Attribute-Neighborhood method that is used for the prediction when we find the similar neighborhood through extracting the representative attribution, which most affect the preference. We improved the efficiency by using the associative user's clustering analysis in order to calculate the preference of specific item within the cluster item vector to the collaborative filtering algorithm. Besides, for the problem of the Sparsity and First-Rater, through using Association Rule Hypergraph Partitioning algorithm associative users are clustered according to the genre. New users are classified into one of these genres by Naive Bayes classifier. In addition, in order to get the similarity value between users belonged to the classified genre and new users, and this paper allows the different estimated value to item which user evaluated through Naive Bayes learning. As applying the preference granted the estimated value to Pearson correlation coefficient, it can make the higher accuracy because the errors that cause the missing value come less. We evaluate our method on a large collaborative filtering database of user rating and it significantly outperforms previous proposed method.

• School Mathematics
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• v.18 no.3
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• pp.479-501
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• 2016

This study was conducted as a learning project for 20 pre-third graders in high school by means of math textbooks, G+, and sample questions from previous CSAT as learning tools for 9 weeks from Dec. 24, 2015. The purpose of the study was to develop 'math textbook project learning(MtPL)', a mixed learning method combined on-line with off-line, and analyze the effects of MtPL on the affective domain of high school students. As a result of the study, it was found that MtPL had positive effects on self-efficacy and self-confidence of students, while the collaborative learning using a textbook and teacher's role worked as instrumental motivation in mathematics learning. The result also implies that the perception of high school students, who think to resolve more difficult math problems to succeed in CSAT, about mathematics learning method has to be modified. Furthermore, it is shown that the preparation of CSAT by utilizing textbook and the use of textbook in math learning have been worked positively for the students.

• Korean Medical Education Review
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• v.14 no.2
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• pp.64-68
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• 2012

Learning in medical school is usually regarded as a very specialized type of learning compared to that of other academic disciplines. Medical students might have general beliefs about their own learning. Beliefs about learning have a critical effect on learning behavior. There are several factors that affect medical students' learning behavior: epistemological beliefs, learning styles, learning strategies, and learning beliefs. Several studies have addressed epistemological beliefs, learning styles, and learning strategies in medical education. There are, however, few studies that have reported on medical students' beliefs about learning. The purpose of this study was to determine what learning beliefs medical students have, what the causes of these beliefs are, and how medical educators teach students who have such beliefs. In this study, the five learning beliefs are assumed and we considered how these beliefs can affect students' learning behaviors. They include: 1) medical students are expected to learn a large amount of information in a short time. 2) memorization is more important than understanding to survive in medical schools. 3) learning is a competition and work is independent, rather than collaborative. 4) reading textbooks is a heavy burden in medical education. 5) the most effective teaching and learning method is the lecture. These learning beliefs might be the results of various hidden curricula, shared experiences of the former and the present students as a group, and personal experience. Some learning beliefs may negatively affect students' learning. In conclusion, the implications of medical students' learning beliefs are significant and indicate that students and educators can benefit from opportunities that make students' beliefs about learning more conscious.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.21-33
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• 2022

Recently, the recommender system has been widely used in various fields such as movies, music, online shopping, and social media, and in the meantime, the recommender model has been developed from correlation analysis through the Apriori model, which can be said to be the first-generation model in the recommender system field. In 2005, many models have been proposed, including deep learning-based models, which are receiving a lot of attention within the recommender model. The recommender model can be classified into a collaborative filtering method, a content-based method, and a hybrid method that uses these two methods integrally. However, these basic methods are gradually losing their status as methodologies in the field as they fail to adapt to internal and external changing factors such as the rapidly changing user-item interaction and the development of big data. On the other hand, the importance of deep learning methodologies in recommender systems is increasing because of its advantages such as nonlinear transformation, representation learning, sequence modeling, and flexibility. In this paper, among deep learning methodologies, RNN, CNN, and GAN-based models suitable for sequential modeling that can accurately and flexibly analyze user-item interactions are classified, compared, and analyzed.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.49-56
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• 2024

There have been emerging many use-cases applying recommendation systems especially in online platform. Although the performance of recommendation systems is affected by a variety of factors, selecting appropriate features is difficult since most of recommendation systems have sparse data. Conventional matrix factorization (MF) method is a basic way to handle with problems in the recommendation systems. However, the MF based scheme cannot reflect non-linearity characteristics well. As deep learning technology has been attracted widely, a deep neural network (DNN) framework based collaborative filtering (CF) was introduced to complement the non-linearity issue. However, there is still a problem related to feature embedding for use as input to the DNN. In this paper, we propose an effective method using singular value decomposition (SVD) based feature embedding for improving the DNN performance of recommendation algorithms. We evaluate the performance of recommendation systems using MovieLens dataset and show the proposed scheme outperforms the existing methods. Moreover, we analyze the performance according to the number of latent features in the proposed algorithm. We expect that the proposed scheme can be applied to the generalized recommendation systems.

• Journal of Korean Academy of Nursing
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• v.36 no.8
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• pp.1308-1314
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• 2006

Purpose. The purposes of this study were to develop a PBL program for continuing nurse education and to evaluate the program after its implementation. Methods. The PBL program was developed in the core cardio-pulmonary nursing concepts through a collaborative approach with a nursing school and a hospital. The PBL packages with simulation on ACLS were implemented to 40 clinical nurses. The entire PBL program consisted of six 3-hour weekly classes and was evaluated by the participants' subjective responses. Results. Two PBL packages in cardio-pulmonary system including clinical cases and tutorial guidelines were developed. The 57.5 % of the participants responded positively about the use of PBL as continuing nurse education in terms of self-motivated and cooperative learning, whereas 20.0% of the participants answered that the PBL method was not suitable for clinical nurses. Some modifications were suggested in grouping participants and program contents for PBL. Conclusion. The PBL method could be utilized to promote nurses' clinical competencies as well as self-learning abilities. Further research is needed in the implementation strategies of PBL-based continuing education in order to improve its effectiveness.

• Convergence Security Journal
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• v.17 no.2
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• pp.165-171
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• 2017

The goal of this paper is to seek an educational method that can cause some communicational improvements to petty officer majoring students through listing and utilizing the academic vocabulary. The academic vocabulary has been listed by using three major steps. The first, the academic vocabulary is needed to extract from the studies based on this field in past 3 years. Second, the academic vocabulary for petty officers is required to compare with high school level of the academic vocabulary. For the last, the academic vocabulary is demanded to be listed for teaching petty officer majoring students. The signification of the academic vocabulary in this study is limited by focusing on the educational skills for writing in petty officer majors. This study presents a way to develop the communicational abilities through using the academic vocabulary into the collaborative learning. For the conclusion, this study presents its limits and further directions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3172-3193
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• 2018

Recently, several studies have shown that linear representation based approaches are very effective and efficient for image classification. One of these linear-representation-based approaches is the Collaborative representation (CR) method. The existing algorithms based on CR have two major problems that degrade their classification performance. First problem arises due to the limited number of available training samples. The large variations, caused by illumintion and expression changes, among query and training samples leads to poor classification performance. Second problem occurs when an image is partially noised (contiguous occlusion), as some part of the given image become corrupt the classification performance also degrades. We aim to extend the collaborative representation framework under limited training samples face recognition problem. Our proposed solution will generate virtual samples and intra-class variations from training data to model the variations effectively between query and training samples. For robust classification, the image patches have been utilized to compute representation to address partial occlusion as it leads to more accurate classification results. The proposed method computes representation based on local regions in the images as opposed to CR, which computes representation based on global solution involving entire images. Furthermore, the proposed solution also integrates the locality structure into CR, using Euclidian distance between the query and training samples. Intuitively, if the query sample can be represented by selecting its nearest neighbours, lie on a same linear subspace then the resulting representation will be more discriminate and accurately classify the query sample. Hence our proposed framework model the limited sample face recognition problem into sufficient training samples problem using virtual samples and intra-class variations, generated from training samples that will result in improved classification accuracy as evident from experimental results. Moreover, it compute representation based on local image patches for robust classification and is expected to greatly increase the classification performance for face recognition task.

• Journal of Practical Engineering Education
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• v.14 no.3
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• pp.543-553
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• 2022

A collaborative robot(Cobot) is one of the production systems presented in the 4th industrial revolution and are systems that can maximize efficiency by combining the exquisite hand skills of workers and the ability of simple repetitive tasks of robots. Also, research on the development of an efficient interface method between the worker and the robot is continuously progressing along with the solution to the safety problem arising from the sharing of the workspace. In this study, a method for controlling the robot by recognizing the worker's hand signal was presented to enhance the convenience and concentration of the worker, and the safety of the worker was secured by introducing the concept of a safety zone. Various technologies such as robot control, PLC, image processing, machine learning, and ROS were used to implement this. In addition, the roles and interface methods of the proposed technologies were defined and presented for using educational media. Students can build and adjust the educational media system by linking the introduced various technologies. Therefore, there is an excellent advantage in recognizing the necessity of the technology required in the field and inducing in-depth learning about it. In addition, presenting a problem and then seeking a way to solve it on their own can lead to self-directed learning. Through this, students can learn key technologies of the 4th industrial revolution and improve their ability to solve various problems.