• Journal of The Korean Association For Science Education
• /
• v.37 no.6
• /
• pp.993-1003
• /
• 2017

Teachers expect team-based project learning to help students develop collaborative and real-world problem solving skills. In practice, however, students tend to solve problems with simple division of labor, and there is a tendency that learning transfer does not occur in solving problems. The purpose of this study is to develop a collaborative learning model based on the transactive memory system (TMS) and to verify its effectiveness. The collaborative learning model based on the TMS is composed of three stages. The first stage is developing TMS. In this stage, the students learn physics concepts and make knowledge about the expertise of group members through peer instruction. The second stage, activating TMS, is building trust through solving well-defined problems for developing near-transfer. And in the third stage, applying TMS, the students solve an ill-defined problem based on real-world context for practicing far-transfer. Based on this model, a 15-week program including two projects on geometric optics and sound waves was developed and applied to 60 college students. The data for five weeks of one project were collected and analyzed. As a result, the TMS of the experimental group with the TMS-based collaborative learning model improved stepwise. Whereas, the difference between the first week and the last week was statistically significant, while the TMS change of the comparison group using the general project learning model was not significant. Also, the experimental group showed that the learning transfer occurred better in the project than the comparison group. A collaborative learning model based on TMS can be used to learn how students gain synergy through collaboration and how students collaboratively transfer the learned concepts in problem solving.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.2
• /
• pp.483-503
• /
• 2014

In this paper, human action recognition using pyramid histograms of oriented gradients and collaborative multi-task learning is proposed. First, we accumulate global activities and construct motion history image (MHI) for both RGB and depth channels respectively to encode the dynamics of one action in different modalities, and then different action descriptors are extracted from depth and RGB MHI to represent global textual and structural characteristics of these actions. Specially, average value in hierarchical block, GIST and pyramid histograms of oriented gradients descriptors are employed to represent human motion. To demonstrate the superiority of the proposed method, we evaluate them by KNN, SVM with linear and RBF kernels, SRC and CRC models on DHA dataset, the well-known dataset for human action recognition. Large scale experimental results show our descriptors are robust, stable and efficient, and outperform the state-of-the-art methods. In addition, we investigate the performance of our descriptors further by combining these descriptors on DHA dataset, and observe that the performances of combined descriptors are much better than just using only sole descriptor. With multimodal features, we also propose a collaborative multi-task learning method for model learning and inference based on transfer learning theory. The main contributions lie in four aspects: 1) the proposed encoding the scheme can filter the stationary part of human body and reduce noise interference; 2) different kind of features and models are assessed, and the neighbor gradients information and pyramid layers are very helpful for representing these actions; 3) The proposed model can fuse the features from different modalities regardless of the sensor types, the ranges of the value, and the dimensions of different features; 4) The latent common knowledge among different modalities can be discovered by transfer learning to boost the performance.

• 대한공업교육학회지
• /
• v.34 no.2
• /
• pp.267-285
• /
• 2009

The collaborative learning has been considered as an efficient teaching model and under the recent basic learning environment, even face-to-face classroom circumstance rapidly increases the courses of blended learning which utilize the merits of e-learning environment. Nonetheless, the study on the strategy for systematic blended learning is quite scarce. In this study, the survey was done for developing the blended learning strategy, based on the collaborative learning model at the face-to-face environment and judging the satisfaction on the courses which the model was applied to. The survey consists of demographic questions, satisfaction in the whole courses, satisfaction in the collaborative learning under the blended learning environment and satisfaction in the blended learning strategy and support tools applied to each step of the learning. The result of this study is as follows. First, in response to the question that the blended learning can complement the face-to-face classroom courses, the respondents represented average 4.09 at 5-point Likert scale. And to the question whether the collaborative learning is more efficient under the blended learning environment than the face-to-face classroom, the response corresponds to 4.06 scale on the average. Second, as for the satisfaction in the blended learning strategy and support tools applied to the each step of the blended learning, the satisfaction degree is analyzed as high as over 4.0 on the average toward all the questions. Third, regarding the support tools used for the blended learning strategy, the learners consider the tools as most helpful in order of chatting, team community, mail & note and archive. Lastly, I would like to suggest that the study result should be highly reflected in constructing the collaborative learning module of learning control system in the future.

• The Journal of the Korea Contents Association
• /
• v.5 no.6
• /
• pp.309-318
• /
• 2005

Learners show lower cooperation and responsibility at e-Learning(Electronic Learning) than face-to-face learners in class. Accordingly the LMS(Learning Management System) focus on collaborative learning design in order to promote the learner's interaction. In this paper, the Experience Learning Support System with JXTA-based P2P(Peer-to-Peer) architecture is proposed for an effective collaborative learning and a blended learning. It intends learners to develop a self-leading learning ability and a creative problem-solving ability through experience learning object's sharing. The experimental results described that the proposed system was more effective in an enhancing learner's learning ability and a cooperative learning than existing system.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.47 no.4
• /
• pp.11-17
• /
• 2010

In this paper, we propose a recommendation system for supporting self-directed programming language education. The system is a recommendation system using collaborative filtering based on learners' level and stage. In this study, we design a recommendation system which uses collaborative filtering based on learners' profile of their level and correlation profile between learning topics in order to increase self-directed learning effects when students plan their learning process in e-learning environment. This system provides a way for solving a difficult problem, that is providing programming problems based on problem solving ability, in the programming language education system. As a result, it will contribute to improve the quality of education by providing appropriate programming problems in learner"s level and e-learning environment based on teaching and learning method to encourage self-directed learning.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.7
• /
• pp.2399-2413
• /
• 2021

A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the cold-start problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.1
• /
• pp.446-465
• /
• 2015

In this modern era of technology and information, e-learning approach has become an integral part of teaching and learning using modern technologies. There are different variations or classification of e-learning approaches. One of notable approaches is Personal Learning Environment (PLE). In a PLE system, the contents are presented to the user in a personalized manner (according to the user's needs and wants). The problem arises when a new user enters the system, and due to the lack of information about the new user's needs and wants, the system fails to recommend him/her the personalized e-learning contents accurately. This phenomenon is known as cold-start problem. In order to address this issue, existing researches propose different approaches for recommendation such as preference profile, user ratings and tagging recommendations. In this research paper, the implementation of a novel interaction-based approach is presented. The interaction-based approach improves the recommendation accuracy for the new-user cold-start problem by integrating preferences profile and tagging recommendation and utilizing the interaction among users and system. This research work takes leverage of the interaction of a new user with the PLE system and generates recommendation for the new user, both implicitly and explicitly, thus solving new-user cold-start problem. The result shows the improvement of 31.57% in Precision, 18.29% in Recall and 8.8% in F1-measure.

• Journal of the Korea Society of Computer and Information
• /
• v.15 no.8
• /
• pp.51-58
• /
• 2010

Programming education needs learning which is adjusted individual learners' level of their learning abilities. Recommendation system is one way of implementing personalized service. In this research, we propose recommendation method which learning items are recommended for individual learners' learning in web-based programming education environment by. Our proposed system for leveled programming education provides appropriate programming problems for a certain learner in his learning level and learning scope employing collaborative filtering method using learners' profile of their level and correlation profile between learning topics. As a result, it resolves a problem that providing appropriate programming problems in learner's level, and we get a result that improving leaner's programming ability. Furthermore, when we compared our proposed method and original collaborative filtering method, our proposed method provides the ways to solve the scalability which is one of the limitations in recommendation systems by improving recommendation performance and reducing analysis time.

• 한국HCI학회:학술대회논문집
• /
• 2007.02a
• /
• pp.612-615
• /
• 2007

This research studies the Virtual Reality simulation of Newton's physics law on rigid body type of objects for physics learning. With network support, collaborative interaction is enabled so that people from different places can interact with the same set of objects in Collaborative Virtual Environment. The taxonomy of the interaction in different levels of collaboration is described as: distinct objects and same object, in which there are same object - sequentially, same object - concurrently - same attribute, and same object - concurrently - distinct attributes. The case studies are the interaction of users in two cases: destroying and creating a set of arranged rigid bodies. We identify a specific type of application for contents authoring with modeling systems integrated with real-time physics and implemented in VR system. In our application called Virtual Dollhouse, users can observe physics law while constructing a dollhouse using existing building blocks, under gravity effects.

• Journal of The Korean Association of Information Education
• /
• v.19 no.1
• /
• pp.87-98
• /
• 2015

Robotics education in elementary school, It is difficult for girls to continue the motivation and willingness to learn because of a negative attitude and low recognition against the machine. In this paper, we studied method to improve the learning attitude through STEAM-based robotics education utilizing storytelling and robot smart learning system for elementary school girls. The curriculum is composed of nine themes which are selected from famous classic fairy tales for girls and we developed robot smart learning system which allows girls to enjoy robot design&control, collaborative learning, and sharing their ideas by using smart-phone. As a t-test results of learning attitude, the two groups showed statistically significant difference, the experimental group was higher average than the control group in terms of learning attitude. The robot smart learning system is effective for collaborative learning activities and maintaining learning motivation of elementary school girls.