• Asian Pacific Journal of Cancer Prevention
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• v.15 no.9
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• pp.3885-3890
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• 2014

Background: This study aimed to determine nursing student knowledge, behavior and beliefs for breast cancer and breast self-examination receiving courses with a traditional lecturing method (TLM) and the Six Thinking Hats method (STHM). Materials and Methods: The population of the study included a total of 69 second year nursing students, 34 of whom received courses with traditional lecturing and 35 of whom received training with the STHM, an active learning approach. The data of the study were collected pre-training and 15 days and 3 months post-training. The data collection tools were a questionnaire form questioning socio-demographic features, and breast cancer and breast self-examination (BSE) knowledge and the Champion's Health Belief Model Scale. The tests used in data analysis were chi-square, independent samples t-test and paired t-test. Results: The mean knowledge score following traditional lecturing method increased from $9.32{\pm}1.82$ to $14.41{\pm}1.94$ (P<0.001) and it increased from $9.20{\pm}2.33$ to $14.73{\pm}2.91$ after training with the Six Thinking Hats Method (P<0.001). It was determined that there was a significant increase in pre and post-training perceptions of perceived confidence in both groups. There was a statistically significant difference between pre-training, and 15 days and 3 months post-training frequency of BSE in the students trained according to STHM (p<0.05). On the other hand, there was a statistically significant difference between pre-training and 3 months post-training frequency of BSE in the students trained according to TLM. Conclusions: In both training groups, the knowledge of breast cancer and BSE, and the perception of confidence increased similarly. In order to raise nursing student awareness in breast cancer, either of the traditional lecturing method or the Six Thinking Hats Method can be chosen according to the suitability of the teaching material and resources.

• 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.

• Journal of The Korean Association For Science Education
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• v.23 no.1
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• pp.35-46
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• 2003

Problem solving ability, having been thought as one of the most important goals of science education is also a primary task for the current education. Indeed, the students' problem solving ability has shown almost no actual progress, despite our long accumulated science education. Under this circumstances, cooperative learning, a way to grow students' positive inter-dependence and problem solving ability in the basis of their active participation and discussion, was proposed as an effective teaching method. But, results have not consistently shown the advantage of cooperative learning over traditional learning for promoting academic achievement in science. Studies have consistently shown greater effectiveness on affective aspects. But, relatively few have focused on biology in Korea. The purpose of this study was to examine the effects of cooperative learning on the achievement and attitude of high school biology students. The pretest-posttest control group design was applied. The sample consisted of 50 11th-grade female students in experimental group(cooperative learning Student Team Achievement Division model) and 50 students in control group(traditional learning). Students in both groups recieved identical content instruction on the unit 'II. Methabolism'. These groups were treated for 13 hours during 5 weeks. Achievement data were collected using a 24-item multiple-choice test(content validity= .85). Science attitude was measured by an instrument which adapted by Kim In Hee(1994). The instrument(Cronbach $\alpha$=.89) included 40 items in four subscales: attitude toward science, social meaning of science, attitude toward science class, and scientific attitude. An analysis of covariance (ANCOVA) was used as the data analysis procedure. For the achievement data, no significant difference exists between the cooperative and traditional groups (p> .05). But, cooperative learning was effective in low-ability students(p < .05). For the science learning attitude data, cooperative learning was more effective than the traditional one(p< .05). Students in the cooperative group acheived better than those in traditional one especially in the subscale of attitude toward science class. There was no meaningful difference of the two methods in both high and average ability students, while cooperative learning was more effective than the traditional one in low ability students(p<.05).

• The Journal of Korean Association of Computer Education
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• v.8 no.1
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• pp.33-42
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• 2005

The purpose of this study is to develop MICHS(Models of Interactive Cyber Home Study) for effective cyber home study(CHS) and a prototype system to support realtime and non-realtime interaction tools such as chatting with users, to study a method to connect MICHS with selective curriculum, and to verify the educational effect of the model. MICHS consists of two models. One model is an educational system model, and another model is a teaching-learning model. The major results of this study are summarized as follows. In the viewpoint of learning achievement, there is no significant difference in achievement between the experimental group(33 leaners) and the comparative group(33 leaners), but this fact can be construed as the possibility that CHS is an alternative system of school education. The interests on CHS of learners in the experimental group were increased twice. This result means that the system of CHS for active interaction needs various and important interaction tools, and the educational vision of CHS. In the viewpoint of system operation, CHS needs higher system requirements such as 100 Mbps Fast Ethernet. If an old system can not process streaming services in request, it should be upgraded to a new system.

• Journal of Information Technology Services
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• v.19 no.1
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• pp.103-112
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• 2020

Job seekers are making various efforts to find a good company and companies attempt to recruit good people. Job search activities through self-introduction essay are nowadays one of the most active processes. Companies spend time and cost to reviewing all of the numerous self-introduction essays of job seekers. Job seekers are also worried about the possibility of acceptance of their self-introduction essays by companies. This research builds a classification model and conducted an experiments to classify self-introduction essays into pass or fail using deep learning and decision tree techniques. Real world data were classified using stratified sampling to alleviate the data imbalance problem between passed self-introduction essays and failed essays. Documents were embedded using Doc2Vec method developed from existing Word2Vec, and they were classified using logistic regression analysis. The decision tree model was chosen as a benchmark model, and K-fold cross-validation was conducted for the performance evaluation. As a result of several experiments, the area under curve (AUC) value of PV-DM results better than that of other models of Doc2Vec, i.e., PV-DBOW and Concatenate. Furthmore PV-DM classifies passed essays as well as failed essays, while PV_DBOW can not classify passed essays even though it classifies well failed essays. In addition, the classification performance of the logistic regression model embedded using the PV-DM model is better than the decision tree-based classification model. The implication of the experimental results is that company can reduce the cost of recruiting good d job seekers. In addition, our suggested model can help job candidates for pre-evaluating their self-introduction essays.

• Journal of Broadcast Engineering
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• v.21 no.4
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• pp.609-620
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• 2016

Recently, there are active studies on a forward collision warning system to prevent the accidents and improve convenience of drivers. For collision evasion, the vehicle detection system is required. In general, existing learning-based vehicle detection methods use the entire appearance of the vehicles from rear-view images, so that each vehicle types should be learned separately since they have distinct rear-view appearance regarding the types. To overcome such shortcoming, we learn Haar-like features from the lower part of the vehicles which contain tail lights to detect vehicles leveraging the fact that the lower part is consistent regardless of vehicle types. As a verification procedure, we detect tail lights to distinguish actual vehicles and non-vehicles. If candidates are too small to detect the tail lights, we use HOG(Histogram Of Gradient) feature and SVM(Support Vector Machine) classifier to reduce false alarms. The proposed forward vehicle detection method shows accuracy of 95% even in the complicated images with many buildings by the road, regardless of vehicle types.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.9-18
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• 2013

In this paper, we design and implement an intelligent system for finding objects with RFID(Radio Frequency IDentification) tag in which an mobile robot can do. The system we developed is a learning system of artificial neural network that uses RSSI(Received Signal Strength Indicator) value as input and absolute coordination value as target. Although a passive RFID is used for location estimation, we consider an active RFID for expansion of recognition distance. We design the proposed system and construct the environment for indoor location estimation. The designed system is implemented with software and the result related learning is shown at test bed. We show various experiment results with similar environment of real one from earning data generation to real time location estimation. The accuracy of location estimation is verified by simulating the proposed method with allowable error. We prepare local test bed for indoor experiments and build a mobile robot that can find the objects user want.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.465-468
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• 2007

An intelligent home network system using low-power and low-cost sensor nodes was designed and implemented. In Intelligent Home Network System, active home appliances control is composed of RSSI (Received Signal Strength Indicator) based user indoor location tracking, dynamic multi-hop routing, and learning integration remote-control. Through the remote-control learning, home appliances can be controlled in wireless network environment. User location information for intelligent service is calculated using RSSI based Triangle measurement method, and then the received location information is passed to Smoothing Algorithm to reduce error rate. In order to service Intelligent Home Network, moreover, the sensor node is designed to be held by user. The gathered user data is transmitted through dynamic multi-hop routing to server, and real-time user location & environment information are displayed on monitoring program.

• Journal of Science of Art and Design
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• v.9
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• pp.117-136
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• 2006

The purpose of the present study is to explore the possibility of computer graphics as a new expression tool for high-grade elementary students' poster making by suggesting specific teaching-learning methods focused on expression using computer graphics to make posters in Unit 9 'Designing Notices' for 6th-grade elementary students. Through this study, we confirmed various possibilities of computer graphics as a new medium for art education and particularly its possibility for children's creative expression activities in expressing images hard to express with paper and brush and applying desired colors to the picture. The use of computer graphics as a new paradigm of art education in the 21st century is expected to contribute to the improvement of diversified visual literacy. Thus, art class in school education needs to take an active attitude in intrdoducing various multimedia.

• The Journal of the Korea Contents Association
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• v.20 no.1
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• pp.573-580
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• 2020

The purpose of this study was to explore and to suggest the effective teaching methods for university students to improve their speaking abilities through peer assessment-feedback and self assessment-feedback based presentation and debate class. The objects and content of the study were the students who took a class "Reading and Communication" of H university, and here examine closely the speaking activities and their effects during the integrated activities of 'reading, speaking and writing'. In speaking education peer and self assessment feedback is to form an emotional consensus among the students and encourage active participation, and enable self-discovery and communication learning to students. As a result of the study, it was found that the variety speaking practice had a very positive effect on the attitude of speaking and the psychological aspect, and it was confirmed that the evaluation and feedback activities had a meaningful learning effect on the students. Effective speech education and guidance measures should be continuously studied and developed.