• Journal of KIISE:Computing Practices and Letters
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• v.7 no.6
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• pp.684-695
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• 2001

Recent recommender system uses a method of combining collaborative filtering system and content based filtering system in order to solve sparsity and first rater problem in collaborative filtering system. Collaborative filtering systems use a database about user preferences to predict additional topics. Content based filtering systems provide recommendations by matching user interests with topic attributes. In this paper, we describe a method for discovery of user preference through combining two techniques for recommendation that allows the application of machine learning algorithm. The proposed collaborative filtering method clusters user using genetic algorithm based on items categorized by Naive Bayes classifier and the content based filtering method builds user profile through extracting user interest using relevance feedback. We evaluate our method on a large database of user ratings for web document and it significantly outperforms previously proposed methods.

• 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 of Information Education
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• v.10 no.2
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• pp.171-182
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• 2006

Science education in elementary school is mostly based on solving investigative tasks conducted by small groups on the basis of the inquiry activities such as experiments, observations, etc. Students should be able to draw conclusions from experiments and observations through their collaborative interactions by exchanging ideas. If learners use the advantages of Blended-Learning as a new pedagogical method comprehending merits of both off-line and on-line learning to complement off-line activities throughout the entire process, the interactions among them may be reinforced, and they would take part in the inquiry learning more actively. Accordingly, the proposed system can help children foster science inquiry learning effectively by using on-line communication facilities to arrange learners' diverse opinions and thoughts coming from off-line inquiry activity process. The result shows that the system enhanced learners' interest about science inquiry learning and improved their learning achievements by exchanging diverse opinions via the Web communication facilities.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.1-15
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• 2021

If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.

• The Journal of Korean Association of Computer Education
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• v.15 no.6
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• pp.65-74
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• 2012

Wiki is an online-based collaborative tool, and has been frequently used more recently as it realizes the paradigm of web 2.0 in educational context. Especially, wiki promotes collaborative knowledge building which is the major interest of this study. The purpose of this research is to investigate the relationships among academic self-efficacy, self-efficacy for group work, wiki anxiety, participation in wiki activity, and learning achievement. Fifty nine college students participated in the wiki activity, and the data from fifty three were used for the multiple regression and path analysis. The results reported that academic self-efficacy and wiki anxiety affected participation in wiki activity, and these two variables also had indirect effects on learning achievement, mediated by participation.

• Journal of The Korean Association of Information Education
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• v.12 no.4
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• pp.385-393
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• 2008

The purpose of this research is to suggest the direction of using blog in e-PBL. In this research we used blog for supporting individual self-regulated learning activity. We distributed space for individual learning and collaborative learning by using blog and community. 39 students participated during the 16 weeks, which includes 8 weeks of traditional e-PBL and 8 weeks of Blog based e-PBL. We conducted questionnaires and interviews, and analyzed learners' reflection notes. Data show learners feel comfortable with using blog in their independent learning activity. And blog activated learners' reflective activity and motivated their self-regulated learning.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.11b
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• pp.864-867
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• 2003

e-러닝이 세간의 관심을 모으는 이유는 교육의 패러다임이 바뀔 수 있다는 점이다. 교육은 모든 분야에 필수적이고, 교육 방식이 바뀐다는 의미는 새로운 시장이 만들어진다는 것이다. 하지만 온라인 교육이 가질 수 있는 장점들을 제대로 활용하지 못한다면 새로운 시장이 만들어진다 하더라도 쉽게 시들어 갈 것이며, 최근에 나와 있는 e-learning과 관련된 사이트들을 보더라도 전자상거래나 쇼핑몰 혹은 검색, 포털 사이트처럼 빠르게 성장할 수 없다 우리는 학창 시절에 어떠한 친구를 만나느냐, 혹은 어떠한 짝꿍을 만나느냐에 따라서 본인의 학습 성취도는 매우 다르다는 것을 경험해 보았을 것이다. 이에 본 논문에서는 학습자들에게 보다 쉽게 짝 찾는 방법과 짝 짓는 방법을 제공하여 학습자들간의 상호협력을 이루어 문제해결 능력과 새로운 지식을 만들어 갈 수 있도록 하였으며, 짝 프로그래밍을 통해서 학습자는 최적의 짝꿍을 만나 지속적인 상호작용으로 흥미와 집중을 유지하여 적극적이고 완전한 학습이 이루어질 수 있도록 하였다. 끝으로 본 연구가 웹 상의 학습자들에게 서로간의 대화를 통해 에러의 원인을 효과적으로 찾아 바로 잡을 수 있는 짝 프로그래밍을 제공했다는 점에서 새로운 학습 시스템의 개발방법을 제시했다고 결론지을 수 있다.

• Journal of Korea Multimedia Society
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• v.21 no.11
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• pp.1297-1304
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• 2018

Online education, commonly referred to as distance education, has developed rapidly. However, it is questionable whether such distance education has been applied to various educational fields and has achieved satisfactory results in terms of learning effect. One of the reasons for not maximizing the benefits of distance education is non-dynamicity in the production and application of educational content. Educational contents production is made up of collaborative work between the instructor who is the contents expert and the developer who is the production expert. For this reason, existing researches have also concentrated on the improvement of each educational effect. In this paper, we propose to replace a production expert from a developer to an instructor. At this time, the important point is that the educational contents produced by the instructor, who is a development non-expert, should still be able to be maintained with high-quality contents utilizing the characteristics of the web. For this purpose, the production system was developed based on open source to maintain the quality similar to the educational contents developed by the production expert. This will increase the effectiveness of education by applying the developed Smart-Blended Learning System to various educational sites.

• The Journal of Korean Academic Society of Nursing Education
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• v.14 no.1
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• pp.63-72
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• 2008

Purpose: This study focused on two aspects: 1) how to design and implement a mobile learning course which is facilitated by a PDA with a web-based class homepage as a tool for mobile learning; 2) how to increase and enhance interactive activities among and between the students and the faculty members by utilizing a PDA as a tool for communication as well as collaboration. Method: To analyze the results of the m-Learning course, data was collected from interviews with the involved two faculty members and a survey from 27 students. Result: The results showed a positive outcome of the m-Learning approach in terms of a more collaborative learning environment in a clinical course where the students practice their clinical activities out of the classroom, far from their faculty members. On the other hand, the problems of the m-Learning approach were that more thorough preparation was needed for the new tools from both the students and the faculty members in preparation in social, cultural, and mental aspects, not withstanding the assumed technical limits of a PDA. Conclusion: m-Learning must be more actively implemented in classes, even though several problems were noticed in terms of both technical aspects of the tools, and social and cultural aspects from the users.

• The Journal of Korean Association of Computer Education
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• v.9 no.3
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• pp.47-65
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• 2006

For the purpose of analysis, the supporting methods for inquiry process is divided into 3 types; when CSILE supports low-level of basic inquiry process, when CSILE supports high-level of integrated inquiry process and when CSILE supports both low-level and high-level of inquiry process. Strauss and Corbin's(1998) grounded theory was used to analyze inquiry process of learning groups. 48 elementary school students in 6th grade participated in this study. Those participants were assigned into 3 groups and each group consisted of 16 students. Then, participants studied a retarded unit in science subject cooperatively for 4 weeks using CSILE program. Through this extensive experiment, 3 types of inquiry model was revealed.