• 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.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.26 no.4
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• pp.263-282
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• 2015

Online education in the field of LIS has continued to spread out in university curricula or with collaborative online programs through consortia among universities. Unlike the traditional online education, however, MOOCs (Massive Open Online Courses) with the recent advent and advances have risen as a new paradigm in education of the future in that these are massive online learner-centered courses, free and open to any person with no limit on enrollment. With no exception to this phenomenon, the LIS field centered by overseas iSchool universities has been offering MOOCs for core LIS courses. This research conducted a case study of utilizing a part of overseas LIS MOOCs in a core LIS course at domestic University-A, in order to explore the potential for utilizing overseas MOOCs in LIS education. The results of conducting a survey and a focus group interview to students discovered that MOOCs content was interesting and useful and many of them were willing to take other MOOCs in the future, despite some language barriers. Based on these findings, this study suggested the need for establishing educational value, administering methods, ways to motivate students, and designing MOOCs by incorporating the characteristics of the LIS field, as ways to utilize MOOCs in LIS education.

• 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 the Korea Society of Computer and Information
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• v.29 no.1
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• pp.273-285
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• 2024

With the active utilization of Online Judge (OJ) systems in the field of education, various studies utilizing learner data have emerged. This research proposes a problem recommendation based on a user-based collaborative filtering approach with learner data to support learners in their problem selection. Assistance in learners' problem selection within the OJ system is crucial for enhancing the effectiveness of education as it impacts the learning path. To achieve this, this system identifies learners with similar problem-solving tendencies and utilizes their problem-solving history. The proposed technique has been implemented on an OJ site in the fields of algorithms and programming, operated by the Chungbuk Education Research and Information Institute. The technique's service utility and usability were assessed through expert reviews using the Delphi technique. Additionally, it was piloted with site users, and an analysis of the ratio of correctness revealed approximately a 16% higher submission rate for recommended problems compared to the overall submissions. A survey targeting users who used the recommended problems yielded a 78% response rate, with the majority indicating that the feature was helpful. However, low selection rates of recommended problems and low response rates within the subset of users who used recommended problems highlight the need for future research focusing on improving accessibility, enhancing user feedback collection, and diversifying learner data analysis.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.151-166
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• 2020

A recommender system is a system that recommends products or services that best meet the preferences of each customer using statistical or machine learning techniques. Collaborative filtering (CF) is the most commonly used algorithm for implementing recommender systems. However, in most cases, it only uses purchase history or customer ratings, even though customers provide numerous other data that are available. E-commerce customers frequently use a search function to find the products in which they are interested among the vast array of products offered. Such search keyword data may be a very useful information source for modeling customer preferences. However, it is rarely used as a source of information for recommendation systems. In this paper, we propose a novel hybrid CF model based on the Doc2Vec algorithm using search keywords and purchase history data of online shopping mall customers. To validate the applicability of the proposed model, we empirically tested its performance using real-world online shopping mall data from Korea. As the number of recommended products increases, the recommendation performance of the proposed CF (or, hybrid CF based on the customer's search keywords) is improved. On the other hand, the performance of a conventional CF gradually decreased as the number of recommended products increased. As a result, we found that using search keyword data effectively represents customer preferences and might contribute to an improvement in conventional CF recommender systems.

• The Journal of the Korea Contents Association
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• v.13 no.10
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• pp.584-593
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• 2013

The purpose of this research is to analyze students' perception of smart learning focusing on its definitions, roles and values in distance higher education. In the online survey, 1,950 students of 'A' open university were participated. The results show that the students viewed the smart learning to be more 'absorbing', 'interactive' and 'collaborative' than the existing e-learning, as it compiles their experiences into learning. However, the respondents' perceptions of smart learning varied among different age groups: more students in their 40s and 50s responded that smart learning was 'customized', 'humanlike', 'interactive', 'comfortable', 'stable', 'familiar', 'unstressful', and 'practical' than students in their 20s and 30s, and they tend to view the main feature of smart learning to be the compilation of learner experiences.

• The Journal of Korean Association of Computer Education
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• v.7 no.6
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• pp.95-105
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• 2004

It is important to understand how pre-service teachers perceive e-learning because their prior-experience with e-learning will have a great impact on their teaching after the graduation. Pre-service teachers (401 students) with cyber learning experience, which is a form of e-learning, were selected in 2004 in May and June. Survey was conducted regarding the instructional experience (working hours, tasks and evaluation, satisfaction about cyber learning and its academic achievement, difficulties and suggestions) and instructional methods (instructional activities, frequencies of interaction, strategies of interaction, collaborative activities, behaviors in the group instructional activities for knowledge development). The results are pre-service teachers tent to spend similar v slightly less working hours for cyber learning, similar or slightly less satisfaction level for the instruction and the academic achievement. It was interesting that female students were more negative than males students, considering female students have been more active in online discussion traditionally. Logical presentation of contents and instructional strategies for the cyber learning were the most wanting suggestions. E-mails and BBS for reference materials were the two most used functions in the online learning. The amount and types of tasks were satisfactory. Students did not interact freely during the group activities, they reported they did not learn much through the group activity. During the group work, they consider they do their roles with responsibility while they have slightly negative responses toward other members' contribution in the group activity. Off line meeting is strongly suggested.

• The Journal of Korean Association of Computer Education
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• v.18 no.1
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• pp.81-97
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• 2015

Although Flipped Classroom(FC) which has recently attracted attention of educational field, showed its various educational effects such as learning academic achievement, attitude, collaborative learning and self-regulated learning. other studies also showed a number of significant problems and challenges in practically implementing. Thus, this study aims to investigate in-service and pre-service teachers awareness of FC in order to explore its educational feasibility for successfully adopting it to classrooms through the alternative solutions to its limitations. To achieve this goal, we firstly conducted literature review on teaching and learning models and guidelines to draw educational prerequisites and then analyzed needs of 156 pre-service teachers and 42 in-service teachers. According to survey results, 80% of teachers are willing to apply FC to their classes and hope to be offered with pre-learning activity materials and guidelines. They consider junior high school students and college students as appropriate learners, social science, science, Korean, and English as suitable subjects, and video content as optimal materials for pre-learning activities.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1293-1304
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• 2022

The personalization recommendation system means analyzing each individual's interests or preferences and recommending information or products accordingly. These personalized recommendations can reduce the time consumers spend searching for information by accessing the products they need more quickly, and companies can increase corporate profits by recommending appropriate products that meet their needs. In this study, products are recommended to consumers using collaborative filtering, matrix factorization, and deep learning, which are representative personalization recommendation techniques. To this end, the data set after purchasing shopping mall products, which is raw data, is pre-processed in the form of transmitting the data set to the input of the recommended system, and the pre-processed data set is analyzed from various angles. In addition, each model performs verification and performance comparison on the recommended results, and explores the model with optimal performance, suggesting which model should be used when building the recommendation system at the mall.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.3
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• pp.632-638
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• 2009

Probabilistic Latent Semantic Analysis has many applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. In this paper, we propose an algorithm using weighted Probabilistic Latent Semantic Analysis Model to find the contextual phrases and opinions from documents. The traditional keyword search is unable to find the semantic relations of phrases, Overcoming these obstacles requires the development of techniques for automatically classifying semantic relations of phrases. Through experiments, we show that the proposed algorithm works well to discover semantic relations of phrases and presents the semantic relations of phrases to the vector-space model. The proposed algorithm is able to perform a variety of analyses, including such as document classification, online reputation, and collaborative recommendation.