• Proceedings of the Korea Information Processing Society Conference
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• 2005.05a
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• pp.321-324
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• 2005

기존의 LMS(Learning Management System)는 웹 기반의 e-Learning 교육의 장점에도 불구하고 학습자의 요구와 수준에 무관하게 학습과 관련한 컨텐츠들이 획일적으로 구성됨으로써, 학습자의 요구를 만족시키지 못하고 있다. 본 논문에서는 LCMS(Learning Content Management System) 와 LMS를 연계한 학습 통계 모듈을 제시하고, LMS에 학습자와 운영자에게 학습정보 데이터를 제공함으로써 학습하는 과정을 추적하고 학습이력을 관리 할 수 있는 학습통계모듈을 설계하고 구현한다. 제시된 모듈에서는 효과적인 학습통계을 위한 검색 방안으로 LCMS의 메타데이터와 다양한 학습관리 정보(CMI)값을 LMS를 호출하는 기능인 API(Application Program Interface) 어댑터를 이용하여 연계된 값과 LMS시스템에 학습지원과 운영지원 기능을 추가하여 나온 결과값을 바탕으로 하였다. 이 학습통계모듈을 통해서 LMS운영자는 학습자의 컨텐츠의 활용을 더욱 확장할 수가 있으며 학습자의 학습정보관리를 하는 LMS의 성능을 향상 시키고자 하였다.

• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.1-10
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• 2021

Log data accumulated in the Learning Management System (LMS) provide high-quality information for the learning process of students. Until now, various studies have been conducted to predict students' academic achievement using LMS log data. However, previous studies were based on relatively small sample sizes of students and courses, limiting the possibility of generalization. This study developed and validated a deep neural network model for the early prediction of academic achievement of college students using massive LMS log data. To this end, we used 78,466,385 cases of LMS log data and 165,846 cases of grade data. The proposed model predicted the excellent-grade students with a high level of accuracy from the beginning of the semester. Meanwhile, the prediction accuracy for the moderate and underachieving groups was relatively low, but the accuracy improved as the time points of the prediction were delayed. This study is meaningful in that we developed an early prediction model based on a deep neural network with sufficient accuracy for practical utilization by only using LMS log data.

• Journal of Digital Convergence
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• v.10 no.10
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• pp.277-282
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• 2012

The speech recognition system can not quickly adapt to varied environmental noise factors that degrade the performance of recognition. In this paper, the echo noise robust HMM learning model using average estimator LMS algorithm is proposed. To be able to adapt to the changing echo noise HMM learning model consists of the recognition performance is evaluated. As a results, SNR of speech obtained by removing Changing environment noise is improved as average 3.1dB, recognition rate improved as 3.9%.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2007.11a
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• pp.123-123
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• 2007

• The Research Journal of the Costume Culture
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• v.27 no.5
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• pp.512-523
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• 2019

Contemporary University students are considered the Z generation who were born after 1995. They are more tech savvy than millennials. To target the generation, traditional class management platforms have evolved to smart LMS that is more customized and accessible for smart devices. Global level information search and collaboration can also be implemented using such smart LMS. However, switching from one LMS to another LMS requires great effort from teachers and support from staffs. This study measured the learners' perception of the system when they were exposed to a new smart-LMS. Blackboard Learn Ultra was used for 15 weeks and at the end of the semester, a questionnaire was administered to the students of these classes. Results indicated that experience with previous LMS discouraged students from adopting Blackboard Learn. Result of TAM modeling indicated that perceived usefulness, compared to perceived ease of use and attitude, was an effective aspect to bring positive acceptance of the system. A qualitative approach and network analysis were also conducted based on students' responses. Both positive and negative responses were detected. Inconvenience due to mechanical aspects was mentioned. Dissatisfaction compared to previous local LMS use was also mentioned. Mobile application and communication effectiveness were positive aspects. Revised course development and promoting how useful the system may help enhance the acceptance of the new system.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.11a
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• pp.247-250
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• 2002

In this paper, We are design & implement to the LMS(Learning Management System) including SCORM standard for effective reusing and management of each digital contents. This system is composed XML based meta-data manager, SQL server and ASP for LMS application, which are the user directed teaming system within effective reusing and management of each digital contents.

• Journal of the Korea Convergence Society
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• v.3 no.2
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• pp.1-5
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• 2012

South Korea is one of major trading partner of Philippines. Many Korean manufacturers and tour companies have branches in the Philippines. Korean companies need to enhance Korean language capabilities for Filipino employee. South Korea has many successive experiences for e-learning. In this paper, we developed Learning Management System (LMS) for Philippines. LMS was implemented in JAVA, ASP and HTML. All lectures are stored in database managed by MS SQL-server.

• Journal of The Korean Association of Information Education
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• v.23 no.2
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• pp.159-167
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• 2019

Recently, a learning management system incorporating ICT technology into learning has helped students improve self-directed learning skills. Self-directed learning using LMS promotes and stimulates learners' participation in learning, focusing on the advantages of efficient use of learning resources and the spread of communication. In this study, we study the impact of self-directed learning using the learning management system on elementary school students' motivation and academic performance. We expect learners will be able to achieve effective academic achievement by learning problems that fit their level through the algorithms of the proposed learning management system. For this study, a total of 16 classes were conducted for eight weeks using the proposed learning management system for 21 elementary school students. Research has shown significant improvement in the learning orientation and interest areas of the learners who participated in the experiment.

• Journal of Advanced Navigation Technology
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• v.16 no.1
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• pp.76-81
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• 2012

With the spread of the various mobile devices, the studies on the learning management system based on the u-learning are actively proceeding. The u-learning-based learning management system is very convenient in that there are no restrictions on the various access devices as well as the access time and place. However, the judgments on the authentication for the user and whether learning is focused on are difficult. In this paper, the voice and user face capture interface rather than the common user event oriented interface was applied to the learning management system. When a user is accessing the learning management system, user's registered password is input and login as voice, and the user's learning attitude is judged through the response utterance of simple words during the process of learning through contents. As a result of evaluating the proposed learning management system, the user's learning achievement and concentration were improved, thus enabling the manager to monitor the user's abnormal learning attitude.

• Educational Technology International
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• v.18 no.2
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• pp.159-191
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• 2017

Academic analytics guides university decision-makers to assign limited resources more effectively. Especially, diverse academic courses clustered by the usage patterns and levels on Learning Management System(LMS) help understanding instructors' pedagogical approach and the integration level of technologies. Further, the clustering results can contribute deciding proper range and levels of financial and technical supports. However, in spite of diverse analytic methodologies, clustering analysis methods often provide different results. The purpose of this study is to present implications by using three different clustering analysis including Gaussian Mixture Model, K-Means clustering, and Hierarchical clustering. As a case, we have clustered academic courses based on the usage levels and patterns of LMS in higher education using those three clustering techniques. In this study, 2,639 courses opened during 2013 fall semester in a large private university located in South Korea were analyzed with 13 observation variables that represent the characteristics of academic courses. The results of analysis show that the strengths and weakness of each clustering analysis and suggest that academic leaders and university staff should look into the usage levels and patterns of LMS with more elaborated view and take an integrated approach with different analytic methods for their strategic decision on development of LMS.