• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.158-170
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• 2022

The purpose of this study is to analyze class satisfaction with peer evaluation in Collaborative Learning-based classes. For collaborative learning-based classes, problem-based learning and project-based learning were selected. Educational implications were derived by designing Instructional procedures of Collaborative Learning-based classes, Peer evaluation types and questionnaire design, Peer evaluation progress of Collaborative Learning-based classes, Class satisfaction research and analysis In Collaborative Learning-based classes. The subjects of the study were participants in Collaborative Learning-based classes selected as problem-based learning and project-based classes. For class satisfaction with peer evaluation in Collaborative Learning-based classes, a survey was conducted on 168 participants A University in Korea. The research tool was designed as Learning procedures for peer evaluation Collaborative Learning-based classes is Team Building, Plan to the Task, To do Task, Mid-check on task, Task completion, Presentation & Evaluation, Reflection & Self-Evaluation. The content validity of items was confirmed by CVR of 12 experts. In the research results, the average class satisfaction of peer evaluation is 4.05(SD=91), followed by class concentration, diligence, voluntary, learning atmosphere. As a result of t-testing the difference in class type between collaborate learning-based classes, the satisfaction of PBL was higher than that of PjBL and a statistically significant difference was observed. The result of this study have significance in providing implications for class design and operation for the application and expansion of peer evaluation in higher education. However, there is a limit to generalization as a result of research using convenience.

• Management Science and Financial Engineering
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• v.11 no.3
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• pp.19-45
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• 2005

Collaborative filtering is one of the most common methods that e-commerce sites and Internet information services use to personalize recommendations. Collaborative filtering has the advantage of being able to use even sparse evaluation data to predict preference scores for new products. To date, however, no in-depth investigation has been conducted on how the data sparsity effect in customers' evaluation data affects collaborative filtering-based recommendation performance. In this study, we analyzed the sparsity effect and used a hybrid method based on customers' evaluations and purchases collected from an online bookstore. Results indicated that recommendation performance decreased monotonically as sparsity increased, and that performance was more sensitive to sparsity in evaluation data rather than in purchase data. Results also indicated that the hybrid use of two different types of data (customers' evaluations and purchases) helped to improve the recommendation performance when evaluation data were highly sparse.

• Journal of Ginseng Research
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• v.41 no.3
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• pp.373-378
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• 2017

Ocotillol-type saponins are one kind of tetracyclic triterpenoids, sharing a tetrahydrofuran ring. Natural ocotillol-type saponins have been discovered in Panax quinquefolius L., Panax japonicus, Hana mina, and Vietnamese ginseng. In recent years, the semisynthesis of 20(S/R)-ocotillol-type saponins has been reported. The biological activities of ocotillol-type saponins include neuroprotective effect, antimyocardial ischemia, antiinflammatory, antibacterial, and antitumor activities. Owing to their chemical structure, pharmacological actions, and the stereoselective activity on antimyocardial ischemia, ocotillol-type saponins are subjected to extensive consideration. In this review, we sum up the discovery, semisynthesis, biological activities, and metabolism of ocotillol-type saponins.

• Asia pacific journal of information systems
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• v.14 no.2
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• pp.131-149
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• 2004

Collaborative filtering is one of popular techniques for personalized recommendation in e-commerce sites. An advantage of collaborative filtering is that the technique can work with sparse evaluation data to predict preference scores of new alternative contents or advertisements. There is, however, no in-depth study about the sparsity effect of customer's evaluation data to the performance of recommendation. In this study, we investigate the sparsity effect and hybrid usages of customers' evaluation data and purchase data using an experiment result. The result of the analysis shows that the performance of recommendation decreases monotonically as the sparsity increases, and also the hybrid usage of two different types of data; customers' evaluation data and purchase data helps to increase the performance of recommendation in sparsity situation.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2004.11a
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• pp.38-38
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• 2004

• Journal of the Korea Society of Computer and Information
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• v.13 no.7
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• pp.19-27
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• 2008

A performance evaluation tool makes an important role in order to improve performance of an embedded software which has restricted computing resources. However, existing performance evaluation tools for embedded softwares cannot be used in collaborative development environment because they support only one developer with performance evaluation work under cross development environment. In this paper, we propose a performance evaluation tool for embedded softwares on collaborative development environment. A proposed tool is based on server and client model. It can have flexibility in offering and integrating the result information for the items. Through the suggested tool. developers can intuitively understand and analysis performance evaluation results each other.

• Journal of Ginseng Research
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• v.44 no.1
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• pp.14-23
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• 2020

Ginseng has been used as a popular herbal medicine in East Asia for at least two millennia. However, 20(R)-ginseng saponins, one class of important rare ginsenosides, are rare in natural products. 20(R)-ginseng saponins are generally prepared by chemical epimerization and microbial transformation from 20(S)-isomers. The C20 configuration of 20(R)-ginseng saponins are usually determined by ¹³C NMR and X-ray single-crystal diffraction. 20(R)-ginseng saponins have antitumor, antioxidative, antifatigue, neuroprotective, and osteoclastogenesis inhibitory effects, among others. Owing to the chemical structure and pharmacological and stereoselective properties, 20(R)-ginseng saponins have attracted a great deal of attention in recent years. In this study, the discovery, identification, chemical epimerization, microbial transformation, pharmacological activities, and metabolism of 20(R)-ginseng saponins are summarized.

• Journal of the Korea Society of Computer and Information
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• v.17 no.4
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• pp.83-90
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• 2012

In e-commerce systems that deal with a large number of items, the function of personalized recommendation is essential. Collaborative filtering that is a successful recommendation algorithm, suffers from the sparsity, cold-start, and scalability restrictions. Additionally, this work raises a new flaw of the algorithm, inconsistent performance of recommendation. This is also not measurable by the current MAE-based evaluation that does not consider the deviation of prediction error, and furthermore is performed independently of precision and recall measurement. To evaluate the collaborative filtering comprehensively, this work proposes an extended evaluation model that includes the current criteria such as MAE, Precision, Recall, deviation, and applies it to cluster-based combined collaborative filtering.

• Journal of Engineering Education Research
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• v.22 no.4
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• pp.61-70
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• 2019

As product development process becomes complex, it becomes more important for engineering students to experience collaborative product development. Especially the collaboration experience based on Product Data Management (PDM) systems is useful, since participants are likely to use the same environment for their professional product development. However, instructors have difficulties to evaluate contribution of each participant to their projects during the practices, since it is hard to trace personal activities for collaborative design processes. To solve this problem, this study suggests a data-driven objective method that analyses product data accumulated in PDM databases to evaluate numerically calculated contributions of participants to their class projects. As a result, the quantitative measures provided by the data-driven analysis with qualitative measures for project results can improve the fairness and quality of evaluation of contributions of participants to collaborative projects. This study implemented the proposed evaluation method with an information system and discussed the result of the application of the system to product development practices.

• Journal of Information Processing Systems
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• v.18 no.2
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• pp.208-222
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• 2022

Through the analysis of the coordination mechanism of the supply chain system of China's automobile manufacturing industry, the factors affecting the supply subsystem, the manufacturing subsystem, the sales subsystem, and the consumption subsystem are sorted out, the supply chain coordination index system based on the influence factor of four subsystems is established. The evaluation models of the coordination degree in the subsystem of the supply chain, the coordination degree among the subsystems, and the comprehensive coordination degree are established by using the efficiency coefficient method and the collaborative entropy method. Experimental results verify the accuracy of the evaluation model using the empirical analysis of the collaborative evaluation index data of China's automobile manufacturing industry from 2000 to 2019. The supply chain synergy of automobile manufacturing industry was low from 2001 to 2005, and it increased to a certain extent from 2006 to 2008 with a small growth rate from 0.10 to 0.15. From 2009 to 2013, the supply chain synergy of automobile manufacturing industry increased rapidly from 0.24 to 0.49, and it also increased rapidly but fluctuated from 2014 to 2019, first rising from 0.68 to 0.84 then dropping to 0.71. These results provide reference for the development of China's automobile manufacturing supply chain system and scientific decision-making basis for the formulation of relevant policies of the automobile manufacturing industry.