• 제목/요약/키워드: Collaborative evaluation

검색결과 319건 처리시간 0.025초

협력학습 기반 수업에서의 동료평가에 대한 수업 만족도 분석 (Analysis of class satisfaction with Peer Evaluation in Collaborative Learning-based classes)

  • 정선경;박남수
    • 융합정보논문지
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    • 제12권3호
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    • pp.158-170
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    • 2022
  • 본 연구의 목적은 협력학습 기반 수업에서의 동료평가에 대한 수업 만족도를 분석하는 것이다. 협력학습 기반 수업에서 동료평가를 위한 수업 절차 설계, 동료평가 유형과 문항 개발, 동료평가 실시, 수업만족도 조사 및 분석하여 교육적 시사점을 도출하고자 하였다. 연구대상은 문제중심학습과 프로젝트 기반 수업으로 선정된 협력학습 기반 수업 참여자를 대상으로 하였다. 협력학습 기반 수업에서의 동료평가에 대한 수업 만족도는 수도권 소재 A대학에서 협력기반 수업 참여자 168명을 대상으로 설문조사를 진행하였다. 연구도구는 동료평가를 위한 협력학습 절차는 팀 구성, 과제 수행 계획, 과제수행, 중간평가, 과제 수행 완료, 발표 및 평가, 자기평가로 설계하였고, 동료평가는 팀내. 팀간 평가로 유형화 하였고, 단계별 동료평가문항을 설계하였다. 동료평가 문항의 내용타당도는 전문가 12인의 CVR로 평정하였다. 연구결과에서 동료평가의 수업만족도는 평균 4.05(SD=.91)로 높게 나타났고 수업의 집중, 수업의 성실성, 자발성, 학습 분위기의 순으로 나타났다. 수업 만족도 수준 차이 검증 결과, 성별과 학년에서는 차이가 나타나지 않았으며, 수업 유형에서는 문제중심학습이 프로젝트기반 학습에 비하여 만족도 수준이 높게 나타났고 통계적으로 유의미하였다. 본 연구의 결과는 고등교육에서의 동료평가 적용 및 확대를 위한 수업설계와 운영의 시사점을 제공하는 데에 의의를 가진다.

Data Sparsity and Performance in Collaborative Filtering-based Recommendation

  • Kim Jong-Woo;Lee Hong-Joo
    • Management Science and Financial Engineering
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    • 제11권3호
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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.

Discovery, semisynthesis, biological activities, and metabolism of ocotillol-type saponins

  • Liu, Juan;Xu, Yangrong;Yang, Jingjing;Wang, Wenzhi;Zhang, Jianqiang;Zhang, Renmei;Meng, Qingguo
    • Journal of Ginseng Research
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    • 제41권3호
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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.

협업 필터링 기반 개인화 추천에서의 평가자료의 희소 정도의 영향 (Sparsity Effect on Collaborative Filtering-based Personalized Recommendation)

  • 김종우;배세진;이홍주
    • Asia pacific journal of information systems
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    • 제14권2호
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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.

협업 개발을 지원하는 임베디드 소프트웨어 성능분석도구 설계 및 구현 (Design and Implementation of a Performance Evaluation Tool for Embedded Softwares on Collaborative Development Environment)

  • 김익수;조용윤
    • 한국컴퓨터정보학회논문지
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    • 제13권7호
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    • pp.19-27
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    • 2008
  • 컴퓨팅 자원 사용에 제약이 많은 임베디드 소프트웨어 개발에 있어서 성능 분석 도구는 품질 개선을 위해 매우 중요한 역할을 한다. 그러나 기존 임베디드 소프트웨어 성능 분석 도구들은 단일 개발자의 교차개발 지원을 위한 성능분석 환경만을 제공하기 때문에 임베디드 소프트웨어를 위한 협업 성능분석 도구로서 활용될 수 없다. 본 논문에서는 협업 환경에서 임베디드 소프트웨어 성능 분석을 효과적으로 수행하기 위한 서버 기반의 새로운 성능 분석도구를 제안한다. 제안하는 성능분석 도구는 개발 소프트웨어에 대해 실행한 성능 분석로그를 협업 개발자들 기호에 맞는 다양한 그래픽 뷰로 생성하여 개발자간의 신속하고 효율적인 정보 공유를 가능하게 함으로써 프로그램의 성능 향상을 위한 개발자의 성능 분석 환경을 크게 개선할 수 있다.

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Advances in the chemistry, pharmacological diversity, and metabolism of 20(R)-ginseng saponins

  • Wang, Chaoming;Liu, Juan;Deng, Jianqiang;Wang, Jiazhen;Weng, Weizhao;Chu, Hongxia;Meng, Qingguo
    • Journal of Ginseng Research
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    • 제44권1호
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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.

협업필터링에서 포괄적 성능평가 모델 (A Comprehensive Performance Evaluation in Collaborative Filtering)

  • 유석종
    • 한국컴퓨터정보학회논문지
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    • 제17권4호
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    • pp.83-90
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    • 2012
  • 대규모의 상품을 다루는 전자상거래 시스템에서 개인화된 추천은 필수적인 기능이 되고 있다. 대표적 추천 알고리즘인 협업필터링은 내용기반 추천에 비하여 뛰어난 추천성능을 제공해 주고 있으나, 희박성, 신규 아이템 문제(Cold-start), 확장성 등의 근본적인 한계를 갖고 있다. 본 연구에서는 추가적으로 협업필터링이 목표 대상자에 따라 비일관된 예측 능력의 차이를 보이는 추천 성능의 편차 문제를 제기하고자 한다. 추천성능의 편차는 기존의 Mean Absolute Error(MAE)에 의해서는 측정되기 어려우며 또한 정확도, 재현율 지표와도 독립적으로 평가되고 있다. 협업알고리즘의 정확한 성능평가를 위해서 본 연구에서는 MAE, MAE 편차, 정확도, 재현율을 포괄적으로 평가할 수 있는 확장 성능평가모델을 제안하고 이를 클러스터링 기반 협업필터링에 적용하여 성능을 비교 분석한다.

협동 제품개발 실습에서 참가자 기여도 평가를 위한 Product Data Analytics 기반 정량적 평가 시스템 적용 (Applying a Product Data Analytics-based Quantitative Contribution Evaluation System for Participants to Collaborative Projects in Product Development Practices)

  • 도남철
    • 공학교육연구
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    • 제22권4호
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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.

Models and Methods for the Evaluation of Automobile Manufacturing Supply Chain Coordination Degree Based on Collaborative Entropy

  • Xiao, Qiang;Wang, Hongshuang
    • Journal of Information Processing Systems
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    • 제18권2호
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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.