Computer Science ›› 2024, Vol. 51 ›› Issue (5): 21-26.doi: 10.11896/jsjkx.230200202

• Discipline Frontier • Previous Articles     Next Articles

Discipline Competition Evaluation Model Based on Multi-attribute Comprehensive Evaluation

XING Cunyuan1, ZHANG Jie2, JIN Ying2   

  1. 1 School of Artificial Intelligence,Nanjing University,Nanjing 210023,China
    2 Department of Computer Science and Technology,Nanjing University,Nanjing 210023,China
  • Received:2023-02-26 Revised:2023-06-14 Online:2024-05-15 Published:2024-05-08
  • About author:XING Cunyuan,born in 2001,undergraduate.His main research interests include machine learning and data mi-ning.
    JIN Ying,born in 1978,Ph.D,professor,is a member of CCF(No.58154M).Her main research interests include computer education,data mining,and virtual reality technology application.
  • Supported by:
    College Computer Course Teaching Steering Committee of Ministry of Education 2020 New Era College Compu-ter Enabling Education Reform Project(2020-JZW-CT-A02) and New Liberal Arts Research and Reform Practice Project of Ministry of Education(2021020001).

Abstract: The college student competition in China is booming,both the holding and participation of events show a positive trend.The discipline competition of college students can reflect the discipline development level and teaching level of participating colleges,and the analysis and comparison of the level of colleges based on the competition data can also promote the colleges' attention and participation in the competition to a certain extent.In previous research and practical application,the evaluation of the school competition level is mostly limited to stacking award scores.The model based on the “award-only” theory is one-sided because it ignores the development level of colleges and universities.Activity,performance,and stability indexes can describe and evaluate the competition level of participating colleges and universities.The optimal weight of the index is determined by scatter degree method so as to obtain the scores of colleges and universities.In addition,according to the characteristics of the discipline competition,different performances of colleges and universities on different courses of the competition can be used as more detailed characteristics.The participating colleges and universities are divided into four types through t-SNE dimension reduction,visualization,and cluster analysis.For different types of colleges and universities,specific suggestions to improve the performance of the competition are proposed in this paper.The data from Jiangsu College Student Computer Design Competition since its inception is used to verify the validity of the model.

Key words: Comprehensive evaluation, Discipline competition, Colleges and universities, Data mining

CLC Number: 

  • TP399
[1]LEI J H.A Research of University Students' Subject ContestManagement System[D].Hangzhou:Zhejiang University,2013.
[2]GUO Y J.Theory,method and application of comprehensiveevaluation[M].Beijing:Science and Technology Press,2007.
[3]FENG S,XU L D.Decision support for fuzzy comprehensiveevaluation of urban development[J].Fuzzy Sets and Systems,1999,105(1):1-12.
[4]ZOU Z H,YI Y,SU J L.Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment[J].Journal of Environmental Sciences,2006,18(5):1020-1023.
[5]CHEN Y Y,ZHANG W Q.Comprehensive decision making and weather forecast[J].Mathematics in Practice and Theory,2000,113:32-39.
[6]QIAO Y,LIU X,ZOU L.Evaluating the quality of education via linguistic aggregation operator[J].ICIC Express Letters,2010,4:1851-1856.
[7]KUO Y F,CHEN P C.Selection of mobile value-added services for system operators using fuzzy synthetic evaluation[J].Expert Systems with Application,2006,30:612-620.
[8]XIA D,CHEN B.A comprehensive decision-making model forrisk management of supply chain[J].Expert Systems with Application,2011,38:4957-4966.
[9]LIANG Z H,YANG K,SUN Y W,et al.Decision support for choice optimal power generation projects:fuzzy comprehensive evaluation model based on the electricity market[J].Energy Policy,2006,34(17):3359-3364.
[10]SAMATSU T,TACHIKAWA K,SHI Y.Usability improve-ment for a car retrieval system employing the important degrees of fuzzy grades[J].International Journal of Innovative Computing,Information and Control,2009,5:5061-5068.
[11]SUN T,LIU Q.Research on the Importance Evaluation of discipline Competition based on approaching ideal point method[J].Marketing Management Review,2017(9):227.
[12]WANG L,LU P,WU Y L.The Construction and Application of Subject Competition Evaluation System in Colleges[J].Journal of Yibin University,2021,21(12):62-68.
[13]ZHENG L.Research on Teaching Evaluation of Discipline Competition Based on Markov Chain [J].Science & Technology Economy Market,2022(3):143-145.
[14]WU B X,CHEN Y,JIANG Z K.Research on optimization of discipline competition ranking model based on ELO system[J].Changjiang Information & Communications,2021,34(10):60-64.
[15]China Association of Higher Education University CompetitionEvaluation and Management System Expert Working Group.National College Student Competition White Paper(2014-2018)[M].Hangzhou:Zhejiang University Press,2019.
[16]LU G D,CHEN L Q,HE Q M,et al.The Evaluation of Academic Competition in Universities:Plan,Method and Exploration[J].China Higher Education Research,2018(2):63-68,74.
[17]Jiangsu Computer Society.Promoting learning by competition,teaching by competition and innovation by competition:Jiangsu University Student Computer Design Competition [EB/OL].(2022-03-21)[2023-03-30].https://www.jscs.org.cn/x4.php?id=65.
[18]Jiangsu Higher Education Association.Notice on the Announcement of the recognition(cultivation) results of the 2022 Provincial Discipline Competition for undergraduate and junior College Students in Colleges and Universities [EB/OL].(2022-06-15)[2023-03-30].http://m.jsgjxh.cn/newsview/27743.
[19]MAATEN L V D,HINTON G.Visualizing Data using t-SNE[J].Journal of Machine Learning Research,2008,9(86):2579-2605.
[20]SHI N Q.Development Status,Common Characteristics and Ex-perience Enlightenment of Higher Vocational Colleges in Suzhou Wuxi Changzhou Metropolitan Area[J].Journal of Wuxi Institute of Technology,2022,21(2):1-6.
[1] BAO Kainan, ZHANG Junbo, SONG Li, LI Tianrui. ST-WaveMLP:Spatio-Temporal Global-aware Network for Traffic Flow Prediction [J]. Computer Science, 2024, 51(5): 27-34.
[2] CHEN Xinyang, CHEN Hanze, ZHOU Jiasheng, HUANG Jiaqing, YU Jiashuo, ZHU Longlong, ZHANG Dong. IntervalSketch:Approximate Statistical Method for Interval Items in Data Stream [J]. Computer Science, 2024, 51(4): 4-10.
[3] WANG Hancheng, DAI Haipeng, CHEN Zhipeng, CHEN Shusen, CHEN Guihai. Large-scale Network Community Detection Algorithm Based on MapReduce [J]. Computer Science, 2024, 51(4): 11-18.
[4] SHEN Zhehui, WANG Kailai, KONG Xiangjie. Exploring Station Spatio-Temporal Mobility Pattern:A Short and Long-term Traffic Prediction Framework [J]. Computer Science, 2023, 50(7): 98-106.
[5] BING Ying’ao, WANG Wenting, SUN Shengze, LIU Xin, NIE Qigui, LIU Jing. Network Reliability Analysis of Power Monitoring System Based on Improved Fuzzy ComprehensiveEvaluation Method [J]. Computer Science, 2023, 50(6A): 220400293-7.
[6] ZHANG Jian, ZHANG Ye. College Students Employment Dynamic Prediction of Multi-feature Fusion Based on GRU-LSTM [J]. Computer Science, 2023, 50(6A): 220500056-6.
[7] ZHAO Xuejian, ZHAO Ke. Bio-inspired Frequent Itemset Mining Strategy Based on Genetic Algorithm [J]. Computer Science, 2023, 50(11A): 220700200-8.
[8] LI Rong-fan, ZHONG Ting, WU Jin, ZHOU Fan, KUANG Ping. Spatio-Temporal Attention-based Kriging for Land Deformation Data Interpolation [J]. Computer Science, 2022, 49(8): 33-39.
[9] YAO Xiao-ming, DING Shi-chang, ZHAO Tao, HUANG Hong, LUO Jar-der, FU Xiao-ming. Big Data-driven Based Socioeconomic Status Analysis:A Survey [J]. Computer Science, 2022, 49(4): 80-87.
[10] KONG Yu-ting, TAN Fu-xiang, ZHAO Xin, ZHANG Zheng-hang, BAI Lu, QIAN Yu-rong. Review of K-means Algorithm Optimization Based on Differential Privacy [J]. Computer Science, 2022, 49(2): 162-173.
[11] HUO Tian-yuan, GU Jing-jing. Dynamic and Static Relationship Fusion of Multi-source Health Perception Data for Disease Diagnosis [J]. Computer Science, 2022, 49(11A): 211100241-9.
[12] XIONG Kai-fang, CHEN Hong-mei, WANG Li-zhen, XIAO Qing. Mining Spatial co-location Pattern with Dominant Feature [J]. Computer Science, 2022, 49(11A): 211000126-7.
[13] ZHANG Ya-di, SUN Yue, LIU Feng, ZHU Er-zhou. Study on Density Parameter and Center-Replacement Combined K-means and New Clustering Validity Index [J]. Computer Science, 2022, 49(1): 121-132.
[14] MA Dong, LI Xin-yuan, CHEN Hong-mei, XIAO Qing. Mining Spatial co-location Patterns with Star High Influence [J]. Computer Science, 2022, 49(1): 166-174.
[15] XU Hui-hui, YAN Hua. Relative Risk Degree Based Risk Factor Analysis Algorithm for Congenital Heart Disease in Children [J]. Computer Science, 2021, 48(6): 210-214.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!