WO2023130775A1 - 一种基于学科评估报告的可视化分析系统 - Google Patents

一种基于学科评估报告的可视化分析系统 Download PDF

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WO2023130775A1
WO2023130775A1 PCT/CN2022/121816 CN2022121816W WO2023130775A1 WO 2023130775 A1 WO2023130775 A1 WO 2023130775A1 CN 2022121816 W CN2022121816 W CN 2022121816W WO 2023130775 A1 WO2023130775 A1 WO 2023130775A1
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evaluation
scientific research
subject
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teaching
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武青松
张颖聪
马鸣
向璨
陈实
吴建才
金阳
王征
罗飞
王智慧
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华中科技大学同济医学院附属协和医院
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    • G06Q10/0639Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393Score-carding, benchmarking or key performance indicator [KPI] analysis

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  • the disclosure relates to the technical field of subject evaluation, and more specifically, the disclosure relates to a visual analysis system based on a subject evaluation report.
  • Academic leaders can be cultivated in their own school or introduced from other schools, but their central position in the academic organization should be guaranteed to ensure that they can fully exercise their academic power without interference from administrative power. , Academic leaders should also assume the responsibility of maintaining the authority and impartiality of academic organizations, as well as the responsibility of cultivating and forming discipline teams.
  • the evaluation process is supervised and the evaluation results are timely. According to the results of the subject evaluation, colleges and universities have adjusted the discipline construction ideas, optimized the professional setting, improved the quality of personnel training and other ways to promote the improvement of the school's overall school-running strength, and formed a "promoted by evaluation” Reform, evaluation to promote construction" virtuous circle.
  • Strengthen social service contribution indicators adopt “representative case” evaluation, and reflect the contribution and characteristics of different regions and types of disciplines to regional economic and social development. The social service contribution and subject reputation of this evaluation will be evaluated through peer experts' scoring.
  • the present disclosure provides a visual analysis system based on subject evaluation reports, which solves the following problems by using one or more implementations of the present disclosure: subject evaluation, result orientation is relatively obvious, overall
  • the evaluation results show the level of the discipline, but there is no specific evaluation result on the impact of the discipline in a certain indicator.
  • the overall level of the development of the discipline may not be obvious, but the development of a single indicator may have obvious advantages, leading to Insufficient comprehensive and thorough understanding of the development and changes of the overall discipline.
  • a visual analysis system based on subject evaluation report including a data acquisition unit, the output end of the data acquisition unit is electrically connected to the input end of the data preprocessing unit, The output end of the data preprocessing unit is electrically connected to the input end of the central processing unit, the output end of the central processing unit is electrically connected to the input end of the evaluation unit, and the output end of the evaluation unit is electrically connected to the input end of the evaluation result analysis unit.
  • the input end of the evaluation result analysis unit is electrically connected to the output end of the data storage unit, the input end of the data storage unit is electrically connected to the output end of the evaluation unit, and the output end of the evaluation result analysis unit is electrically connected to the output end of the evaluation result analysis unit.
  • the input terminals of the visualization unit are electrically connected.
  • the data acquisition unit is used to acquire teaching and research team data, teaching achievement data, scientific research team data, scientific research achievement data and scientific research conditions of related disciplines;
  • the data preprocessing unit is used to The format of the acquired data is organized and unified;
  • the evaluation unit is used to evaluate and analyze the subject combined with the acquired data;
  • the evaluation result analysis unit is used to analyze the evaluation results and compare them with the previous evaluation results to obtain The changes and development trends of the evaluation results of the discipline corresponding to the indicators among multiple evaluation results;
  • the visualization unit displays the evaluation results and the evaluation results of the indicators in a visual manner.
  • the teaching and research team data includes the subject teaching and research leader and the subject team, the subject teaching and research leader includes influence in the teaching field, representative teaching and research achievements and academic level, and the subject team includes members of the teaching and research team Participating talent team, overall academic level and rationality of talent structure, talent structure includes personnel age, support, education background, expertise; said teaching achievements include talent training and teaching and research results, said talent training includes the ratio of undergraduates to teachers, The ratio of graduate students to teachers, the ratio of doctoral students to teachers, the level of professional construction of students, the level of papers and scientific research achievements during school.
  • the teaching and research achievements include the awarding of teaching achievements and the establishment of teaching reform projects;
  • the data of the scientific research team include Scientific research personnel data and scientific research leaders.
  • the scientific research personnel data includes the qualifications of scientific research team members, the reasonable structure of team members and scientific research projects.
  • the scientific research leaders include the scientific research level of scientific research personnel, participation in scientific research projects and scientific research projects;
  • Including scientific research projects and scientific research achievements, scientific research projects include the research significance and role of scientific research projects, the scientific research achievements include the level of scientific research achievements, the gold content of papers, the status of scientific research awards and the transformation of scientific research achievements;
  • the described scientific research conditions include scientific research platforms and basic scientific research conditions,
  • the scientific research platform includes the construction of scientific research bases and scientific research centers, and the basic conditions of scientific research include scientific research equipment, books and materials, and the use of scientific research funds.
  • the evaluation unit uses an evaluation model, and the establishment of the evaluation model includes the following steps: S1, establishing a hierarchical index system for subject evaluation: S2, constructing a pairwise comparison judgment matrix; Expert opinion, compare between two factors, use scale 1-9 to construct a judgment matrix for comparison, and use the method of collective discussion to determine; S3, carry out weight and consistency test: according to the AHP and the introduced matrix scale , construct a judgment matrix, and calculate its largest characteristic root, perform single sorting at each level and check the consistency of the judgment matrix, and use the square root method to calculate the steps as follows:
  • V is the total score of subject evaluation
  • C i is the weight of the i-th evaluation index
  • V i is the score of each subject in the i-th evaluation index
  • m is the number of evaluation indicators.
  • Fig. 1 shows a schematic diagram of system connection according to some embodiments of the present disclosure.
  • the visual analysis system may include a data acquisition unit, the output end of the data acquisition unit may be electrically connected to the input end of the data preprocessing unit, and the data preprocessing unit
  • the output end of the processing unit can be electrically connected to the input end of the central processing unit, the output end of the central processing unit can be electrically connected to the input end of the evaluation unit, the output end of the evaluation unit can be electrically connected to the input end of the evaluation result analysis unit, and the evaluation
  • the input end of the result analysis unit may be electrically connected to the output end of the data storage unit, the input end of the data storage unit may be electrically connected to the output end of the evaluation unit, and the output end of the evaluation result analysis unit may be electrically connected to the input end of the visualization unit.
  • the data acquisition unit can be used to acquire teaching and research team data, teaching achievement data, scientific research team data, scientific research achievement data and scientific research conditions of related disciplines;
  • the data preprocessing unit can be used to obtain data The format of the assessment is organized and unified;
  • the assessment unit can be used to evaluate and analyze the subject combined with the acquired data;
  • the assessment result analysis unit can be used to analyze the assessment results and compare them with the previous assessment results to obtain the corresponding indicators.
  • the visualization unit can display the evaluation results and the evaluation results of the indicators in a visual way.
  • teaching and research team data may include subject teaching and research leaders and subject teams, subject teaching and research leaders include influence in the teaching field, representative teaching and research achievements, and academic levels, and subject teams include members of the teaching and research team participating The talent team, the overall academic level and the rationality of the talent structure.
  • the talent structure includes the age, support, education, and expertise of the personnel;
  • the teaching results can include talent training and teaching and research results, and the talent training includes the ratio of undergraduates to teachers, and the ratio of graduate students to The ratio of teachers, the ratio of doctoral students to teachers in school, the level of professional construction of students, the level of papers and scientific research achievements during the school, teaching and research achievements include the award of teaching achievements and the establishment of teaching reform projects;
  • the data of scientific research team can include the data of scientific research personnel and scientific research leaders , the data of scientific research personnel can include the qualifications of scientific research team members, the reasonable structure of team members and scientific research projects. It can include the research significance and role of scientific research projects.
  • Scientific research achievements can include the level of scientific research achievements, the gold content of papers, the status of scientific research awards and the transformation of scientific research achievements; scientific research conditions can include scientific research platforms and basic conditions of scientific research. Scientific research platforms can include the construction of scientific research bases, scientific research centers Construction, the basic conditions of scientific research can include scientific research equipment, books and materials, and the use of scientific research funds.
  • V is the total score of subject evaluation
  • C i is the weight of the i-th evaluation index
  • V i is the score of each subject in the i-th evaluation index
  • m is the number of evaluation indicators.
  • this disclosure can analyze the evaluation results by using the evaluation module, the visualization unit and the evaluation result analysis unit.
  • Corresponding charts are generated from the historical data of the evaluation results and the overall evaluation results, reflecting the changes in the overall evaluation results of the discipline, and at the same time accurately judging the development and changes of the discipline in the corresponding items of each indicator, which can make a clear definition of the overall development of the discipline
  • the planning and development goals are clearer for the overall subject evaluation and subsequent development process.
  • This disclosure can use the corresponding subject indicators to clearly specify the various indicators of the subject, and at the same time use the corresponding expert opinions to compare the corresponding factors, establish a judgment matrix, and use the method of collective discussion to see and confirm, which overcomes the differences in the opinions of various experts. The resulting errors make the overall evaluation results more scientific and reasonable.

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Abstract

一种基于学科评估报告的可视化分析系统,涉及学科评估技术领域,包括数据获取单元,所述数据获取单元的输出端与数据预处理单元的输入端电连接,所述数据预处理单元的输出端与中央处理器的输入端电连接。所述系统可以精准判断该学科在各指标对应项目的发展变化情况,可针对性地对学科整体发展作出明确规划和发展目标,使整体的学科评估和后续发展过程更为清晰明确。

Description

一种基于学科评估报告的可视化分析系统
相关申请的交叉引用
本公开要求于2022年01月07日提交、申请号为202210015384.9且名称为“一种基于学科评估报告的可视化分析系统”的中国专利申请的优先权,其全部内容通过引用合并于此。
技术领域
本公开涉及学科评估技术领域,更具体地说,本公开涉及一种基于学科评估报告的可视化分析系统。
背景技术
重点学科建设和评估是高等学校的一项基础性工作,是提高高等学校整体办学水平的有效途径和战略措施.对高等学校重点学科建设进行评估,有利于加强重点学科建设的指导和组织管理,实现重点学科内部及相关学科间资源的合理配置和交流,促进学科结构调整,提高学科整体水平。近年来,各级教育行政部门、高校和学界对如何科学合理地开展高校重点学科评估作了大量积极的探索工作,取得了一些成效,但仍存在不少问题.本文采用层次分析法计算各评价指标的权重,建立评价模型,并应用到本校重点学科管理和评估的实践中。
重点学科的建设需要有学科带头人、学术骨干和一般研究人员共同参与。学科带头人应对本学科省内外、国内外,同类或不同类、同层次或不同层次院校同类学科的发展现状、特点和趋势,本学科自身的现状、实力、潜力、优势和差距要有充分的了解,能指导本学科制定发展规划、稳定优势研究方向、开拓新的研究方向。学术骨千和一般研究人员应在学科带头人的带领下开展相关研究工作,保持学科的整体竞争力.学科带头人作为学科组织和学科队伍的核心,对重点学科的建设发展具有决定作用。学科带头人可以采取在本校培养的方式,也可采取外校引进的方式,但都应该保障其在学术组织中的中心地位,保证其可以充分地行使学术权力而不受行政权力的干涉.同时,学科带头人也应当承担维护学术组织的权威和公正的责任,承担培养和组建学科团队的责任。
学科评估作为一种评价学科建设发展成效的重要举措,其重要性和影响力逐渐受到社会公众普遍关注。经过文献资料梳理研究可以发现,学科评估的概念早期在西方社会出现,是学科门类划分清晰后的直接产物,西方发达国家教育界最先意识到,学科发展的进程中,需要有一种科学合理的标准评判衡量学科建设的成效,进而确保学科建设一直处于正常的发展轨道。例如 美国和欧洲,学科评估一般是由高等教育领域的第三方非政府组织(学会、行业协会等)进行组织,具有固定的周期性,且各高等学校自愿参与,评估过程接受监督,评估结果及时公布,体现出较强的权威性”。根据学科评估的结果,高校通过调整学科建设思路,优化专业设置,提升人才培养质量等多种方式,促进学校整体办学实力的提升,形成“以评促改、以评促建”良性循环。
教育部从2002年开始,对具有研究生培养和学位授予资格的单位按一级学科进行整体水平评估,至今已完成四轮评估。在刚刚完成的第四轮学科评估中,对于结果和作用,各方予以了高度关注,且争议较大。尽管教育部一再强调,学科评估“不数帽子,不排名次”,但作为具有官方背景的评估结果,一定会引起后续的溢出效应。在未来的五年内,此次学科评估的结果会影响到各高校国家竞争性科研基金的获取,高水平人才的培养和引进,以及毕业生的就业层次和方向。与第三次学科评估相比,指标体系更加强调“质量、成效、特色、分类”的管理理念,但是教育部的学科评估具有导向性和激励性,高校的学科建设都在围绕指标体系开展。一场初衷为以评促建、以评助建,引导学校把准备评估的过程,变成学校梳理学科家底,规范学科建设管理,谋划学科发展的过程,并未实现,取而代之的又是一场轰轰烈烈的功利性和操作性运动。学科评估自身存在多种矛盾,其根源就在于评估的双重目的性:支持过程导向的发展和改进;支持结果导向的等级评价。这是两种截然不同的评估活动,评估的初衷重视过程,但评估的指标体系全然为结果导向,初衷和指标设计南辕北辙,这无疑体现了评估授权方矛盾的心理:既希望高校出于自律,在准备评估的过程中自查自省,补短板,求发展。
把人才培养质量放在首位,建立“培养过程质量”“在校生质量”“毕业生质量”三维度评价模式,首次试点开展在校生和用人单位调查,跟踪学生在学期间和毕业后职业发展的质量。需要填报本学科15名有代表性的在校研究生和20名有代表性成就的研究生毕业的校友。学生交流指标方面,无论是本校学生出境交流,还是海外学生来华交流,都只计算学习达90天以上的学生(含学位生)。
淡化条件资源,突出成效产出。改变以往单一的“以学术头衔评价学术水平”的师资队伍评价方法,改用列举“代表性骨干教师”的方法,由专家综合考察师资队伍的水平、结构、国际化程度和可持续发展能力。师资队伍质量的评判从“数牌牌”向比结构比能力进行转变。不具体统计各类人才数,而以队伍结构和代表性人物进行代替。一支老中青比例、梯队合理的师资队伍才能展现学科的可持续发展能力,因此,在填报25名教师情况的时候,不能单单考虑教师的实力,还要充分考虑结构问题(如要求青年教师不少于10人)。专任教师数将会设定上限,达到上限均为满分。另外,“生师比”是第三轮评估中的一个指标项,但在第四轮评估指标体系中并未出现。
坚持“定量与定性、国内与国外、质量与数量”三结合的学术论文评价方法,如改进“ESI高被引论文”,打造“中国版ESI高被引论文”品牌,实现更加科学的评价。将“ESI学科分类”与“中国学科目录”相对应,将ESI的前1%统计范围扩展到前3%,为学术论文质量的定量评价注入中国元素,强化中国论文评价话语权(如30篇代表性论文中中文期刊论文不少于10篇)。在“代表性论文”中增加考察“结构质量”,限定每位教师只能填写5篇代表性论文。
成果转化方面,理工、农学和医学统计近四年获得授权并已转化或应用的发明专利与国防专利,并需要提供相应的成果应用证明。
增强社会服务贡献指标,采用“代表性案例”评价,体现不同地区、类型学科对地区经济社会发展的贡献与特色。此次评估的社会服务贡献和学科声誉都将通过同行专家打分的形式给予评价。
鼓励学科交叉合作,通过“归属度”方法科学评价跨学科成果,即同一科研人员的科研成果可以在其所跨学科之间进行分配。
首次将教材编写列入科研成果。主要是为了落实教育部加强教材建设的意见,鼓励教师积极参与高质量教材编写,促进学科基础建设质量提升。不过,只有近四年出版的“十二五”国家级规划教材才能纳入统计,以突出教材的代表性和权威性。
学科的评估,结果导向较为明显,整体的评估结果得出该学科的水平,但是对该学科在某一指标中的影响结果并未有具体的评估结果,学科的发展整体水平可能并不明显,但是单一指标的发展可能存在明显优势,导致对整体学科的发展和变化了解不够全面彻底,因此需要一种基于学科评估报告的可视化分析系统来解决上述问题。
发明内容
为了克服现有技术的上述缺陷,本公开提供了一种基于学科评估报告的可视化分析系统,通过利用本公开内容的一个或多个实施方式解决了:学科的评估,结果导向较为明显,整体的评估结果得出该学科的水平,但是对该学科在某一指标中的影响结果并未有具体的评估结果,学科的发展整体水平可能并不明显,但是单一指标的发展可能存在明显优势,导致对整体学科的发展和变化了解不够全面彻底的问题。
为实现上述目的,根据本公开的一些实施方式,提供了一种基于学科评估报告的可视化分析系统,包括数据获取单元,所述数据获取单元的输出端与数据预处理单元的输入端电连接,所述数据预处理单元的输出端与中央处理器的输入端电连接,所述中央处理器的输出端与评估单元的输入端电连接,所述评估单元的输出端与评估结果分析单元的输入端电连接,所述评估结果分析单元的输入端与数据存储单元的输出端电连接,所述数据存储单元的输 入端与评估单元的输出端电连接,所述评估结果分析单元的输出端与可视化单元的输入端电连接。
在本公开的一些实施例中:所述数据获取单元用于获取相关学科的教研团队数据、教学成果数据、科研团队数据、科研成果数据和科研条件;所述数据预处理单元用于对各项获取数据的格式进行整理统一;所述评估单元用于结合获取的数据对该学科进行评估分析;所述评估结果分析单元用于对评估结果进行分析,同时与以往评估结果进行比对,得出对应指标的该学科的评估结果在多次评估结果中的变化和发展趋势;所述可视化单元以可视化方式将评估结果及其中指标的评估结果进行展示。
在本公开的一些实施例中:所述教研团队数据包括学科教研领头人和学科团队,学科教研领头人包括在教学领域的影响力、代表性教研成果和学术水平,学科团队包括教研团队的成员参与人才团队、整体学术水平和人才结构合理程度,人才结构包括人员的年龄、支撑、学历、特长;所述教学成果包括人才培养和教研成果,所述人才培养包括在校本科生与教师比例,在校研究生与教师比例、在校博士生与教师比例、学生专业建设水平、论文水平与在校期间科研成果,所述教研成果包括教学成果获奖情况和教改课题立项情况;所述科研团队数据包括科研人员数据和科研带头人,科研人员数据包括科研团队成员资质、团队成员结构合理情况和科研项目,所述科研带头人包括科研人员科研水平、参与科研项目和主持科研项目;所述科研成果数据包括科研项目和科研成果,科研项目包括科研项目研究意义及作用,所述科研成果包括科研成果水平、论文含金量、科研获奖情况和科研成果转化情况;所述科研条件包括科研平台和科研基础条件,科研平台包括科研基地建设、科研中心建设,科研基础条件包括科研仪器设备和图书资料、科研经费使用。
在本公开的一些实施例中:所述评估单元采用评估模型进行,所述评估模型的建立包括以下步骤:S1、建立学科评估的递阶层次指标体系:S2、构造两两比较判断矩阵;根据专家意见,对两两因素之间进行比较,比较取1-9尺度构造判断矩阵,采用集体讨论的方法进行确定;S3、进行权重及进行一致性检验:根据层次分析模型及引入的矩阵标度,构造判断矩阵,并计算其最大的特征根,各层次的单排序及进行判断矩阵一致性检验,采用方根法计算步骤如下:
计算判断矩阵每一行元素的乘积,
Figure PCTCN2022121816-appb-000001
计算M i的n次方根
Figure PCTCN2022121816-appb-000002
i=1,2,…,n;对向量
Figure PCTCN2022121816-appb-000003
正规化,即
Figure PCTCN2022121816-appb-000004
i=1,2,…,n 则W=[W 1,W 2,…,W n] T即为所求的特征向量;计算判断矩阵的最大特征根
Figure PCTCN2022121816-appb-000005
标识向量AW的第i个元素;验证判断矩阵的一致性,当阶数大于2时,判断矩阵的一致性指标CI与同阶平均随机一致性指标RI之比成为随机一致性比率,即为CR,当
Figure PCTCN2022121816-appb-000006
时,即认为判断矩阵具有满意的一致性,对于一、二阶段判断矩阵,不需要判断一致性;对多个评估指标得分累加,得到学科评估的数学模型为:
Figure PCTCN2022121816-appb-000007
其中V为学科评估总分,C i为第i个评价指标的权重,V i为各学科在第i个评价指标中的得分,m为评价指标的数量。
附图说明
图1示出了根据本公开的一些实施例的系统连接示意图。
具体实施方式
下面将结合本公开实施例中的附图,对本公开实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本公开一部分实施例,而不是全部的实施例。基于本公开中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本公开保护的范围。
本公开的一些实施方式提供了一种基于学科评估报告的可视化分析系统,所述可视化分析系统可以包括数据获取单元,数据获取单元的输出端可以与数据预处理单元的输入端电连接,数据预处理单元的输出端可以与中央处理器的输入端电连接,中央处理器的输出端可以与评估单元的输入端电连接,评估单元的输出端可以与评估结果分析单元的输入端电连接,评估结果分析单元的输入端可以与数据存储单元的输出端电连接,数据存储单元的输入端可以与评估单元的输出端电连接,评估结果分析单元的输出端可以与可视化单元的输入端电连接。
在本公开的一些实施例中,数据获取单元可以用于获取相关学科的教研团队数据、教学成果数据、科研团队数据、科研成果数据和科研条件;数据预处理单元可以用于对各项获取数据的格式进行整理统一;评估单元可以用于结合获取的数据对该学科进行评估分析;评估结果分析单元可以用于对评估结果进行分析,同时与以往评估结果进行比对,得出对应指标的该学科的评估结果在多次评估结果中的变化和发展趋势;可视化单元可以以可视化方式将评估结果及其中指标的评估结果进行展示。
在本公开的一些实施例中,教研团队数据可以包括学科教研领头人和学 科团队,学科教研领头人包括在教学领域的影响力、代表性教研成果和学术水平,学科团队包括教研团队的成员参与人才团队、整体学术水平和人才结构合理程度,人才结构包括人员的年龄、支撑、学历、特长;教学成果可以包括人才培养和教研成果,人才培养包括在校本科生与教师比例,在校研究生与教师比例、在校博士生与教师比例、学生专业建设水平、论文水平与在校期间科研成果,教研成果包括教学成果获奖情况和教改课题立项情况;科研团队数据可以包括科研人员数据和科研带头人,科研人员数据可以包括科研团队成员资质、团队成员结构合理情况和科研项目,科研带头人包括科研人员科研水平、参与科研项目和主持科研项目;科研成果数据可以包括科研项目和科研成果,科研项目可以包括科研项目研究意义及作用,科研成果可以包括科研成果水平、论文含金量、科研获奖情况和科研成果转化情况;科研条件可以包括科研平台和科研基础条件,科研平台可以包括科研基地建设、科研中心建设,科研基础条件可以包括科研仪器设备和图书资料、科研经费使用。
在本公开的一些实施例中,所述评估单元可以采用评估模型进行,所述评估模型的建立可以包括以下步骤:S1、建立学科评估的递阶层次指标体系:S2、构造两两比较判断矩阵;根据专家意见,对两两因素之间进行比较,比较取1-9尺度构造判断矩阵,采用集体讨论的方法进行确定;S3、进行权重及进行一致性检验:根据层次分析模型及引入的矩阵标度,构造判断矩阵,并计算其最大的特征根,各层次的单排序及进行判断矩阵一致性检验,采用方根法计算步骤如下:计算判断矩阵每一行元素的乘积,
Figure PCTCN2022121816-appb-000008
i=1,2,…,n;计算M i的n次方根
Figure PCTCN2022121816-appb-000009
i=1,2,…,n;对向量
Figure PCTCN2022121816-appb-000010
正规化,即
Figure PCTCN2022121816-appb-000011
i=1,2,…,n,则W=[W 1,W 2,…,W n] T即为所求的特征向量;计算判断矩阵的最大特征根
Figure PCTCN2022121816-appb-000012
标识向量AW的第i个元素;验证判断矩阵的一致性,当阶数大于2时,判断矩阵的一致性指标CI与同阶平均随机一致性指标RI之比成为随机一致性比率,即为CR,当
Figure PCTCN2022121816-appb-000013
时,即认为判断矩阵具有满意的一致性,对于一、二阶段判断矩阵,不需要判断一致性;对多个评估指标得分累加,得到学科评估的数学模型为:
Figure PCTCN2022121816-appb-000014
其中V为学科评估总分,C i为第i个评价指标的权重,V i为各学科在第i个评价指标中的得分,m为评价指标的数量。
综上可知,本公开可以通过采用评估模块、可视化单元和评估结果分析单元,对评估结果进行分析,评估结果中具体细分出该学科在各指标中的评估结果,同时结合该学科各指标的评估结果和整体评估结果的历史数据生成相应的图表,反映出该学科整体的评估结果变化的同时,精准判断该学科在各指标对应项目的发展变化情况,可针对性的对学科整体发展作出明确规划和发展目标,对整体的学科评估和后续发展过程更为清晰明确。
本公开可以采用相应的学科指标,对学科的各项指标进行明确指明,同时采用相应专家意见对对应因素进行比较,建立判断矩阵,采用集体讨论的方式见确定,克服了由于各专家意见观点不同造成的误差情况,使整体的评估结果更为科学合理。
本公开公开实施例附图中,只涉及到与本公开实施例涉及到的结构,其他结构可参考通常设计,在不冲突情况下,本公开同一实施例及不同实施例可以相互组合。
以上所述仅为本公开的一个或多个实施例而已,并不用于限制本公开,凡在本公开的精神和原则之内,所作的任何修改、等同替换、改进等,均应包含在本公开的保护范围之内。

Claims (10)

  1. 一种基于学科评估报告的可视化分析系统,包括:数据获取单元,所述数据获取单元的输出端与数据预处理单元的输入端电连接,所述数据预处理单元的输出端与中央处理器的输入端电连接,所述中央处理器的输出端与评估单元的输入端电连接,所述评估单元的输出端与评估结果分析单元的输入端电连接,所述评估结果分析单元的输入端与数据存储单元的输出端电连接,所述数据存储单元的输入端与评估单元的输出端电连接,所述评估结果分析单元的输出端与可视化单元的输入端电连接。
  2. 根据权利要求1所述的一种基于学科评估报告的可视化分析系统,其中,所述数据获取单元用于获取相关学科的教研团队数据、教学成果数据、科研团队数据、科研成果数据和科研条件。
  3. 根据权利要求1所述的一种基于学科评估报告的可视化分析系统,其中,所述数据预处理单元用于对各项获取数据的格式进行整理统一。
  4. 根据权利要求1所述的一种基于学科评估报告的可视化分析系,其中,所述评估单元用于结合获取的数据对该学科进行评估分析。
  5. 根据权利要求1所述的一种基于学科评估报告的可视化分析系统,其中,所述评估结果分析单元用于对评估结果进行分析,同时与以往评估结果进行比对,得出对应指标的该学科的评估结果在多次评估结果中的变化和发展趋势。
  6. 根据权利要求1所述的一种基于学科评估报告的可视化分析系统,其中,所述可视化单元以可视化方式将评估结果及其中指标的评估结果进行展示。
  7. 根据权利要求2所述的一种基于学科评估报告的可视化分析系统,其中,所述教研团队数据包括学科教研领头人和学科团队,学科教研领头人包括在教学领域的影响力、代表性教研成果和学术水平,学科团队包括教研团队的成员参与人才团队、整体学术水平和人才结构合理程度,人才结构包括人员的年龄、支撑、学历、特长。
  8. 根据权利要求2所述的一种基于学科评估报告的可视化分析系统,其 中,所述教学成果包括人才培养和教研成果,所述人才培养包括在校本科生与教师比例,在校研究生与教师比例、在校博士生与教师比例、学生专业建设水平、论文水平与在校期间科研成果,所述教研成果包括教学成果获奖情况和教改课题立项情况。
  9. 根据权利要求2所述的一种基于学科评估报告的可视化分析系统,其中,所述科研团队数据包括科研人员数据和科研带头人,科研人员数据包括科研团队成员资质、团队成员结构合理情况和科研项目,所述科研带头人包括科研人员科研水平、参与科研项目和主持科研项目;所述科研成果数据包括科研项目和科研成果,科研项目包括科研项目研究意义及作用,所述科研成果包括科研成果水平、论文含金量、科研获奖情况和科研成果转化情况;所述科研条件包括科研平台和科研基础条件,科研平台包括科研基地建设、科研中心建设,科研基础条件包括科研仪器设备和图书资料、科研经费使用。
  10. 根据权利要求1所述的一种基于学科评估报告的可视化分析系统,其特征在于,所述评估单元采用评估模型进行,所述评估模型的建立包括以下步骤:
    S1、建立学科评估的递阶层次指标体系:
    S2、构造两两比较判断矩阵;根据专家意见,对两两因素之间进行比较,比较取1-9尺度构造判断矩阵,采用集体讨论的方法进行确定;
    S3、进行权重及进行一致性检验:根据层次分析模型及引入的矩阵标度,构造判断矩阵,并计算其最大的特征根,各层次的单排序及进行判断矩阵一致性检验,采用方根法计算步骤如下:
    计算判断矩阵每一行元素的乘积,
    Figure PCTCN2022121816-appb-100001
    计算M i的n次方根
    Figure PCTCN2022121816-appb-100002
    对向量
    Figure PCTCN2022121816-appb-100003
    正规化,即
    Figure PCTCN2022121816-appb-100004
    则W=[W 1,W 2,…,W n] T即为所求的特征向量;
    计算判断矩阵的最大特征根
    Figure PCTCN2022121816-appb-100005
    标识向量AW的第i个元素;
    验证判断矩阵的一致性,当阶数大于2时,判断矩阵的一致性指标CI与同阶平均随机一致性指标RI之比成为随机一致性比率,即为CR,当
    Figure PCTCN2022121816-appb-100006
    时,即认为判断矩阵具有满意的一致性,对于一、二阶段判断矩阵,不需要判断一致性;
    对多个评估指标得分累加,得到学科评估的数学模型为:
    Figure PCTCN2022121816-appb-100007
    其中V为学科评估总分,C i为第i个评价指标的权重,V i为各学科在第i个评价指标中的得分,m为评价指标的数量。
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104915793A (zh) * 2015-06-30 2015-09-16 北京西塔网络科技股份有限公司 基于大数据分析挖掘的公共信息智能分析平台
CN105184886A (zh) * 2015-09-01 2015-12-23 浪潮集团有限公司 一种云数据中心智能巡检系统及方法
CN109214662A (zh) * 2018-08-20 2019-01-15 田金荣 一种金融风险在线监控系统
CN112862234A (zh) * 2020-12-25 2021-05-28 三盟科技股份有限公司 一种高校学科评估方法及系统
CN114529142A (zh) * 2022-01-07 2022-05-24 华中科技大学同济医学院附属协和医院 一种基于学科评估报告的可视化分析系统

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104915793A (zh) * 2015-06-30 2015-09-16 北京西塔网络科技股份有限公司 基于大数据分析挖掘的公共信息智能分析平台
CN105184886A (zh) * 2015-09-01 2015-12-23 浪潮集团有限公司 一种云数据中心智能巡检系统及方法
CN109214662A (zh) * 2018-08-20 2019-01-15 田金荣 一种金融风险在线监控系统
CN112862234A (zh) * 2020-12-25 2021-05-28 三盟科技股份有限公司 一种高校学科评估方法及系统
CN114529142A (zh) * 2022-01-07 2022-05-24 华中科技大学同济医学院附属协和医院 一种基于学科评估报告的可视化分析系统

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