基于AHP和2-tuple理论的高校教师课堂教学质量评价

课堂教学是整个教学工作的中心环节,要提高高校的教学质量,必须先提高课堂教学质量。开展课堂教学质量评价是提高课堂教学质量的重要手段。因此,本文建立了高校教师课堂教学质量评价模型。首先,建立了高校教师课堂教学质量评价指标体系,在此基础上,利用层次分析法(AHP)确定了评价指标的权重,然后利用二元语义(2-tuple)理论建立了课堂教学质量的总体评价模型,为提高课堂教学质量起到了导向的作用。

Optimized interval 2-tuple linguistic aggregation operator based on PGSA and its application in MAGDM

This study proposes a multiple attribute group decisionmaking(MAGDM)approach on the basis of the plant growth simulation algorithm(PGSA)and interval 2-tuple weighted average operators for uncertain linguistic weighted aggregation(ULWA).We provide an example for illustration and verification and compare several aggregation operators to indicate the optimality of the assembly method.In addition,we present two comparisons to demonstrate the practicality and effectiveness of the proposed method.The method can be used not only to aggregate MAGDM problems but also to solve multi-granularity uncertain linguistic information.Its high reliability,easy programming,and high-speed calculation can improve the efficiency of ULWA characteristics.Finally,the proposed method has the exact characteristics for linguistic information processing and can effectively avoid information distortion and loss.

Crowdsourcing-Based Framework for Teaching Quality Evaluation and Feedback Using Linguistic 2-Tuple

Crowdsourcing is widely used in various fields to collect goods and services from large participants.Evaluating teaching quality by collecting feedback from experts or students after class is not only delayed but also not accurate.In this paper,we present a crowdsourcing-based framework to evaluate teaching quality in the classroom using a weighted average operator to aggregate information from students’questionnaires described by linguistic 2-tuple terms.Then we define crowd grade based on similarity degree to distinguish contribution from different students and minimize the abnormal students’impact on the evaluation.The crowd grade would be updated at the end of each feedback so it can guarantee the evaluation accurately.Moreover,a simulated case is shown to illustrate how to apply this framework to assess teaching quality in the classroom.Finally,we developed a prototype and carried out some experiments on a series of real questionnaires and two sets of modified data.The results show that teachers can locate the weak points of teaching and furthermore to identify the abnormal students to improve the teaching quality.Meanwhile,our approach provides a strong tolerance for the abnormal student to make the evaluation more accurate.

2-Tuple and Rough Set Based Reduction Model for Multi-sensory Evaluation Indicators

In order to lessen adverse influences of excessive evaluative indicators of the initial set in multi-sensory evaluation,a2-tuple and rough set based reduction model is built to simplify the initial set of evaluative indicators. In the model,a great variety of descriptive forms of the multi-sensory evaluation are also taken into consideration. As a result,the method proves effective in reducing redundant indexes and minimizing index overlaps without compromising the integrity of the evaluation system. By applying the model in a multi-sensory evaluation involving community public information service facilities,the research shows that the results are satisfactory when using genetic algorithm optimized BP neural network as a calculation tool. It shows that using the reduced and simplified set of indicators has a better predication performance than the initial set,and 2-tuple and rough set based model offers an efficient way to reduce indicator redundancy and improves prediction capability of the evaluation model.

Evaluating Emergency Response Capacity of Chemical Industrial Park Using an Hybrid Fuzzy AHP Method

This paper presents an expert-based fuzzy analytic hierarchy process( AHP) model for evaluating emergency response capacity of Chemical Industrial Park( ERCCIP) by jointly using an improved fuzzy preference programming( FPP) and 2-tuple fuzzy linguistic approach. An evaluation index system for ERCCIP is proposed. The weight of sub-criteria and criteria of the evaluation index system for ERCCIP are determined using the improved FPP. And the ratings of sub-criteria are assessed in linguistic values according to the experts' subjective opinions. Finally,the aggregated ratings of criteria and the overall ERCCIP are calculated.

A Web-Based Integrated Intelligent System for Sensory Fabric Hand Evaluation

This paper presents a web-based integrated system for on-line sensory fabric hand evaluation. The methods of fuzzy techniques, neural networks, classical factorial analysis and other data analysis are used in the system to analyze the objective and subjective data, and to build the relationship between them. Given the objective data of a new fabric sample, the system can provide its sensory hand data and its total hand grade. In meantime, the total hand grade can be obtained directly from the sensory fabric hand data if provided. The sensory evaluation system is developed in Internet environment using Java language and SQL server database management system.

QUALITATIVE REASONING BY COMPUTING WITH WORDS IN HERRERA-MARTíNEZ’S LINGUISTIC MODEL

Most of modern systems for information retrieval, fusion and management have to deal with more and more qualitative information （by linguistic labels） besides information expressed quantitatively （by numbers）, since human reports are better and easier expressed in natural language than with numbers. In this paper, Herrera-Martfnez＇s 2-Tuple linguistic representation model is extended for reasoning with uncertain and qualitative information in Dezert-Smarandache Theory （DSmT） framework, in order to overcome the limitations of current approaches, i.e., the lack of precision in the final results of linguistic information fusion according to 1-Tuple representation （ q1 ）- The linguistic information which expresses the expert＇s qualitative beliefs is expressed by means of mixed 2 Tuples （equidistant linguistic labels with a numeric biased value）. Together with the 2-Tuple representation model, some basic operators are presented to carry out the fusion operation among qualitative information sources. At last, through simple example how 2-Tuple qualitative DSmT-based （q2 DSmT） fusion rules can be used for qualitative reasoning and fusion under uncertainty, which advantage is also showed by comparing with other methods.

科研经费使用行为的关键影响因素分析

本文首先分析了科研经费违法违规使用行为的影响因素,构建了包括制度因素、监管因素和个体因素在内的三维结构模型。在此基础上,考虑到影响因素之间的交互作用,用Linguistic 2-Tuples模型和DEMATEL方法来处理和集结专家给出的语言判断信息,对科研经费使用行为的影响因素进行归类和排序。研究发现,科研人员的经费使用意识是科研经费使用行为最关键的影响因素,同时也最容易受到其它因素的影响;科研经费的使用制度是制度因素中最活跃的影响因素;科研经费违法违规使用的惩处力度最容易对其它因素产生影响。最后,本文基于上述分析进一步给出了相应的对策与建议。

信息公开情境下科研资源配置水平双维度评价

针对我国科研资源配置中存在的重复配置和过度行政化的问题,本文从科研信息平台的技术水平(技术维度)和科研资源的管理水平(管理维度)双维度构建了信息公开情境下科研资源配置水平的评价指标体系,给出了基于Linguistic 2-Tuples的多属性群决策评价方法及矩阵判断模型,并进而通过实际数据对我国科研资源配置水平进行评价。研究发现,我国科研资源配置呈现技术维度较弱的特征,即当前科研信息平台的技术水平在更大程度上制约了科研资源的优化配置;科研信息平台的信息共享度既是权重最大的指标,也是当前科研资源配置中最薄弱的环节;还存在信息使用的不便利性以及专家评审体系不规范等问题。基于此,进一步提出了改进我国科研资源配置水平的针对性建议。