College of Basic Sciences, Tianjin Agricultural University, Tianjin 300384, China

Abstract In the team construction, fair and impartial performance evaluation is a very important part. The evaluation results will directly affect the team’s work enthusiasm. Through consulting extensive data and materials, this paper firstly built a micro-course teaching team evaluation indicator system. Secondly, using the analytic hierarchy process (AHP), it calculated the weight of each indicator. Finally, using the fuzzy comprehensive evaluation method, it evaluated the micro-course. This study is expected to provide a methodological support for the construction and management of the micro-course teaching team.

Key words Evaluation indicator system, Analytic hierarchy process (AHP), Fuzzy comprehensive evaluation

1 Introduction

With the rapid development of science and technology at present, the traditional education concepts and teaching models should be improved accordingly. In order to meet the diverse learning demands, it is imperative to build a micro-course teaching team. The construction of the micro-course teaching team directly affects the cultivation of students’ self-learning ability and the organic combination of online and offline learning. Besides, in order to better implement the construction and management of the micro-course teaching team, it is crucial to carry out a fair, reasonable and efficient performance evaluation of the micro-curriculum teaching team. In the indicators, there are qualitative indicators and quantitative indicators influencing the micro-course teaching team, thus it is feasible to adopt the AHP-fuzzy comprehensive evaluation method to make a comprehensive evaluation.

2 Building of the evaluation indicator system for micro-course teaching team

According to the basic requirements and features of micro-courses proposed by the Higher Education Department in 2010NationalTeachingTeamApplicationGuide, and with reference to the fine course evaluation indicators, we established the micro-course teaching team indicator system. As shown in Fig. 1, according to the influence degree of the performance of micro-course teaching team, we screened three Level I indicators and 12 Level II indicators. Three Level I indicators are team structure, team construction, and team achievement; 12 Level II indicators are the number of members, age structure, academic structure, title structure, academic exchange, skill complementarity, communication ability, devotion spirit, research projects, monographs, micro-course awards, and student evaluation.

Fig.1Teachingteamevaluationindicatorsystem

3 Calculation of indicator weight by analytic hierarchy process (AHP)

3.1DeterminingtheindicatorweightbeingakeypartoftheevaluationindicatorsystemFirstly, based on the survey results of peers and experts and the Saaty quantity scale, we compared the indicators and built a judgment matrix. Secondly, with the aid of Matlab software, we calculated the maximum eigenvalue of the judgment matrix and the weight of theλmaxlevel I indicators (Table 1).

Table1WeightoflevelIindicators

AA1A2A3Weight of factor (ω)A111/21/50.122 0A2211/30.229 7A35310.648 3λmax3.003 7

Table2WeightoflevelIIindicators

MatrixλmaxCICRWeight of sub-factor wi (i=1, 2, 3)Judgment matrix A14.185 50.061 80.068 7(0.346 2, 0.204 8, 0.285 5, 0.163 5)Judgment matrix A24.069 00.023 00.025 6(0.097 1, 0.090 4, 0.337 9, 0.474 5)Judgment matrix A34.032 80.010 90.012 1(0.092 6, 0.084 3, 0.239 2, 0.583 9)

It can be seen from Table 3 that in the performance evaluation of the micro-course teaching team, the student evaluation is the most important indicator, followed by the micro-course awards and devotion spirit.

Table3Weightofindicatorsofmicro-courseteachingteamevaluationA

Level IindicatorWeightLevel IIindicatorWeightWeight of LevelII indicator toLevel I indicatorTeam 0.122 0Number of members 0.346 20.042 2structure A1 Age structure 0.204 80.025 0Academic structure 0.285 50.034 8Title structure 0.163 50.019 9Team 0.229 7Academic exchange 0.09710.022 3construction A2Skill complementarity0.090 40.020 8Communication ability 0.337 90.077 6Devotion spirit 0.474 50.109 0Team 0.648 3Research projects 0.092 60.060 0achievement A3Monographs 0.084 30.054 6Micro-course awards 0.239 20.155 0Student evaluation 0.583 90.378 5

4 Evaluation by fuzzy comprehensive evaluation method

According to the common evaluation criteria, we built the comment setV={V1,V2,V3,V4}={excellent, good, qualified, unqualified}. Through questionnaires, we calculated the percentage of each indicator in the corresponding indicator layer, and built three fuzzy evaluation matrices.

According to "first level judgment = weight of sub-factor × judgment matrix", we obtained:

B1=ω1×R1=(0.577 6, 0.355 1, 0.067 3, 0)，

B2=ω2×R2=(0.463 6, 0.422 7, 0.113 7, 0)，

B3=ω3×R3=(0.499 2, 0.359 2, 0.141 6, 0)

The total single factor judgment matrix:

According to "second level judgment = weight of factor × judgment matrix", we obtained:

According to the principle of maximum membership, we obtained the final judged maximum value (0.500 6, 0.373 3, 0.126 1, 0)max, so the final evaluation grade of the team is excellent. It is very fuzzy and can not reflect the degree of excellence. If the second level judgment results as the weight vector, the score of the team’s performance evaluation can be obtained by the weighted average method. For instance,

5 Conclusions

From the above results, we can reach the conclusion that the micro-course teaching team performance evaluation indicators are team achievements, team construction, and team structure in the order of importance. The AHP-fuzzy comprehensive evaluation method can reasonably evaluate the performance of the team, well combine the artificial qualitative description and the scientific quantitative description, weaken the subjective description and improve the credibility of the indicator weight.