高校学术队伍在学科评估中的价值分析(最终英文版).doc

The value analysis of faculty on assessment of disciplines Li Junfeng Qiu WanhuaSchool of Economics & Management , Beihang university, Beijing，P.R.China 1001914Abstract: Assessment of disciplines, which is to help the construction of disciplines at universities, is an authoritative assessment carried out by Educational Ministry. As the faculty is one of the important indicators in the assessment process, more and more universities has become more concerned about the value of the construction of faculty on the development of its disciplines. This article aims to provide suggestions to the development of disciplines through the value analysis of faculty on the assessment. Keywords: assessment of disciplines faculty value analysi1 IntroductionThe assessment of national leading disciplines carried out by Chinese educational ministry reflects the development of a discipline in universities in China. It is obtained from the comparison of discipline construction among universities that own it, and is based on the status qua and characteristics of higher education at this stage in China. This assessment plays an active role in knowing the development of the discipline, helping building the discipline and identifying the gap between world leading disciplines and ours. It also improves the distribution of asset. Among the four leading indicators in the assessment process, it is important for us to know which one contributes more to the final assessment result then providing us feasible suggestions to a better construction of the discipline.At the present, a lot of researches about methods of discipline assessment are seen while few researches are about the relationship between assessment results and assessment indicators in this field. This article, by analyzing numbers and age distributions of professor, associate professor and lecturer in universities especially the group of academicians and excellent experts, has researched the correlation between situations of faculty construction and discipline assessment results thus identifying the contributions from different kinds of talents in the universities, and offer us a way to quicken the construction of disciplines. The author has chosen the disciplines of material sciences and engineering, management sciences and engineering, computer sciences and engineering, mechanical engineering, mechanics, instrument sciences and engineering, control sciences and engineering as his research samples for the reason that they rank top five in national leading disciplines assessment.2 The basic faculty information analysisThe average data of basic information about faculty at the universities in top five disciplines are displayed from Figure 2.1 to Figure 2.3, in which the numbers of academicians, extraordinary experts, professors, associate professors and lecturers, and the numbers of people who own doctors degree are given. Sample disciplines are material sciences and engineering(MSE), management sciences and engineering(MgSE), computer sciences(CS), mechanical engineering(ME). In addition, to identify the importance of academicians and extraordinary experts, they are called high level talents in this paper.Figure 2.1 The statistics of faculty in different disciplinesFigure 1.2 the statistics of high level faculty in different disciplinesFigure 1.3 The statistics of PH.D faculty in different disciplines Looking at Figure 2.1 we see that almost all top five disciplines are equipped with prestigous facultys which include a certain number of professors and associate professors, indicating the significance of the numbers of this group of people. Besides, we can see that compared with disciplines like material science, computer science, mechanics etc, management science has less talents in total, whatever from the numbers of people who own doctors degree or the numbers of professors, associate professors.It is obviously seen in Figure 2.2 that high level talents plays an extremely important role in the development of a discipline. Especially in the field of basic displines like material science and mechanics, high level talents have a larger proportion. Figure 2.3 shows that the number of people who own doctors degree has taken up to 70% of all, which means talents with high academic degrees occupy a large proportion in faculty s. From the data shown above it can be drawn that the urgent need for high level talents at the universities has become an indispensible part in the development of a univerisity and social progress. They are essential in enhancing the academic status of a university, and helping them to be more competitive. We can say that the one who has more high level talents has more authority in deciding the development of a discipline. 3 Age distribution analysisAge distribution of professors, associate professors and lecturers are displayed from Figure 3.1 to Figure 3.3. Sample disciplines are material sciences and engineering, management sciences and engineering and mechanical engineering. Age distribution in faculty is also one of the indicators in assessing the leading disciplines. By analyzing the form of age distribution, we can understand clearly about the question like how faculty has been constructed, how to realize the sustainable development of faculty and research program. All of this will offer precious reference to other universities.Figure 3.1 Age distribution of faculty in material sciences and engineeringFigure 3.2 Age distribution of faculty in management sciences and engineeringFigure 3.3 Age distribution of faculty in computer sciencesFigure 3.4 Age distribution of faculty in mechanical engineeringFrom the Figures above we can see that the professors ages mainly fall between 36 and 55 years old, especially between 36 and 45 years old. Professors under 35 years old are rarely seen in each discipline except a few excellent ones. Further investigation shows that policies about selecting professors under 35 years old differ in each university. Professors above 61 years old are distributed variably in each discipline where most of them work in the filed of mechanics and material science engineering. We have 13 professors and 10 professors in mechanics and material science engineering respectively while the average number for other disciplines is 4. All of these professors are academicians and academic leaders, which matches the above mentioned discovery that high level talents are relatively intensive in basic disciplines. Associate professors ages are mainly under 35 years old, or fall between 36 and 45 years old. Lecturers ages are generally below 35 years old. Distributions of other age phrases are barely seen. 4 The value analysis of discipline ranking and faculty factorial indicators 4.1 Introduction about related theoryAs any changes in the nature are related with two things, relationship between variables that is used to describe characteristics of numbers must be existed. Correlation studies are adopted to measure the degree of independence between two variables. In statistics, we usually use r as correlation coefficient to indicate how closely the two variables are related. The magnitude of the correlation coefficient varies from -1 to 1 as follows:If, there is a positive correlation between two variables, that is, the direction of association between two variables keeps the same;If, there is a negative correlation between two variables, that is, the direction of association between two variables appears oppositely;When, one variable is entirely dependent on another variable, that is, there is a perfect relationship between two variables;When, there is no linear correlation between two variables.As a rule of thumb, we usually judge the correlations according to following categories:When ，strong correlation;When , medium correlation;When ，weak correlation.When ,extremely weak correlation，or no correlation.4.2 Analysis of correlation outcomeCorrelation studies take Spearmans method, giving the correlation coefficient for discipline ranking and different factorial indicators and using scatter gram and curve graph to find out how much strength a factorial indicator upon its discipline ranking. Suppose we set the magnitude of ranking as 1, the bigger the correlation coefficient for the discipline ranking and different factorial indicators is, the more significant influence does it upon its ranking, and vice versa. Negative coefficient means they are negatively related. For example, in the filed of material science and engineering, the coefficient for ranking and high level talents is -0.87, which means the factor of high level talents exerts a great influence upon its ranking. Generally, more high level talents it has a better ranking it will have. The data comes from disciplines of material sciences and engineering, management sciences and engineering, mechanical engineering, mechanics at the universities whose faculty ranks top ten in China. The outcome from SPSS analysis is as follows (MOR for magnitude of ranking, T.N for total number, HLF for high level faculty, Ac for academicians, E.E for extraordinary experts, A.Prof. for associate professors and L for lecturers) :Table 1 Analysis of correlation about material sciences and engineeringItemsMOR T.N HLFAcE.EProf.A.ProfLPH.DR1-0.28-0.89-0.87-0.61-0.46-0.08-0.26-0.43Table 2 Analysis of correlation about management sciences and engineering ItemsMORT.NHLFAcE.EProf.A.ProfLPH.Dr10.15-0.29-0.31-0.020.370.15-0.030.16Table 3 Analysis of correlation about mechanical engineeringItemsMORT.NHLFAcE.EProf.A.ProfLPH.Dr1-0.58-0.84-0.60-0.42-0.76-0.53-0.30-0.88Table 4 Analysis of correlation about mechanicsItemsMORT.NHLFAcE.EProf.A.ProfLPH.Dr1-0.5-0.83-0.60-0.60-0.33-0.70-0.10-0.61By studying the correlation between ranking and total number of talents, high level talents, professors and associate professors in the above mentioned four disciplines, we can see that different factors of faculty have different influence on ranking. And difference also exists in different disciplines. In the field of material science and engineering and mechanics, there is a strong correlation between the number of high level talents and ranking of which the coefficient are -0.891、-0.839、-0.831 respectively, thus revealing that compared with other factors the number of high level talents has more significant influence on ranking. The number of high level talents has been lessened in the ranking list from top to bottom. However, in the field of management science and engineering, the coefficient for high level talents and ranking is relatively small. The main reason for this phenomenon is that few high level talents are existed in this field, and are presented like scattered distribution. Meanwhile, the coefficients for other factors of faculty and ranking are invariably small, which means other indicators except faculty exert more influence on ranking in the field of management science and engineering, such as the research outcome, the number of papers, the number of lab and so on. Moreover, in this field, the coefficient for the number of professors and ranking is +0.37, which tells us top universities have less professors than those whose ranking is behind them. It further says that the number of professors is no longer an important factor in assessment. By using linear relations and curves, this article gave us a relationship between ranking and the number of high level talents. It is very clear to note the influence of numbers of high level talents upon ranking. Conditioned to the length, this article did not give us the curves of ranking and other factors like professors and academicians.4.3 Complements to the result analysisWhen analyzing the correlation between ranking and the data about faculty, we have to be clear that different kinds of talents contribute differently to ranking. Undoubtedly, academician and extraordinary experts are leaders, who play the most significant role in their field, and are followed by professors, associate professors and lecturers. During the statistical process, we just identified the relationship between the numbers of different kinds of talents and ranking, which did not include the importance of different kinds of talents in a team. It is possible that the coefficient for associate professors or lecturers and ranking may be higher than it for high level talents and r