Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

College students’ innovation and entrepreneurship ability based on nonlinear model

College students’ innovation and entrepreneurship ability based on nonlinear model 1IntroductionSince the entrepreneurial ability of college students is an extension of the entrepreneurial ability among college students, some factors that affect entrepreneurial ability will also affect the development of college students’ entrepreneurial ability. Some scholars believe that an individual's entrepreneurial behavior, entrepreneurial cognition, and entrepreneurial environment constitute a dynamic and interactive relationship, which affects the generation and development of their entrepreneurial ability. Because of the uniqueness of college students, some exceptional factors affect the development of college students’ entrepreneurial ability [1]. These factors are multifaceted. Some scholars believe that the influencing factors of college students’ self-employment ability mainly include five aspects: skill level, motivation level, entrepreneurial ability level, environment level, and family level. Some scholars believe that the factors that affect the entrepreneurial ability of higher vocational graduates include internal factors such as the quality of college students and external factors such as the macroeconomic environment and family social conditions. Among many factors, the entrepreneurial education of college students has a crucial influence on the development of college students’ entrepreneurial ability. Some scholars, based on empirical research in the United States, Asia, and Europe, have found that the practical realization of entrepreneurship education in colleges and universities depends on the the active and extensive participation of various stakeholders in society [2]. A robust internal organization promotes entrepreneurship education; promotes the establishment of technology parks, incubators, technology transfer offices, etc; recruits entrepreneurial teachers to promote changes in education models; formulates corresponding incentive measures for entrepreneurial research, publishing, and teaching; ensures that the concept of entrepreneurship is embedded in the disciplines and comprehensive courses of each department; and encourages a wide range of interdisciplinary activities. Based on the university–industry–government triple-helix theory, some scholars have explored measures to strengthen students’ entrepreneurial ability in entrepreneurial universities from the aspects of university self-construction, government, and industry. Some scholars believe that entrepreneurship education content should permeate into classroom teaching, entrepreneurship education activities should be reflected in the classrooms, and the results of entrepreneurship education should be implemented in social practice [3]. Some scholars believe that innovative experiments have a tremendously positive effect on cultivating college students’ entrepreneurial ability.Many domestic scholars have conducted few targeted studies on the factors influencing college students’ entrepreneurial ability, but they have touched on this issue more or less from different aspects. However, these studies are relatively lacking in system and generality. Scholars at home and abroad have made active explorations on the model of entrepreneurial ability, and many of them have studied the model of college students’ entrepreneurial ability. But there is no mature theoretical result that is widely recognized [4]. Due to the broad and narrow divergence levels in understanding college students’ entrepreneurial ability, there are inevitably differences in the models constructed based on this understanding. In the above research results, some scholars have summarized the empirical research of predecessors through literature analysis and concluded that entrepreneurial ability has the following six capabilities: opportunity recognition, interpersonal relationship, concept, organization, strategy, and commitment. Some scholars have further expanded the entrepreneurial competency model. Its composition includes eight dimensions: opportunity ability, organizational ability, relationship ability, strategic ability, commitment ability, conceptual ability, emotional ability, and learning ability. Some scholars have constructed an entrepreneurial competence model framework based on foreign scholars’ research on entrepreneurial competence models, which also includes narrowly defined entrepreneurial competence and business and management competence, interpersonal competence, and conceptual and relationship competence. Finally, some scholars used the competency model to divide the 20 influencing factors into five factors to construct an index model of college students’ entrepreneurial ability. There are five dimensions: leadership communication, autonomous learning, frustration resistance, emotional control, and decision-making influence [5]. Among them, the influence of leadership communication on entrepreneurial ability is significantly more substantial than that of the other four abilities. This shows that an entrepreneur requires high leadership communication skills on the one hand. On the other hand, it also requires a relatively balanced emotion control ability, frustration resistance ability, independent learning ability, and decision-making influence.Disciplinary competition in colleges and universities is one of the virtual channels and methods to implement the quality construction requirements of colleges and universities, cultivate innovative talents, and improve the awareness of innovation. This research on the evaluation of college students’ creative ability belongs to the problem of multiattribute complexity. Some scholars use the analytic hierarchy process (AHP) and MFCE method to evaluate innovation and entrepreneurship and implement opinions comprehensively. On the other hand, some scholars have used the fuzzy AHP (FAHP) method to develop an index system and fuzzy comprehensive evaluation for the entrepreneurial ability of science and engineering college students.Some scholars use the AHP of expert opinions to determine the importance of the evaluation index of college students’ innovation ability. However, weight is an essential indicator of evaluation research, and a single method to determine attribute weight will inevitably lead to a one-sided evaluation. Therefore, this paper proposes to analyze the multiple complex factors that are interrelated and that restrict each other in the innovation ability evaluation system using the AHP and the coefficient of variation method. At the same time, we build an independent basic weight set. On this basis, we use the game theory to analyze its internal competition relationship and obtain the optimal comprehensive weight of the index. This provides a theoretical basis for the educational evaluation and decision-making of college students’ innovative abilities.2Constructing an evaluation index system for innovation abilityThere are many factors involved in the evaluation of college students’ innovative ability cultivation in subject competitions. This article focuses on accumulating data using questionnaire surveys for the evaluation of students who participated in subject competitions within 2 years of graduation, as well as at the university [6]. We decompose the content of the questionnaire into target level, criterion level, and index level, with innovation capability as the evaluation objective. All indicators of the layered three-dimensional structure evaluation system are evaluated on a 10-point system. Table 1 shows the established evaluation index system for the innovation ability of discipline competitions.Table 1Evaluation index systemTarget layerInnovation ability A0Criterion layerCreative thinking ability A1Innovation ability A2Scientific and technological innovation achievement A3Innovative education benefit A4Index layerLogic analysis ability B11Practical ability B21Paper Publication B31Scholarship B41Knowledge integration ability B12Teamwork ability B22Project Participation B32Self-employment B42Question the ability to find problems B13Communication and coordination ability B23Technical works B33Continue to study B43Abstract thinking ability B14Writing ability B24Science and technology project B34Problem-solving ability B153Evaluation method3.1AHP to determine the subjective weightThe AHP divides the various evaluation factors into order. This method compares the relative importance of each level of factors, and at the same time, sorts the impact of indicators according to the weight value [7]. The specific steps are as follows.(1) We select the average value of the evaluation index as the relative scale for pairwise cross comparison and construct the judgment matrix of each layer.Taking the index layer as an example to construct the judgment matrix, we set the mean value of index i as Vi and the mean value of index j as Vj. If Vi < Vj, it means that index j is more critical than index i. When the difference is <0.1, the scale is “2”; when the difference is 0.1–0.5, the scale is “3”. Based on this recursion, we build a hierarchy relationship scale table with 2–9 scales (Table 2) and then use the evaluation scale comparison table to obtain the judgment matrix value aij. The criterion layer constructs the judgment matrix as the index layer. See Table 3 for the hierarchical relationship evaluation scale of scales 2–9. We take the criterion layer A1–A4 and the index layer B11–B15 as examples to construct the judgment matrix as shown in Eq. (1).(1)f1=[1 1/25 22 15 31/5 1/51 1/41/2 1/34 1]f2=[1 1/2 1/3 1/3 1/42 11/2 1/3 1/43 22 1/2 1/43 32 1 1/44 44 4 1]{f_1} = \left[ {\matrix{ {1\,1/25\,2} \hfill \cr {2\,15\,3} \hfill \cr {1/5\,1/51\,1/4} \hfill \cr {1/2\,1/34\,1} \hfill \cr } } \right]{f_2} = \left[ {\matrix{ {1\,1/21/3\,1/3\,1/4} \hfill \cr {2\,11/2\,1/3\,1/4} \hfill \cr {3\,22\,1/2\,1/4} \hfill \cr {3\,32\,1\,1/4} \hfill \cr {4\,44\,4\,1} \hfill \cr } } \right]Table 2Index layer scale comparison tableRangeScalingRangeScaling<0.121.5–260.1–0.532–2.570.5–142.5–381–1.553–3.59Table 3Standard-level scale comparison tableRangeScalingRangeScaling0.1–124–561–235–672–346–783–457–89(2) We calculate the normalized index weight and obtain the most significant characteristic root λmax and consistency test of the judgment matrix.(2)ωi=(∏j=1maij)1/m∑(∏j=1maij)1/m(i,j=1,2,⋯,m){\omega _i} = {{{{(\prod\nolimits_{j = 1}^m {a_{ij}})}^{1/m}}} \over {\sum {{(\prod\nolimits_{j = 1}^m {a_{ij}})}^{1/m}}}}(i,j = 1,2, \cdots ,m)(3)CR=CI/RI{C_R} = {C_I}/{R_I}Among them, CI = (λmax − m)/(m − 1) is the consistency index of the deviation rate; RI is the average random consistency index, which represents the average consistency index of the average deviation rate. We use MATLAB to repeat the random judgment matrix calculation results 10,000 times, as shown in Table 4.Table 4Average random consistency index value standardmRI1–2030.5840.951.1261.2471.3281.4191.45101.49111.51(3) Calculate the weights of multiple-level indicators. Considering the influence of the first-level indicators at the criterion level on the index factors, we calculate the comprehensive weight as the product of the two-level weights. The results are shown in Table 5.Table 5Analytic hierarchy process to determine the weight distribution of indicatorsIndexA1B11B12B13B14B15Sibling weight0.29290.06720.09360.14460.20990.4846Comprehensive weight0.01970.02740.04240.06150.1419IndexA2B21B22B23B24Sibling weight0.46470.20940.29660.41950.0742Comprehensive weight0.09730.13780.19490.0345IndexA3B31B32B33B34Sibling weight0.06340.11550.2310.49010.1634Comprehensive weight0.00730.01460.03110.0104IndexA4B41B42B43Sibling weight0.17910.08110.3420.5769Comprehensive weight0.01450.06130.1033Analysis of the results in Table 6 shows that the value of CR in the table is <0.1. Therefore, we can judge that its construction matrix is reasonable and adequate and that it meets the consistency requirements.Table 6Consistency judgment of analytic hierarchy processLevelλmaxCRCIA1–A44.0980.03270.0363B11–B155.25340.05660.0634B21–B244.12130.04490.0404B31–B324.12130.04490.0404B41–B433.02910.02510.01453.2Coefficient of variation method to determine objective weightThe coefficient of variation method uses the standard deviation and mean of each index data to directly calculate the index weight. The change in difference between indicator data is proportional to the weight. The greater the difference, the greater is the weight, and the more critical is the impact on the evaluation result [8]. The specific calculation method is as follows:(1) Calculate the coefficient of variation of the i-th group of indicators(4)Ci=σimi{C_i} = {{{\sigma _i}} \over {{m_i}}}where σi is the standard deviation of the i-th group of index data, and mi¯\overline {{m_i}} is the arithmetic mean of the i-th group of index data.(2) We use the weight calculation in Eq. (5) of the multiple-level indicators to obtain the weight distribution of the indicators.(5)ωi=Vi∑Vi{\omega _i} = {{{V_i}} \over {\sum {V_i}}}3.3Game theory combination weighting methodThe game theory analysis method finds the minimization difference between the optimal weight and the different weights of each index to obtain the optimal evaluation index weight. The specific steps are as follows:(1) We set up a vector set of basic weights for the evaluation indicators of subject competition innovation ability: wk = {wk1, wk2, ⋯, wkm}, k = 1, 2,⋯L, where wk is the set of weights determined by the k-weighting method, and m is the number of evaluation indicators [9]. In this paper, L = 2, m = 16. Let a = {a1, a2} be the linear combination coefficient, and the comprehensive weight vector w satisfies any linear combination of the two vectors as follows:(6)ω=∑k=1Lak⋅ωkT(αk>0).\omega = \sum\limits_{k = 1}^L {a_k} \cdot \omega _k^T({\alpha _k} > 0).(2) Optimize the weight vector ak of the two weight vectors. We obtain the optimal weight w* by minimizing the range between w and wk. In other words, the optimization objective function is as follows:(7)minak>0‖∑k=12ak⋅ωkT−ωk‖2\mathop {\min }\limits_{{a_k} > 0} {\left\| {\sum\limits_{k = 1}^2 {a_k} \cdot \omega _k^T - {\omega _k}} \right\|_2}(3) Obtain the linear equation system form corresponding to the objective function according to the matrix differential property.(8)[ω1ω1Tω1ω2Tω2ω1Tω2ω2T][a1a2]=[ω1ω1Tω2ω2T]\left[ {\matrix{ {{\omega _1}\omega _1^T} & {{\omega _1}\omega _2^T} \cr {{\omega _2}\omega _1^T} & {{\omega _2}\omega _2^T} \cr } } \right]\left[ {\matrix{ {{a_1}} \hfill \cr {{a_2}} \hfill \cr } } \right] = \left[ {\matrix{ {{\omega _1}\omega _1^T} \hfill \cr {{\omega _2}\omega _2^T} \hfill \cr } } \right]We solve the matrix Eq. (8) to obtain the combination coefficient a1 = 0.4377, a2 = 0.8028. The normalized processing obtains the weight vector a1*=0.3528a_1^* = 0.3528, a2*=0.6472a_2^* = 0.6472and the final evaluation index combination total weight.(9)ω*=∑k=12ak*ωkT{\omega ^*} = \sum\limits_{k = 1}^2 a_k^*\omega _k^TWe use MATLAB to calculate the combined total weights, as shown in Table 7.Table 7The method of coefficient of variation and game theory to determine the weight distribution of indicatorsIndexCoefficient of variationGame theory combinationIndexCoefficient of variationGame theory combinationB110.05150.0309B240.04960.0398B120.05100.0357B310.09320.0376B130.05030.0452B320.09140.0417B140.05000.0574B330.08790.0511B150.04540.1079B340.09190.0392B210.04580.0791B410.11400.0496B220.04560.1053B420.04610.0559B230.04530.1421B430.04110.0814It can be seen from Table 7 that the index elements B15, B22, and B23 have larger weights, followed by B21 and B43, and the remaining ones are even less critical. We compare the actual survey and statistical results of subject competitions between 2017 and 2020. The example shows that the main index factors that influence the innovation ability of college students in subject competition are the ability to solve problems, the ability of teamwork, and the ability of communication and coordination [10]. However, the aspects of innovation and efficiency produced through subject competitions are relatively weak, related to the relatively weak subjective conversion consciousness of students’ achievements and the insufficient accumulation of knowledge and abilities to a certain extent.Figure 1 is a graph of the weighting result of the evaluation method. The analysis available is affected by the correlation between subjective factors and indicator elements. The AHP highlights the weight of the ability factor and weakens the weight of the benefit factor [11]. The coefficient of variation method analyzes the original sample data of the indicator elements, and the two criterion levels of scientific and technological innovation achievements and scientific and technological education benefits fluctuate considerably. This highlights the weight of the benefit factor result. The weighted result obtained by the combination weight method of game theory based on the theory of minimization of difference equilibrium is located between the two. The weight of the 16 index factors has been balanced to a certain extent. Therefore, we have obtained relatively good weighting results.Fig. 1Results of weight comparison.4ConclusionWe start by constructing subjective evaluation indicators and objective evaluation indicators based on the game theory to balance personal empowerment and objectiveness. Then, the weight set is assigned to determine the complete optimal weight result. Finally, in response to the above theoretical results, suggestions for improving college students’ creative ability are proposed: creating a relaxed learning environment for practicing innovative personality development, encouraging students to understand cutting-edge technologies in science and technology, condensing scientific research issues, and proposing innovative ideas and opinions; building a multilevel open development platform; providing a suitable environment for students to obtain hands-on practice; strengthening the ability of teachers of innovation and entrepreneurship; and guiding the cultivation and development of students’ creative ability. In addition, attention should be paid to cultivating students’ innovative ideological awareness and practical ability, strengthening the accumulation of knowledge and ability, and enhancing the transformation of results to implement each training link. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Mathematics and Nonlinear Sciences de Gruyter

College students’ innovation and entrepreneurship ability based on nonlinear model

Download PDF
8 pages

Loading next page...
 
/lp/de-gruyter/college-students-innovation-and-entrepreneurship-ability-based-on-13267aQBAu

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
de Gruyter
Copyright
© 2021 Xue Zhang et al., published by Sciendo
eISSN
2444-8656
DOI
10.2478/amns.2021.1.00064
Publisher site
See Article on Publisher Site

Abstract

1IntroductionSince the entrepreneurial ability of college students is an extension of the entrepreneurial ability among college students, some factors that affect entrepreneurial ability will also affect the development of college students’ entrepreneurial ability. Some scholars believe that an individual's entrepreneurial behavior, entrepreneurial cognition, and entrepreneurial environment constitute a dynamic and interactive relationship, which affects the generation and development of their entrepreneurial ability. Because of the uniqueness of college students, some exceptional factors affect the development of college students’ entrepreneurial ability [1]. These factors are multifaceted. Some scholars believe that the influencing factors of college students’ self-employment ability mainly include five aspects: skill level, motivation level, entrepreneurial ability level, environment level, and family level. Some scholars believe that the factors that affect the entrepreneurial ability of higher vocational graduates include internal factors such as the quality of college students and external factors such as the macroeconomic environment and family social conditions. Among many factors, the entrepreneurial education of college students has a crucial influence on the development of college students’ entrepreneurial ability. Some scholars, based on empirical research in the United States, Asia, and Europe, have found that the practical realization of entrepreneurship education in colleges and universities depends on the the active and extensive participation of various stakeholders in society [2]. A robust internal organization promotes entrepreneurship education; promotes the establishment of technology parks, incubators, technology transfer offices, etc; recruits entrepreneurial teachers to promote changes in education models; formulates corresponding incentive measures for entrepreneurial research, publishing, and teaching; ensures that the concept of entrepreneurship is embedded in the disciplines and comprehensive courses of each department; and encourages a wide range of interdisciplinary activities. Based on the university–industry–government triple-helix theory, some scholars have explored measures to strengthen students’ entrepreneurial ability in entrepreneurial universities from the aspects of university self-construction, government, and industry. Some scholars believe that entrepreneurship education content should permeate into classroom teaching, entrepreneurship education activities should be reflected in the classrooms, and the results of entrepreneurship education should be implemented in social practice [3]. Some scholars believe that innovative experiments have a tremendously positive effect on cultivating college students’ entrepreneurial ability.Many domestic scholars have conducted few targeted studies on the factors influencing college students’ entrepreneurial ability, but they have touched on this issue more or less from different aspects. However, these studies are relatively lacking in system and generality. Scholars at home and abroad have made active explorations on the model of entrepreneurial ability, and many of them have studied the model of college students’ entrepreneurial ability. But there is no mature theoretical result that is widely recognized [4]. Due to the broad and narrow divergence levels in understanding college students’ entrepreneurial ability, there are inevitably differences in the models constructed based on this understanding. In the above research results, some scholars have summarized the empirical research of predecessors through literature analysis and concluded that entrepreneurial ability has the following six capabilities: opportunity recognition, interpersonal relationship, concept, organization, strategy, and commitment. Some scholars have further expanded the entrepreneurial competency model. Its composition includes eight dimensions: opportunity ability, organizational ability, relationship ability, strategic ability, commitment ability, conceptual ability, emotional ability, and learning ability. Some scholars have constructed an entrepreneurial competence model framework based on foreign scholars’ research on entrepreneurial competence models, which also includes narrowly defined entrepreneurial competence and business and management competence, interpersonal competence, and conceptual and relationship competence. Finally, some scholars used the competency model to divide the 20 influencing factors into five factors to construct an index model of college students’ entrepreneurial ability. There are five dimensions: leadership communication, autonomous learning, frustration resistance, emotional control, and decision-making influence [5]. Among them, the influence of leadership communication on entrepreneurial ability is significantly more substantial than that of the other four abilities. This shows that an entrepreneur requires high leadership communication skills on the one hand. On the other hand, it also requires a relatively balanced emotion control ability, frustration resistance ability, independent learning ability, and decision-making influence.Disciplinary competition in colleges and universities is one of the virtual channels and methods to implement the quality construction requirements of colleges and universities, cultivate innovative talents, and improve the awareness of innovation. This research on the evaluation of college students’ creative ability belongs to the problem of multiattribute complexity. Some scholars use the analytic hierarchy process (AHP) and MFCE method to evaluate innovation and entrepreneurship and implement opinions comprehensively. On the other hand, some scholars have used the fuzzy AHP (FAHP) method to develop an index system and fuzzy comprehensive evaluation for the entrepreneurial ability of science and engineering college students.Some scholars use the AHP of expert opinions to determine the importance of the evaluation index of college students’ innovation ability. However, weight is an essential indicator of evaluation research, and a single method to determine attribute weight will inevitably lead to a one-sided evaluation. Therefore, this paper proposes to analyze the multiple complex factors that are interrelated and that restrict each other in the innovation ability evaluation system using the AHP and the coefficient of variation method. At the same time, we build an independent basic weight set. On this basis, we use the game theory to analyze its internal competition relationship and obtain the optimal comprehensive weight of the index. This provides a theoretical basis for the educational evaluation and decision-making of college students’ innovative abilities.2Constructing an evaluation index system for innovation abilityThere are many factors involved in the evaluation of college students’ innovative ability cultivation in subject competitions. This article focuses on accumulating data using questionnaire surveys for the evaluation of students who participated in subject competitions within 2 years of graduation, as well as at the university [6]. We decompose the content of the questionnaire into target level, criterion level, and index level, with innovation capability as the evaluation objective. All indicators of the layered three-dimensional structure evaluation system are evaluated on a 10-point system. Table 1 shows the established evaluation index system for the innovation ability of discipline competitions.Table 1Evaluation index systemTarget layerInnovation ability A0Criterion layerCreative thinking ability A1Innovation ability A2Scientific and technological innovation achievement A3Innovative education benefit A4Index layerLogic analysis ability B11Practical ability B21Paper Publication B31Scholarship B41Knowledge integration ability B12Teamwork ability B22Project Participation B32Self-employment B42Question the ability to find problems B13Communication and coordination ability B23Technical works B33Continue to study B43Abstract thinking ability B14Writing ability B24Science and technology project B34Problem-solving ability B153Evaluation method3.1AHP to determine the subjective weightThe AHP divides the various evaluation factors into order. This method compares the relative importance of each level of factors, and at the same time, sorts the impact of indicators according to the weight value [7]. The specific steps are as follows.(1) We select the average value of the evaluation index as the relative scale for pairwise cross comparison and construct the judgment matrix of each layer.Taking the index layer as an example to construct the judgment matrix, we set the mean value of index i as Vi and the mean value of index j as Vj. If Vi < Vj, it means that index j is more critical than index i. When the difference is <0.1, the scale is “2”; when the difference is 0.1–0.5, the scale is “3”. Based on this recursion, we build a hierarchy relationship scale table with 2–9 scales (Table 2) and then use the evaluation scale comparison table to obtain the judgment matrix value aij. The criterion layer constructs the judgment matrix as the index layer. See Table 3 for the hierarchical relationship evaluation scale of scales 2–9. We take the criterion layer A1–A4 and the index layer B11–B15 as examples to construct the judgment matrix as shown in Eq. (1).(1)f1=[1 1/25 22 15 31/5 1/51 1/41/2 1/34 1]f2=[1 1/2 1/3 1/3 1/42 11/2 1/3 1/43 22 1/2 1/43 32 1 1/44 44 4 1]{f_1} = \left[ {\matrix{ {1\,1/25\,2} \hfill \cr {2\,15\,3} \hfill \cr {1/5\,1/51\,1/4} \hfill \cr {1/2\,1/34\,1} \hfill \cr } } \right]{f_2} = \left[ {\matrix{ {1\,1/21/3\,1/3\,1/4} \hfill \cr {2\,11/2\,1/3\,1/4} \hfill \cr {3\,22\,1/2\,1/4} \hfill \cr {3\,32\,1\,1/4} \hfill \cr {4\,44\,4\,1} \hfill \cr } } \right]Table 2Index layer scale comparison tableRangeScalingRangeScaling<0.121.5–260.1–0.532–2.570.5–142.5–381–1.553–3.59Table 3Standard-level scale comparison tableRangeScalingRangeScaling0.1–124–561–235–672–346–783–457–89(2) We calculate the normalized index weight and obtain the most significant characteristic root λmax and consistency test of the judgment matrix.(2)ωi=(∏j=1maij)1/m∑(∏j=1maij)1/m(i,j=1,2,⋯,m){\omega _i} = {{{{(\prod\nolimits_{j = 1}^m {a_{ij}})}^{1/m}}} \over {\sum {{(\prod\nolimits_{j = 1}^m {a_{ij}})}^{1/m}}}}(i,j = 1,2, \cdots ,m)(3)CR=CI/RI{C_R} = {C_I}/{R_I}Among them, CI = (λmax − m)/(m − 1) is the consistency index of the deviation rate; RI is the average random consistency index, which represents the average consistency index of the average deviation rate. We use MATLAB to repeat the random judgment matrix calculation results 10,000 times, as shown in Table 4.Table 4Average random consistency index value standardmRI1–2030.5840.951.1261.2471.3281.4191.45101.49111.51(3) Calculate the weights of multiple-level indicators. Considering the influence of the first-level indicators at the criterion level on the index factors, we calculate the comprehensive weight as the product of the two-level weights. The results are shown in Table 5.Table 5Analytic hierarchy process to determine the weight distribution of indicatorsIndexA1B11B12B13B14B15Sibling weight0.29290.06720.09360.14460.20990.4846Comprehensive weight0.01970.02740.04240.06150.1419IndexA2B21B22B23B24Sibling weight0.46470.20940.29660.41950.0742Comprehensive weight0.09730.13780.19490.0345IndexA3B31B32B33B34Sibling weight0.06340.11550.2310.49010.1634Comprehensive weight0.00730.01460.03110.0104IndexA4B41B42B43Sibling weight0.17910.08110.3420.5769Comprehensive weight0.01450.06130.1033Analysis of the results in Table 6 shows that the value of CR in the table is <0.1. Therefore, we can judge that its construction matrix is reasonable and adequate and that it meets the consistency requirements.Table 6Consistency judgment of analytic hierarchy processLevelλmaxCRCIA1–A44.0980.03270.0363B11–B155.25340.05660.0634B21–B244.12130.04490.0404B31–B324.12130.04490.0404B41–B433.02910.02510.01453.2Coefficient of variation method to determine objective weightThe coefficient of variation method uses the standard deviation and mean of each index data to directly calculate the index weight. The change in difference between indicator data is proportional to the weight. The greater the difference, the greater is the weight, and the more critical is the impact on the evaluation result [8]. The specific calculation method is as follows:(1) Calculate the coefficient of variation of the i-th group of indicators(4)Ci=σimi{C_i} = {{{\sigma _i}} \over {{m_i}}}where σi is the standard deviation of the i-th group of index data, and mi¯\overline {{m_i}} is the arithmetic mean of the i-th group of index data.(2) We use the weight calculation in Eq. (5) of the multiple-level indicators to obtain the weight distribution of the indicators.(5)ωi=Vi∑Vi{\omega _i} = {{{V_i}} \over {\sum {V_i}}}3.3Game theory combination weighting methodThe game theory analysis method finds the minimization difference between the optimal weight and the different weights of each index to obtain the optimal evaluation index weight. The specific steps are as follows:(1) We set up a vector set of basic weights for the evaluation indicators of subject competition innovation ability: wk = {wk1, wk2, ⋯, wkm}, k = 1, 2,⋯L, where wk is the set of weights determined by the k-weighting method, and m is the number of evaluation indicators [9]. In this paper, L = 2, m = 16. Let a = {a1, a2} be the linear combination coefficient, and the comprehensive weight vector w satisfies any linear combination of the two vectors as follows:(6)ω=∑k=1Lak⋅ωkT(αk>0).\omega = \sum\limits_{k = 1}^L {a_k} \cdot \omega _k^T({\alpha _k} > 0).(2) Optimize the weight vector ak of the two weight vectors. We obtain the optimal weight w* by minimizing the range between w and wk. In other words, the optimization objective function is as follows:(7)minak>0‖∑k=12ak⋅ωkT−ωk‖2\mathop {\min }\limits_{{a_k} > 0} {\left\| {\sum\limits_{k = 1}^2 {a_k} \cdot \omega _k^T - {\omega _k}} \right\|_2}(3) Obtain the linear equation system form corresponding to the objective function according to the matrix differential property.(8)[ω1ω1Tω1ω2Tω2ω1Tω2ω2T][a1a2]=[ω1ω1Tω2ω2T]\left[ {\matrix{ {{\omega _1}\omega _1^T} & {{\omega _1}\omega _2^T} \cr {{\omega _2}\omega _1^T} & {{\omega _2}\omega _2^T} \cr } } \right]\left[ {\matrix{ {{a_1}} \hfill \cr {{a_2}} \hfill \cr } } \right] = \left[ {\matrix{ {{\omega _1}\omega _1^T} \hfill \cr {{\omega _2}\omega _2^T} \hfill \cr } } \right]We solve the matrix Eq. (8) to obtain the combination coefficient a1 = 0.4377, a2 = 0.8028. The normalized processing obtains the weight vector a1*=0.3528a_1^* = 0.3528, a2*=0.6472a_2^* = 0.6472and the final evaluation index combination total weight.(9)ω*=∑k=12ak*ωkT{\omega ^*} = \sum\limits_{k = 1}^2 a_k^*\omega _k^TWe use MATLAB to calculate the combined total weights, as shown in Table 7.Table 7The method of coefficient of variation and game theory to determine the weight distribution of indicatorsIndexCoefficient of variationGame theory combinationIndexCoefficient of variationGame theory combinationB110.05150.0309B240.04960.0398B120.05100.0357B310.09320.0376B130.05030.0452B320.09140.0417B140.05000.0574B330.08790.0511B150.04540.1079B340.09190.0392B210.04580.0791B410.11400.0496B220.04560.1053B420.04610.0559B230.04530.1421B430.04110.0814It can be seen from Table 7 that the index elements B15, B22, and B23 have larger weights, followed by B21 and B43, and the remaining ones are even less critical. We compare the actual survey and statistical results of subject competitions between 2017 and 2020. The example shows that the main index factors that influence the innovation ability of college students in subject competition are the ability to solve problems, the ability of teamwork, and the ability of communication and coordination [10]. However, the aspects of innovation and efficiency produced through subject competitions are relatively weak, related to the relatively weak subjective conversion consciousness of students’ achievements and the insufficient accumulation of knowledge and abilities to a certain extent.Figure 1 is a graph of the weighting result of the evaluation method. The analysis available is affected by the correlation between subjective factors and indicator elements. The AHP highlights the weight of the ability factor and weakens the weight of the benefit factor [11]. The coefficient of variation method analyzes the original sample data of the indicator elements, and the two criterion levels of scientific and technological innovation achievements and scientific and technological education benefits fluctuate considerably. This highlights the weight of the benefit factor result. The weighted result obtained by the combination weight method of game theory based on the theory of minimization of difference equilibrium is located between the two. The weight of the 16 index factors has been balanced to a certain extent. Therefore, we have obtained relatively good weighting results.Fig. 1Results of weight comparison.4ConclusionWe start by constructing subjective evaluation indicators and objective evaluation indicators based on the game theory to balance personal empowerment and objectiveness. Then, the weight set is assigned to determine the complete optimal weight result. Finally, in response to the above theoretical results, suggestions for improving college students’ creative ability are proposed: creating a relaxed learning environment for practicing innovative personality development, encouraging students to understand cutting-edge technologies in science and technology, condensing scientific research issues, and proposing innovative ideas and opinions; building a multilevel open development platform; providing a suitable environment for students to obtain hands-on practice; strengthening the ability of teachers of innovation and entrepreneurship; and guiding the cultivation and development of students’ creative ability. In addition, attention should be paid to cultivating students’ innovative ideological awareness and practical ability, strengthening the accumulation of knowledge and ability, and enhancing the transformation of results to implement each training link.

Journal

Applied Mathematics and Nonlinear Sciences – de Gruyter

Published: Jan 1, 2022

Keywords: nonlinear model; analytic hierarchy process; colleges and universities; innovation and entrepreneurship ability evaluation; 34A34

There are no references for this article.