Construction of Moral Education Evaluation Model Based on Quality Cultivation of College Students

Yuan, Xiaolin

doi:https://doi.org/10.1155/2022/5641782

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Volume 2022 | Article ID 5641782 | https://doi.org/10.1155/2022/5641782

Construction of Moral Education Evaluation Model Based on Quality Cultivation of College Students

Xiaolin Yuan¹

Academic Editor: Baiyuan Ding

Received09 Nov 2021

Revised23 Nov 2021

Accepted03 Dec 2021

Published07 Jan 2022

Abstract

Contemporary young college students are greatly impacted in the aspects of moral cognition and moral choice, which results in the weak moral will of some college students, vague moral concepts, and weak ideals and beliefs, which seriously affect the formation and development of college students’ moral quality. Therefore, the moral education evaluation model based on college students’ quality cultivation is constructed. Firstly, the present situation and defects of college students’ quality training are analyzed. Based on this, association rules in data mining method are constructed and introduced to extract valuable knowledge hidden in the data to assist education managers to make effective decisions and improve management level. Finally, the evaluation index is selected and the weighted principal component TOP-SIS model is constructed to realize the evaluation of moral education based on college students’ quality cultivation. The experimental results show that the evaluation results of the model are consistent with the actual situation, high degree of fit and freedom, and good practical performance.

1. Introduction

With the comprehensive deepening of economic globalization, the surging of new ideas, and the intersection and collision of multiculturalism, it has a profound impact on people’s original moral concepts [1, 2]. Therefore, whether colleges and universities can grasp the pulse of the times and solve the moral confusion and value conflict of college students not only plays a vital role in comprehensively promoting the ideological and political work of colleges and universities, but also concerns whether college students can practice the socialist core values with practical actions [3].

Reference [4] uses Amos to test the doctoral education quality evaluation model. The results show that the education quality evaluation model can be divided into four parts: input quality, process quality, output quality, and development quality. There is a significant positive effect between these four parts. Reference [5] takes multiple regression analysis of time series as the main means to explore the linear relationship between the scientific and technological innovation service function of local universities and the characteristics of innovation and entrepreneurship education resources and gives relevant conclusions and policy analysis. In [6], aiming at the phenomenon of “soft evaluation” in college curriculum academic evaluation, taking the evaluation triangle as the theoretical basis, and based on the comprehensive analysis of relevant research results at home and abroad, this paper constructs an “evidence” based academic evaluation model of college mixed learning curriculum (e-abc model). Based on the above research, this paper constructs an evaluation model of moral education based on college students’ quality training, introduces association rules, extracts valuable knowledge, and assists education managers to make effective decisions and improve management level, building a weighted principal component TOP-SIS model to realize the evaluation of moral education. Compared with previous studies, the advantages of the model are that the evaluation results of the model are basically consistent with the actual situation, high degree of fit and freedom, and good practical performance.

2. Current Situation of College Students’ Moral Education

(1)Moral education is divorced from the reality of society. Morality originates from social life and is developed and perfected in social practice. Practice is the precondition for the existence and development of morality. Therefore, the moral character is the human forms and the development in the interactive contact practice; the personal practice cannot be less [7]. However, at present, the moral education of college students in some schools is often superior to the students’ real life and divorced from the students’ moral knowledge and behavior. In view of this serious situation, some colleges and universities still have weak response to the social reality in the process of moral education, and the theory of moral education lacks keeping pace with the times and self-innovation [8]. This kind of moral education is out of touch with the real society and lacks appeal and influence. It is impossible to make a convincing interpretation of the puzzles brought by the complicated real world to the moral cognition of college students from the theoretical level, which leads to the conflict between the moral behaviors of college students and their moral ideas, or even a set of ideas to switch between the superficial “moral spiritual world” and the inner “moral real world” and become a moral “double-faced person.”(2)The blending degree of moral education and knowledge education is insufficient. Herbart, a German philosopher and educator known as “the father of educational science,” argues that teaching without moral education is a means to no end, and moral education (or character education) without teaching is an end without means [9, 10]. Obviously, in Herbart’s teaching idea, the moral education and the knowledge education need to unify. That is to say, moral education cannot fight alone but can play the role of “leading” and “running through” knowledge education only when it permeates into the teaching contents and process of various subjects. As the carrier of spreading morality, moral education should be embodied in the rich content of students’ real life, which coincides with the function of knowledge education. Therefore, moral education and knowledge education complement each other in purpose and are unified in whole [11]. However, due to such problems as social value function orientation, teacher education concept, teaching evaluation index, or teacher assessment, in actual teaching practice, some teachers only pay attention to impart professional knowledge and train thinking ability to students but neglect to cultivate and inspire moral spirit and value rationality. Students only learn instrumental knowledge and lack the promotion of moral character [12]. Moral education is basically out of line with knowledge education. What is more regrettable is that some moral educators understand moral education narrowly as the education of moral knowledge, inculcate moral knowledge into education instead of moral character, and lack the consideration of the true feelings of college students, so that moral education evolves into the study of moral concepts, moral rules, and moral feelings, moral education becomes pure knowledge teaching, and education becomes the materialization of human beings.(3)Moral education is separated from virtue cultivation. Colleges and universities cultivate moral education. It is the duty and responsibility of moral education in colleges and universities to cultivate the moral character of college students. This kind of moral education should be made up of the interaction between the external normalization and the internal cultivation of virtue, and the two are integrated into each other, so as to make the moral subject conscious, voluntary, and self-motivated in the historical circumstances and the practice of the times [13]. However, some scholars always emphasize the enlightenment and restraint of external norms, equate moral education in colleges and universities with “educators turn the moral norms and requirements respected by the society into a kind of education of individual moral character of the educated,” regard it as the ultimate goal of moral education to train college students to obey the basic moral rules and constraints, and neglect the cultivation of inner moral character, which leads to the confusion and perplexity of cultivating college students into “obligatory moral” who blindly obey the basic social rules. It is precisely because of separating the cultivation process of external norms and internal virtues, ignoring the difference and connection between external norms and internal virtues, paying attention only to the obedience of external norms, and ignoring the moral value pursuit of the main body of university students that the two-way integration of the two aspects is deficient, there is no humanistic concern, only empty requirements of moral norms, and it is difficult to penetrate and touch the moral mind of university students, and it also deviates from the “original heart” of cultivating and promoting the moral character of university students [14].(4)Moral education is divorced from moral subjects. There is no doubt that people’s moral character is not born but is gradually formed in the process of learning and practicing. Because of the differences in growing environment and life experience, people’s moral cognition and behavior show greater differences. College moral education must also pay attention to this difference, study the students’ moral development level, teach students according to their needs, and use various methods synthetically. While moral indoctrination is essential in this process, it may lead to a gradual distance between moral education and the educated [15]. In moral education in colleges and universities, some moral educators, without fully understanding the students’ living situation, actual needs, thoughts, and their different needs for morality, regard students as pure objects, ignore the subjective role of students and the differences of students, adopt monologue indoctrination education, and impose moral truth on students, and teachers seem to become the mouthpiece of morality, thus making it difficult for moral education to touch students’ souls and obtain value identification, aggravating students’ dissatisfaction and disgust for moral education and contradicting moral education from the heart. The biggest weakness of this educational model is that it neglects the subjectivity of moral education, mental development, and ideological reality of college students, which easily leads to the disconnection of moral knowledge and practice.

3. Moral Education Evaluation Model Data Mining

3.1. Data Mining Process and Main Functions

Data mining refers to the process of searching hidden information from a large number of data through algorithms. It is mainly a technology used by various disciplines. It plays a very important role in various fields. Data mining is a hot issue in the field of artificial intelligence and database. The so-called data mining refers to a nontrivial process of revealing implicit, previously unknown and potentially valuable information from a large amount of data in the database. Data mining is a decision support process of moral education evaluation. It highly automatically analyzes the data of moral education evaluation, makes inductive reasoning, excavates potential models, and helps decision makers adjust education and teaching strategies, reduce risks, and make correct decisions The process of extracting effective knowledge hidden in noisy and fuzzy data information [16, 17] comes from the knowledge extraction in the database, namely, KDD (Knowledge Discovery Database) [18], and it is also the theoretical basis of the proposed data mining. People use KDD to describe the whole process of data mining and use data mining to describe the basic process of data mining using mining algorithm. The process of discovering valuable knowledge in the database, known as the KDD process, is shown in Figure 1.

According to Figure 1, KDD is composed of data collection and processing, data information mining, and result analysis, etc. Among them, data mining is mainly based on association analysis and cluster analysis and prediction of various statistical analysis tools to find useful knowledge in large-scale data and through model evaluation will be valuable model as knowledge to assist relevant personnel to make a scientific and rational decision.

The main function of data mining mainly refers to making valuable knowledge-based decisions by predicting future development trends. The main function of data mining technology is not realized by a single way, but by a group of methods. The main task of data mining is to find valuable knowledge or information from relevant databases. Its main functions are as follows.(1)Correlation analysis of data In a database, the analysis of the correlation between data is the key link to discover the important knowledge. If there is some special law between the values of two or more variables, then it can be called correlation [19].(2)Cluster analysis between data In the database, the data information records can be divided into different meaningful data subsets; the process is data clustering. Among them, the smaller the distance between the data in the same data class, the more similar they are, and the less similar they are by contraries. Data clustering can enhance people’s cognition of objective facts, and it is the precondition of concept representation and deviation analysis. At present, there are several data clustering algorithms in common use: mean clustering, density clustering, and fuzzy clustering, etc. [20].(3)Forecast analysis In some instances, the public may have to predict some vacant values. If the data for the vacant value is a numeric type, it is generally called a forecast. Prediction process is according to historical data to find the laws between the data information, build a corresponding model, and based on the model predict the next data.(4)Data evolution analysis In the database, the analysis of the evolution of data information indicates that there are laws and trends in the target of a certain behavior changing with time, and the model of such trends is established [21].

3.2. Data Mining and Quality Evaluation of Moral Education

From the above analysis, we can see that association rules are one of the most significant methods in data mining. The application of association rules in the management of moral education quality is more and more extensive [22]. There are many objects involved in the management of college moral education quality, such as curriculum and evaluation index, etc. Association rules can extract valuable knowledge hidden in data, assist educational administrators to make effective decisions, and improve the management level.

4. Constructing the Quality Evaluation Model of Moral Education

4.1. Selection of Evaluation Indicators

The evaluation index of moral education level is the key to construct a high-precision evaluation model, and the index system is the concrete regulation of moral education level.

In order to construct an objective and accurate evaluation model of moral education quality, a hierarchical and multiobjective evaluation index system is constructed by AHP [23]. The AHP is selected to obtain the evaluation index to ensure the completeness of the evaluation system.

AHP operation is divided into four steps: design and build a hierarchical structure model, build a comparison matrix, calculate the weight value, and get the combination weight vector and implement consistency test.

Based on the comprehensive analysis of each evaluation index, the preparation of the curriculum, the effect of moral education, and the content are taken as the first index. See Table 1 for details.

According to the above AHP running steps, we can see that it is necessary to construct the criterion judgment matrix. Based on experience, use 1 to 9 as the corresponding judgment matrix scale [24]. The judgment matrix scale is as follows: when the scale is 1, it means and are equally important; when the scale is 3, it means that is slightly more important than ; when the scale is 5, it means that is significantly more important than B; when the scale is 7, it means that is very important than ; when the scale is 9, it means that A is extremely important than ; when the scale is 2, 4, 6, 8, and are in the middle of two adjacent judgments [25–27].

4.2. Evaluating the Properties of Matrix Composition Operator

The properties of the synthesis operator are analyzed through the following nonlinear fuzzy matrix [28]:

In the above matrix, represents the index salience influence coefficient vector, and , where . With higher impact of indicator , will increase accordingly.

Assuming , the nonlinear matrix composition operator is expressed in the form of

Because in the nonlinear evaluation, there is generally membership grade , so in the process of nonlinear matrix synthesis, it is necessary to change the membership grade of the evaluation target matrix so that the membership grade is higher than 1, then the matrix synthesis operator has the following properties:

In the formula, . When , the equation holds; then , .

And because , and the function , at , it is monotonically decreasing, indicating that the higher the value, the higher the value, so the minimum value of is greater [29].

In the nonlinear synthesis matrix, when the influence of all the indexes is different, some indexes have greater influence.

Assuming , there is the following expression:

The above expression is obtained by monotonically decreasing function with respect to .

Formula (5) is successively obtained on according to function and .

Assuming that all the evaluation indexes of one evaluation target are larger than other targets, the former evaluation result should be larger than the latter. An increase in the value of a single target indicator leads to an increase in the final indicator value, which remains steady and does not lead to sudden jumps [30].

4.3. Moral Education Evaluation Model of TOP-SIS

4.3.1. Weighted Principal Component TOP-SIS Model

The weighted principal component TOP-SIS value moral education model is selected to evaluate the moral education of a city. The main processes of data screening and statistics are as follows: first, analyze the educational subjects, including schools, society, and families. At the same time, three teams of big data technology, analysis, and education should be established to collect and summarize data information through the daily learning and life of college students, comprehensively grasp and understand the moral performance of college students, guide college students to establish a correct moral outlook, and abide by the correct TOP-SIS value and moral code of conduct. Second, from the analysis of the form of education, it mainly covers the content of TOP-SIS value and moral education, the practice of TOP-SIS value and moral education, and the networking of TOP-SIS value and moral education.

Based on the above research, the weighted principal component TOP-SIS model is constructed, and the process is as follows:(1)Select SPSS software to carry out principal component analysis on the secondary indicators moral education factors in the evaluation index system, and according to the principle that the eigenvalue and cumulative contribution rate are greater than 1 and 80%, respectively, the principal component with the quantity of is represented by , , , and the extracted principal component is set as the secondary indicator of the evaluation model to establish the principal component expression [31, 32]. Input the original data of standardization processing into the principal component expression; get the final principal component score matrix expressed by , .(2)The normalized principal component decision matrix shall be established by means of vector normalization according to the corresponding principal component scores of each first-grade index. The weight matrix of principal component contribution rate and cumulative contribution rate expressed by is established, and the weighted canonical matrix expressed by is obtained.(3)Obtain the maximum reasonable value of each level of indicators and the relative proximity between the maximum reasonable value and different schemes through the TOP-SIS method, and use the maximum reasonable value and the relative proximity to evaluate the comprehensive evaluation indicators of moral education.(4)Obtain the moral education indices of 10 cities of a certain province and the final ranking of each city through the calculation formula of relative moral education [33].

4.3.2. TOP-SIS Value Function Model

The TOP-SIS model of value moral education is often used in the decision analysis of fixed scheme determination. The TOP-SIS model of value moral education is a method to minimize and maximize the distance between positive ideal scheme and negative ideal scheme and the optimal scheme. The TOP-SIS model of value moral education is used to rank many objects to be evaluated which have measurement attributes.(1)Select the vector normalization method to obtain the standardized decision matrix .(2)The weight and weight coefficient of vector matrix are represented by and , and the weighted gauge matrix is established and represented by .(3) is the positive ideal solution; is the negative ideal solution and can be obtained; .(4)Obtain the distance from different schemes to the positive ideal solution and the distance to the negative ideal solution, and evaluate the pros and cons of each scheme by using the relative proximity of the positive ideal solution to different schemes. The range of relative proximity is ; when the value is close to 0 and 1, the scheme is close to the worst level and the optimal level, respectively.

Using the relative proximity of 10 cities in a province to obtain the comprehensive relative proximity and using the comprehensive relative proximity to evaluate the moral education of each city in a province, the higher the comprehensive relative proximity is, the higher the moral education of the city is, otherwise the contrary [34].

4.3.3. Index Correlation Judgment

Before nonlinear evaluation, it is necessary to clarify which indicators in the evaluation objectives have linear correlation, because the indicators with linear relationship not only have no additional information, but also easily bring more redundant data [35, 36].

Combined with the grey correlation analysis method, the pairwise comparison sequence is regarded as two indicators with linear relationship among the objectives to be evaluated. The detailed process is as follows:(1)Experts study the known assessment indicators and mark the indicators with linear relationship as the target of grey relational grade analysis.(2)Assume that and are two indicators with linear relationship, and represent ideal scheme, evaluated scheme, and negative ideal scheme, respectively. Using the index as the comparison unit, all the indexes are dimensionless. If the distance between the corresponding points is small, the consistency of the change of the series is strong, otherwise weak. So, according to , the distance between indices and is in .(3)Define operation expression: In the above formulas, represents the grey correlation coefficients of indices and in any scheme, and represent the maximum and minimum values of absolute difference, respectively, and is a resolution coefficient, which can control the influence of on data transformation and enhance the difference between the correlation coefficients.(4)The association coefficient shall be averaged, and the definition of association degree shall be(5)If the correlation degree is higher than a certain threshold, it indicates that indicators and have great correlation, and one of them can be eliminated.

Accurate value evaluation type: suppose that represents the evaluation value, represents the best evaluation value obtained by each ideal value index, and e represents the worst evaluation value obtained by all negative ideal value indexes. Therefore, is the optimization degree ; describes the degree of deterioration . Then the calculation formula of comprehensive score is

Interval value evaluation type: suppose represents interval evaluation value, and and represent ideal and negative ideal scheme evaluation values, respectively. And represent the lower limit of interval value, are the upper limit, and . At this time, the following three situations will occur: the value range of the scheme to be evaluated is all in the ideal value range or negative ideal value range, and there may be intersection with the two ranges. When the third condition occurs, , , or any of the following conditions are met:

When , the calculation formula of intermediate rate is

Then the comprehensive score is expressed as

4.3.4. Relative Development of Moral Education Calculation

There are great differences in the maximum reasonable values of population, resources, and other indicators in 10 cities in a province. The use of unified evaluation criteria makes the evaluation results more reasonable. Select the unified evaluation index to process the data to obtain the relative score formula of the evaluation index for the development of moral education, as follows:where and , respectively, represent the standardized score and the maximum reasonable value of the indicators within the first-level indicators of each city in a province; represents the corresponding value of each city in a province in the weighted norm matrix.

The weighted summation formula is as follows:where and , respectively, represent the comprehensive score of moral education of each city in a province and the primary index weight in the comprehensive evaluation index system of moral education.

4.3.5. Index Contribution Rate Operation

For complex evaluation objects, not all the indicators have the significance of participating in the evaluation. According to the information theory, the function of each index in the evaluation system depends on the amount of decision information it has. The more the information is, the higher the effect is.

The Del entropy method combines the information of all indexes to judge the importance of indexes, that is, entropy weight. Assume that the initial indicator attribute matrix is represented by , and is the attribute value of Plan under the indicator, so the contribution of Plan to the indicator attribute is expressed as

This contribution rate includes a kind of information that can describe the sum of contribution rates of the three methods to the index through entropy .

In a formula, represents a constant, usually a value of , and is guaranteed.

If the index belongs to interval type, the contribution rate and entropy of the interval upper bound and lower bound of the index in all schemes are obtained, and the value of interval entropy is taken as the total contribution rate.

Assuming that the contribution degree of two indicators is basically the same, that is, is close to 1, it indicates that the indicator does not play any role in the decision-making process, and the weight attribute of the indicator is 0; otherwise, if the contribution degree of an indicator is small, it indicates that the information content of the indicator is larger, and the role is larger. When the contribution of the index is higher than 0.85, the index is less important in the decision-making process. The contribution of other indicators can be obtained according to the following formula:

In the formula, represents the total number of indicators corresponding to level indicators.

The final score of the final development of moral education in each city is obtained by using the first-level indicators of each city in a province relative to the moral education index. The level of moral education in a province can be reflected by the final score of moral education. The higher the score obtained, the higher the development of moral education in the education industry of the city.

5. Experimental Design and Test

Bring the original index data of a city into SPSS software for principal component analysis. Due to the different units of measurement of the original indicators, it is impossible to compare directly. Therefore, it is necessary to standardize the original indicator data of 10 cities to eliminate the impact of dimensions on the evaluation results.

5.1. KMO and Bartlett Test

In order to ensure the validity of the empirical analysis results of factor analysis and extraction of comprehensive factors, it is necessary to conduct correlation test on the standardized index data, using the commonly used KMO and Bartlett sphericity correlation test methods, and the test results are shown in Table 2.

Table 2 shows that the final KMO value is 0.643, and the KMO value satisfies the prerequisite requirements for the quality of PCA data, and the PCA can be carried out for the index data; the Bartlett sphericity test result for the index data is = 0, and the index data is verified again to meet the PCA requirements.

5.2. Validation of Accuracy of Principal Component Analysis Results

The obtained component score coefficient matrix results are shown in Table 3.

By analyzing the component score coefficient matrix in Table 3, we have the following.

The formula of component score F₁ is as follows:

The formula of component score F₂ is as follows:

The formula of component score F₃ is as follows:

The formula of component score F₄ is as follows:

The comprehensive factor score is obtained by using the linear combination of the original indicators. The comprehensive factor score formula is as follows:

In formula (21), is the comprehensive factor, is the main factor score, and is the score weight. The evaluation equation of moral education obtained by formula (22) is as follows:

The single and comprehensive scores of principal components are positive, indicating that the urban moral education is stronger than the average development of a province, and a negative value indicates that the urban moral education is weaker than the average development of 10 cities in a province. The higher the comprehensive score and individual score, the better the city’s moral education and the stronger its competitiveness.

5.3. Comparison of Fitting Degree of Different Models

Using the Amos based education quality evaluation model proposed in [4] and the time series multiple regression based education quality evaluation model proposed in [5] as the control experimental group, and integrating the experimental environment and data, the fitting experimental results of different models are shown in Figure 2:

(a)

(b)

(c)

The higher the fitting degree between the evaluation result and the actual situation is, the higher the evaluation precision is. In Figure 2, the accuracy of the model is lower than that of the paper. Based on the data mining technology, the model extracts the valuable knowledge hidden in the data of moral education quality evaluation by using the association rules and assists the education managers to make effective decisions, which provides a reliable support for improving the fitting degree between the evaluation results and the actual situation. In order to set up an objective and accurate evaluation model of moral education quality, a hierarchical and multiobjective evaluation index system is established by using AHP. Selecting AHP to obtain the evaluation index not only ensures the completeness of the evaluation system, but also improves the accuracy of evaluation results to a certain extent [37].

5.4. Comparison of Degrees of Freedom between Different Models

The higher the degree of freedom is, the lower the evaluation complexity is. As can be seen from Figure 3, the moral education level evaluation model constructed in this paper has a higher degree of freedom under different experimental times, but the evaluation complexity is lower than that of literature achievements. The proposed model uses the fuzzy comprehensive evaluation method to construct the moral education quality evaluation model, which not only fully considers the fault tolerance of the evaluation process, but also effectively improves the degree of freedom of modeling, that is, reduces the complexity of evaluation.

(a)

(b)

(c)

To sum up, the moral education evaluation model based on college students’ quality training has a high degree of fit and freedom, which can effectively reduce the complexity of education evaluation and has good applicability.

6. Conclusion

In order to strengthen the moral will and moral concept of college students, an evaluation model of moral education is established based on the analysis of college students’ quality cultivation, using data mining method to extract valuable knowledge hidden in the data, to assist education managers to make effective decisions, and improve management standards. According to the evaluation index, the weighted principal component TOP-SIS model is constructed to complete the evaluation of moral education. The following experimental results are obtained:(1)The moral education evaluation model based on college students’ quality training has a high degree of fit between the evaluation results and the actual situation, and the evaluation accuracy is high.(2)The model fully considers the fault tolerance of the evaluation process and effectively improves the degree of freedom of modeling, and the evaluation complexity is low, which verifies the practicability of the model.

Data Availability

The raw data supporting the conclusions of this article will be made available by the author, without undue reservation.

Conflicts of Interest

The author declares that there are no conflicts of interest regarding this work.

Acknowledgments

This work was supported by Hainan Provincial Natural Science Foundation of China “Research on the cognitive and emotional processing mechanism of moral judgment” (Project no. 719MS056).

References

X. R. Wu, B. Q. Liu, and T. T. Yuan, “A new generation of smart class: concept, platform and system Architecture,” China Educational Technology, vol. 3, pp. 81–88, 2019.
View at: Google Scholar
S. K. Mydhili, S. Periyanayagi, and S. Baskar, “Machine learning based multi scale parallel K-means++ clustering for cloud assisted internet of things,” Peer-to-Peer Networking and Applications, vol. 13, no. 2, pp. 2023–2035, 2020.
View at: Publisher Site | Google Scholar
T. Yilmaz, R. Ozcan, I. S. Altingovde, and Ö. Ulusoy, “Improving educational web search for question-like queries through subject classification,” Information Processing & Management, vol. 56, no. 1, pp. 228–246, 2019.
View at: Publisher Site | Google Scholar
Y. Z. Luo, X. M. Chen, and J. L. Zhang, “Evaluation of doctoral education quality:model construction and empirical test,” Research in Higher Education of Engineering, vol. 2, pp. 171–177, 2019.
View at: Google Scholar
J. L. Tu and Q. X. Tao, “Relevance analysis and evaluation model of innovation and entrepreneurship education resources and service function of science and technology innovation in local universities,” Science & Technology Progress and Policy, vol. 37, no. 8, pp. 159–166, 2020.
View at: Google Scholar
H. J. Wang and N. Zhang, “Construction of “Evidence-based” Academic assessment model for blended learning courses in higher education,” E-education Research, vol. 41, no. 5, p. 7, 2020.
View at: Google Scholar
S. J. Andriole, “Social media analytics, wearable technology, and the internet-of-things,” IT Professional, vol. 21, no. 5, pp. 11–15, 2019.
View at: Publisher Site | Google Scholar
M. D. Abdulrahaman, N. Faruk, A. A. Oloyede et al., “Multimedia tools in the teaching and learning processes: a systematic review,” Heliyon, vol. 6, no. 11, Article ID e05312, 2020.
View at: Publisher Site | Google Scholar
Z. Chen and N. Polytechnic, “Research on the new characteristics of ideological and political education in colleges under the background of network self-media,” Journal of Ningbo Polytechnic, vol. 55, no. 13, pp. 560–572, 2019.
View at: Google Scholar
Q. H. Zheng, B. Dong, B. Y. Qian et al., “The state of the art and future tendency of smart education,” Journal of Computer Research and Development, vol. 56, no. 1, pp. 213–228, 2019.
View at: Publisher Site | Google Scholar
D., A. Satarupa, “Smart university utilising the concept of the internet of things(IoT),” Neurochemistry International, vol. 129, no. 10, p. 2022, 2019.
View at: Google Scholar
H. Tanveer, T. Balz, N. S. Sumari, R.-u. Shan, and H. Tanweer, “Pattern analysis of substandard and inadequate distribution of educational resources in urban-rural areas of Abbottabad, Pakistan,” Geojournal, vol. 85, no. 5, pp. 1397–1409, 2020.
View at: Publisher Site | Google Scholar
T. L. Friesz, “Socio-economic determinants of student mobility and inequality of access to higher education in Italy,” Networks and Spatial Economics, vol. 19, no. 1, pp. 125–148, 2019.
View at: Publisher Site | Google Scholar
V. Mkrttchian, L. Gamidullaeva, A. Finogeev, and S. Chernyshenko, “Big data and internet of things (IoT) technologies’ influence on higher education: current state and future prospects,” International Journal of Web-Based Learning and Teaching Technologies, vol. 16, 2021.
View at: Publisher Site | Google Scholar
C. Wiley, “Standardised module evaluation surveys in UK higher education: establishing students’ perspectives,” Studies In Educational Evaluation, vol. 61, pp. 55–65, 2019.
View at: Publisher Site | Google Scholar
K. Khosravi, J. R. Cooper, P. Daggupati, B. Thai Pham, and D. Tien Bui, “Bedload transport rate prediction: application of novel hybrid data mining techniques,” Journal of Hydrology, vol. 585, no. 6, Article ID 124774, 2020.
View at: Publisher Site | Google Scholar
N. W. Borsato, S. L. Martell, and J. D. Simpson, “Identifying stellar streams in Gaia DR2 with data mining techniques,” Monthly Notices of the Royal Astronomical Society, vol. 492, no. 1, pp. 1370–1384, 2020.
View at: Publisher Site | Google Scholar
K. Snehal, S. Gaikwad, S. Iyer, and D. Lunga, “Proceedings of KDD 2020 workshop on data-driven humanitarian mapping: harnessing human-machine intelligence for high-stake public policy and resilience planning,” Papers, vol. 9, Article ID 210900435, 2021.
View at: Google Scholar
S. Pyne and T. Laskey, “Broadening the cybercartographic research and education network,” Cybercartography in a Reconciliation Community - Engaging Intersecting Perspectives, vol. 8, pp. 217–236, 2019.
View at: Publisher Site | Google Scholar
M. Dimitris, K. Georgoulias, and G. Chryssolouris, “The Teaching Factory Network: a new collaborative paradigm for manufacturing education,” Procedia Manufacturing, vol. 97, no. 7, pp. 120–132, 2019.
View at: Publisher Site | Google Scholar
W. L. Shi and Y. H. Zhang, “Intelligent education platform design based on big data analysis technology,” Modern Electronics Technique, vol. 43, no. 9, pp. 158–161, 2020.
View at: Google Scholar
J. Lai and M. Bower, “Evaluation of technology use in education: findings from a critical analysis of systematic literature reviews,” Journal of Computer Assisted Learning, vol. 36, no. 3, pp. 1459–1471, 2020.
View at: Publisher Site | Google Scholar
G. Bodur, S. Gumus, and N. G. Gursoy, “Perceptions of Turkish health professional students toward the effects of the internet of things (IOT) technology in the future,” Nurse Education Today, vol. 79, pp. 98–104, 2019.
View at: Publisher Site | Google Scholar
D. A. Tamburri and G. Casale, “Cognitive distance and research output in computing education: a case-study,” IEEE Transactions on Education, vol. 62, no. 2, pp. 99–107, 2019.
View at: Publisher Site | Google Scholar
S. Pundir, M. Wazid, D. P. Singh, A. K. Das, J. J. P. C. Rodrigues, and Y. Park, “Intrusion detection protocols in wireless sensor networks integrated to internet of things deployment: survey and future challenges,” IEEE Access, vol. 8, pp. 3343–3363, 2020.
View at: Publisher Site | Google Scholar
A. Gee and H. Abbass, “Transparent machine education of neural networks for swarm shepherding using curriculum design,” International Joint Conference on Neural Networks, vol. 67, no. 13, pp. 57–69, 2019.
View at: Publisher Site | Google Scholar
G. R. Browne, H. Bender, J. Bradley, and A. Pang, “Evaluation of a tertiary sustainability experiential learning program,” International Journal of Sustainability in Higher Education, vol. 35, no. 5, pp. 2–13, 2020.
View at: Publisher Site | Google Scholar
K. Moriarty, “Collective impacts on a global education emergency: the power of network response,” Prospects, vol. 49, no. 1, pp. 81–85, 2020.
View at: Publisher Site | Google Scholar
J. O’Hara, M. Brown, G. Mcnamara, and P. Shevlin, “The potential, limitations and evaluation of education networks in a monocentric system,” Revista de Investigación Educativa, vol. 44, no. 1, pp. 34–57, 2020.
View at: Publisher Site | Google Scholar
L. I. Ye, “Research on the existing problems and solutions of network ideological and political education in colleges and universities,” Journal of Hlongjiang College of Education, vol. 56, no. 1, pp. 781–792, 2019.
View at: Google Scholar
Ö. K. Kalkan, A. Altun, and B. Atar, “Role of teacher-related factors and educational resources in science literacy: an international perspective,” Studies In Educational Evaluation, vol. 67, Article ID 100935, 2020.
View at: Publisher Site | Google Scholar
A. Ghaleb, “Rasch validation of the Arabic version of the teacher efficacy for inclusive practices (TEIP) scale - ScienceDirect,” Studies In Educational Evaluation, vol. 62, pp. 104–110, 2019.
View at: Google Scholar
S. Wild and L. Schulze Heuling, “Re-evaluation of the D21-digital-index assessment instrument for measuring higher-level digital competences,” Studies In Educational Evaluation, vol. 68, no. 1, Article ID 100981, 2021.
View at: Publisher Site | Google Scholar
A. Liqiong, Y. B. Jing, B. Xiaoyan, H. Luoya, S. Shaomei, and Z. Yan, “Based on Delphi method and Analytic Hierarchy Process to construct the Evaluation Index system of nursing simulation teaching quality,” Nurse Education Today, vol. 79, pp. 67–73, 2019.
View at: Google Scholar
I. Klima Ronen, “Action research as a methodology for professional development in leading an educational process,” Studies In Educational Evaluation, vol. 64, Article ID 100826, 2020.
View at: Publisher Site | Google Scholar
C. Spoden, J. Fleischer, and A. Frey, “Person misfit, test anxiety, and test-taking motivation in a large-scale mathematics proficiency test for self-evaluation,” Studies In Educational Evaluation, vol. 67, Article ID 100910, 2020.
View at: Publisher Site | Google Scholar
S. A. Wind, E. Jones, C. Bergin, and K. Jensen, “Exploring patterns of principal judgments in teacher evaluation related to reported gender and years of experience,” Studies In Educational Evaluation, vol. 61, pp. 150–158, 2019.
View at: Publisher Site | Google Scholar

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Copyright © 2022 Xiaolin Yuan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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