Research and Development Talents Training in China Universities—Based on the Consideration of Education Management Cost Planning
- The BRB expert system takes into consideration the IF-THEN rule with embedding belief structure into THEN part for knowledge representation, in which the IF- THEM rule is one of the most common forms for expressing various types of know- ledge ; therefore, it makes the EBRB expert system more powerful in keeping human knowledge. Moreover, the use of belief structure further extends the ability of traditional IF-THEN rules to represent a variety of uncertain information .
- The BRB expert system is a data-driven, knowledge-driven, or hybrid-driven model, and its rule base is constructed using historical data and expert knowledge. Meanwhile, the BRB expert system takes the ER algorithm  as an inference engine for rule reasoning; therefore, it can not only achieve knowledge fusion with uncertain information but also have transparent rule integration process. All these components form a powerful expert system for handling complex practical problems.
- The BRB expert system belongs to a “white box” model, which mainly refers to the visible modeling and inference processes, especially because of the fact that domain experts can participate in these processes. The inferential results of the BRB expert system have good traceability and interpretability so that decision makers can fully understand and explain its working principle more easily when applying BRB expert systems.
2. Preliminaries for Education Management Cost Prediction
2.1. Brief Review of the BRB Expert System
2.2. Optimization of BRB Expert System
2.3. Inference of BRB Expert System
3. Education Management Cost Prediction Based on the BRB Expert System
4. Empirical Analysis on China Education Management Cost Planning
4.1. Data Collection and Indicator Explanation
4.2. Analysis of Education Management Cost Investments during 2001–2019
4.3. Verification of BRB Expert System for Cost Prediction
4.4. Analysis of Future Education Management Cost Planning
6.1. Theoretical Implications
- Due to the problem of sparse data in the field of education management cost prediction, the system modeling has to suffer from the over-fitting problem. The data increment of education management input and output indicators were used to enrich data for modeling expert system, and the resulting BRB expert system is able to overcome the over-fitting problem.
- To overcome the subjectivity of parameters given by experts, the global parameter learning model was introduced to enhance the BRB expert system construction, so that the parameter values of the BRB expert system can be optimized according to the historical education management input and output data.
- The results of comparative studies demonstrated that the data increment and parameter learning could effectively improve the performance of the BRB expert system. The government expenditures on education, science, and technology predicted by the BRB expert system were much lower than the other prediction models.
6.2. Policy Suggestions
- To optimize the setting of professional knowledge learning in universities based on the demand of economic market and social development, the course learning in universities for R&D talents should be improved and designed according to the current market environment and industry demand, so that they can better understand the market demand and master relevant professional technology through school–enterprise cooperation and exchange, so as to make up for the shortage of professional technicians in relevant fields in the market.
- To advocate expanding teaching and to cultivate R&D talents’ basic quality and professional skills for social practice by relying on the practice carrier outside the universities professional classroom, so that they can train professional skills and improve practical ability in expanding teaching.
- To establish a diversified training mode for R&D talents, and adopt a variety of talent training methods to improve the innovation of scientific and technological capability is an important strategy for R&D talents training in China universities. Decision makers of education management should also pay attention to improve the professional and technical level of R&D talents so as to ensure that the talents trained in universities can be truly applicable to new products and technologies innovation of actual economic activities.
6.3. Limitations and Future Researches
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Antecedent Attributes (Weights)||Belief Distribution of Consequent Attribute|
|TMTV (δ1)||NNP (δ2)||Low||Middle||High|
|Indicator Name||Abbr.||Corresponding Relationship|
|Number of R&D employees||NRDE||Antecedent|
|Number of new products||NNP||Antecedent|
|Number of invention patent applications||NIPA||Antecedent|
|Technical market transaction volume||TMTV||Antecedent|
|Government expenditure on education||GEE||Consequent|
|Government expenditure on science and technology||GEST||Consequent|
|GEE||82.40 (1)||164.17 (2)||2683.36 (3)||9.62% (1)||17.57% (2)||400.76% (3)|
|GEST||18.59 (1)||38.81 (2)||446.91 (3)||12.99% (1)||22.74% (2)||245.18% (3)|
|GEE||82.40 (2)||56.27 (1)||384.16 (4)||256.94 (3)||9.62% (2)||4.45% (1)||35.22% (4)||27.43% (3)|
|GEST||18.59 (1)||25.27 (2)||76.82 (4)||54.86 (3)||12.99% (1)||13.21% (2)||36.92% (4)||23.09% (3)|
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Yang, L.-H.; Liu, B.; Liu, J. Research and Development Talents Training in China Universities—Based on the Consideration of Education Management Cost Planning. Sustainability 2021, 13, 9583. https://doi.org/10.3390/su13179583
Yang L-H, Liu B, Liu J. Research and Development Talents Training in China Universities—Based on the Consideration of Education Management Cost Planning. Sustainability. 2021; 13(17):9583. https://doi.org/10.3390/su13179583Chicago/Turabian Style
Yang, Long-Hao, Biyu Liu, and Jun Liu. 2021. "Research and Development Talents Training in China Universities—Based on the Consideration of Education Management Cost Planning" Sustainability 13, no. 17: 9583. https://doi.org/10.3390/su13179583