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The evaluation of university management performance using the CS-RBM algorithm.

Huifang Guo1

  • 1Zhengzhou Vocational College of Finance and Taxation, Zhengzhou, Henan, China.

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|October 9, 2023
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A new Crow Search Restricted Boltzmann Machine (CS-RBM) algorithm enhances college performance evaluation. This method significantly reduces errors and speeds up iterations for sustainable higher education development.

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CS-RBMCrow search algorithmDeep learningPerformance measurement system

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Area of Science:

  • Educational Management
  • Artificial Intelligence
  • Performance Assessment

Background:

  • Higher education reforms in China necessitate effective performance assessment for sustainable development.
  • Traditional evaluation methods are inefficient, being time-consuming and labor-intensive.
  • There is a need for innovative, data-driven approaches to educational management.

Purpose of the Study:

  • To introduce and validate the Crow Search Restricted Boltzmann Machine (CS-RBM) prediction algorithm for assessing institutional performance.
  • To improve the efficiency and accuracy of performance evaluations in colleges and universities.
  • To support the sustainable development of higher education institutions through enhanced assessment.

Main Methods:

  • Integration of the Crow Search (CS) algorithm with an enhanced Restricted Boltzmann Machine (RBM) algorithm.
  • Utilizing user evaluation form reports to score project performance indicators.
  • Developing a comprehensive project performance assessment model.

Main Results:

  • The CS-RBM algorithm demonstrated a 45.6% reduction in prediction errors compared to standard methods.
  • An increase of 34.7% in iteration speed was observed with the CS-RBM algorithm.
  • The CS-RBM algorithm achieved over 98% accuracy on the tested data set.

Conclusions:

  • The CS-RBM algorithm offers a precise and effective solution for performance evaluation in higher education.
  • This novel approach significantly enhances the speed and accuracy of institutional assessments.
  • CS-RBM holds considerable potential for advancing the sustainable development of colleges and universities.