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Updated: Jan 17, 2026

Interactive and Visualized Online Experimentation System for Engineering Education and Research

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Design of performance evaluation method for higher education reform based on adaptive fuzzy algorithm.

Dakun Yang¹, Muhammad Sheraz Arshad Malik²

¹School of Marxism, Anyang Institute of Technology, Anyang, China.

Peerj. Computer Science

|September 24, 2025

Summary

This study introduces an Adaptive Neural Fuzzy Inference System (ANFIS) for evaluating university teacher performance. The ANFIS model offers a more accurate and objective assessment compared to traditional methods.

Keywords:

Adaptive neuro fuzzy system Factor analysis method University evaluation

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Last Updated: Jan 17, 2026

Interactive and Visualized Online Experimentation System for Engineering Education and Research

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Published on: November 24, 2021

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

Area of Science:

Educational Technology
Artificial Intelligence in Education
Performance Management Systems

Background:

University teacher performance evaluation is crucial for academic quality and institutional management.
Existing evaluation methods often lack objectivity, comprehensiveness, and adaptability.
There is a need for a scientific and data-driven approach to assess teaching and research contributions.

Purpose of the Study:

To develop and validate an Adaptive Neural Fuzzy Inference System (ANFIS) based framework for university teacher performance evaluation.
To enhance the objectivity, accuracy, and adaptability of performance assessments.
To provide a scientific mechanism for institutional management and decision-making support.

Main Methods:

Development of a comprehensive evaluation index system with 16 sub-indicators across teaching, research, and faculty information.
Application of factor analysis to optimize data processing and reduce indicator redundancy.
Implementation of the Adaptive Neural Fuzzy Inference System (ANFIS), a hybrid model combining fuzzy logic and neural networks.

Main Results:

The ANFIS model demonstrated superior performance over conventional methods like backpropagation (BP) neural networks and support vector machines (SVMs).
Empirical validation confirmed the ANFIS model's enhanced accuracy, precision, and overall effectiveness in performance evaluation.
The system dynamically optimizes its parameters and rule base through continuous learning from training data.

Conclusions:

The proposed ANFIS-based framework offers a novel and practical solution for evaluating university teacher performance.
This approach provides a more accurate reflection of teaching and research outcomes.
The system serves as valuable decision-making support for academic management, improving institutional effectiveness.