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Related Experiment Video

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

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Published on: August 29, 2025

Enhanced teaching team evaluation system for vocational colleges.

Qiming Tian¹, Wanle Chi¹, Dafeng Gong¹

¹Department of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou, 325035, China.

Scientific Reports

|November 10, 2025

Summary

A new artificial intelligence model, CLDMAO, enhances the artemisinin optimization algorithm to accurately predict teaching team evaluations in vocational colleges. This AI-driven approach improves prediction accuracy and identifies key factors for robust evaluation systems.

Keywords:

Artemisinin optimization Comprehensive learning Dispersed foraging strategy Global optimization Teaching quality evaluation

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Using Learning Outcome Measures to assess Doctoral Nursing Education

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Published on: June 21, 2010

Area of Science:

Artificial Intelligence
Optimization Algorithms
Educational Technology

Background:

Effective teaching team evaluation is crucial for vocational college reforms.
Existing AI systems lack accuracy in predicting teaching evaluations.
The artemisinin optimization (AO) algorithm shows promise but struggles with local optima.

Purpose of the Study:

To develop an enhanced AI algorithm for accurate teaching team evaluation.
To improve the global optimization capabilities of the AO algorithm.
To identify key factors for constructing a comprehensive teaching evaluation system.

Main Methods:

Enhanced the AO algorithm with comprehensive learning and dispersed foraging mechanisms, creating CLDMAO.
Benchmarked CLDMAO against state-of-the-art algorithms using CEC 2017 test functions.
Developed a binary CLDMAO-KNN model for the teaching team evaluation system.

Main Results:

CLDMAO demonstrated superior performance, ranking first in most CEC 2017 test functions.
The binary CLDMAO-KNN model achieved excellent results in error rate, fitness, and feature selection.
The model successfully identified factors for building a complete evaluation system.

Conclusions:

The CLDMAO algorithm offers a significant advancement in AI-based optimization.
The CLDMAO-KNN model provides an effective solution for predicting teaching team evaluations.
This research facilitates the development of more accurate and insightful educational evaluation systems.