通过人工神经网络 (ANN) 和黑洞优化 (BHO) 的结合,评估大学的教学质量
1School of Marxism, Dalian Polytechnic University, Dalian, 116034, China.
Heliyon
|October 20, 2023
概括
本研究介绍了一种使用集体学习和黑洞优化 (BHO) 的自动化方法,以准确评估英语教学质量. 改进的方法达到98.53%的准确性,改善了以前的方法.
科学领域:
- 教育技术的教育技术.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 自动评估教育质量对于优化教学方法至关重要.
- 机器学习 (ML) 为这种评估提供了工具,但经典模型在定量估计中面临准确性挑战.
- 集合系统和模型优化是提高ML准确性的潜在解决方案.
研究的目的:
- 开发一个自动化系统来评估英语教学质量.
- 提高使用ML的定量质量估计的准确性.
- 识别关键指标并提高组合学习模型的性能.
主要方法:
- 一个两阶段的方法: 1) 使用黑洞优化 (BHO) 算法识别突出的质量指标.
- 2) 通过一组人工神经网络 (ANN) 估计质量,BHO优化ANN配置和权重.
- 一个平均化策略将ANN的输出结合起来进行最终的预测.
主要成果:
- 提出的方法在估计英语教学质量方面实现了98.53%的平均准确性.
- 这比现有方法提高了2.19%.
- BHO算法有效地识别了最小的指标,并优化了整体ANN模型.
结论:
- 由BHO优化开发的组合学习方法显著改善了自动英语教学质量评估.
- 这种方法提供了一种更准确,更有效的方法来评估教学效率.
- 这些发现突显了先进的ML技术在教育评估中的潜力.
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