优化英语教学质量评估 (ETQE) 的决策树,使用人工蜂群 (ABC) 优化
1Fundamental Education Department, Tourism College of Changchun University, Changchun 130000, Jilin, China.
Heliyon
|September 4, 2023
概括
本研究介绍了一种基于人工智能的英语教学质量评估 (ETQE) 方法. 该方法使用优化的指标准确预测教学质量,改进了以前的方法.
科学领域:
- 教育中的人工智能
- 教育数据挖掘教育数据挖掘
- 机器学习用于质量评估
背景情况:
- 由于教育系统的不断变化,对自动英语教学质量评估 (ETQE) 的需求日益增加.
- 现有的ETQE模型缺乏在各种条件下通用性和最佳性能.
- 确定关键教学质量 (TQ) 指标对于有效的ETQE至关重要.
研究的目的:
- 为准确和实用的ETQE提出一种基于人工智能的新方法.
- 为指标选择和质量预测制定一个两阶段的方法.
- 在不同的教育环境中验证方法的性能.
主要方法:
- 利用人工蜂群 (ABC) 算法进行最佳指标子集选择 (24名候选人).
- 采用分类和回归树 (CART) 模型,由ABC优化,用于质量预测.
- 在面对面 (中学) 和在线 (大学) 教学环境中评估了该方法.
主要成果:
- 在面对面和在线环境中实现了超过98.99%的预测准确性.
- 与现有的ETQE方法相比,证明了至少1.11%的改进.
- 由人工智能驱动的模型显示出高效率和通用性,适用于现实世界的应用.
结论:
- 拟议的基于人工智能和元启发式算法的方法为ETQE提供了一个强大的解决方案.
- 两阶段方法有效地识别了相关指标,并预测了教学质量.
- 该方法的高准确性和通用性支持其在实际教育质量评估中的采用.
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