基于聚类技术的大学教育信息化水平评估研究
1Jiangsu Food and Pharmaceutical Science College, Huai'an, 223003, Jiangsu, China.
Heliyon
|February 19, 2024
概括
本研究介绍了一种基于人工智能的方法,用于评估高等教育中的教育信息化 (EI) 水平. 使用11个关键指标和支持向量机器,该方法实现了93.64%的准确性,提高了教育技术整合评估.
科学领域:
- 教育技术的教育技术.
- 教育中的人工智能
- 高等教育信息学信息学
背景情况:
- 在现代教育中,信息技术 (IT) 的整合至关重要.
- 评估IT在教育中的有效性需要强大的方法.
- 人工智能 (AI) 提供了自动化此类评估的潜力.
研究的目的:
- 开发和验证基于人工智能的框架,用于评估高等教育中的教育信息化 (EI) 水平.
- 确定评估EI最具影响力的指标.
- 提高EI评估的准确性和效率.
主要方法:
- 引入了14个指标来衡量高等教育中的EI.
- 采用基于集群的策略来对指标进行排名并选择最佳子集.
- 使用的支持矢量机器 (SVM) 用于EI级别分类.
- 使用强化学习策略优化SVM超参数.
主要成果:
- 确定了11个与教育行为相关的指标作为EI评估的最佳指标.
- 在评估EI水平时获得了93.64%的平均准确性.
- 它的性能至少比以前的方法高4.09%.
结论:
- 拟议的AI驱动方法有效地评估了大学的EI水平.
- 选择的11个指标的子集为评估提供了一个非常准确的基础.
- 这种方法在评估高等教育中的IT集成方面取得了重大进展.
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