一个神经模糊模型用于评估和预测学生的计算思维技能
1Department of Mathematics and Science Education, Education Faculty, Biruni University, Istanbul, Turkey. afiliz@biruni.edu.tr.
Scientific reports
|October 15, 2025
概括
这项研究使用自适应神经模糊推理系统 (ANFIS) 预测中学生的计算思维技能. 该ANFIS模型准确地估计了技能得分,显示了它对教育AI应用的潜力.
科学领域:
- 教育中的人工智能
- 计算思维 计算思维
- 教育数据挖掘教育数据挖掘
背景情况:
- 计算思维 (CT) 对数字时代至关重要.
- 预测CT技能有助于教育发展和个性化学习.
- 现有的方法可能无法完全捕捉CT技能获取的细微差别.
研究的目的:
- 预测中学学生的计算思维能力.
- 评估适应性神经模糊推理系统 (ANFIS) 对CT技能预测的有效性.
- 探索学术成绩,学年级和CT技能之间的关系.
主要方法:
- 使用了ANFIS方法,一种混合的神经模糊系统.
- 雇员的年级水平和学业成绩作为输入变量.
- 通过个人信息表格和计算思维尺度收集数据.
主要成果:
- 在ANFIS预测和实际CT技能得分之间发现了强烈的正相关性.
- 在实际和预测得分之间没有观察到统计学上显著的差异.
- ANFIS模型在估计CT技能方面表现出很高的准确性.
结论:
- 该ANFIS方法是一个可行的和有效的方法来预测学生的计算思维技能.
- 这项研究强调了人工智能在预测教育特征方面的成功应用.
- 调查结果支持整合人工智能工具,以加强教育评估和支持.
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