学生特征和ICT使用作为计算思维的预测者:一种可解释的AI方法

Tongtong Guan¹, Liqiang Zhang², Xingshu Ji¹

¹Research Institute of Science Education, Beijing Normal University, Beijing 100875, China.

Journal of Intelligence

|November 26, 2025

概括

计算思维 (CT) 的发展受到学生的特征和信息和通信技术 (ICT) 的使用的影响. 机器学习模型揭示了复杂的非线性关系,为教育策略提供了见解.

科学领域:

教育技术的教育技术
计算机科学教育计算机科学教育
学习分析学习分析

背景情况:

计算思维 (CT) 是一个至关重要的21世纪技能.
学生的个体特征和信息和通信技术 (ICT) 的使用大大影响了CT的发展.
大规模的国际数据对于理解全球CT趋势至关重要.

研究的目的:

通过使用一种新的预测建模框架,识别和解释CT发展的关键预测因素.
分析学生水平和ICT使用变量对CT的非线性影响.
整合机器学习和可解释AI (XAI) 用于教育数据挖掘.

主要方法:

分析了2023年国际计算机和信息素养研究 (ICILS) 81,871名八年级学生的数据.
应用一个预测建模框架,整合两个机器学习技术.
使用夏普利添加式解释 (SHAP) 来解释复杂的非线性关系.

主要成果:

学生层面的因素 (例如,教育期望,家里的书籍) 和ICT使用显示出显著的非线性预测模式 (U形,反向U形,单调).
基于SHAP的方法有效地解释了CT发展的复杂非线性影响,优于传统的线性模型.
这项研究表明了教育数据挖掘和XAI的新型整合.

结论:

信息通信技术的发展受到学生特征和ICT使用的复杂相互作用的影响,往往表现出非线性趋势.
这些发现为设计ICT集成教学和有针对性的干预措施提供了可操作的见解,以促进CT.
未来的研究应该探索纵向数据,以了解CT发育轨迹和因果机制.

关键词:

信息和通信技术 (ICT) 是一种信息技术.计算思维是一种计算思维.教育数据挖掘教育数据挖掘可以解释的人工智能AI

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