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科学创造力是拔尖创新人才的核心素养及科学教育的核心目标,其精准测评对拔尖创新人才选拔培养及科学教育质量提升具有重要意义。针对传统同感评估技术(CAT)依赖专家评分导致的主观偏倚、耗时费力、效率低下以及规模化应用受限等问题,构建了基于机器学习与认知—神经—行为多模态数据融合的科学创造力智能测评方法。通过整合表征静态认知结构的科学知识语义网络、反映动态认知调控的眼动注意模式、揭示神经机制的脑成像及创造力行为等多模态特征,应用SVM、 XGBoost和ANN机器学习算法实现了科学创造力的智能测评。进一步构建了“硬投票—软投票—专家验证”融合策略下的四级进阶智能评价体系,在测评规模、成本与精度间实现了阶梯式动态平衡,满足从大规模初筛到个体化精准诊断的全场景测评需求。该研究深化了科学教育视域下拔尖创新人才选拔与培养的认知—神经—行为三元协同理论,为《教育强国建设规划纲要(2024-2035年)》提出的“完善拔尖创新人才发现与培养机制”及智能评价改革提供可扩展的量化决策支持系统及方法论支撑。
Abstract：Scientific creativity(SC) is both a core competency of top innovative talents and a central objective of science education. Its precise assessment is crucial for selecting and cultivating such talents and enhancing the quality of science education. To address the limitations of the traditional Consensual Assessment Technique(CAT)—including subjective bias, time intensiveness, inefficiency, and scalability constraints due to its reliance on expert scoring—this study develops an intelligent evaluation method for SC based on machine learning and the fusion of cognitiveneural-behavioral multimodal data. By integrating multimodal features—including semantic networks of scientific knowledge(representing static cognitive structures), eye-tracking attention patterns(reflecting dynamic cognitive regulation), neuroimaging data(revealing neural mechanisms), and creativity-related behavioral metrics—this research applies machine learning algorithms(SVM, XGBoost, and ANN) to achieve intelligent assessment of SC. Furthermore, a four-tiered progressive intelligent evaluation framework was constructed under a “hard voting-soft voting-expert validation” fusion strategy. This framework achieves a stepwise dynamic balance among evaluation scale, cost, and accuracy, fulfilling comprehensive assessment needs ranging from large-scale preliminary screening to individualized precision diagnosis. This study deepens the cognitive-neural-behavioral tripartite synergy theory for selecting and cultivating top innovative talents within science education. It provides an extensible quantitative decision-support system and methodological foundation for implementing the directive to “ refine mechanisms for discovering and nurturing top innovative talents” outlined in the Education Powerhouse Construction Plan Outline(2024-2035) and advancing intelligent evaluation reform.
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基本信息:
中图分类号:G434
引用信息:
[1]张阳,李阳萍,韩葵葵,等.基于认知—神经—行为多模态数据融合的科学创造力智能测评方法[J].中国电化教育,2026,No.471(04):39-47.
基金信息:
陕西省自然科学基础研究计划面上项目“科学教育LLM智能体驱动下科学思维与创造力人机融合培养模式与智能评价机制研究”（项目编号：2025JC-YBMS-793）; 陕西省“十四五”教育科学规划“AI驱动的思维型科学探究综合实践活动中五育融合育人模式创新实践研究”（项目编号：SGH24Y2202）研究成果
2026-04-10
2026-04-10