【作 者】：
郑 娅 峰
【关 键 词 】：
智能技术； 虚拟科学探究学习； 过程评价； 适应性反馈； 平台设计
【栏 目】：
课程与教学
【中文摘要】：
虚拟科学探究学习过程的自动评价和适应性反馈是提升探究学习效果的重要支撑手段。研究首先从虚拟科学探究学习过程要素表征和分析模型、自动分析与实时评价、适应性反馈三个方面概述了智能技术在虚拟科学探究学习中的应用现状，总结了当前技术应用面临的深层次探究要素表征难、不确定探究过程刻画难、适应性反馈生成难等现实挑战。其次，在此基础上，提出了基于活动流的底层计算模型构建、复杂探究过程动态监测与自动评价、可解释性归因的自适应反馈内容生成三个关键技术。再次，基于关键技术设计了虚拟科学实验自主探究学习平台的技术架构。最后，研究总结了当前研究的创新之处并提出未来建议，为智能技术赋能虚拟科学探究学习领域开展更深入的技术探索提供有益参考。
【英文摘要】：
Automatic evaluation and adaptive feedback of virtual science inquiry learning are important supporting means to improve the effectiveness of inquiry learning. Firstly, this study outlines the application status of intelligent technology in virtual science inquiry learning from three aspects, namely, the element representation and analysis model, the automatic analysis and real-time evaluation, and the adaptive feedback. And this study summarizes the practical challenges faced by the current application of the technology, such as the difficulty in deeply exploring the element representation, portraying uncertain inquiry processes, and generating adaptive feedback. Secondly, on this basis, three key technologies are proposed: the construction of underlying computing model based on activity flow, the dynamic monitoring and automatic evaluation of complex inquiry process, and the generation of self-adaptive feedback content with explainable attribution. Thirdly, based on the key technologies, a technical architecture of the independent inquiry learning platform for virtual science experiments is designed. Finally, the study summarizes the innovation of the current research and puts forward future suggestions to provide a useful reference for further technical exploration in the field of virtual science inquiry learning enabled by intelligent technology.