用自适应例题提升学生认知参与度，效果因知识水平而异。
Adaptive Scaffolding for Cognitive Engagement in an Intelligent Tutoring System
- 根据学生水平动态选配引导式或带错例题，调节认知投入
- 低基础学生用贝叶斯方法提升明显，接近高基础学生水平
- 高基础学生用深度强化学习表现更优，适合进阶训练
ICAP框架将认知参与度分为被动、主动、建构和互动四个层次，更高参与度有助于学习。但如何在智能辅导系统中个性化设计能激发最优参与度的学习活动仍是难题。本文开发并评估了一种自适应支架系统，通过动态选择两种不同ICAP模式的例题（主动：引导例题；建构：带错例题）来调节认知参与。在逻辑推理领域的智能辅导系统中，对比了贝叶斯知识追踪（BKT）与深度强化学习（DRL）两种自适应策略，以及非自适应基线方法。对113名学生的实验表明，两种自适应策略均显著提升了测试成绩。其中，BKT对先验知识较低的学生提升最大，帮助其追赶高知识水平同龄人；而DRL在高先验知识学生中表现出更高的后测分数。该研究揭示了认知参与与自适应机制之间复杂的交互关系及其对学习结果的影响。
原文摘要 · Abstract (English)
The ICAP framework defines four cognitive engagement levels: Passive, Active, Constructive, and Interactive, where increased cognitive engagement can yield improved learning. However, personalizing learning activities that elicit the optimal level of cognitive engagement remains a key challenge in intelligent tutoring systems (ITS). In this work, we develop and evaluate a system that adaptively scaffolds cognitive engagement by dynamically selecting worked examples in two different ICAP modes: (active) Guided examples and (constructive) Buggy examples. We compare Bayesian Knowledge Tracing (BKT) and Deep Reinforcement Learning (DRL) as adaptive methods against a non-adaptive baseline method for selecting example type in a logic ITS. Our experiment with 113 students demonstrates that both adaptive policies significantly improved student performance on test problems. BKT yielded the largest improvement in posttest scores for low prior knowledge students, helping them catch up with their high prior knowledge peers, whereas DRL yielded significantly higher posttest scores among high prior knowledge students. This paper contributes new insights into the complex interactions of cognitive engagement and adaptivity and their results on learning outcomes.
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