Deductive domains are typical of many cognitive skills in that no single
problem-solving strategy is always optimal for solving all problems. It was
shown that students who know how and when to use each strategy (StrTime)
outperformed those who know neither and stick to the default strategy
(Default). In this work, students were trained on a logic tutor that supports a
default forward-chaining and a backward-chaining (BC) strategy, then a
probability tutor that only supports BC. We investigated three types of
interventions on teaching the Default students how and when to use which
strategy on the logic tutor: Example, Nudge and Presented. Meanwhile, StrTime
students received no interventions. Overall, our results show that Nudge
outperformed their Default peers and caught up with StrTime on both tutors.

这篇论文研究了如何通过干预方法来帮助学生学习如何在逻辑教学中使用最佳求解策略，并通过使用反向链接策略教授概率教程来比较干预效果。结果表明，教育干预方法 “Nudge” 能够提高学生的学习成绩。

推动力的力量：在智能教学系统中探索元认知技能训练的三种干预方法

The Power of Nudging: Exploring Three Interventions for Metacognitive Skills Instruction across Intelligent Tutoring Systems

A growing body of work studies how to answer a question or verify a claim by
generating a natural language "proof": a chain of deductive inferences yielding
the answer based on a set of premises. However, these methods can only make
sound deductions when they follow from evidence that is given. We propose a new
system that can handle the underspecified setting where not all premises are
stated at the outset; that is, additional assumptions need to be materialized
to prove a claim. By using a natural language generation model to abductively
infer a premise given another premise and a conclusion, we can impute missing
pieces of evidence needed for the conclusion to be true. Our system searches
over two fringes in a bidirectional fashion, interleaving deductive
(forward-chaining) and abductive (backward-chaining) generation steps. We
sample multiple possible outputs for each step to achieve coverage of the
search space, at the same time ensuring correctness by filtering low-quality
generations with a round-trip validation procedure. Results on a modified
version of the EntailmentBank dataset and a new dataset called Everyday Norms:
Why Not? show that abductive generation with validation can recover premises
across in- and out-of-domain settings.

提出了一个处理模糊的假设的新系统，使用自然语言生成模型来从一个前提中推出另一个前提并以双向的方式搜索。 使用回路验证程序过滤质量不佳的生成来确保正确性。 该系统在修改后的 EntailmentBank 数据集和名为 “Everyday Norms：Why Not?“的新数据集上表现出可以恢复跨领域和非领域的前提的能力。