Tutoring systems improve learning through tailored interventions, such as worked examples, but often suffer from the aptitude-treatment interaction effect where low prior knowledge learners benefit more. We applied the ICAP learning theory to design two new types of worked examples, Buggy (students fix bugs), and Guided (students complete missing rules), requiring varying levels of cognitive engagement, and investigated their impact on learning in a controlled experiment with 155 undergraduate students in a logic problem solving tutor. Students in the Buggy and Guided examples groups performed significantly better on the posttest than those receiving passive worked examples. Buggy problems helped high prior knowledge learners whereas Guided problems helped low prior knowledge learners. Behavior analysis showed that Buggy produced more exploration-revision cycles, while Guided led to more help-seeking and fewer errors. This research contributes to the design of interventions in logic problem solving for varied levels of learner knowledge and a novel application of behavior analysis to compare learner interactions with the tutor.

翻译：辅导系统通过定制化干预（如解题示例）提升学习效果，但常受能力-处理交互效应影响，即先验知识薄弱的学习者获益更多。本研究应用ICAP学习理论设计了两类新型解题示例：纠错型（学生修正错误）与引导型（学生补全缺失规则），二者需要不同层次的认知投入，并通过155名本科生在逻辑问题求解辅导系统中的受控实验探究其学习影响。实验表明，接受纠错型与引导型示例的学生在后测中表现显著优于接受被动解题示例的对照组。纠错型问题有助于高先验知识学习者，而引导型问题则更利于低先验知识学习者。行为分析显示，纠错型示例引发更多探索-修正循环，而引导型示例则导致更多求助行为与更少错误。本研究为针对不同知识水平学习者的逻辑问题求解干预设计提供了新思路，并通过行为分析在辅导系统交互比较中的创新应用作出了贡献。