Salden Ron J C M, Aleven Vincent A W M M, Renkl Alexander, Schwonke Rolf
Human-Computer Interaction Institute, Carnegie Mellon UniversityPsychological Institute, University of Freiburg.
Top Cogn Sci. 2009 Jan;1(1):203-13. doi: 10.1111/j.1756-8765.2008.01011.x.
The current research investigates a combination of two instructional approaches, tutored problem solving and worked examples. Tutored problem solving with automated tutors has proven to be an effective instructional method. Worked-out examples have been shown to be an effective complement to untutored problem solving, but it is largely unknown whether they are an effective complement to tutored problem solving. Further, while computer-based learning environments offer the possibility of adaptively transitioning from examples to problems while tailoring to an individual learner, the effectiveness of such machine-adapted example fading is largely unstudied. To address these research questions, one lab and one classroom experiment were conducted. Both studies compared a standard Cognitive Tutor with two example-enhanced Cognitive Tutors, in which the fading of worked-out examples occurred either in a fixed way or adaptively. Results indicate that the adaptive fading of worked-out examples leads to higher transfer performance on delayed posttests than the other two methods.
当前的研究调查了两种教学方法的组合,即辅导式问题解决和实例演练。事实证明,使用自动辅导工具进行辅导式问题解决是一种有效的教学方法。已证明演练实例是对非辅导式问题解决的有效补充,但它们是否是辅导式问题解决的有效补充在很大程度上尚不清楚。此外,虽然基于计算机的学习环境提供了在适应个体学习者的同时从实例到问题进行自适应过渡的可能性,但这种机器自适应的实例渐隐效果在很大程度上尚未得到研究。为了解决这些研究问题,进行了一项实验室实验和一项课堂实验。两项研究都将标准认知辅导工具与两种实例增强型认知辅导工具进行了比较,在实例增强型认知辅导工具中,演练实例的渐隐以固定方式或自适应方式进行。结果表明,与其他两种方法相比,演练实例的自适应渐隐在延迟后测中能带来更高的迁移成绩。