In acquiring new conceptual knowledge, learners often engage in the generation of examples that illustrate the to-be-learned principles and concepts. Learners are, however, bad at judging the quality of self-generated examples, which can result in suboptimal regulation decisions. A promising means to foster judgment accuracy in this context is providing external standards in form of expert examples after learners have generated own examples. Empirical evidence on this support measure, however, is scarce. Furthermore, it is unclear whether providing learners with poor examples, which include typical wrong illustrations, as negative example standards after they generated own examples would increase judgment accuracy as well. When they generated poor examples themselves, learners might realize similarities between their examples and the negative ones, which could result in more cautious and hence likely more accurate judgments concerning their own examples. Against this background, in a 2 × 2 factorial experiment we prompted N = 128 university students to generate examples that illustrate previously encountered concepts and self-evaluate these examples afterwards. During self-evaluation, we varied whether learners were provided with expert example standards (with vs. without) and negative example standards (with vs. without). In line with previous findings, expert example standards enhanced learners’ judgment accuracy. The newly developed negative example standards showed inconsistent and partly even detrimental effects regarding judgment accuracy. The results substantiate the notion that expert example standards can serve as a promising means to foster accurate self-evaluations in example generation tasks, whereas negative example standards should be treated with caution.

在获取新的概念性知识时，学习者经常会生成示例来说明要学习的原理和概念。然而，学习者不善于判断自我生成示例的质量，这可能会导致监管决策不理想。在这种情况下，提高判断准确性的一个有前途的方法是在学习者生成自己的示例后以专家示例的形式提供外部标准。然而，这种支持措施的经验证据很少。此外，尚不清楚在学习者生成自己的例子后，向学习者提供不良例子（包括典型的错误插图）作为反面例子标准是否也会提高判断准确性。当他们自己生成糟糕的例子时，学习者可能会意识到他们的例子与负面例子之间的相似之处，这可能会导致对自己的例子更加谨慎，从而可能做出更准确的判断。在此背景下，在 2 × 2 析因实验中，我们提示N  = 128 名大学生生成示例来说明以前遇到的概念，并在事后对这些示例进行自我评估。在自我评估过程中，我们改变了是否向学习者提供专家示例标准（有与没有）和反面示例标准（有与没有）。与之前的研究结果一致，专家示例标准提高了学习者的判断准确性。新制定的反例标准对判断准确性表现出不一致甚至部分有害的影响。结果证实了这样的观点，即专家示例标准可以作为在示例生成任务中促进准确自我评估的有前途的手段，而负面示例标准应谨慎对待。