Solving problems in educational settings, as in daily-life scenarios, involves constantly assessing one’s own confidence in each considered solution. Metacognitive research has exposed cues that may bias confidence judgments (e.g., familiarity with question terms). Typically, metacognitive research methodologies require examining misleading cues one-by-one, while recent research has revealed the integration of multiple cues stemming from the same stimuli. However, this research leaves open important questions about including the weight balance among cues and their changes across task design (e.g., instructions) and/or population characteristics (e.g., background knowledge). The present study presents the Bird’s-Eye View of Cue Integration (BEVoCI) methodology. It is based on hierarchical multiple regression models, allowing efficient exposure of multiple biases at once, their relative weights, and their malleability across task designs and populations. Notably, the BEVoCI can be applied both to planned studies and to existing datasets. I demonstrate its application in both ways. In Experiment 1 and Experiment 2, I introduce two nonverbal problem-solving tasks, the Comparison of Perimeters (CoP) and the novel Missing Tan Task (MTT), while Experiment 3 reanalyzes data collected by others, comprising algebra problems solved by children and adults. The experiments demonstrate exposing biases, their malleability across conditions, and the non-straightforward association between performance improvement and overcoming biases, and the results of Experiment 3 provide strong support for the generalizability of the methodology. Pinpointing sources of bias is essential for guiding educational design efforts.

在教育环境中解决问题，就像在日常生活中一样，需要不断评估自己对每个考虑的解决方案的信心。元认知研究揭示了可能使信心判断产生偏差的线索（例如，对问题术语的熟悉程度）。通常，元认知研究方法需要一个一个地检查误导性线索，而最近的研究揭示了来自同一刺激的多个线索的整合。然而，这项研究留下了一些重要的问题，包括线索之间的权重平衡及其在任务设计（例如，指令）和/或人口特征（例如，背景知识）中的变化。本研究提出了提示整合的鸟瞰图(BEVoCI) 方法。它基于分层多元回归模型，允许一次有效地暴露多重偏差、它们的相对权重以及它们在任务设计和人群中的可塑性。值得注意的是，BEVoCI 既可以应用于计划的研究，也可以应用于现有的数据集。我以两种方式演示它的应用。在实验 1 和实验 2 中，我介绍了两个非语言问题解决任务，周长比较 (CoP) 和新颖的 Missing Tan 任务 (MTT)，而实验 3 重新分析了其他人收集的数据，包括儿童和成人解决的代数问题. 实验证明了暴露偏差、它们在不同条件下的可塑性，以及性能改进和克服偏差之间的非直接关联，实验 3 的结果为该方法的普适性提供了强有力的支持。查明偏差来源对于指导教育设计工作至关重要。