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.