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Bird’s-Eye View of Cue Integration: Exposing Instructional and Task Design Factors Which Bias Problem Solvers

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Abstract

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.

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Notes

  1. R command for Experiment 1: model.success<-lme(Accuracy_dv_c ~ 1 + Serial_order_iv_c + Perimeter_area_congruency_iv_c + Basic_shape_area_iv_c + Difference_in_edges_iv_c+ Response_time_iv_c, random = ~1|Username, data = raw_data). All predictors were centralized (denoted by c). The model for predicting confidence was the same with Confidence_dv_c instead of Accuracy_dv_c.

  2. More details about the coding scheme can be found at https://www.jmap.org/JMAPArchives/CurrentVersion/JMAPAI_REGENTS_BOOK_BY_PI_TOPIC.pdf.

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Acknowledgements

I would like to thank Meira Ben-Gad for editorial assistance and Paul Feigin for statistical consulting.

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This work was supported by the Israel Science Foundation.

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  1. Faculty of Data and Decision Sciences, Technion—Israel Institute of Technology, 3200003, Haifa, Israel

    Rakefet Ackerman

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The research methodologies were approved by the Behavioral Sciences Research Ethics Committee of the Technion—Israel Institute of Technology (approval number 2020-015). Informed consent was collected from all participants at the outset of each experiment.

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Ackerman, R. Bird’s-Eye View of Cue Integration: Exposing Instructional and Task Design Factors Which Bias Problem Solvers. Educ Psychol Rev 35, 55 (2023). https://doi.org/10.1007/s10648-023-09771-z

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