Math matters: A novel, brief educational intervention decreases whole number bias when reasoning about COVID-19.

Abstract

At the onset of the coronavirus disease (COVID-19) global pandemic, our interdisciplinary team hypothesized that a mathematical misconception—whole number bias (WNB)—contributed to beliefs that COVID-19 was less fatal than the flu. We created a brief online educational intervention for adults, leveraging evidence-based cognitive science research, to promote accurate understanding of rational numbers related to COVID-19. Participants from a Qualtrics panel (N = 1,297; 75% White) were randomly assigned to an intervention or control condition, solved health-related math problems, and subsequently completed 10 days of daily diaries in which health cognitions and affect were assessed. Participants who engaged with the intervention, relative to those in the control condition, were more accurate and less likely to explicitly mention WNB errors in their strategy reports as they solved COVID-19-related math problems. Math anxiety was positively associated with risk perceptions, worry, and negative affect immediately after the intervention and across the daily diaries. These results extend the benefits of worked examples in a practically relevant domain. Ameliorating WNB errors could not only help people think more accurately about COVID-19 statistics expressed as rational numbers, but also about novel future health crises, or any other context that involves information expressed as rational numbers.

Confidence in COVID problem solving: What factors predict adults’ item-level metacognitive judgments on health-related math problems before and after an educational intervention?

“…2 for problem features). These features of the problem may have served as cues that influenced participants’ monitoring judgments, even though Thompson et al ( 2021 ) reported that accuracy on posttest Problem 3 was higher than expected, potentially because of news sources reporting on Italy’s and China’s infection rates. In fact, monitoring judgments were lowest on this final problem, regardless of accuracy (Table 4 ).…”

“…People may be likely to avoid numerical health information if they believe that they cannot interpret the information (Afifi & Weiner, 2004 ; Sweeny et al, 2010 ), if they have high math anxiety (Rolison et al, 2016 ), or if they have negative attitudes toward the numbers (Sidney et al, 2021 ). People’s level of certainty in their interpretations of numerical health information, their comprehension of health statistics, and their own perceived disease susceptibility can influence their medical decision-making (Desender et al, 2018 ; Peters et al, 2019 ; Taber & Klein, 2016 ). If people are not confident in their ability to accurately interpret quantitative health information, they may turn to other types of information, such as affective factors and personal values, to make health decisions, which could have downstream implications for uptake of preventive health recommendations (e.g., social distancing, wearing a mask, getting vaccinated).…”

“…To address participants’ tendency towards whole number bias (Thompson et al, in press ), Thompson et al ( 2021 ) randomly assigned participants to an educational intervention or control condition. In the intervention condition, participants saw a worked example 2 demonstrating, step-by-step, how to solve the target problems.…”

Perceptions of ease and difficulty, but not growth mindset, relate to specific math attitudes

“…Even WNs can be expressed as fractions (Sidney, Thompson, Matthews, & Hubbard, 2017). Teaching numerical notations in tandem may help students overcome common misconceptions, such as whole number bias (Alibali & Sidney, 2015; Ni & Zhou, 2005; Thompson et al., in press), and result in an integrated representation of WNs and rational numbers (Siegler et al., 2011, 2013).…”

Risk perception, decision-making, and risk communication in the time of COVID-19.

“…Indeed some studies included here reveal the influence of known biases, such whole number bias (Fitzsimmons, 2021) and denominator neglect (Joslyn et al, 2021) which may have distorted peoples' understanding of the relevant statistics. The good news is that the same evidence revealed that targeted interventions were effective in reducing these biases (Fitzsimmons, 2021;Joslyn et al, 2021).…”