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Creators/Authors contains: "Donovan, Brian M."

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  1. Free, publicly-accessible full text available February 23, 2025
  2. Free, publicly-accessible full text available February 23, 2025
  3. Abstract

    Argumentation is fundamental to science education, both as a prominent feature of scientific reasoning and as an effective mode of learning—a perspective reflected in contemporary frameworks and standards. The successful implementation of argumentation in school science, however, requires a paradigm shift in science assessment from the measurement of knowledge and understanding to the measurement of performance and knowledge in use. Performance tasks requiring argumentation must capture the many ways students can construct and evaluate arguments in science, yet such tasks are both expensive and resource‐intensive to score. In this study we explore how machine learning text classification techniques can be applied to develop efficient, valid, and accurate constructed‐response measures of students' competency with written scientific argumentation that are aligned with a validated argumentation learning progression. Data come from 933 middle school students in the San Francisco Bay Area and are based on three sets of argumentation items in three different science contexts. The findings demonstrate that we have been able to develop computer scoring models that can achieve substantial to almost perfect agreement between human‐assigned and computer‐predicted scores. Model performance was slightly weaker for harder items targeting higher levels of the learning progression, largely due to the linguistic complexity of these responses and the sparsity of higher‐level responses in the training data set. Comparing the efficacy of different scoring approaches revealed that breaking down students' arguments into multiple components (e.g., the presence of an accurate claim or providing sufficient evidence), developing computer models for each component, and combining scores from these analytic components into a holistic score produced better results than holistic scoring approaches. However, this analytical approach was found to be differentially biased when scoring responses from English learners (EL) students as compared to responses from non‐EL students on some items. Differences in the severity between human and computer scores for EL between these approaches are explored, and potential sources of bias in automated scoring are discussed.

     
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  4. Abstract

    Recently, it has been argued that improving students' genomics literacy could prevent students from developing erroneous beliefs about social identity, such as the belief that racial groups differ cognitively and behaviorally because of their genes; a belief called genetic essentialism. To date, however, little research has explored if or how a conceptual understanding of genomics protects against the development of genetic essentialism. Using a randomized control trial (RCT) (N= 721, 9th–12th graders), we explore if students with more genomics literacy are more able to conceptually change their genetic essentialist beliefs after engaging in a learning experience designed to refute essentialist thinking. The results of the RCT demonstrated that students with higher genomics literacy (relative to those with lower genomics literacy) exhibited greater reductions in the perception of racial differences and greater reductions in belief in genetic essentialism after learning how patterns of human genetic variation refute genetic essentialism. These results suggest that genetics education can protect students from developing a belief in genetic essentialism when it provides them with opportunities to learn multifactorial genetics and population thinking in conjunction with how these concepts refute essentialist thinking.

     
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  5. Abstract

    When people are exposed to information that leads them to overestimate the actual amount of genetic difference between racial groups, it can augment their racial biases. However, there is apparently no research that explores if the reverse is possible. Does teaching adolescents scientifically accurate information about genetic variation within and between US census races reduce their racial biases? We randomized 8thand 9thgrade students (n = 166) into separate classrooms to learn for an entire week either about the topics of (a) human genetic variation or (b) climate variation. In a cross‐over randomized trial with clustering, we demonstrate that when students learn about genetic variation within and between racial groups it significantly changes their perceptions of human genetic variation, thereby causing a significant decrease in their scores on instruments assessing cognitive forms of prejudice. We then replicate these findings in two computer‐based randomized controlled trials, one with adults (n = 176) and another with biology students (n = 721, 9th–12thgraders). These results indicate that teaching about human variation in the domain of genetics has potentially powerful effects on social cognition during adolescence. In turn, we argue that learning about the social and quantitative complexities of human genetic variation research could prepare students to become informed participants in a society where human genetics is invoked as a rationale in sociopolitical debates.

     
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