Ravit Golan Duncan

Scientific modeling is a core practice of scientific inquiry. Students' engagement in modeling can be enhanced by attending to epistemic criteria, which in science are standards used to evaluate the validity and accuracy of scientific models. While prior research has focused on students' development and use of epistemic criteria in scientific inquiry environments, less is known about students' metacognitive knowledge about why epistemic criteria matter—their metacognitive epistemic justifications. Investigating students' metacognitive knowledge of criteria can reveal their understandings of the criteria guiding scientific practice (such as modeling), their relative value, and their relationship to epistemic products. We present findings from clinical interviews of student dyads conducted at the end of the implementation of a fifth‐grade model‐based inquiry unit in which the class developed and used epistemic criteria for scientific models. Students' justifications of epistemic criteria revealed their metacognitive knowledge about why criteria, such as evidentiary fit, are important in modeling practice. We also identified themes in students' justifications for the relative importance of different epistemic criteria and the role such justifications played in resolving disagreements about epistemic matters. Our findings illustrate the potential value of engaging students in discourse about metacognitive justifications for epistemic criteria.

Genetics is a core topic in the biology curriculum of many countries. Learning genetics is difficult for multiple reasons, including the need to reason about complex mechanisms—and to link mechanisms—that occur at different time and space scales. Previous research in genetics education explores students' understandings of inheritance patterns or their understandings of molecular genetics; only a handful of studies examined how students understand the connection between molecular mechanisms and inheritance patterns. Moreover, we know little about how such understandings develop over the secondary and undergraduate levels. To address this gap, we conducted a cross‐sectional interview study with middle school, high school, undergraduate, and graduate students after they had experienced “status‐quo” instruction in genetics. We analyzed student responses using a cognitive model of molecular genetics that describes two key domain‐specific knowledge resources: heuristics and explanatory schemas. Our findings extended this model to account for reasoning about inheritance patterns as well as identifying the knowledge resources used by students with different levels of education. This allowed us to present a snapshot of a progression describing the gradual use of these heuristics and schemas across different grades. We discuss the implications of the tentative progression for instruction aimed at supporting reasoning across time and space scales in genetics.

Learning progressions are theoretical models that describe learning of scientific ideas and practices over time. These hypothetical progressions need to be tested and refined in order to productively inform instruction and assessment. In this paper, we report our attempts to revise a learning progression in genetics. In particular, we focused on two constructs that embody core ideas in classical genetics and one molecular construct. The revisions are based on analysis of pre‐ and postinterview data obtained from sixty 11th grade students before and after they engaged in a 10‐week unit that addressed these concepts. We found that while many of the students held ideas that aligned with the progression, there were several distinct dimensions of student reasoning that were not captured and led to substantial revisions of the constructs including: (a) the splitting of the construct dealing with meiosis (E) into two subconstructs (E1‐physical passage of genetic information and E2 – the role of sex cells), (b) the addition of new levels to constructs dealing with the universal nature and organization of the genetic code (A) and construct (F). For Construct A, the lower levels were expanded to include ideas about the localization of DNA in cells and to include ideas about the composition of DNA that were not captured in the progression. Revisions to Construct F included the expansion of existing levels and the addition of modes of inheritance such as codominance and incomplete dominance. The research we present offers insights about a methodological approach that can be used to test and refine progressions, as well as insights about student learning in genetics as we further describe and expand the stepping‐stone ideas in the progression and discuss further the multidimensional nature of learning progressions.

Mechanisms are central in scientific explanations. However, developing mechanistic explanations is difficult for students especially in domains in which mechanisms involve abstract components and functions, such as genetics. One of the core components of genetic mechanisms are proteins and their functions. Students struggle to reason about the role of proteins while learning genetics and show limited ability to provide mechanistic explanations of genetic phenomena. In genetics education there are currently two competing theoretical frameworks regarding what domain‐specific knowledge about proteins is important for reasoning about genetic mechanisms. One framework assumes knowledge about specific protein functions in the body, a tool kit of functions; the other framework assumes more abstracted knowledge about protein interactions that are common to all protein functions. These frameworks implicate different instructional frameworks: One offers to provide concrete examples of protein functions while the other offers a more general description of protein activity. Our aim in this study was to ascertain the ways in which students' reasoning about proteins' role in genetic phenomena (both familiar and novel) relates to the two theoretical frameworks. Toward this end we engaged 7th grade students in learning about proteins functions in the mechanisms underlying genetic traits using an online simulation environment that embodied key aspects of both frameworks. We analyzed students' responses to the final test questions in which they were asked to generatively reason about the underlying mechanisms of two novel genetic traits. Our findings suggest that students use proteins in their explanations mainly when they can explain the protein function and that knowledge about a few specific functions is insufficient to support conceptualization of new functions. Moreover, knowledge of general protein activities common to most functions is also insufficient. We suggest a new combined approach to supporting students' understanding of proteins' role in genetic mechanisms.

Practices of generating, analyzing, and using evidence play a central role in the Framework for K‐12 Science Education and the NGSS. However, the construct of evidence remains largely underspecified in these documents, providing insufficient guidance on how to engage students with the broad and complex nature of evidentiary reasoning. This creates a risk of perfunctory and simplified implementation of evidence‐based practices that misses the intent of the standards and does little to prepare students for reasoning with the complex, varied, and contentious evidence encountered in popular media or in advanced education. To address these challenges, we propose a theoretical framework, which we callGrasp of Evidence, that complexifies the concept of evidence in ways that facilitate introducing more authentic forms of evidence and more sophisticated ways of engaging with evidence in science classrooms. Our approach focuses on promoting alaygrasp of evidence needed by competent outsiders as they engage with science in their everyday lives. The framework posits five dimensions. The first four dimensions capture what students should understand about how experts work with evidence: evidence analysis, evidence evaluation, evidence interpretation, and evidence integration. The fifth dimension focuses on how laypeople can use evidence reports themselves. Each of these dimensions of practice involves specific epistemic aims, epistemic ideals, and reliable epistemic processes for reasoning with and about evidence. We discuss these dimensions and their contribution to the conceptualization of evidence as well as provide some initial instructional implications and potential directions for future research.

Learning progressions (LPs) are hypothetical models of learning developed with the aim of informing the design of standards, curriculum, instruction, and assessment in K-16 settings. The promise of LPs to inform designs for learning across grades and grade bands is an attractive prospect to learning scientists, particularly in math and science, who seek to fundamentally change learning settings through design. LPs in science and mathematics share several core features. They are organized around a few core disciplinary ideas and practices and describe the development of students’ understandings as intermediate steps or levels of growing sophistication. Descriptions of these levels are grounded in research on student learning in the domain, and progress along the levels is mediated by targeted instruction and curriculum. LPs by their very nature are conjectural models of learning over time that need to be empirically validated. Examination of a variety of existing progressions shows that their core features can be operationalized in different ways. These features include (a) the scope of the progression, (b) the type of constructs (core ideas) included; (c) how progress along a progression is conceptualized; and (d) the methods used to develop and refine LPs. We discuss each of these features using examples drawn from science and mathematics progressions. We further examine concerns and criticisms of LPs raised in the literature, including issues of construct and consequential validity and some of the challenges with classroom and policy applications. We agree with many of the criticisms raised, particularly the importance of accounting for the complex, contextual, and nuanced nature of learning in LPs. We anticipate that this young field will continue to evolve, improve, and contribute to learning, teaching, assessment, and policy.