This study aims to better understand norms that are potentially relevant for student engagement in peer critique within the modeling context.By analyzing multiple data from a 5 th /6 th grade classroom, we identified social and socioscientific norms within small groups.Students demonstrated productive within-group engagement in peer critique governed by norms, while encountering some difficulty with adopting certain socioscientific norms.

ABSTRACT 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.

Peer critique is essential for collaborative learning of science but presents challenges for students.Drawing on distributed and synergistic scaffolding, we designed multiple supports within a CSCL modeling environment, including integrated evidence and peer-feedback mechanisms, to help 6 th -grade students develop peer critique practices.Through analysis of students' comments and engagement patterns during critique activities, we found that students demonstrated progressive uptake of peer critique practices that the scaffolds were designed to promote, evident in more balanced, specific critiques over time.These findings inform the design of distributed scaffolds for supporting peer critique in science classrooms.

The nurturing of learners’ ways of knowing is vital for supporting their intellectual growth and their participation in democratic knowledge societies. This paper traces the development of two interrelated theoretical frameworks that describe the nature of learners’ epistemic thinking and performance and how education can support epistemic growth: the AIR and Apt-AIR frameworks. After briefly reviewing these frameworks, we discuss seven reflections on educational theory development that stem from our experiences working on the frameworks. First, we describe how our frameworks were motivated by the goal of addressing meaningful educational challenges. Subsequently, we explain why and how we infused philosophical insights into our frameworks, and we also discuss the steps we took to increase the coherence of the frameworks with ideas from other educational psychology theories. Next, we reflect on the important role of the design of instruction and learning environments in testing and elaborating the frameworks. Equally important, we describe how our frameworks have been supported by empirical evidence and have provided an organizing structure for understanding epistemic performance exhibited in studies across diverse contexts. Finally, we discuss how the development of the frameworks has been spurred by dialogue within the research community and by the need to address emerging and pressing real-world challenges. To conclude, we highlight several important directions for future research. A common thread running through our work is the commitment to creating robust and dynamic theoretical frameworks that support the growth of learners’ epistemic performance in diverse educational contexts. © The Author(s) 2024.

Disagreement is often perceived negatively, yet it can be beneficial for learning and scientific inquiry. However, students tend to avoid engaging in disagreement. Peer critique activities offer a promising way to encourage students to embrace disagreement, which supports learning as students articulate their ideas, making them available for discussion, revision, and refinement. This study aims to better understand how students express disagreement during peer critique within small groups and how that affects moving their inquiry forward. It explores 5th-grade students' management of disagreement within a computer-supported collaborative modeling environment. Using conversation analysis, we identified various forms of disagreements employed by students when engaging with different audiences. We observed a tendency for students to disagree softly; that is, disagreement was implied and/or mitigated. Students' resolution of both direct and soft disagreements effectively promoted their collective knowledge advancement, including building shared scientific understanding and improving their models, while maintaining a positive socio-emotional climate. These findings have implications for designing CSCL environments with respect to supporting students in providing and responding to peer critiques at the group level.

This study aims to better understand norms that are potentially relevant for student engagement in peer critique within the modeling context.By analyzing multiple data from a 5 th /6 th grade classroom, we identified social and socioscientific norms within small groups.Students demonstrated productive within-group engagement in peer critique governed by norms, while encountering some difficulty with adopting certain socioscientific norms.

Scientific modeling is supported by epistemic criteria to evaluate the "goodness" of scientific products.A central epistemic criterion is causal mechanism which means identifying the underlying causes which generate changes within a system.During a five-week model-based inquiry unit students identified causes of the phenomenon, eutrophication, but it was unclear whether student understood the causes hierarchically, i.e., whether some causes led to others, and whether some were more critical to include in a model than others.In a model evaluation task, students prioritized a model that was complete and had all the causes, deprioritized the model missing the direct causal mechanism, and placed models missing the initial causes in the middle.The results indicate that students thought about mechanisms in terms of chaining which means students thought about how causes led to outcomes within a system.

IntroductionThis study reports on a classroom intervention where upper-elementary students and their teacher explored the biological phenomena of eutrophication using the Modeling and Evidence Mapping (MEME) software environment and associated learning activities. The MEME software and activities were designed to help students create and refine visual models of an ecosystem based on evidence about the eutrophication phenomena. The current study examines how students utilizing this tool were supported in developing their mechanistic reasoning when modeling complex systems. We ask the following research question: How do designed activities within a model-based software tool support the integrations of complex systems thinking and the practice of scientific modeling for elementary students? MethodsThis was a design-based research (DBR) observational study of one classroom. A new mechanistic reasoning coding scheme is used to show how students represented their ideas about mechanisms within their collaboratively developed models. Interaction analysis was then used to examine how students developed their models of mechanism in interaction. ResultsOur results revealed that students' mechanistic reasoning clearly developed across the modeling unit they participated in. Qualitative coding of students' models across time showed that students' mechanisms developed from initially simplistic descriptions of cause and effect aspects of a system to intricate connections of how multiple entities within a system chain together in specific processes to effect the entire system. Our interaction analysis revealed that when creating mechanisms within scientific models students' mechanistic reasoning was mediated by their interpretation/grasp of evidence, their collaborative negotiations on how to link evidence to justify their models, and students' playful and creative modeling practices that emerged in interaction. DiscussionIn this study, we closely examined students' mechanistic reasoning that emerge in their scientific modeling practices, we offer insights into how these two theoretical frameworks can be effectively integrated in the design of learning activities and software tools to better support young students' scientific inquiry. Our analysis demonstrates a range of ways that students represent their ideas about mechanism when creating a scientific model, as well as how these unfold in interaction. The rich interactional context in this study revealed students' mechanistic reasoning around modeling and complex systems that may have otherwise gone unnoticed, suggesting a need to further attend to interaction as a unit of analysis when researching the integration of multiple conceptual frameworks in science education.

Reforms in science education have called for students to engage in scientific practices. A scientific practice that has received increased attention is modeling. Part of the practice of modeling is the development, use, and evaluation of epistemic criteria which are the standards scientists use to evaluate the validity and accuracy of scientific models. For students to effectively engage with epistemic criteria, they need to be given collective cognitive responsibility, or epistemic agency. This study investigates how the design and implementation of a fifth-grade model-based inquiry unit supported students' epistemic agency to revise an initial class criteria list for scientific models. The findings indicate that reflecting upon peer critique of their scientific models in a written task, and the ways that teachers engaged students in a class discussion to revise the initial class list, supported students' epistemic agency to revise an initial class criteria list for scientific models.

In collaborative problem solving (CPS), people's actions are interactive, interdependent, and temporal. However, it is unclear how actions temporally relate to each other and what are the temporal similarities and differences between successful vs. unsuccessful CPS processes. As such, we apply a temporal analysis approach, Multilevel Vector Autoregression (mlVAR) to investigate CPS processes. Our data were collected from college students who collaborated in triads via a video-conferencing tool (Zoom) to collaborately engage a physics learning game. Video recordings of their verbal interactions were transcribed, coded using a validated CPS framework, and organized into sequences of 10-second windows. Then, mlVAR was applied to the successful vs. unsuccessful CPS sequences to build temporal models for each. A comparison of the models together with a qualitative analysis of the transcripts revealed six temporal relationships common to both, six unique to successful level attempts, and another eight unique to unsuccessful level attempts only. Generally, for successful outcomes, people were likely to answer clarification questions with reasons and to ask for suggestions according to the current game situation, while for unsuccessful CPS level attempts, people were more likely to struggle with unclear instructions and to respond to inappropriate ideas. Overall, our results suggest that mlVAR is an effective approach for temporal analyses of CPS processes by identifying relationships that go beyond a coding and counting approach.