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To learn about real world phenomena, scientists have traditionally used models with clearly interpretable elements. However, modern machine learning (ML) models, while powerful predictors, lack this direct elementwise interpretability (e.g. neural network weights). Interpretable machine learning (IML) offers a solution by analyzing models holistically to derive interpretations. Yet, current IML research is focused on auditing ML models rather than leveraging them for scientific inference. Our work bridges this gap, presenting a framework for designing IML methods—termed ’property descriptors’—that illuminate not just the model, but also the phenomenon it represents. We demonstrate that property descriptors, grounded in statistical learning theory, can effectively reveal relevant properties of the joint probability distribution of the observational data. We identify existing IML methods suited for scientific inference and provide a guide for developing new descriptors with quantified epistemic uncertainty. Our framework empowers scientists to harness ML models for inference, and provides directions for future IML research to support scientific understanding. [ABSTRACT FROM AUTHOR]

Copyright of Journal of the Association of Arab Universities for Higher Education Research is the property of Journal of the Association of Arab Universities for Higher Education Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Learning to plan modeling-based investigations (MBIs) presents significant challenges for pre-service elementary teachers. Although they understand disciplinary core ideas and modeling practices, they address both as competing learning goals and, as novices, they have not experienced how to structure MBIs for their future students. In the context of a science teacher education course, we have designed a set of pedagogical supports around a biology-specific epistemic tool to help pre-service elementary teachers meet those challenges. This study builds the case of how a group of four pre-service elementary teachers learns to plan MBIs while working with the designed supports. We use the professional vision as a theoretical and analytical framework to characterize the group's learning through discourses to build shared professional understandings of planning MBIs around biology core ideas. Analysis focused on their highlighting and coding of relevant aspects of disciplinary core ideas, modeling, and structuring MBIs, as well as their creation and use of material representations. The findings illustrate that: a) epistemic negotiation supports the group in integrating biology core ideas and modeling practices when developing explanatory models, and b) pedagogical negotiation, through the de-construction of those explanatory models, supports the group structuring of a MBI, including a natural phenomenon and questions students can investigate in a coherent sequence. We discuss how a biology-specific epistemic tool embedded in pedagogical supports helps pre-service teachers learn to plan MBIs by facilitating both epistemic and pedagogical negotiations. [ABSTRACT FROM AUTHOR]

Scientific modeling is a syllogistic system of definitive premise, sound inference and consistent explanation to understand, define, quantify, visualize or simulate feature of the target. Single-model is defined to an informative representation for identifying a property of a target object/phenomenon, and meta-model integrates the relevant single-models to explain phenomenological realities. Human recognition-behavioral adaptation is an information-metabolism system to maintain homeostasis of human-self, and that has been investigated in neurological, psychiatric and psychological aspects. I analyzed human recognition-behavioral adaptation-system via scientific modeling. Neurological meta-model of human recognition-behavioral adaptation system was synthesized as complex-network of the functional neuronal modules, and the meta-model was integrated to Mentality-model in the psychiatric aspect, and to Personality-model in the psychological aspect. The integrated meta-models successfully explained phenomenological realities in the aspects. From the above, I comprehended that the meta-model of human recognition-behavioral adaptation-system has been developed to Biopsychosocial model integrating the biological, psychological and socio-environmental factors. [ABSTRACT FROM AUTHOR]

Research has explored how science teachers can create equitable learning environments. In addition, research demonstrates that representations can be powerful tools for supporting disciplinary learning and inviting and leveraging students' diverse ideas and practices. Yet, professional development (PD) about representations has primarily focused on teachers' knowledge of disciplinary practices rather than on how teachers can value and build upon students' representations as resources for equitable sensemaking. In this paper, we present cases from a year-long professional development program with in-service elementary teachers designed to support science teaching with representations. Through our work with these teachers, we have illustrated an approach to using representations that supports equitable sensemaking by: (1) making space for students to create personally meaningful representations, (2) amplifying students' representations, and (3) helping students iteratively refine their representations and ideas. These findings extend literature about inclusive science teaching by illustrating how focusing on students' representations can support equitable sensemaking and by addressing tensions that emerge between equitable teaching, science standards that prioritize canonical knowledge and practices, and monoglossic language ideologies. [ABSTRACT FROM AUTHOR]

Attending to emotion in science classrooms can expand the range of resources valued for science learning, and it can offer insights into students' investigations. However, research that characterizes emotion as a part of disciplinary science learning is relatively nascent. In response, we explore the dynamic relationships between feelings, sensemaking, and practices in a sixth‐grade STEM classroom, guided by (a) Kimmerer's (2013) account of braiding Eurocentric disciplinary science with other resources, including feelings, and (b) Vea's (2020) framework of emotional configurations. We analyze data from multiple iterations of a 9‐week curriculum about guppies' survival needs and the dynamics of ecosystems, illustrating how students' feelings and their sensemaking through practices involving observation were intertwined in generative and mutually‐reinforcing ways. First, we show how the teacher and researcher made space for students to express feelings, in part in response to a student who used feelings to challenge "business as usual" classroom discourse on a day when guppies were discovered to have died. Then, we show how, in subsequent implementations, feelings, sensemaking, and practices were braided together to shape classroom investigations. We argue that attending to feelings in this way, as valued resources to be integrated with sensemaking and practices, is an important step toward equitable science teaching and learning and toward understanding learning in student‐driven contexts. [ABSTRACT FROM AUTHOR]

The use of modeling-based instruction is very effective when learning science content in multiple disciplines for native speakers. However, the benefits of using this pedagogy with students learning English as a second language or English Learners (ELs) have not been widespread, especially at the upper secondary level. This article discusses the use of a modeling-based natural selection unit that was developed for 14–16-year-old ELs in a sheltered EL biology class in the United States. The activity described for ELs includes not only activity details but also specifics about the inclusion and use of research-based scaffolding techniques. The efficacy of the unit was assessed in terms of changes to both conceptual understanding and scientific reasoning skills for the ELs and two inquiry-based non-EL biology classrooms. This modeling-based pedagogy allowed the ELs to not only increase their conceptual understanding of evolution but also their reasoning skills while also removing any knowledge gap with their non-EL peers in terms of evolution. The implication is that modeling-based science activities might hold the key to allowing EL students to achieve at the same conceptual levels as their non-EL peers. [ABSTRACT FROM AUTHOR]

Collaboration between ecologists and learning scientists can give rise to powerful models for scientific outreach within ecology. This paper presents a process by which learning scientists and ecologists codesigned a science curriculum that invites students to join an ecological community of practice. In the Journey to El Yunque middle school science curriculum, students engage with simulation models generated from data gathered by Luquillo Long Term Ecological Research (LUQ LTER) scientists. Journey to El Yunque students can explore post‐hurricane population changes in yagrumo (Cecropia schreberiana), tabonuco (Dacryodes excelsa), coquís (Eleutherodactylus coquí), snails (Caracolus caracola), anoles (Anolis stratulus and A. gundlachi), veiled stinkhorn mushrooms (Dictyophora indusiata), and caterpillars (Historis odius). Ecology‐based revisions to Journey to El Yunque have included adding models of the effects of repeated hurricanes on limiting factors, based in part on findings from a canopy trimming experiment. Revisions based on classroom testing include simplifying student‐facing model controls to allow students to focus on the essential model components. The ongoing collaboration that keeps the Journey to El Yunque curriculum on the cutting edge of ecological and educational advances has been sustained for over two decades. We attribute the longevity of this work to (1) the long‐term nature of LUQ LTER, (2) a sustained interdisciplinary collaboration, and (3) our long‐term relationships with schools. [ABSTRACT FROM AUTHOR]

Abstract Collaboration between ecologists and learning scientists can give rise to powerful models for scientific outreach within ecology. This paper presents a process by which learning scientists and ecologists codesigned a science curriculum that invites students to join an ecological community of practice. In the Journey to El Yunque middle school science curriculum, students engage with simulation models generated from data gathered by Luquillo Long Term Ecological Research (LUQ LTER) scientists. Journey to El Yunque students can explore post‐hurricane population changes in yagrumo (Cecropia schreberiana), tabonuco (Dacryodes excelsa), coquís (Eleutherodactylus coquí), snails (Caracolus caracola), anoles (Anolis stratulus and A. gundlachi), veiled stinkhorn mushrooms (Dictyophora indusiata), and caterpillars (Historis odius). Ecology‐based revisions to Journey to El Yunque have included adding models of the effects of repeated hurricanes on limiting factors, based in part on findings from a canopy trimming experiment. Revisions based on classroom testing include simplifying student‐facing model controls to allow students to focus on the essential model components. The ongoing collaboration that keeps the Journey to El Yunque curriculum on the cutting edge of ecological and educational advances has been sustained for over two decades. We attribute the longevity of this work to (1) the long‐term nature of LUQ LTER, (2) a sustained interdisciplinary collaboration, and (3) our long‐term relationships with schools.

The world is facing global ecological changes, making it essential to prepare the future generation with the necessary understanding to effectively navigate and address complex tasks. Previous research has shown that both systems thinking and scientific modeling are particularly relevant in investigating the comprehensive understanding of such complex phenomena. However, there has been little research on the interrelation between systems thinking and scientific modeling. To address this research gap, we conducted a thinking-aloud study with nine high school students by confronting them with a simulation of a dynamic ecological system. Our qualitative content analysis of the students’ statements indicates an interrelation between systems thinking and scientific modeling. The students infrequently show systems thinking during the exploration, whereas when developing a graphical model, the students are involved in identifying the system organization and analyzing the system behavior. When predicting future system states, students engage in modeling the system evolution. Furthermore, during verbalizing analogies and experiences, students refer to the system organization and behavior, whereas in mental modeling, students additionally model the system evolution. These results illustrate a central difference between the two perspectives. Thus, scientific modeling focuses on students’ activities during their understanding process, while systems thinking addresses students’ analysis of systems and their properties. While the phenomenon exploration may not require systems thinking, pattern recognition and model development are frequently associated with identifying the system organization and analyzing the system behavior. Systems thinking must also be applied when deriving possible future system states by modeling the system evolution, an activity that is closely related to the prediction phase of scientific modeling. Interestingly, in our study, the students also demonstrated the modeling of system evolution in their mental modeling. In conclusion, a complementary consideration of systems thinking and scientific modeling affords a deeper understanding of students’ cognitive processes in dealing with complex phenomena.

Science education reform efforts have emphasized the incorporation of more authentic and meaningful scientific practices into formal learning environments. With this focus on practices, such as scientific modeling, a powerful tool for sense‐making and reasoning, comes a need to observe and understand if and how it is actually occurring, particularly in elementary science learning environments. To address this need, and moving forward an international, collaborative research effort centered around scientific modeling, we undertook a cross‐national observational study in which we used an observation protocol to characterize scientific modeling practices in elementary classrooms in both the United States and Germany. Drawing on existing video‐based datasets from multiple projects, this study's findings provide empirical evidence for model‐based teaching and learning and explore the relative frequencies of relevant modeling subpractices (i.e., construction, use, evaluation, and revision of scientific models) in regular, day‐to‐day elementary science classrooms. This study also illustrates the differences and similarities between German and American science instruction. In general, students in both countries experience model construction more often than other modeling practices. German teachers more consistently foreground anchoring phenomena and students' questions/preconceptions as part of the modeling process, thus leading to slightly higher overall modeling scores—despite a complete lack of model evaluation and revision in any of the observed German classrooms. Ultimately, however, young learners in both nations seem to experience alarmingly little opportunities to engage in any of the modeling subpractices. Particularly in terms of modeling as a means to make sense of phenomena, constrained by teachers most commonly using finished representations (i.e., models) of phenomena and processes to teach science/deliver content. Our results indicate the need for a greater focus on all scientific modeling subprocesses, particularly the integration of scientific modeling with already established, more general (e.g., inquiry‐based) strategies for effective science teaching and an emphasis on actualizing modeling as an authentic sense‐making endeavor. [ABSTRACT FROM AUTHOR]

Assessing scientific modeling competence (SMC) is challenging because of the multi‐dimensionality of the construct and the potential variability of student performance across tasks. To deal with the challenges, this study applied Kane's validity framework to assess high‐school students' SMC in Newtonian mechanics and examined how students' performance depended on tasks. We first specified students' SMC in three dimensions: Conceptualization, Deploying Scientific Knowledge, and Representation, incorporating 11 components (e.g., conceptualization of objects). We assessed 305 high‐school students' modeling performance on two Newtonian mechanics tasks, taking task dependency into account. We applied the Many‐facet Rasch measurement (MFRM) to examine students' ability and discriminate students' performance variability across tasks. MFRM analyses revealed that among the three dimensions of SMC, Representation is most challenging for students to achieve compared to Conceptualization and Deploying Scientific Knowledge, especially among higher‐performing students. Five out of the 11 SMC components were task‐dependent, suggesting significant score variance due to the variability of tasks. After excluding task variance, we found that students were grouped into four levels (i.e., primitive non‐model, intuitive model, qualitative model, and quantitative model), and the majority of students were distributed at the medium two levels. We conclude the study by introducing three putative theories that may account for task dependency: depth of cognition, knowledge transfer, and construct irrelevant interest. The study suggests that task dependency does not necessarily compromise the interpretation of scores, as long as assessors appropriately consider potential theories in their interpretive arguments when designing tasks and interpreting scores. [ABSTRACT FROM AUTHOR]

National and state science learning standards urge K-12 educators to offer authentic Science, Technology, Engineering, and Mathematics learning experiences. One way to fulfill this goal is to prepare preservice science teachers to integrate computer science skills, such as coding, into science education learning contexts that can benefit from it. This study implemented Coding in Scientific Modeling Lessons (CS-ModeL) in a science teacher education course. CS-ModeL is the name of an instructional module and of an online tool, and they aim to support preservice science teachers' use of coding in scientific modeling and lesson design. Preservice teachers used block-based coding to create science simulations, performed analogous physical experiments, and designed lessons in which they support scientific modeling with coding. This mixed methods study investigated if and how participation in CS-ModeL affected preservice teachers' epistemological understanding of scientific models and modeling along with their understanding of computer science concepts. This study also examined coding-enhanced scientific modeling activities in their designed lessons. Results showed an overall improvement in participants' epistemological understanding of models and modeling, and in their understanding of computer science concepts. Participants' lessons featured activities in which block-based coding simulations are used either as a research tool or as an exploration tool. Additionally, most lessons targeted computer science practices, but not concepts. It was also found that participants' lessons were not aligned with their epistemological understanding of models and modeling. Study limitations, implications for research and practice, and directions for future research are discussed. [ABSTRACT FROM AUTHOR]

Copyright of Asia-Pacific Science Education is the property of Brill Academic Publishers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The emphasis of using computing tools in scientific practice has called for new forms of scientific modeling. Therefore, researchers are paying increasing attention to computational scientific modeling in which students use the computational power of computers to model and learn about science phenomena. However, computational scientific modeling is challenging since it involves not only scientific concepts but also uses computational representations to model the science concepts. This study hypothesizes that prompting students to self-explain critical subgoals of computational scientific modeling before taking part in the construction of models would help them construct correct computational models. This study recruited 65 10th grade students in a 6-week program. They were randomly assigned to a self-explanation group (n = 29) where students learned with the self-explanation for subgoals of computational scientific modeling, and a demonstration group (n = 36) where the teacher directly demonstrated the modeling process before students took part in the modeling activity. This study collected the students' performance in a paper-based and hands-on modeling test, and also their modeling actions in the hands-on test to understand the impact of the self-explanation scaffolding on their computational scientific modeling. The results showed that the two groups demonstrated similar levels of improvements in the paper-based test, suggesting that both types of scaffolding are helpful for computational scientific modeling learning. However, the self-explanation group demonstrated significantly better modeling quality in the hands-on test. Furthermore, the spotlight analysis found the moderation effect of the modeling actions on the relation between the two treatments and the model quality. The self-explanation group constructed high quality models if they took only a low number of modeling actions. Conversely, frequent modeling actions are necessary for the demonstration group to construct quality scientific models. The results suggest that the self-explanation scaffolding is more effective since students did not simply rely on the trial-and-error strategy, but adopted a strategic approach to constructing scientific models. • Computational scientific modeling translates science concepts into executable model. • Students receiving the self-explanation prompts demonstrated better model quality. • Frequent modeling actions were not required for the self-explanation group. • The self-explanation group adopted a strategic approach to constructing models. • Students receiving demonstration scaffolding relies on trial-and-error strategy. [ABSTRACT FROM AUTHOR]

This paper argues for theoretical modeling and model-construction as central (but not necessarily the only) types of activities that philosophers of social ontology (in the analytic tradition) engage in. This claim is defended through a detailed case study and revisionary interpretation of Raimo Tuomela's account of the we-perspective. My interpretation is grounded in Ronald Giere's account of scientific models, and argued to be compatible with, but less demanding than Tuomela's own description of his account as a philosophical theory of the social world. My approach is also suggested to be applicable to many (but not necessarily all) other methodologically naturalist accounts of collective intentionality and social ontology. [ABSTRACT FROM AUTHOR]

A powerful idea put forward in the recent philosophy of science literature is that scientific models are best understood as instruments, tools or, more generally, artifacts. This idea has thus far been developed in combination with the more traditional representational approach: accordingly, current artifactualist accounts treat models as representational tools. But artifactualism and representationalism are independent views, and adopting one does not require acceptance of the other. This paper argues that a leaner version of artifactualism, free of representationalist assumptions, is both desirable and viable. Taking seriously the idea that models are artifacts can help us philosophically to make sense of how and why scientific modeling works even without reference to representation. [ABSTRACT FROM AUTHOR]

Scientific modeling provides mathematical abstractions of real-world systems and builds software as implementations of these mathematical abstractions. Ocean science is a multidisciplinary discipline developing scientific models and simulations as ocean system models that are an essential research asset. In software engineering and information systems research, modeling is also an essential activity. In particular, business process modeling for business process management and systems engineering is the activity of representing processes of an enterprise, so that the current process may be analyzed, improved and automated. In this paper, we employ process modeling for analyzing scientific software development in ocean science to advance the state in engineering of ocean system models and to better understand how ocean system models are developed and maintained in ocean science. We interviewed domain experts in semi-structured interviews, analyzed the results via thematic analysis, and modeled the results via the Business Process Modeling Notation (BPMN). The processes modeled as a result describe an aspired state of software development in the domain, which are often not (yet) implemented. This enables existing processes in simulation-based system engineering to be improved with the help of these process models. [ABSTRACT FROM AUTHOR]

Representational views of the mind traditionally face askeptical challenge on perceptual knowledge: if ourexperience of the world is mediated by representations builtupon perceptual inputs, how can we be certain that ourrepresentations are accurate and our perceptual apparatusreliable? J. J. Gibson's ecological approach provides analternative framework, according to which direct perceptionof affordances does away with the need to posit internalmental representations as intermediary steps betweenperceptual input and behavioral output. Gibson accordinglyspoke of his framework as providing “reasons for realism.” Inthis paper I suggest that, granting Gibson his reasons forperceptual realism, the Gibsonian framework motivates anti-realism when it comes to scientific theorizing and modeling.If scientists are Gibsonian perceivers, then it makes sense totake their use of models in indirect investigations of real-world phenomena not as representations of the phenomena,but rather as autonomous tools with their own affordances.

This study investigated eight experienced science teachers' and eight senior high school students' metavisualization when they drew models to represent their concepts of carbon cycling. Qualitative data collection techniques including think‐aloud tasks and follow‐up retrospective interviews were employed. The purposes of the study included: (1) to propose a framework differentiating performance levels leading to metavisual competence; and (2) to identify students' metavisualization difficulties by comparing experienced teachers' and novice students' performances. Four aspects of metavisualization were investigated, including use of epistemic knowledge of visualization, demonstration of metacognition in visualization, use of judgment criteria, and use of metavisual strategies. Levels of progression from none or less to sufficient metavisual competence were proposed based on the participants' metavisualization performances. Three types of epistemic knowledge of visualization were identified, namely, a view of visualization as a learning or expression tool, a static view of representation and target, and a dynamic view between purposeful visualization and knowledge construction. Five types of metavisual strategies were also identified, namely, resourcing, focusing, inducting, deducing, and perfecting strategies. Comparison of the teachers' and students' metavisualization indicates that the experienced teachers demonstrated distinctive metacognition and metavisual strategies that helped them achieve the goal of fluent visualization. The findings provide insights into how to support individuals' development of metavisual competence for scientific modeling. [ABSTRACT FROM AUTHOR]

The incorporation of discussions about the nature of scientific knowledge in science teaching activities is a fundamental and quite challenging aspect. This paper aims to present an empirical contrast of theoretical models exploring the particular case of the movement of bodies with variable mass as a way to highlight the role of the scientific modeling process in an experimental situation. We investigate the implications of assuming certain idealizations in the contrasted models, explaining the representative character of scientific knowledge in the development of experimental investigations, incorporating the influence of different resistive forces on these bodies, such as air drag forces and rolling resistance. Thus, we seek to expose an example of a situation with the potential to be explored by teachers in overcoming naive epistemological conceptions by students. Also, the investigation involves the use of open-source computational resources in the construction of numerical solutions of theoretical models and the collection of experimental data, favoring the use of free technologies in physics teaching. Four of the six empirically contrasted theoretical models also had their analytical solutions deduced and compared with their respective numerical solutions. The results show that the choice of the best theoretical model that represents the collected data involves more than the analysis of the quality of the adjustment of the models because it demands an evaluation of the hypotheses assumed in the theoretical models and the decisions made by the researcher. [ABSTRACT FROM AUTHOR]

This paper analyzes three contrasting strategies for modeling intentional agency in contemporary analytic philosophy of mind and action, and draws parallels between them and similar strategies of scientific model-construction. Gricean modeling involves identifying primitive building blocks of intentional agency, and building up from such building blocks to prototypically agential behaviors. Analogical modeling is based on picking out an exemplary type of intentional agency, which is used as a model for other agential types. Theoretical modeling involves reasoning about intentional agency in terms of some domain-general framework of lawlike regularities, which involves no detailed reference to particular building blocks or exemplars of intentional agency (although it may involve coarse-grained or heuristic reference to some of them). Given the contrasting procedural approaches that they employ and the different types of knowledge that they embody, the three strategies are argued to provide mutually complementary perspectives on intentional agency. [ABSTRACT FROM AUTHOR]

The purpose of this study was to understand how students engage in computer-supported modeling-based activities (CSMBAs), and the relationship between their engagement and their modeling competence. Different facets of learning engagement were measured through multiple data, including performance on modeling tasks, self-reported level of engagement, and online behavior patterns of science modeling. The research participants were 76 11th-grade students in Taiwan. The research instruments included online student worksheets, an engagement questionnaire, computer logs, and modeling competence tests. Students' online worksheets were scored and used to group them into three performance groups—the low-level-performance group (LPG), the middle-level-performance group (MPG) and the high-level-performance group (HPG). ANOVA statistics lag sequential analysis (LSA), and ANCOVA statistics were used for the data analysis. The results showed that, first, in analyzing the engagement questionnaires, students' negative cognitive engagement, negative behavioral engagement, and negative social engagement all played important roles in their low performance in the CSMBAs. Second, through the use of LSA, it was found that the LPG students lacked evaluative behavior, while the HPG students emphasized reflective behavior. Third, analysis of the students' pre- and post-modeling competence tests showed that those who were in the HPG and MPG scored significantly higher than those in the LPG in two dimensions of the modeling competence post-tests. The results indicate that efforts made in completing tasks in CSMBAs can lead to better modeling competence. Implications for developing future CSMBAs and for promoting student engagement are suggested. [ABSTRACT FROM AUTHOR]

Modeling and using technology are two practices of particular interest to K-12 science educators. These practices are inextricably linked among professionals, who engage in modeling activity with and across a variety of representational technologies. In this paper, we explore the practices of five sixth-grade girls as they generated models of smell diffusion using drawing, stop-motion animation, and computational simulation during a multi-day workshop. We analyze video, student discourse, and artifacts to address the questions: In what ways did learners’ modeling practices, reasoning about mechanism, and ideas about smell shift as they worked across this variety of representational technologies? And, what supports enabled them to persist and progress in the modeling activity? We found that the girls engaged in two distinct modeling cycles that reflected persistence and deepening engagement in the task. In the first, messing about, they focused on describing and representing many ideas related to the spread of smell at once. In the second, digging in, they focused on testing and revising specific mechanisms that underlie smell diffusion. Upon deeper analysis, we found these cycles were linked to the girls’ invention of “oogtom,” a representational object that encapsulated many ideas from the first cycle and allowed the girls to restart modeling with the mechanistic focus required to construct simulations. We analyze the role of activity design, facilitation, and technological infrastructure in this pattern of engagement over the course of the workshop and discuss implications for future research, curriculum design, and classroom practice.

Metacognitive skills are critical in learning but difficult toteach. Thus the question becomes how can we facilitatemetacognitive tutoring? We present an exploratory learningenvironment called MILA-T with embedded metacognitivetutors imitating five functional roles of teachers inclassrooms. We tested MILA–T in a controlled experimentwith 237 middle school students. We examine the impact ofMILA–T on the models of a natural phenomenon constructedby the students. We find that students with access to MILA–Twrote better evidential justifications for their models, andthus, deliver better-justified models for the phenomenon. Wealso find that these improvements persisted during a transfertask. These results lend support for organizing metacognitivetutoring around the functional roles of teachers for supportinginquiry-driven modeling

This article aims to answer what I call the "constitution question of engineering modeling": in virtue of what does an engineering model model its target system? To do so, I will offer a category-theoretic, structuralist account of design, using the olog framework. Drawing on this account, I will conclude that engineering and scientific models are not only cognitively but also representationally indistinguishable. I will finally propose an axiological criterion for distinguishing scientific from engineering modeling. [ABSTRACT FROM AUTHOR]

In this qualitative case study, we purposively selected a passive student in elementary science class from a small group and examined what factors contributed to her socially and momentarily construction of epistemic emotions. We used various data such as emotion diaries, transcripts of the video recordings, postlesson interviews, and field notes to document her involvement in the six lessons of the scientific modeling of the human respiratory system. Our analysis revealed that she constructed the epistemic emotions of frustration, anxiety, and joy. Her nonparticipation and constructed epistemic frustration in Lesson 1 were because of her unfamiliarity with the task and her perception that her ideas will be rejected by others. Due to her lack of previous knowledge, that desire to engage in the small group argumentative discussions was affected by her perception that her ideas would be rejected. With slightly increased familiarity and a sense of acceptance in Lessons 2 and 3, she displayed passive participation and constructed epistemic anxiety because she was not certain whether her opinions were scientifically correct or not. With the uncertainty of her ideas, she was not able to develop a personal motivational drive to engage in effortful participation. Finally, she exhibited active participation and constructed epistemic joy in Lessons 5 and 6 when she became familiar with scientific argumentation and scientific modeling, in addition to her developed sense of full acceptance. These results indicated the intertwined relations of affective, cognitive, and social aspects of learning on student's participation and construction of epistemic emotions. [ABSTRACT FROM AUTHOR]

Research suggests that translanguaging can be transformative for teaching and learning by making students' diverse linguistic resources a meaningful part of classroom discourse. Building on this study, researchers have explored how translanguaging practices can support learning in STEM (science, technology, engineering, and mathematics), primarily in the context of bilingual classrooms. However, in the United States, most students learn in English‐dominant classrooms. In response, researchers and educators have begun to explore strategies for inviting and leveraging translanguaging in English‐dominant classrooms, primarily focusing on literacy learning. Less is known about supporting translanguaging in English‐dominant STEM classrooms, particularly with monolingual teachers. In an English‐dominant sixth‐grade STEM classroom engaging in a 9‐week ecology unit, we explored how scientific modeling could not only provide a context for inviting translanguaging, but how it could also provide a setting where modeling and translanguaging could be experienced as analogous meaning‐making practices. Our findings demonstrate that translanguaging has the potential to support new kinds of learning in English‐dominant STEM classrooms, not only about STEM content and practices but also about what counts as legitimate and valuable participation in these spaces. [ABSTRACT FROM AUTHOR]

Mechanisms consist of component parts and processes organized in a specific way to produce changes that may give rise to one or more phenomena. I aim to examine the generative mechanism of generative material models in the production of new material models. A generative material model in biology is a living material model (such as model organisms) that is capable of generating new material models (e.g., new model organisms). I contend that generative mechanisms of a generative material model are not to be conflated with biological mechanisms: the former is a mechanism manipulated and altered by the modeler while the latter is a naturally occurring mechanism. I examine the ways in which idealization may contribute to the generative mechanism in generating new model organisms. I articulate that the component parts and activities in a generative mechanism are to a large extent shaped by idealization in generative material modeling. Although biological mechanisms do have a role to play in the generation of model organisms, I propose that we shall view the generative mechanism as a hybrid of biological mechanisms and modeling activities. I shall argue that the notion of generative mechanisms as hybrid mechanisms is different from the extant conceptions of mechanisms in various aspects. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Textbook Research is the property of National Academy for Educational Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Teachers' instructional practices are critical for guiding students' engagement in scientific practices within the classroom knowledge building community. In this study, we seek to understand how teachers' instructional supports of epistemic aspects involved in scientific modeling may influence students' meaningful engagement in the practice. In particular, we examine two upper elementary teachers' epistemological messages about modeling over the course of a model‐based unit and focus group conversations in consensus model activities to explore the relationship between teachers' instructional supports and students' practice. The results suggest that each teacher was sharing complex messages about various aspects of the modeling practice ranging from procedural to epistemic. Further, those messages were then reflected in students' work. Our findings indicate that teachers' (a) foregrounding of epistemological messages, (b) consistency in emphasizing those messages, and (c) unpacking of those messages may play an important role in how epistemological ideas about modeling are taken up by students. We conclude the paper by discussing implications for supporting teachers in meaningfully enacting modeling practice. [ABSTRACT FROM AUTHOR]

Making sense of why something succeeded or failed is central to scientific practice: it provides an interpretation of what happened, i.e. an hypothesized explanation for the results, that informs scientists’ deliberations over their next steps. In philosophy, the realism debate has dominated the project of making sense of scientists’ success and failure claims, restricting its focus to whether truth or reliability best explain science’s most secure successes. Our aim, in contrast, will be to expand and advance the practice-oriented project sketched by Arthur Fine in his work on the Natural Ontological Attitude. An important obstacle to articulating a positive program, we suggest, has been overlooking how scientists adopt standardized rules and procedures in order to define and operationalize meanings for success and failure relative to their situated goals. To help fill this gap, we introduce two new ideas, design specifications and track records, and show how they advance our ability to make sense of scientific modeling practices while maintaining a deflationary stance toward the realism debate. [ABSTRACT FROM AUTHOR]

The incorporation of discussions about the nature of scientific knowledge in science teaching activities is a fundamental and quite challenging aspect. This paper aims to present an empirical contrast of theoretical models exploring the particular case of the movement of bodies with variable mass as a way to highlight the role of the scientific modeling process in an experimental situation. We investigate the implications of assuming certain idealizations in the contrasted models, explaining the representative character of scientific knowledge in the development of experimental investigations, incorporating the influence of different resistive forces on these bodies, such as air drag forces and rolling resistance. Thus, we seek to expose an example of a situation with the potential to be explored by teachers in overcoming naive epistemological conceptions by students. Also, the investigation involves the use of open-source computational resources in the construction of numerical solutions of theoretical models and the collection of experimental data, favoring the use of free technologies in physics teaching. Four of the six empirically contrasted theoretical models also had their analytical solutions deduced and compared with their respective numerical solutions. The results show that the choice of the best theoretical model that represents the collected data involves more than the analysis of the quality of the adjustment of the models because it demands an evaluation of the hypotheses assumed in the theoretical models and the decisions made by the researcher. [ABSTRACT FROM AUTHOR]

Representationalism—the view that scientific modeling is best understood in representational terms—is the received view in contemporary philosophy of science. Contributions to this literature have focused on a number of puzzles concerning the nature of representation and the epistemic role of misrepresentation, without considering whether these puzzles are the product of an inadequate analytical framework. The goal of this paper is to suggest that this possibility should be taken seriously. The argument has two parts, employing the "can't have" and "don't need" tactics drawn from philosophy of mind. On the one hand, I propose that representationalism doesn't work: different ways to flesh out representationalism create a tension between its ontological and epistemological components and thereby undermine the view. On the other hand, I propose that representationalism is not needed in the first place—a position I articulate based on a pragmatic stance on the success of scientific research and on the feasibility of alternative philosophical frameworks. I conclude that representationalism is untenable and unnecessary, a philosophical dead end. A new way of thinking is called for if we are to make progress in our understanding of scientific modeling. [ABSTRACT FROM AUTHOR]

Scientific modeling and coding are critical skills to be integrated into K-12 instruction. Research has shown that preservice teachers are often ill-prepared for teaching scientific modeling, and lack opportunities to learn coding within teacher education programs. The present study reports the implementation of an instructional module and online system, called Coding in Scientific Modeling Lessons (CS-ModeL), which was designed to scaffold preservice science teachers' learning to code simulations and design scientific modeling lessons that feature simulation coding. In this study, we examined preservice teachers' epistemic discourse during simulation coding, perceptions of coding for future teaching, and coding-enhanced scientific modeling lessons. This was a qualitative single case study that involved six participants enrolled in a science teacher education course. Participants worked in pairs during scientific modeling activities, and each pair was considered an embedded unit within the single case. Data sources included transcripts of screen recordings captured during simulation coding, transcripts of individual semi-structured interviews, and lessons in which participants used simulation coding as part of scientific modeling activities. Qualitative thematic analysis was conducted. Findings revealed that participants' epistemic discourse led to correction of science misconceptions. However, lack of debugging and conflict argumentation skills detracted from their epistemic discourse quality. Participants perceived coding as a beneficial skill for K-12 students though they voiced concerns about teaching with coding unassisted. Participants failed to design truly interdisciplinary and authentic scientific modeling activities including simulation coding. Study limitations and future research directions are discussed. [ABSTRACT FROM AUTHOR]

Learning standards for K-12 science education emphasize the importance of engaging students in practices that scientists perform in their profession. K-12 teachers are expected to engage students in scientific modeling, which entails constructing, testing, evaluating, and revising their own models of science phenomena while pursuing an epistemic goal. However, conceptualizing models of unobservable science phenomena without support is daunting for students. We propose that creating science simulations with block-based coding in Scratch is a promising approach to support student's scientific modeling and learning to code. However, research indicates that preservice and in-service science teachers often hold a deficient understanding of scientific modeling instruction, and lack experience teaching with coding. Professional learning on use of block-based coding in scientific modeling instruction is needed though such interdisciplinary research is scarce. In this paper, we review pertinent literature and propose five guidelines for teacher educators striving to offer such professional learning. The guidelines informed the design and development of coding in scientific modeling lessons (CS-ModeL), which is a module and an online tool for scaffolding teachers' learning to code science simulations, and to integrate simulation coding activities into scientific modeling lessons, respectively. We discuss how guidelines informed the design and development of CS-ModeL, as well as plans for future research. [ABSTRACT FROM AUTHOR]

Abstraction is one of the important processes in scientific modeling. It has always been implied that abstraction is an agent-centric activity that involves the cognitive processes of scientists in model building. I contend that there is an autonomous aspect of abstraction in many modeling activities. I argue that the autonomous process of abstraction is continuous with the agent-centric abstraction but capable of evolving independently from the modeler's abstraction activity. [ABSTRACT FROM AUTHOR]

In this case study, we describe an alternative storyline design approach that we adopted to translate an informal, out-of-school summer science experience with a strong emphasis on developmental neuroscience and data literacy into a more inclusive, replicable, and scalable experience for formal high school science instruction. Combining elements of problem- and project-based learning, a storyline is a curriculum model that engages students in the application of investigative science and engineering practices to incrementally build conceptual models that explain an observable (anchoring) phenomenon. Published reports on the storyline design process describe procedures and tools that are well suited to the creation of novel instructional units. However, these design methods are difficult to apply to projects aimed at translating pre-existing science experiences and resources into classroom storyline units. In this descriptive case study, we discuss a series of alternative design procedures that we utilized to achieve this adaptation. Our overarching project goal was to create the resources necessary to engage high school students in the construction of a multidimensional explanatory model for an unusual movement disorder that assimilates converging lines of behavioral, neuroanatomical, neurophysiological, molecular genetic, developmental, and cellular data. The methods described in this case study establish a design template for other biomedical scientists who are interested in adopting a storyline approach to bring aspects of their work or educational projects into science classrooms and into closer alignment with a new vision for science teaching and learning articulated in the National Research Council's A Framework for K-12 Science Education and the Next Generation Science Standards .

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To organize for the improvement of science instruction teachers need opportunities to collaboratively learn from practice, in practice, and to engage in the revision of classroom tools. In this paper, we examine how a professional learning community (PLC), comprised of middle school teachers and researchers, worked on the improvement of Ambitious Science Teaching (AST) practices and developed instructional practices and tools supporting model‐based inquiry. This paper focuses on the first year of a 5‐year research–practice partnership in which teachers and researchers routinely coplanned, cotaught, and codebriefed science lessons via improvement cycles. We conducted an analysis of teacher‐designed tools, reflective talk, and classroom observations. All teachers engaged in increasingly sophisticated forms of AST practices over the year and began to use a similar tool to scaffold scientific modeling with students. Yet, there were two distinct variations that evolved with grade‐level teams. One team developed a practice and tool supporting students' final form articulation of ideas with models and the other team developed a practice and tools supporting the revision of models over a unit of instruction. We argue that both grade‐level teams engaged in productive learning and that PLC benefited from having different perspectives on relatively similar practices for scaffolding students' scientific modeling. On the basis of the findings, we propose three key components to a practice‐based theory for how PLCs negotiate tools as a part of the improvement of teaching practices: anchoring improvement in a particular tool and practice, supporting variation in teacher learning and making teachers' pedagogical reasoning explicit. [ABSTRACT FROM AUTHOR]

A feature of science is its production of evidence-based explanations. Scientific models can both provide causal explanations and be predictive of natural phenomena. Modeling-based inquiry (MBI) is a pedagogical strategy that promotes students' deep learning about phenomena via engagement in authentic scientific practices. Some university instructors have begun to facilitate MBI in their courses, notably those aimed at aspiring K-12 science educators who, per the Next Generation Science Standards, are encouraged to implement MBI. Yet exploration of curriculum and teaching with MBI in postsecondary environments is scarce. We detail a novel MBI curriculum implemented in a postsecondary ecology course that included students interested in future careers in education. The curriculum engages students in modeling why there is greater biological diversity in tropical than in temperate regions. This biological phenomenon continues to be of great interest to the scientific community. We briefly detail how the curriculum impacted students' understanding of participation in aspects of scientific practices and their comfort with facilitating MBI. [ABSTRACT FROM AUTHOR]

Developing scientific literacy about water systems is critical for K‐12 students. However, even with opportunities to build knowledge about the hydrosphere in elementary classrooms, early learners may struggle to understand the water cycle (Forbes et al., ; Gunckel et al., ; Zangori et al., ; Zangori et al.,). Scientific modeling affords opportunities for students to develop representations, make their ideas visible, and generate model‐based explanations for complex natural systems like the water cycle. This study describes a comprehensive evaluation of a 5‐year, design‐based research project focused on the development, implementation, revision, and testing of an enhanced, model‐centered version of the Full Option Science System (FOSS) Water (2005) unit in third grade classrooms. Here, we build upon our previous work (Forbes et al., a; b; Vo et al., ; Zangori et al., ; Zangori et al.,) by conducting a comparative analysis of student outcomes in two sets of classrooms: (1) one implementing the modeling‐enhanced version of the FOSS Water unit developed by the research team (n = 6), and 2) another using the standard, unmodified version of the same curricular unit (n = 5). Results demonstrate that teachers in both conditions implemented the two versions of the curriculum with relative fidelity. On average, students exposed to the modeling‐enhanced version of the curriculum showed greater gains in their model‐based explanations for the hydrosphere. Engagement in scientific modeling allowed students to articulate hydrologic phenomena by (1) identifying various elements that constitute the hydrosphere, (2) describing how these elements influenced the movement of water in the hydrosphere, and (3) demonstrating underlying processes that govern the movement of water in the hydrosphere. [ABSTRACT FROM AUTHOR]