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Mechanistic reasoning about an artifact or system involves thinking about its underlying entities and the properties, activities, and cause-effect relationships of those entities. Previous studies of children's mechanistic reasoning about engineering solutions have mostly focused on specific mechanical systems such as gear trains. Yet there is growing interest in more contextualized, community-connected engineering design experiences for elementary students. Important questions remain about how the specific features of community contexts influence student opportunities for engineering design practice and reasoning. In this study, we explore whether comparisons in students' mechanistic reasoning can be made across a range of five different community design contexts. For this qualitative descriptive study, we focus on interview data collected after each of five community-connected engineering-enriched science curriculum units: accessible playground design (3rd grade, N = 8, district A, schools 1 and 2), displaced animal relocation design (3rd grade, N = 10, district A, school 1), migration stopover site design (4th grade, N = 4, district A, school 2), retaining wall design (4th grade, N = 13, district B, school 1), and water filter design (5th grade, N = 9 students, district A, school 3). The findings showed that all students named entities and described entity factors for the design solutions for all five units. For the playground, displaced animals, and stopover sites units, some students described the design artifacts without explicitly expressing connections between entity factors and/or the way factors linked up to the design performance. We argue that particular features of the design tasks influenced students' approaches to explaining their design solutions. Therefore, we can claim that comparisons can be made across different community-connected engineering design contexts in terms of children's mechanistic reasoning. [ABSTRACT FROM AUTHOR]

In physiology education, students must learn to recognize and construct causal explanations. This challenges students, in part, because causal explanations in biology manifest in different varieties. Unlike other natural sciences, causal mechanisms in physiology support physiological functions and reflect biological adaptations. Therefore, students must distinguish between questions that prompt a proximate or an ultimate explanation. In the present investigation, we aimed to determine how these different varieties of student knowledge coordinate within students' written explanations. Prior research in science education demonstrates that students present specific challenges when distinguishing between proximate and ultimate explanations: students appear to conflate the two or construct other nonmechanistic explanations. This investigation, however, demonstrates that analytic frameworks can distinguish between students' proximate and ultimate explanations when they are provided explanatory scaffolds that contextualize questions. Moreover, these scaffolds and prompts help students distinguish between physiological functions and the cellular and molecular mechanisms that underpin them. Together, these findings deliver insight into the context-sensitive nature of student knowledge in physiology education and offer an analytic framework for identifying and characterizing student knowledge in physiology. NEW & NOTEWORTHY: Why ask why? How questions posed in physiology task students with developing their mechanistic reasoning. Why questions sometimes undermine this reasoning. Prior research, however, also illustrates that framing the context of a question explicitly supports students in distinguishing between question types. We further illustrate how providing such context in the form of explanatory scaffolds and prompts allows students to tap different and useful varieties of knowledge when constructing written explanations. [ABSTRACT FROM AUTHOR]

How anthropomorphic reasoning functions in scientific thinking has been a controversial topic. There is evidence it is problematic as well as evidence it can play productive roles, for scientists and for students. In science education, however, the prevailing view remains that it is an impediment. For this study, we have chosen examples of what we claim are productive instances in elementary students' reasoning, and we analyze them to understand how anthropomorphisms functioned to support scientific thinking. We argue that one productive role is to support temporary shifts from mechanistic reasoning to more general storytelling, in particular to fill gaps as students work to explain phenomena. That is, we propose that children may come to mechanistic explanation as a form of storytelling. Part of their value is in allowing students to "invent science" based on their existing knowledge, supporting them to understand science as sensemaking. [ABSTRACT FROM AUTHOR]

Previous studies have documented the promising results from student‐constructed representations, including stop‐motion animation (SMA), in supporting mechanistic reasoning (MR), which is considered an essential thinking skill in science education. Our current study presents theoretically and empirically how student‐constructed SMA contributes to promoting MR. As a theoretical perspective, we propose a framework hypothesizing the link between elements of MR and the construction nature of SMA, that is, chunking and sequencing. We then examined the extent to which this framework was consistent with a multiple‐case study in the domain of static electricity involving five secondary school students constructing and using their own SMA creation for reasoning. In addition, students' reasoning in pre‐ and postconstruction of an SMA was examined. Our empirical findings confirmed our framework by showing that all students identified the basic elements of MR, that is, entities and activities of entities, when engaging in chunking and sequencing. Chunking played a role in facilitating students to identify entities responsible for electrostatic phenomena, and sequencing seemed to elicit students to specify activities of these entities. The analysis of students' reasoning in pre‐ and postconstruction of SMA found that student‐generated SMA has a potential effect on students' retention of the use of MR. Implications for instruction with SMA construction to support MR are discussed. [ABSTRACT FROM AUTHOR]

Despite its proven added value, modeling‐based learning (MbL) in science is not commonly incorporated into the early grades. Our purpose in this descriptive case study was to enrich our understanding of how kindergarten children enact MbL by examining these children's constructed models and their accompanying oral descriptions of their models. For this purpose, we adopted a drawing‐based modeling approach in which children used annotated drawings to represent their models. The participants consisted of four groups of 5‐ to 6‐year‐olds (68 children total) who studied the solution of substances in water. We analyzed child‐developed models (artifact analysis) and their oral presentations (discourse analysis), seeking to provide rich, detailed descriptions of the characteristics of these models. Our findings suggest that children in the study developed five different types of models using three different depiction strategies. Our findings also suggest that when developing and presenting their models of a physical phenomenon, our kindergarten children tended to rely on analogical reasoning to identify similar, known situations corresponding to the phenomenon under study. They then invoked mechanistic reasoning to develop representations of the phenomenon under study based on the analogy they used. The spectrum of mechanistic reasoning used by the children, and the analysis of the structure and components of their constructed models serve as evidence suggesting that despite their limited experiences with formal science education, as well as with MbL in science, participating children could successfully engage in authentic MbL activities. We contend that this is aligned with the idea of modeling resources, suggesting that it is more productive to help children to develop more reliable access to modeling resources they already have, even though they are usually not aware of their connection to MbL, such as prior scientific knowledge, experience, and MbL skills. [ABSTRACT FROM AUTHOR]

Evidence-based medicine (EBM) has long deemphasized mechanistic reasoning and pathophysiological rationale in assessing the effectiveness of interventions. The EBM+ movement has challenged this stance, arguing that evidence of mechanisms and comparative studies should both be seen as necessary and complementary. Advocates of EBM+ provide a combination of theoretical arguments and examples of mechanistic reasoning in medical research. However, EBM+ proponents have not provided recent examples of how downplaying mechanistic reasoning resulted in worse medical results than would have occurred otherwise. Such examples are necessary to make the case that EBM+ responds to a problem in clinical practice that urgently demands a solution. In light of this, we examine the failed rollout of efavirenz as a first-line HIV treatment in Zimbabwe as evidence of the importance of mechanistic reasoning in improving clinical practice and public health policy decisions. We suggest that this case is analogous to examples commonly given to support EBM. [ABSTRACT FROM AUTHOR]

The ability of school students to use health-related knowledge for their and their community's needs is referred to as health literacy and is regarded as a combination of knowledge and motivational factors. In the case of cancer literacy, high-school students have some knowledge about risk factors, but not much is known about their understanding of the mechanisms by which these risk factors cause cancer. In addition, motivational factors, such as psychological perceptions of cancers, are not well-characterised in this population. Hence, data are insufficient to support the development of educational programmes for enhancing cancer literacy. We characterised 10th-grade students' knowledge and illness perceptions using open questions and Brief Illness Perception Questionnaire and searched for an association between the two. We found that students have much more causal knowledge than mechanistic knowledge about cancer. We also found that the ability to reason about the mechanisms by which cancer develops is associated with the perceived severity of the disease. Thus, the mechanisms leading to cancer should be taught rather than focusing on risk factors. This study also provides evidence for a possible interplay between a specific type of knowledge (mechanistic) about a given phenomenon (cancer) and psychological perceptions of that phenomenon. [ABSTRACT FROM AUTHOR]

Systems thinking is a powerful concept in biology which can help students understand how the diversity of contexts in the biology curriculum all have similar underlying characteristics. However, it is not currently a common feature in secondary education as it is not part of the National Curriculum of England. Research on systems thinking has suggested that students struggle with systems thinking in secondary school biology but there is little consensus on how systems thinking can be implemented in schools. Most research focuses on developing pedagogical activities that function as stand-alone units. In this article I complement this work by suggesting that systems thinking needs to be an integrated component of the entire biology curriculum and I offer a simple framework for analysing the extent of systems thinking in curricula, assessments, and student thinking. The intention is that with the creation of pragmatic frameworks and simple pedagogy for systems thinking, the latter will become more prevalent in secondary school biology curricula. [ABSTRACT FROM AUTHOR]

Many studies have characterized students' difficulties in understanding and reasoning about scientific mechanisms. Some of those studies have drawn implications on teaching mechanisms and how to guide students while reasoning mechanistically. In this theoretical article, I claim that one component that has not garnered much attention in the science education literature, unlike other components of mechanistic explanations, is the black box construct, that is, missing mechanistic parts within mechanistic explanations (explanatory black box). By reviewing the literature on mechanisms and mechanistic explanations in the philosophy of science and cognitive psychology, I argue that explanatory black boxes are an inherent part of mechanistic explanations and that their recognition is essential for learning mechanisms, scientific literacy, and understanding the nature of science. Examples from biology education are provided as a case of a complex multileveled scientific field. In the absence of a pedagogical approach for teaching explanatory black boxes, I turn to studies and frameworks from computer science education that may guide educators on how to begin discussing this construct in the science classroom. [ABSTRACT FROM AUTHOR]

ABSTRACT Understanding molecular processes and coordinating the various activities across levels of organization in biological systems is a complicated task, yet many curricular guidelines indicate that undergraduate students should master it. Employing mechanistic reasoning can facilitate describing and investigating biological phenomena. Biofilms are an important system in microbiology and biology education. However, few empirical studies have been conducted on student learning of biofilms or how students utilize mechanistic reasoning related to systems thinking to explain biofilm formation. Using mechanistic reasoning and the theory of knowledge integration as conceptual and analytical frameworks, we examined the features of 9 undergraduate biology students’ mechanistic models of a specific transition point in biofilm development. From these data, we constructed a model of knowledge integration in the context of biofilms, which categorizes students’ knowledge based on features of their descriptions (e.g., entities, correct connections, and the nature of connections). We found that 4 of 9 students produced a fragmented model, 4 of 9 students produced a transitional model, and 1 student produced a connected model. Overall, students often did not discuss cell-cell communication mechanics in their mechanistic models and rarely included the role of gene regulation. Most connections were considered nonnormative and lacked important entities, leading to an abundance of unspecified causal connections. We recommend increasing instructional support of mechanistic reasoning within systems (e.g., identifying entities across levels of organization and their relevant activities) and creating opportunities for students to grapple with their understanding of various biological concepts and to explore how processes interact and connect in a complex system.

In this paper, we describe and evaluate a study on the use of mechanism comics for writing solutions to a task in a written exam for the course “Organic Chemistry I for Pre-Service Chemistry Teachers.” The students had to design a reaction mechanism for a reaction that was unknown to them and write captions explaining every step of their reaction mechanism. The students’ work was evaluated using the method of qualitative content analysis in four rounds by both authors. The majority of the captions were coded as “descriptive” and only a minority as “causal.” This means that the students mostly described “what” happened, but seldom “why” this happened. Implicit electron movement was also described more often than explicit electron movement. The majority of the captions were technically correct. In summary, the students were capable of designing and describing a reaction mechanism for a previously unknown reaction. The quality of their reasoning could be improved, however. In the new course, the quality of students’ mechanistic reasoning and then especially their explanations of “why” mechanistic steps occur will be given much clearer emphasis.

Rationale: Exercise interventions and policies are widely prescribed in both sport and healthcare. Research investigating exercise interventions and policies is generally conducted using an Evidence‐Based framework, placing an emphasis on evidence gathered from randomised controlled trials (RCTs). Aims and objectives: To explore the idea that, in addition to the assessment of evidence from RCTs when investigating exercise interventions, mechanistic studies ought to also be assessed and considered. Methods: This article assesses the rationale supporting the use of RCTs as evidence for exercise interventions, and the use of evidence of mechanisms in establishing efficacy, determining external validity, and tailoring interventions. Results and conclusions: The article argues that evidence from mechanistic studies ought to be considered alongside evidence from RCTs because: as RCTs investigating exercise interventions tend to be of low quality, mechanistic studies ought to be used to reinforce the evidence base; further, evidence from mechanistic studies is highly useful for both questions of extrapolation and implementation. This article argues for this on theoretical grounds, and also draws on a number of case studies. [ABSTRACT FROM AUTHOR]

This study investigated whether contexts of failure improve preschool children's mechanistic reasoning. We showed 3- to 6-year-old children (N = 55, M = 5;2) how to make an unfamiliar toy work to play a goal-directed game. Between conditions we manipulated children's success in making the toy work by surreptitiously turning a hidden causal switch ON (Success) or OFF (Failure) before they interacted with the toy. We then measured children's exploration of the toy, explanations for how the toy worked, and generalizations about how a new functioning toy would work. Children in the Failure condition were more likely to discover the hidden causal mechanism and talk about it in their explanations about the toy. Younger children spent more time exploring the toy than older children but were not more likely to discover the hidden causal mechanism. The findings are discussed as they relate to the emergence of spontaneous mechanistic exploration over development, how this then supports mechanistic reasoning, and the role of failure in children's early causal reasoning. • Preschool children search more about causal mechanisms in the context of failure. • Preschool children talk more about causal mechanisms in the context of failure • 3- to 4-year-olds explore causal systems longer than 5- to 6-year-olds [ABSTRACT FROM AUTHOR]

What one knows, what one does not know, and what one wants—as well as the dynamics among them—play major roles in psychology. We herein discuss such dynamics—namely, learning—as a desire-driven, generative process that increases knowledge and wisdom through cycles in which incoming information (a) exposes new areas of ignorance, (b) changes one's preferences, and (c) creates an appetite for more knowledge. Evidence is presented that shows that when salient feedback along two described dimensions—numerical and/or mechanistic—conflicts with one's estimates, predictions, or explanations, it can trigger surprise, which in turn produces wisdom-enhancing conceptual change. Highlighting ignorance's importance in increasing wisdom, we describe (1) interventions utilizing surprising information (for instance, from our Numerically Driven Inferencing paradigm), and (2) a specific focus on global warming as a touchstone for increasing wisdom, which includes (3) a direct-to-the-public website for fostering conceptual changes regarding that central phenomenon of climate change (www.HowGlobalWarmingWorks.org).

What one knows, what one does not know, and what one wants—as well as the dynamics among them—play major roles in psychology. We herein discuss such dynamics—namely, learning—as a desire-driven, generative process that increases knowledge and wisdom through cycles in which incoming information (a) exposes new areas of ignorance, (b) changes one's preferences, and (c) creates an appetite for more knowledge. Evidence is presented that shows that when salient feedback along two described dimensions—numerical and/or mechanistic—conflicts with one's estimates, predictions, or explanations, it can trigger surprise, which in turn produces wisdom-enhancing conceptual change. Highlighting ignorance's importance in increasing wisdom, we describe (1) interventions utilizing surprising information (for instance, from our Numerically Driven Inferencing paradigm), and (2) a specific focus on global warming as a touchstone for increasing wisdom, which includes (3) a direct-to-the-public website for fostering conceptual changes regarding that central phenomenon of climate change (www.HowGlobalWarmingWorks.org).

Mechanistic reasoning is a powerful form of reasoning central to scientific explanations. Despite mechanistic reasoning being an important dimension of scientific practice and a central dimension of science curricula, students face difficulties in developing such type of reasoning. Many authors have been proposing tools for supporting its development; drawing is one such tool. Studies that purposefully explore how drawing leverages and supports students' mechanistic reasoning while engage in a process of constructing explanations are still scarce. The goal of the current study was to understand how students' mechanistic reasoning emerges and is enacted when students are jointly involved in drawing creation. For that, we drew on a recent framework that identifies essential characteristics of students' mechanistic reasoning and also on theories of distributed and embodied cognition. In this paper, we present a pair of middle school students who jointly explain a chemical phenomenon by creating drawings and reasoning with them. Using a fine‐grain analysis we examined the elements of students' mechanistic reasoning in relation to drawing creation and how talk and embodied actions on and with the drawings were used to support students' reasoning. Findings reveal that drawings played a key role in paving the way for students reasoning about mechanisms and in enacting mechanistic reasoning. In particular, drawings were essential for pushing students to look for a mechanism, for enabling and supporting mechanistic reasoning‐in‐action, and for facilitating productive interactions between the students that ended up in the construction of a sophisticated mechanistic explanation. [ABSTRACT FROM AUTHOR]

At least historically, it was common for medical practitioners to believe causal hypotheses on the basis of standalone mechanistic reasoning. However, it is now widely acknowledged that standalone mechanistic reasoning is insufficient for appropriately believing a causal hypothesis in medicine, thanks in part to the so-called problem of masking. But standalone mechanistic reasoning is not the only type of mechanistic reasoning. When exactly then is it appropriate to believe a causal hypothesis on the basis of mechanistic reasoning? In this paper, I argue that it has proved difficult to provide a satisfying answer to this question. I also argue that this difficulty is predicted by recent work in knowledge-first epistemology. I think this shows that recent work in epistemology has important implications for practice in the philosophy of science. It is therefore worth paying closer attention in the philosophy of science to this recent work in knowledge-first epistemology. [ABSTRACT FROM AUTHOR]

This article reports on a case study that aims to help students develop mechanistic reasoning through constructing a model based stop-motion animation of a physical phenomenon. Mechanistic reasoning is a valuable thinking strategy for students in trying to make sense of scientific phenomena. Ten ninth-grade students used stop-motion software to create an animation of projectile motion. Retrospective think-aloud interviews were conducted to investigate how the construction of a stop-motion animation induced the students' mechanistic reasoning. Mechanistic reasoning did occur while the students engaged in creating the animation, in particular chunking and sequencing. Moreover, all students eventually exhibited mechanistic reasoning including abstract concepts, e.g., not directly observable agents. Students who reached the highest level of mechanistic reasoning, i.e., chaining, demonstrated deeper conceptual understanding of content. [ABSTRACT FROM AUTHOR]

Rationale: Evidence‐based medicine (EBM), the dominant approach to assessing the effectiveness of clinical and public health interventions, focuses on the results of association studies. EBM+ is a development of EBM that systematically considers mechanistic studies alongside association studies. Aims and objectives: To explore examples of the importance of mechanistic evidence to coronavirus research. Methods: We have reviewed the mechanistic evidence in four major areas that are relevant to the management of COVID‐19. Results and conclusions: (a) Assessment of combination therapy for MERS highlights the need for systematic assessment of mechanistic evidence. (b) That hypertension is a risk factor for severe disease in the case of SARS‐CoV‐2 suggests that altering hypertension treatment might alleviate disease, but the mechanisms are complex, and it is essential to consider and evaluate multiple mechanistic hypotheses. (c) Confidence that public health interventions will be effective requires a detailed assessment of social and psychological components of the mechanisms of their action, in addition to mechanisms of disease. (d) In particular, if vaccination programmes are to be effective, they must be carefully tailored to the social context; again, mechanistic evidence is crucial. We conclude that coronavirus research is best situated within the EBM+ evaluation framework. [ABSTRACT FROM AUTHOR]

Structural formulas are the fundamental tool to represent and explain reaction processes in organic chemistry. They contain mostly implicit information about the parameters that constitute the reaction process. Due to their abstractness and complexity, learners often experience difficulties accessing and understanding the information contained within. This can lead to incorrect approaches and may hinder the development of mechanistic thinking skills. In this article, we present a novel concrete 3D model that allows learners to model the dynamics of SN‐reaction processes. The model represents the reaction process and the energetic and structural changes during the chemical reaction in a dynamic and controllable way using only one single concrete model. Due to the realized mechanics and the properties of the model components, the model generates a direct, haptically perceptible feedback when used. Learners can relate this explicit information to the reaction process and use it to form and test hypotheses about its structural and energetic course. Thus the model provides a "haptic kinetic". Due to the complete haptic perceivability, the model is suitable for inclusive settings with blind, visually impaired and seeing learners. [ABSTRACT FROM AUTHOR]

Through analysis of a classroom lesson led by a decorated teacher, we illustrate how instructional practices favor students seeking empirical patterns at the expense of using mechanistic reasoning. In the lesson, when students spontaneously come up with hypothetical mechanisms to explain why a light bulb in an electric circuit does or does not light, the teacher, following the guidance of standardized curricula, redirects them toward pattern‐seeking. We argue that this bias toward pattern‐seeking in Chinese national standards and curricula, along with ambiguity in those documents on what an "explanation" is, sits in tension with students' productive abilities and propensities for engaging in mechanistic explanation. In discussion, we extend the argument to show a less severe but similar bias toward pattern‐seeking in the United States' Next Generation Science Standards. [ABSTRACT FROM AUTHOR]

Some philosophers have argued that evidence of underlying mechanisms does not provide evidence for the effectiveness of a medical intervention. One such argument appeals to the unreliability of mechanistic reasoning. However, mechanistic reasoning is not the only way that evidence of mechanisms might provide evidence of effectiveness. A more reliable type of reasoning may be distinguished by appealing to recent work on evidential pluralism in the epistemology of medicine. A case study from virology provides an example of this so‐called reinforced reasoning in medicine. It is argued that in this case study, the available evidence of underlying mechanisms did in fact play a role in providing evidence in favour of a medical intervention. This paper therefore adds a novel and recent case study to the literature in support of evidential pluralism in medicine. [ABSTRACT FROM AUTHOR]

While the importance of mechanisms in determining causality in medicine is currently the subject of active debate, the role of mechanistic reasoning in clinical practice has received far less attention. In this paper we look at this question in the context of the treatment of a particular individual, and argue that evidence of mechanisms is indeed key to various aspects of clinical practice, including assessing population-level research reports, diagnostic as well as therapeutic decision making, and the assessment of treatment effects. We use the pulmonary condition bronchiectasis as a source of examples of the importance of mechanistic reasoning to clinical practice. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Physiology demands systems thinking: reasoning within and between levels of biological organization and across different organ systems. Many physiological mechanisms explain how structures and their properties interact at one level of organization to produce emergent functions at a higher level of organization. Current physiology principles, such as structure-function relations, selectively neglect mechanisms by not mentioning this term explicitly. We explored how students characterized mechanisms and functions to shed light on how students make sense of these terms. Students characterized mechanisms as 1) processes that occur at levels of organization lower than that of functions; and 2) as detailed events with many steps involved. We also found that students produced more variability in how they characterized functions compared with mechanisms: students characterized functions in relation to multiple levels of organization and multiple definitions. We interpret these results as evidence that students see mechanisms as holding a more narrow definition than used in the biological sciences, and that students struggle to coordinate and distinguish mechanisms from functions due to cognitive processes germane to learning in many domains. We offer the instructional suggestion that we scaffold student learning by affording students opportunities to relate and also distinguish between these terms so central to understanding physiology. [ABSTRACT FROM AUTHOR]

Understanding molecular processes and coordinating the various activities across levels of organization in biological systems is a complicated task, yet many curricular guidelines indicate that undergraduate students should master it. Employing mechanistic reasoning can facilitate describing and investigating biological phenomena. Biofilms are an important system in microbiology and biology education. However, few empirical studies have been conducted on student learning of biofilms or how students utilize mechanistic reasoning related to systems thinking to explain biofilm formation. Using mechanistic reasoning and the theory of knowledge integration as conceptual and analytical frameworks, we examined the features of 9 undergraduate biology students' mechanistic models of a specific transition point in biofilm development. From these data, we constructed a model of knowledge integration in the context of biofilms, which categorizes students' knowledge based on features of their descriptions (e.g., entities, correct connections, and the nature of connections). We found that 4 of 9 students produced a fragmented model, 4 of 9 students produced a transitional model, and 1 student produced a connected model. Overall, students often did not discuss cell-cell communication mechanics in their mechanistic models and rarely included the role of gene regulation. Most connections were considered nonnormative and lacked important entities, leading to an abundance of unspecified causal connections. We recommend increasing instructional support of mechanistic reasoning within systems (e.g., identifying entities across levels of organization and their relevant activities) and creating opportunities for students to grapple with their understanding of various biological concepts and to explore how processes interact and connect in a complex system., Competing Interests: The authors declare no conflict of interest., (Copyright © 2023 Flowers et al.)

• Children judged the helpfulness of information about an artifact's complexity. • 7–9-year-olds robustly favored relevant over irrelevant information. • 5–6-year-olds only did so in cases of extreme relevance contrasts. • Children gradually develop the ability to detect which object features imply complexity. Mechanistic complexity is an important property that affects how we interact with and learn from artifacts. Although highly complex artifacts have only recently become part of human material culture, they are ever-present in contemporary life. In previous research, children successfully detected complexity contrasts when given information about the functions of simple and complex objects. However, whether children spontaneously favor relevant information about an object's causal mechanisms and functions when trying to determine an object's complexity remains an open question. In Study 1, 7- to 9-year-olds and adults, but not 5- and 6-year-olds, rated information about relevant actions (e.g., the difficulty in fixing an object) as more helpful than information about irrelevant actions (e.g., the difficulty in spelling an object's name) for making determinations of mechanistic complexity. Only in Study 2, in which the relevance contrasts were extreme, did the youngest age group rate relevant actions as more helpful than irrelevant actions. In Study 3, in which participants rated the complexity of the actions themselves, participants performed differently than in the previous studies, suggesting that children in the prior studies did not misinterpret the study instructions as prompts to rate the actions' complexity. These results suggest that the ability to detect which object properties imply complexity emerges during the early school years. Younger children may be misled by features that are not truly diagnostic of mechanistic complexity, whereas older children more easily disregard such features in favor of relevant information. [ABSTRACT FROM AUTHOR]

Traditionally, mechanistic reasoning has been assigned a negligible role in standard EBM (evidence-based medicine) literature, although some recent authors have argued for an upgrade. Even so, the mechanistic reasoning that has received attention has almost exclusively been positive--both in an epistemic sense of claiming that there is a mechanistic chain and in a health-related sense of there being claimed benefits for the patient. Negative mechanistic reasoning has been neglected, both in the epistemic and in the health-related sense. I distinguish three main types of negative mechanistic reasoning and subsume them under a new definition of mechanistic reasoning in the context of assessing medical interventions. This definition is wider than a previous suggestion in the literature. Each negative type corresponds to a range of evidential strengths, and it is argued that there are differences with respect to typical evidential strengths. The variety of negative mechanistic reasoning should be acknowledged in EBM, and it presents a serious challenge to proponents of so-called medical hierarchies of evidence. [ABSTRACT FROM AUTHOR]

Rationale: Exercise interventions and policies are widely prescribed in both sport and healthcare. Research investigating exercise interventions and policies is generally conducted using an Evidence-Based framework, placing an emphasis on evidence gathered from randomised controlled trials (RCTs)., Aims and Objectives: To explore the idea that, in addition to the assessment of evidence from RCTs when investigating exercise interventions, mechanistic studies ought to also be assessed and considered., Methods: This article assesses the rationale supporting the use of RCTs as evidence for exercise interventions, and the use of evidence of mechanisms in establishing efficacy, determining external validity, and tailoring interventions., Results and Conclusions: The article argues that evidence from mechanistic studies ought to be considered alongside evidence from RCTs because: as RCTs investigating exercise interventions tend to be of low quality, mechanistic studies ought to be used to reinforce the evidence base; further, evidence from mechanistic studies is highly useful for both questions of extrapolation and implementation. This article argues for this on theoretical grounds, and also draws on a number of case studies., (© 2022 The Authors. Journal of Evaluation in Clinical Practice published by John Wiley & Sons Ltd.)

Objective: To examine the role of explainability in machine learning for healthcare (MLHC), and its necessity and significance with respect to effective and ethical MLHC application., Study Design and Setting: This commentary engages with the growing and dynamic corpus of literature on the use of MLHC and artificial intelligence (AI) in medicine, which provide the context for a focused narrative review of arguments presented in favour of and opposition to explainability in MLHC., Results: We find that concerns regarding explainability are not limited to MLHC, but rather extend to numerous well-validated treatment interventions as well as to human clinical judgment itself. We examine the role of evidence-based medicine in evaluating inexplicable treatments and technologies, and highlight the analogy between the concept of explainability in MLHC and the related concept of mechanistic reasoning in evidence-based medicine., Conclusion: Ultimately, we conclude that the value of explainability in MLHC is not intrinsic, but is instead instrumental to achieving greater imperatives such as performance and trust. We caution against the uncompromising pursuit of explainability, and advocate instead for the development of robust empirical methods to successfully evaluate increasingly inexplicable algorithmic systems., (Copyright © 2021. Published by Elsevier Inc.)