Search

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.

Mechanistic reasoning (MR), which is a form of causal reasoning, is an essential aspect of science education. To support students in developing MR, students in this series of studies were tasked with constructing and using a model in the form of a stop-motion animation (SMA). In SMA students create a sequence of frames (images) representing a natural phenomenon. After creating the SMA, they were asked to explain the phenomenon on the basis of their own model. This dissertation describes four separate studies that were conducted to address the main research question: How can student-constructed SMAs be used as a pedagogical approach to support students in developing MR? Our studies show, both theoretically and empirically, that the specific nature of SMA construction, i.e., breaking up a natural phenomenon in chunks and then sequencing these chunks in order to create the SMA, played a crucial role as a cognitive support for students in developing key elements of MR. Based on the findings, our studies suggest practical implications regarding for implementing SMA construction activities in a science classroom to support students’ MR. The results contribute to a more comprehensive understanding of the implementation of SMA-based modeling in science classrooms that supports students in developing MR, in particular, and in science learning, in general.

There is a growing research interest in mechanistic reasoning (MR) in the field of science education, as this type of reasoning is perceived as an essential thinking skill for science education. This literature review synthesized 60 science education studies on MR published from 2006 to 2021. The findings showed three common aspects of conceptualizations of MR in science education: (1) causality in relation to MR, (2) use of entities and their associated activities, and (3) use of entities at (at least) one scale level below the scale level of a target phenomenon. While most of the reviewed studies related the importance of MR to cognitive aspects, a smaller number associated its value with scientific modelling. Three main difficulties in generating MR were categorized: (1) identifying and using unobservable entities, (2) assigning activities to entities, and (3) identifying and using an appropriate number of entities. Various types of support for fostering MR were identified. Implications and future studies are discussed.

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] 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

Because reasoning about mechanism is critical to disciplined inquiry in science, technology, engineering, and mathematics (STEM) domains, this study focuses on ways to support the development of this form of reasoning. This study attends to how mechanistic reasoning is constituted through mathematical description. This study draws upon Smith’s (2007) characterization of mathematical description of scientific phenomena as ‘‘bootstrapping,’’ where negotiating the relationship between target phenomena and represented relations is fundamental to learning. In addition, the development of mathematical representation presents a viable pathway towards STEM integration. In this study, participants responded to an assessment of mechanistic reasoning while cognitive interviews were conducted to characterize their reasoning about mechanism and mathematical description of the systems of levers represented in the items. Participant item responses were modeled using item response theory and participant talk and gesture were coded according to developed analytic frameworks. Participants were elementary, middle, and high school students as well as college undergraduates, and adults without college education. The results suggest a relationship between participants’ tendencies to describe these systems mathematically and their mechanistic reasoning ability. Moreover, there are specific elements of mechanistic reasoning that are more highly associated with mathematical description. In addition, there is a relationship between a participant’s propensity to both mathematically describe and mechanistically trace mechanical systems.

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).

Experts in the field of molecular and cellular biology (MCB) use domain-specific reasoning strategies to navigate the unique complexities of the phenomena they study and creatively explore problems in their fields. One primary goal of instruction in undergraduate MCB is to foster the development of these domain-specific reasoning strategies among students. However, decades of evidence-based research and many national calls for undergraduate instructional reform have demonstrated that teaching and learning complex fields like MCB is difficult for instructors and learners alike. Therefore, how do students develop rich understandings of biological mechanisms? It is the aim of this dissertation work to explore features of expertise and knowledge building in undergraduate MCB by investigating knowledge organization and problem-solving strategies. Semi-structured clinical think-aloud interviews were conducted with introductory and upper-division students in MCB. Results suggest that students must sort ideas about molecular mechanism into appropriate mental categories, create connections using function-driven and mechanistic rather than associative reasoning, and create nested and overlapping ideas in order to build a nuanced network of biological ideas. Additionally, I characterize the observable components of generative multi-level mechanistic reasoning among undergraduate MCB students constructing explanations about in two novel problem-solving contexts. Results indicate that like MCB experts, students are functionally subdividing the overarching mechanism into functional modules, hypothesizing and instantiating plausible schema, and even flexibly consider the impact of mutations across ontological and biophysical levels. However "filling in" these more abstract schema with molecular mechanisms remains problematic for many students, with students instead employing a range of developing mechanistic strategies. Through this investigation of expertise and knowledge buildin

Traditionally, mechanistic reasoning has been assigned a negligible role in standard EBM (evidence-based medicine) literature, although some recent authors have argued for an upgrading. Even so, 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 the typical evidential strengths. The variety of negative mechanistic reasoning should be acknowledged in EBM, and presents a serious challenge to proponents of so-called medical hierarchies of evidence., QC 20151211

The thesis consists of an introduction and two papers. In the introduction a brief historical survey of empirical investigations into the effectiveness of medicinal interventions is given. Also, the main ideas of the EBM (evidence-based medicine) movement are presented. Both included papers can be viewed as investigations into the reasonableness of EBM and its hierarchies of evidence. Paper I: Typically, in a clinical trial patients with specified symptoms are given either of two or more predetermined treatments. Health endpoints in these groups are then compared using statistical methods. Concerns have been raised, not least from adherents of so-called alternative medicine, that clinical trials do not offer reliable evidence for some types of treatment, in particular for highly individualized treatments, for example traditional homeopathy. It is argued that such concerns are unfounded. There are two minimal conditions related to the nature of the treatments that must be fulfilled for evaluability in a clinical trial, namely (1) the proper distinction of the two treatment groups and (2) the elimination of confounding variables or variations. These are delineated, and a few misunderstandings are corrected. It is concluded that the conditions do not preclude the testing of alternative medicine, whether individualized or not. Paper II: Traditionally, mechanistic reasoning has been assigned a negligible role in standard EBM literature, although some recent authors have argued for an upgrading. Even so, 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 reasonin, QC 20150413