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 building, I characterize several of the ways in which knowledge integration and generative explanation building are productively constrained by domain-specific features, expand on several discovered barriers to productive knowledge organization and mechanistic explanation building, and suggest instructional implications for undergraduate learning.

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 reasoning in the context of assessing medical interventions. Although this definition is wider than a previous suggestion in the literature, there are still other instances of reasoning that concern mechanisms but do not (and should not) count as mechanistic reasoning. One of the three distinguished types, which is negative only in the health-related sense, has a corresponding positive counterpart, whereas the other two, which are epistemically negative, do not have such counterparts, at least not that are particularly interesting as evidence. Accounting for negative mechanistic reasoning in EBM is therefore partly different from accounting for positive mechanistic reasoning. Each negative type corresponds to a range of evidential strengths, and it is argued that there are differences with respect to the typical 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. / <p>QC 20150413</p>

<p>In recent years, science has shifted from a focus on reductionist explanations of biological phenomena to a more integrated, systems approach. This shift has made its way into curricular recommendations for undergraduate education. To understand complex biological phenomena, it has been argued that students employ mechanistic reasoning, in which one describes a mechanism by identifying the activities that produce change, the entities which engage in activities, and the starting and ending conditions. Students’ use of mechanistic reasoning requires engaging in the complex task of simultaneously integrating and coordinating multiple elements across space and time. In addition, students must link and organize their scientific ideas and then structure their thoughts into a network of knowledge, as described by the theory of knowledge integration. Previous studies that have explored students’ understanding of scientific concepts using knowledge integration as a lens found that students’ nonmechanistic ideas prevented them from identifying gaps in the connections between their ideas and from forming normative knowledge. Thus, this dissertation investigates the features of undergraduate biology students’ systems thinking using knowledge integration and mechanistic reasoning as conceptual and analytical frameworks. Using a semi-structured interview, we asked students to describe functional definitions of and relationships between three fundamental modules in biology (i.e., gene regulation, cell-cell communication, and the relationship between genotype and phenotype). In the first study, we found that the majority of students did not have normative functional definitions for how and why gene regulation occurs or how phenotype is regulated. When describing the relationships in an open context, most students expressed unidirectional, linear knowledge networks which lacked Mechanistic connections. In our second study, we examined how students described a transition point in biofilm development after being cued to think about the three modules. Though students struggled to transfer over relevant ideas to the biofilm context (such as gene regulation and cell-cell communication processes), we found that explanations were more specified in the nature of connections and content including more Mechanistic descriptions. In the third study, we explored features of biology students’ and instructors’ knowledge networks in an open context and situated to a context of the participants’ choice. Within an open context, most students described multidirectional, non-linear knowledge networks similar to instructors. In the specific context, the majority of students described non-linear knowledge networks, but some students modified their structures to be linear. Although the structures became less complex in the specific context, the nature of connections and content became more specified. Across all studies, we found that context greatly affected students’ systems thinking as revealed by the changing features of the knowledge networks. Specifically, context helped students identify what relationships they deeply understood and could transfer and allowed for the creation of a detailed explanation relevant to the specific biological phenomenon. For students to develop a broad systems perspective of biology, we recommend instructors engage students in the process of knowledge integration. Embed opportunities for students to think about biology concepts in various contexts, particularly where students grapple with nuanced and complex transfer of ideas. These practices will encourage students to form causal, mechanistic linkages between concepts and build an integrated, expert-like understanding of biology.</p>