Educational potential of teaching evolution as an interdisciplinary science

Evolution education continues to struggle with a range of persistent challenges spanning aspects of conceptual understanding, acceptance, and perceived relevance of evolutionary theory by students in general education. This article argues that a gene-centered conceptualization of evolution may inherently limit the degree to which these challenges can be effectively addressed, and may even precisely contribute to and exacerbate these challenges. Against that background, we also argue that a trait-centered, generalized, and interdisciplinary conceptualization of evolution may hold significant learning potential for advancing progress in addressing some of these persistent challenges facing evolution education. We outline a number of testable hypotheses about the educational value of teaching evolutionary theory from this more generalized and interdisciplinary conception

Is defining life pointless? Operational definitions at the frontiers of Biology

Despite numerous and increasing attempts to define what life is, there is no consensus on necessary and sufficient conditions for life. Accordingly, some scholars have questioned the value of definitions of life and encouraged scientists and philosophers alike to discard the project. As an alternative to this pessimistic conclusion, we argue that critically rethinking the nature and uses of definitions can provide new insights into the epistemic roles of definitions of life for different research practices. This paper examines the possible contributions of definitions of life in scientific domains where such definitions are used most (e.g., Synthetic Biology, Origins of Life, Alife, and Astrobiology). Rather than as classificatory tools for demarcation of natural kinds, we highlight the pragmatic utility of what we call operational definitions that serve as theoretical and epistemic tools in scientific practice. In particular, we examine contexts where definitions integrate criteria for life into theoretical models that involve or enable observable operations. We show how these definitions of life play important roles in influencing research agendas and evaluating results, and we argue that to discard the project of defining life is neither sufficiently motivated, nor possible without dismissing important theoretical and practical research

Investigating Individual Differences in the Conceptual Change of Biology Misconceptions Using Computer-Based Explanation Tasks

The current study examined the effects of computer-based self-explanations (i.e., generated by the learner) and instructional explanations (i.e., provided to the learner) on undergraduate biology students’ revision of photosynthesis and respiration misconceptions. Individual differences, particularly students’ prior knowledge, significantly impact the effectiveness of instructional tasks. Oftentimes, an instructional task is effective only for learners at a particular prior knowledge level. Cognitive Load Theory suggests that too much or too little instructional support can overwhelm a learner’s working memory. When used for building knowledge, self-explanations and instructional explanations, like those employed in the current study, both interact with prior knowledge. Prior research has indicated that instructional explanations may only benefit students with low prior knowledge, and self-explanations may only benefit students with high prior knowledge. The current study addressed whether such effects extend to the use of explanation tasks to facilitate knowledge revision, in which existing misconceptions are revised. Four hundred and thirty eight undergraduate major and non-major biology students completed an online activity for course credit. Participants were randomly assigned to one of three conditions (self-explanation, instructional explanation, or no explanation) and then prompted with a set of photosynthesis questions, each of which was followed by their assigned instructional task and a cognitive load measure. One week later, participants returned to the activity to take a posttest. Results indicated students entered the activity with high rates of photosynthesis and respiration misconceptions. Further regression analyses indicated that only self-explanations, not instructional explanations, increased learning compared to no explanations. Trends in effect sizes suggest self-explanations only benefited students with sufficient prior knowledge. Higher cognitive load was associated with less learning in both explanation conditions, but not in the no explanation condition. The current results suggest that self-explanations may effectively promote knowledge revision, assuming students are familiar with the content, while instructional explanations may not foster knowledge revision in a computer-based setting. Implications for adaptive instruction that targets knowledge revision are addressed

Modeling a Pandemic: Investigating Student Learning about Disease Spread in the Context of Agent-Based Modeling

The COVID-19 pandemic has highlighted a need for students to learn about public health issues, including the transmission of disease and methods for the prevention of epidemics. This study presents data from a project focused on developing computational microworlds to help middle school students learn about these topics. The microworld is designed to help students model and test their ideas about how a disease spreads through a population and how an epidemic can be prevented. I employed a lab-based case study approach to conduct one-on-one 1.5-hour interviews through Zoom with four middle-school students (ages 12-14). During the interview, the student was asked questions about the spread and prevention of disease and then invited to model and test their ideas in the microworld. This study presents an analysis of students’ pre and post instructional knowledge of disease spread and prevention, which they shared while constructing their initial and later models. I present student ideas in categories of disease transmission, recovery from disease, and disease protection strategies. The paper also analyzes students’ knowledge refinement through the building, testing, and debugging of a disease spread and prevention model. I model student refinement of thinking through steps of building initial models and predicting results, testing initial models, making sense of the results, debugging and retesting models, observing final models, and explaining results, resulting in three types of thinking shifts, and two types of thinking refinements. My findings suggest middle school students can learn about strategies for disease prevention through computational modeling