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One reason for the widespread use of the energy concept across the sciences is that energy analysis can be used to interpret the behavior of systems even if one does not know the particular mechanisms that underlie the observed behavior. By providing an approach to interpreting unfamiliar phenomena, energy provides a lens on phenomena that can set the stage for deeper learning about how and why phenomena occur. However, not all energy ideas are equally productive in setting the stage for new learning. In particular, researchers have debated the value of teaching students to interpret phenomena in terms of energy forms and transformations. In this study, we investigated how two different approaches to middle school energy instruction—one emphasizing energy transformations between forms and one emphasizing energy transfers between systems—prepared students to use their existing energy knowledge to engage in new learning about a novel energy‐related phenomenon. To do this, we designed a new assessment instrument to elicit student initial ideas about the phenomenon and to compare how effectively students from each approach learned from authentic learning resources. Our results indicate that students who learned to interpret phenomenon in terms of energy transfers between systems learned more effectively from available learning resources than did students who learned to interpret phenomena in terms of energy forms and transformations. This study informs the design of introductory energy instruction and approaches for assessing how students existing knowledge guides new learning about phenomena.

 

Abstract Uncertainty is ubiquitous in science, but scientific knowledge is often represented to the public and in educational contexts as certain and immutable. This contrast can foster distrust when scientific knowledge develops in a way that people perceive as a reversals, as we have observed during the ongoing COVID-19 pandemic. Drawing on research in statistics, child development, and several studies in science education, we argue that a Bayesian approach can support science learners to make sense of uncertainty. We provide a brief primer on Bayes’ theorem and then describe three ways to make Bayesian reasoning practical in K-12 science education contexts. There are a) using principles informed by Bayes’ theorem that relate to the nature of knowing and knowledge, b) interacting with a web-based application (or widget—Confidence Updater) that makes the calculations needed to apply Bayes’ theorem more practical, and c) adopting strategies for supporting even young learners to engage in Bayesian reasoning. We conclude with directions for future research and sum up how viewing science and scientific knowledge from a Bayesian perspective can build trust in science. 

Machine learning (ML) has become commonplace in educational research and science education research, especially to support assessment efforts. Such applications of machine learning have shown their promise in replicating and scaling human‐driven codes of students' work. Despite this promise, we and other scholars argue that machine learning has not yet achieved its transformational potential. We argue that this is because our field is currently lacking frameworks for supporting creative, principled, and critical endeavors to use machine learning in science education research. To offer considerations for science education researchers' use of ML, we present a framework, Distributing Epistemic Functions and Tasks (DEFT), that highlights the functions and tasks that pertain to generating knowledge that can be carried out by either trained researchers or machine learning algorithms. Such considerations are critical decisions that should occur alongside those about, for instance, the type of data or algorithm used. We apply this framework to two cases, one that exemplifies the cutting‐edge use of machine learning in science education research and another that offers a wholly different means of using machine learning and human‐driven inquiry together. We conclude with strategies for researchers to adopt machine learning and call for the field to rethink how we prepare science education researchers in an era of great advances in computational power and access to machine learning methods.

 

Students' motivation plays an important role in successful science learning. However, motivation is a complex construct. Theories of motivation suggests that students' motivation must be conceptualized as a motivational system with numerous components that interact in complex ways and influence metacognitive processes such as self‐evaluation. This complexity is further increased because students' motivation and success in science learning influence each other as they develop over time. It is challenging to study the co‐development of motivation and learning due to these complex interactions which can vary widely across individuals. Recently, person‐centered approaches that capture students' motivational profiles, that is, the multiplicity of motivational factors as they co‐occur in students, have been successfully used in educational psychology to better understand the complex interplay between the co‐development of students' motivation and learning. We employed a person‐centered approach to study how the motivational profiles, constructed from goal‐orientation, self‐efficacy, and engagement data ofN = 401 middle school students developed over the course of a 10‐week energy unit and how that development was related to students' learning. We identified four characteristic motivational profiles with varying temporal stability and found that students' learning over the course of the unit was best characterized by considering the type of students' motivational profiles and the transitions that occurred between them. We discuss implications for the design and implementation of interventions and future research into the complex interplay between motivation and learning.

 

Energy is a central concept in science in every discipline and also an essential player in many of the issues facing people everywhere on the globe. However, studies have shown that by the end of K‐12 schooling, most students do not reach the level of understanding required to be able to use energy to make sense of a wide range of phenomena. Many researchers have questioned whether the conceptual foundations of traditional approaches to energy instruction may be responsible for students' difficulties. In response to these concerns, we developed and tested a novel approach to middle school physical science energy instruction that was informed by the recommendations of the Framework for K‐12 Science Education (National Research Council, 2012a) and the Next Generation Science Standards (NGSS) (NGSS Lead States, 2013). This new approach differs substantially from more traditional approaches to energy instruction in that it does not require energy forms and it emphasizes connections between energy, systems, and fields that mediate interaction‐at‐a‐distance. We investigated student learning during this novel approach and contrasted it with student learning within a comparable unit based on a more traditional approach to energy instruction. Our findings indicate that students who learned in the new approach outperformed students who learned in the traditional approach in every quantitative and qualitative aspect considered in this study, irrespective of their prior knowledge of energy. They developed more parsimonious knowledge networks in relation to energy that focused primarily around the concept of energy transfer. This study warrants further investigation into the value of this new approach to energy instruction in both middle and high school.