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With the rise of the popularity of Bayesian methods and accessible computer software, teaching and learning about Bayesian methods are expanding. However, most educational opportunities are geared toward statistics and data science students and are less available in the broader STEM fields. In addition, there are fewer opportunities at the K-12 level. With the indirect aim of introducing Bayesian methods at the K-12 level, we have developed a Bayesian data analysis activity and implemented it with 35 mathematics and science pre-service teachers. In this article, we describe the activity, the web app supporting the activity, and pre-service teachers’ perceptions of the activity. Lastly, we discuss future directions for preparing K-12 teachers in teaching and learning about Bayesian methods. 

What do we know about data science learning at the grades K–12 (precollegiate) level? This article answers this question by using the notion of agency to provide a framework to review the diverse research agendas and learning environments relevant to data science education. Examining research on data science education published in three recent special issues, we highlight key findings from scholars working in different communities using this lens. Then, we present the results of a co-citation coupling analysis for articles published in one of three recent data science education special issues with research spanning various levels and contexts. This co-citation analysis showed that while there are some common touchpoints, research on data science learning is taking place in a siloed manner. Based on our review of the literature through the lens of agency and our analysis, we discuss how the data science education community can synthesize research across disciplinary and grade-level divides. 

Abstract BackgroundSituational engagement in science is often described as context-sensitive and varying over time due to the impact of situational factors. But this type of engagement is often studied using data that are collected and analyzed in ways that do not readily permit an understanding of the situational nature of engagement. The purpose of this study is to understand—and quantify—the sources of variability for learners’ situational engagement in science, to better set the stage for future work that measures situational factors and accounts for these factors in models. ResultsWe examined how learners' situational cognitive, behavioral, and affective engagement varies at the situational, individual learner, and classroom levels in three science learning environments (classrooms and an out-of-school program). Through the analysis of 12,244 self-reports of engagement collected using intensive longitudinal methods from 1173 youths, we found that the greatest source of variation in situational engagement was attributable to individual learners, with less being attributable to—in order—situational and classroom sources. Cognitive engagement varied relatively more between individuals, and affective engagement varied more between situations. ConclusionsGiven the observed variability of situational engagement across learners and contexts, it is vital for studies targeting dynamic psychological and social constructs in science learning settings to appropriately account for situational fluctuations when collecting and analyzing data. 

As the field of education, and especially gifted education, gradually moves toward open science, our research community increasingly values transparency and openness brought by open science practices. Yet, individual researchers may be reluctant to adopt open science practices due to low incentives, barriers of extra workload, or lack of support to apply these in certain areas, such as qualitative research. We encourage and give guidelines to reviewers to champion open science practices by warmly influencing authors to consider applying open science practices to quantitative, qualitative, and mixed-methods research and providing ample support to produce higher-quality publications. Instead of imposing open science practices on authors, we advocate reviewers suggest small, non-threatening, specific steps to support authors without making them feel overwhelmed, judged, or punished. We believe that these small steps taken by reviewers will make a difference to create a more supportive environment for researchers to adopt more open science practices. 

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. 

There are significant participation gaps in computing, and the way to address these participation gaps lies not simply in getting students from underrepresented groups into a CS1 classroom, but supporting students to pursue their interest in computing further beyond CS1. There are many factors that may influence students’ pursuit of computing beyond introductory courses, including their sense that they can do what CS courses require of them (their self-efficacy) and positive emotional experiences in CS courses. When interest has been addressed in computing education, research has treated it mostly as an outcome of particular pedagogical approaches or curricula; what has not been studied is how students’ longer-term interest develops through more granular experiences that students have as they begin to engage with computing. In this paper, we present the results of a study designed to investigate how students’ interest in computing develops as a product of their momentary self-efficacy and affective experiences. Using a methodology that is relatively uncommon to computer science education—the experience sampling method, which involves frequently asking students brief, unobtrusive questions about their experiences—we surveyed CS1 students every week over the course of a semester to capture the nuances of their experiences. 74 CS1 students responded 14-18 times over the course of a semester about their self-efficacy, frustration, and situational interest. With this data, we used a multivariate, multi-level statistical model that allowed us to estimate how students’ granular, momentary experiences (measured through the experience sampling method surveys) and initial interest, self-efficacy, and self-reported gender (measured through traditional surveys) relate to their longer-term interest and achievement in the course. We found that students’ momentary experiences have a significant impact on their interest in computing and course outcomes, even controlling for the self-efficacy and interest students reported at the beginning of the semester. We also found significant gender differences in students’ momentary experiences, however, these were reduced substantially when students’ self-efficacy was added to the model, suggesting that gender gaps could instead be self-efficacy gaps. These results suggest that students’ momentary experiences in CS1, how they experience the course week to week, have an impact on their longer-term interest and learning outcomes. Furthermore, we found that male and female students reported different experiences, suggesting that improving the CS1 experiences that students have could help to close gender-related participation gaps. In all, this study shows that the granular experiences students have in CS1 matter for key outcomes of interest to computing education researchers and educators and that the experience sampling method, more common in fields adjacent to computer science education, provides one way for researchers to integrate the experiences students have into our accounts of why students become interested in computing. 

School closures during the COVID-19 pandemic presented a threat to student learning and motivation. Suspension of achievement testing created a barrier to understanding the extent of this threat. Leveraging data from a mathematics learning software as a substitute assessment, we found that students had lower engagement with the software during the pandemic, but students who did engage had increased performance. Students also experienced changes in motivation: lowered mathematics expectancy, but also lower emotional cost for mathematics. Results illustrate the potential and pitfalls of using educational technology data in lieu of traditional assessments and draw attention to access and motivation during at-home schooling.