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Situational 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.

We 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.

Given 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.

 

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. 

While computer science (CS) education researchers have frequently examined what happens in courses, programs of study, or occupations in general, they have less frequently addressed finer-grained experiences that spark students' interest in CS. One excellent way to study these types of student experiences is the Experience Sampling Method (ESM). ESM involves collecting data on individuals' experiences at much more frequent intervals than traditional survey research. This aspect of ESM makes it well-suited to examine time-specific aspects of students' experiences, as well as changes due to the disruptive effects of COVID-19. 

Teachers participate in professional learning activities to enhance their pedagogical knowledge and share best practices—and the increasing role of technologies in education, including social media, is shifting how this professional learning occurs. The COVID-19 pandemic provided an opportunity to consider the role of social media for professional learning. Using intensive longitudinal methods, we repeatedly surveyed 14 teachers’ use of social media both before and during the pandemic (N = 386 total responses). We found patterns in social media platforms uptake and their purposes, but teachers’ use of social media was largely idiosyncratic. Also, teachers demonstrated notable shifts in social media use after the pandemic started; multilevel models indicated that teachers were more likely to use social media to connect and share, especially, as well as learn and follow, compared with before the pandemic. Higher levels of COVID-19-related family stress were also associated with more use of social media to find materials.