skip to main content

Explore Research Products in the NSF-PAR

  • Home
  • Search Results
  • Page 1 of 1

Search for: All records

Creators/Authors contains: "Rosenberg, Joshua M."

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; ; ; ( , International Journal of STEM Education)
    Abstract Background

    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.

     Results

    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.

     Conclusions

    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.

     
    more » « less
  2. ; ; ; ( , Science & Education)
    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. 
    more » « less
    Full Text Available 
  3. ; ; ( , Proceedings of the 2022 ACM Conference on International Computing Education Research (ICER ’22))
    Full Text Available 
  4. ; ; ; ; ( , British Journal of Educational Technology)
    Full Text Available 
  5. ; ; ; ( , Journal of Research on Technology in Education)
    null (Ed.)
    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. 
    more » « less
    Full Text Available 
  6. ; ; ( , Journal of Research in Science Teaching)
    Abstract

    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.

     
    more » « less
  7. ; ; ; ; ; ( , AERA Open)
    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. 
    more » « less
    Full Text Available 
  8. ; ; ( , Journal of Research in Science Teaching)
  9. ; ; ; ; ; ; ; ; ; ; et al ( , Organizational Behavior and Human Decision Processes)
    null (Ed.)
    Full Text Available