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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.

While educators' uses of social media for purposes such as professional learning and networking are now well-established, our understanding of how educational institutions use social media--including to engage key stakeholders during periods of crisis--is limited. In this study, we used a public data mining research approach to examine how K-12 school districts in the United States used Twitter as a communication tool during a critical period of the COVID-19 pandemic, March-April, 2020. Through a three-step grounded theory approach of 1,357 district tweets from 492 school districts, we found that districts worked to build community and share time-sensitive announcements in alignment with social media crisis communication recommendations. Announcements were more common during the early stages of the pandemic (and were engaged more collaboratively), with community-building posts more common later on. This study demonstrates how researchers can use publicly available (social media) data to understand districts' communication priorities and strategies during and beyond periods of crisis.

During the COVID-19 pandemic, schools abruptly transitioned to emergency remote instruction. Consequently, expectations for parental involvement in school mathematics rose to unprecedented levels. We sought to understand the experiences of parents to reimagine possibilities for engagement in mathematics during and beyond the pandemic. Leveraging data from tweets using #mathathome and survey responses from parents, we identified who supported continued mathematics learning at home and explored the nature of the mathematics taught there. We found that Twitter and survey data sources described two largely distinct groups of those supporting parents to continue mathematics education at home, but similar findings emerged from analyses of each data source, suggesting that themes were common among different groups. Namely, we saw a commitment to continued mathematics learning and engagement with a range of mathematics topics. These topics mostly focused on elementary-level content, especially counting, through everyday activities/objects and mathematical sense-making. Most parents used resources provided by the school alongside resources they identified and provided on their own. School responses to emergency remote instruction were mostly asynchronous, and parents expressed a need for more opportunities to interact directly with their children's teachers. We discuss what the mathematics education community might learn from these experiences to support parental engagement during and beyond periods of remote emergency instruction.

System-wide educational reforms are difficult to implement in the United States, but despite the difficulties, reforms can be successful, particularly when they are associated with broad public support. This study reports on the nature of the public sentiment expressed about a nationwide science education reform effort, the Next Generation Science Standards (NGSS). Through the use of data science techniques to measure the sentiment of posts on Twitter about the NGSS (N = 565,283), we found that public sentiment about the NGSS is positive, with only 11 negative posts for every 100 positive posts. In contrast to findings from past research and public opinion polling on the Common Core State Standards, sentiment about the NGSS has become more positive over time--and was especially positive for teachers. We discuss what this positive sentiment may indicate about the success of the NGSS in light of opposition to the Common Core State Standards.

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.

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.

Conservative activism around the purported influence of Critical Race Theory (CRT) on K-12 education has swept the country in recent years. While others have documented the sources of these messages, how school districts have responded to these critiques has not yet been investigated. Drawing on research on how social media algorithms elevate polarizing information and activate emotions, we analyze public social media posts on school/district Facebook pages mentioning the phrase "critical race" to examine how educators address the claim of teaching CRT and how the local community responds. We use sentiment analysis to examine the emotions of these posts and how they are distributed across states. We also explore the sentiment of subsequent community reactions reflected in the comments of each post, including negative emotions such as anger and fear, and positive emotions such as trust. This study has implications for how school districts can help to stop cycles of fearful rhetoric and engage with stakeholders in ways that unite a school community around shared priorities.

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.

Facebook is widely used and researched. However, though the data generated by educational technology tools and social media platforms other than Facebook have been used for research purposes, very little research has used Facebook posts as a data source--with most studies relying on self-report studies. While it has historically been impractical (or impossible) to use Facebook as a data source, the CrowdTangle platform allows academic researchers to freely access the massive collection of posts on public Facebook pages and groups. In this paper, we first outline how interactions and textual features in these public Facebook data in concert with established methods from educational data mining and learning analytics can be used to scrutinize educational discourse and knowledge sharing at scale. We then provide a primer that offers considerations for researchers before collecting these data (i.e., conducting research ethically and framing the study). The tutorial also covers matters directly pertaining to using CrowdTangle: accessing the CrowdTangle platform, uploading or identifying pages (or groups), and downloading historical data and it includes code using the statistical software and programming language R. We conclude with ideas for future directions for using Facebook posts as data with a focus on how educational researchers can leverage the scale of the available data and the time periods for which data is available to study educational affairs (i.e., issues or topics) and individuals (i.e., people or organizations) and to scrutinize how Facebook itself is used.

Many school leaders responded to the controversies over critical race theory with denial. That approach can backfire, according to Emily Hodge, Francesca López, and Joshua Rosenberg. They suggest proactive messaging, which can help prevent leaders from falling into the trap of reactively defending their practices and letting the direction of the conversation be guided by those with the most extreme views.

Public schools and districts use social media to share announcements and communicate with parents and the community, but alongside such uses run risks to students' privacy. Using a novel data set of 18 million posts on Facebook by schools and school districts in the United States, we sought to establish how frequently photos of students were shared. Through sequential mixed-methods, we estimated that around 4.9 million posts included identifiable images of students and that approximately 726,000 of these posts also included students' first and last names and their approximate location. We discuss these findings' implications from a data ethics perspective.

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.

With improving technology and monitoring efforts, the availability of scientific data is rapidly expanding. The tools that scientists and engineers use to analyse data are changing in response. At the same time, science education standards have shifted to emphasize the importance of students making sense of data in science classrooms. However, it is not yet known whether these exciting new datasets and tools are used science classrooms, and what it would take to facilitate their use. To identify opportunities, research is needed to capture the data practices currently performed in classrooms, and the roles of technology for student learning. Here, we report findings from a survey conducted in the United States of 330 science teachers on the data sources, practices and technologies common to their classroom. We found that teachers predominantly involve their students in analysing relatively small data sets that they collect. In support of this work, teachers tend to use the technologies that are available to them--namely, calculators and spreadsheets. In addition, we found that a subset of teachers used a wide variety of data sources of varying complexity. We discuss what these findings suggest for practice, research and policy, with an emphasis on supporting teachers based on their needs.

Science educators have begun to explore how students have opportunities to not only view and manipulate simulations, but also to analyze the complex sources of data they generate. While scholars have documented the characteristics and the effects of using simulations as a source of data in face-to-face, K-12 classrooms, how simulations can be taken up and used in such a way in fully-online classes is less-explored. In this study, we present results from our initial qualitative investigation of students' use of a simulation in such a way across three lessons in an online, Advanced Placement high school physics class. In all, 13 students participated in the use of a computational science simulation that we adapted to output quantitative data across the lesson sequence. Students used the simulation and developed a class data set, which students then used to understand, interpret, and model a thermodynamics-related concept and phenomenon. We explored the progression of students' conceptual understanding across the three lessons, students' perceptions of the strengths and weaknesses of the simulation, and how students balanced explaining variability and being able to interpret their model of the class data set. Responses to embedded assessment questions indicated that a few developed more sophisticated conceptual understanding of the particle nature of matter and how it relates to diffusion, while others began the lesson sequence with an already-sophisticated understanding, and a few did not demonstrate changes in their understanding. Students reported that the simulation helped to make a complex idea more accessible and useful and that the data generated by the simulation made it easier to understand what the simulation was representing. When analyzing the class data set, some students focused on fitting the data, not considering the interpretability of the model as much, whereas other students balanced model fit with interpretability and usefulness. In all, findings suggest that the lesson sequence had educational value, but that modifications to the design of the simulation and lesson sequence and to the technologies used could enhance its impact. Implications and recommendations for future research focus on the potential for simulations to be used to engage students in a variety of scientific and engineering practices in online science classes.

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.

For many schools and districts in the United States, Facebook has emerged as an important tool for sharing timely information, building a sense of community, highlighting staff and students, and many other purposes. However, neither researchers nor schools and districts have paid enough attention to how their Facebook use may pose a risk to the privacy of individuals -- often students who are minors. Joshua Rosenberg, Macy Burchfield, Conrad Borchers, Benjamin Gibbons, Daniel Anderson, and Christian Fischer describe their recent studies showing that 15-20 million photos of students have been shared on publicly accessible Facebook pages of public schools and districts. They estimate that at least 150,000 of these photos -- and perhaps as many as a million or more -- depict students who are identifiable by name and school or district. They review some of the risks to students that might result from such social media posts and offer practical steps that schools and districts can take to minimize these risks.

While the perspectives and practices of many educational stakeholders in relation to STEM have been documented, research has yet to deeply explore pre-service teachers' (PST) understandings and how to support their sensemaking about STEM during pre-service teacher education. To address this, we used two STEM integration frameworks, focused on discipline-based practices and modeling with data, to inform the design of a STEM unit that brought together thirty secondary mathematics and science PSTs. With an eye on PSTs' development of a situated understanding of STEM, a qualitative analysis of pre/post surveys and STEM lesson sketches revealed that PSTs collectively shifted from initial views of STEM as blurred lines between subjects to a more nuanced understanding of where and how subjects cross and meet. PSTs also described STEM activities through a more plausible understanding of student and teacher actions and viewed data as a bridge to connect disciplines and the real-world. Furthermore, PSTs' descriptions of anticipated challenges to planning for and implementing STEM mirrored those of in-service teachers. We discuss these findings through the lens of sensemaking theory, highlighting shifts in the sum of PSTs' individual understandings to a more collective and situated understanding of STEM. We also discuss implications focused on the role of data in STEM activities and the affordances of secondary mathematics and science PSTs sensemaking together to about STEM.

Research on teachers' use of social media has typically assumed that it is a) driven by a need for professional learning and b) best understood in terms of individual motivations. In this study, we use a dataset of nearly 600,000 tweets posted to one or more of 48 Regional Educational Twitter Hashtags associated with 44 U.S. states. To explore the influence of local contextual factors on hashtag- and account-level activity in these hashtags, we use an analytic approach heretofore uncommon in social media-focussed education research: generalised linear and multilevel modelling. At the hashtag level, higher numbers of teachers within a state, proportions of students receiving subsidised meals, student-to-teacher ratios, and amounts of state spending per child are associated with more activity within a regional hashtag; by contrast, more left-leaning state governments and citizenries are associated with less activity. At the account level, more experienced accounts and accounts in more right-leaning states contribute more tweets to these hashtags. These findings reinforce established understandings of Twitter as a site for teacher learning; however, they also underline the importance of acknowledging other important purposes of teachers' Twitter use, including receiving emotional support and engaging in activism.

Assessing students' participation in science practices presents several challenges, especially when aiming to differentiate meaningful (vs. rote) forms of participation. In this study, we sought to use machine learning (ML) for a novel purpose in science assessment: developing a construct map for students' "consideration of generality," a key epistemic understanding that undergirds meaningful participation in knowledge-building practices. We report on our efforts to assess the nature of 845 students' ideas about the generality of their model-based explanations through the combination of an embedded written assessment and a novel data analytic approach that combines unsupervised and supervised machine learning methods and human-driven, interpretive coding. We demonstrate how unsupervised machine learning methods, when coupled with qualitative, interpretive coding, were used to revise our construct map for generality in a way that allowed for a more nuanced evaluation that was closely tied to empirical patterns in the data. We also explored the application of the construct map as a framework for coding used as a part of supervised machine learning methods, finding that it demonstrates some viability for use in future analyses. We discuss implications for the assessment of students' meaningful participation in science practices in terms of their considerations of generality, the role of unsupervised methods in science assessment, and combining machine learning and human-driven approach for understanding students' complex involvement in science practices.

Though science teachers use curricular materials from a range of sources, the nature of the science education materials that exist in the public domain or that are licensed for free use has not been the focus of much prior research. In this study, Open Educational Resources (OER) that can be accessed through the OER Commons platform were examined in terms of their characteristics and use using public Internet data mining methods. The author evaluated 8,937 life science, physical science, and applied science resources in terms of their material type, grade level, license type, number of endorsements by approved organizations (e.g., a state department of education), alignment with the Next Generation Science Standards (NGSS), and number of views. Many resources were readings and laboratory investigations, and most were for postsecondary science classes, though many were intended for high school, middle school, and elementary classrooms. Relatively few resources were endorsed, and fewer still were explicitly aligned with the NGSS, suggesting the need for greater alignment of standards across states. To provide a richer set of accessible curricular resources for educators, several implications for practice and policy are considered.

Computer programming is becoming an essential skill for young students regardless of their education or career goals. Therefore, for students to develop and for educators and researchers to accurately measure self-efficacy in and value for programming is important. Although student motivation in subject matter can be measured using self-report surveys, these types of instruments are prone to error due to inherent biases. In this quasi-experimental and cross-sectional study, we examined whether using a computer programming pretest before taking a perception survey (i.e., providing examples of the key concept in the survey beforehand) had an impact on students' self-reported self-efficacy and utility value, thus helping collect more accurate data. Results showed no significant difference on self-efficacy and value scores between those who received a pretest and those who did not. In further analysis, however, it was found that pretest performance was positively correlated with self-efficacy and value. In addition, boys reported significantly higher self-efficacy and value than girls, confirming gender disparity outlined in previous research. An exploratory, but important, finding was that there was an interaction between gender and test performance for the self-efficacy and utility value. While boys' who had high pretest scores also reported higher levels of self-efficacy and utility value, it was the opposite for girls.

Understanding how prepared teachers are to use technology to enhance their teaching can assist researchers to support them better, yet the theoretical basis for understanding teachers' self-beliefs is in need of stronger empirical support. The first objective of this study was to replicate and extend prior research that empirically tested portions of the technology acceptance model (TAM) amongst pre-service teachers. The second objective was to empirically test an alternative set of antecedents of behavioural intentions to use technology based on the expectancy-value theory (EVT). Four theoretical models were tested using structural equation modelling with a sample of pre-service teachers (N = 249). Results provide support for the TAM and the extended TAM models, however, no support was found for the combined EVT and TAM models. We discuss the implications to promote teacher integration of technology in the classroom by creating facilitating conditions.

The Next Generation Science Standards (NGSS) chat (#NGSSchat) is a social media-based professional network used to discuss topics related to the NGSS in the United States. While successful reforms involve and coordinate the work of multiple stakeholders, recent research points out a striking lack of coordination between the individuals working in different educational roles--to the detriment of intended changes in the system. In this study, we analyzed more than 7,000 posts from individuals participating in #NGSSchat on Twitter (n = 247) during 2 years of 1-hr synchronous discussions. We studied the depth and types of conversations that took place, the extent to which the involvement of teachers, administrators, researchers, and organizations was balanced, and what explains participation in the network over time. Using a mixed-methods approach involving social network analysis, we found that conversations were primarily transactional, or social, and substantive, or providing opportunities for sense-making about the standards or for participants to transform their practice and that individuals from diverse roles participated, with teachers comprising the plurality of those involved. Additionally, researchers, administrators, and teachers were the most active in the network, with no differences in both initiating, or sending, and being the recipients of, or receiving, replies as a part of conversations. Finally, we found that being a teacher or administrator, as well as receiving replies from individuals who were important in the network, were positively related to sustained involvement in the network in the following year. We discuss how #NGSSchat--as a social media-based professional network--demonstrates similar features in other effective networks, and how social media-based networks invite new visions for how to implement ambitious, large-scale changes in science education.

Out-of-school-time programs for youth that are focused on STEM content are often seen as affording opportunities to increase youth engagement, interest, and knowledge in STEM domains, yet we know relatively little about how youth actually experience such programs. In this article, we explore how experiences and activities employed in the delivery of summer STEM programs are associated with youth engagement during programming, and whether youth characteristics moderate these relationships. Data were collected from 203 youth (ages 10-16) in nine summer programs using multiple methods including video, experience sampling, and surveys. Through the use of cross-classified, multi-level models, we found that youth reported higher engagement in program activities they perceived to be more challenging and relevant, and in activities, they perceived to have more affordances for learning or developing skills. Gender moderated these relationships such that the positive relationships observed among males were muted or nonexistent for girls. We further identify that program activities are differently associated with fostering challenge, relevance, and learning. Findings have implications for out-of-school STEM programming for youth.

Schools and school districts use social media for a variety of reasons, but alongside the benefits of schools' social media use come potential risks to students' privacy. Using a novel dataset of around 18 million Facebook posts by schools and districts in the United States, we explore the extent to which personally identifiable information of students may be revealed. We find that around 4.9 million posts depict one or more students' faces, and approximately 726,000 posts also identify the full name of one or more students. We also examine which Facebook page characteristics and structural factors might be associated with posts that depict or identify students. We find that districts with a higher student poverty rate and districts that share a greater number of posts were more likely to depict students. We discuss these findings and recommendations for educational leaders and researchers through the lens of data ethics. [ABSTRACT FROM AUTHOR]

Validity evidence based on test content is critical to meaningful interpretation of test scores. Within high-stakes testing and accountability frameworks, content-related validity evidence is typically gathered via alignment studies, with panels of experts providing qualitative judgments on the degree to which test items align with the representative content standards. Various summary statistics are then calculated (e.g., categorical concurrence, balance of representation) to aid in decision-making. In this paper, we propose an alternative approach for gathering content-related validity evidence that capitalizes on the overlap in vocabulary used in test items and the corresponding content standards, which we define as "textual congruence." We use a text-based, machine learning model, specifically topic modeling, to identify clusters of related content within the standards. This model then serves as the basis from which items are evaluated. We illustrate our method by building a model from the "Next Generation Science Standards," with textual congruence evaluated against items within the Oregon statewide alternate assessment. We discuss the utility of this approach as a source of triangulating and diagnostic information and show how visualizations can be used to evaluate the overall coverage of the content standards across the test items.

New ed-tech tools crop up frequently, leaving teachers little to no time to become familiar with them or adapt them to learning. Professional development that focuses on tool-centered approaches are often insufficient to meet teachers' needs in contemporary classrooms. Therefore, teachers need to develop a level of comfort with using new technology and experimenting with new tools. The authors explain a course they developed which helps teachers adopt a creatively focused technology fluent (CFTF) mindset, a research-based approach that's fueled by creative pursuits to incorporate technology for teaching and learning without dependence on specific tools.

Teacher confidence with technology is essential during times of rapid changes in digital technologies. In this study, we draw on theoretical accounts from creativity research and the educational technology literature to characterize an approach to teaching--a creatively focused technology fluent (CFTF) mindset. Following our work with five cohorts of educational technology master's degree students in hybrid classes designed to support this mindset (n = 74), we report evidence on such an approach. Teachers reported growth in their confidence in using not only technologies they directly experienced but also significant increases in confidence with technologies overall (even with tools they did not use/learn). We discuss implications of these findings with an emphasis upon how teacher educators can support creative teaching with technology regardless of the available technologies.