The COVID-19 pandemic forced widespread school closures and a shift to remote learning. A growing body of research has examined the effects of remote learning on student outcomes. But the accuracy of the school modality measures used in these studies is questionable. The most common measures--based on self-reports or district website…

Collected Biannually since 1968, the Civil Rights Data Collection (CRDC) represents the U.S. Department of Education's most substantial effort to understand data related to students' educational opportunities throughout K-12 schooling, particularly for historically marginalized student populations. Due to the COVID-19 pandemic, the Department…

With the widespread use of visuals in the media and the rapid development of visual technologies, media consumers today increasingly face visual information and visual decision-making. Developing visual literacy is thus crucial to empower people to effectively navigate the visual world. This paper uses two studies to describe the development and…

Purpose: Mobile learning has seen tremendous growth over the years. Like any other software application, usability is one of the key concerns in its successful implementation. There is a lack of study that provides a comprehensive overview of usability testing of mobile learning applications. Motivated by this a mapping study is conducted.…

This policy analysis examined the intersection of a statistical model and locus of control to recommend an accountability model that best upholds the criteria of equity, efficacy, and impact on student learning for Missouri schools. Using Eugene Bardach's eightfold path and informed by the quality education in schools framework, this analysis…

There can be many reasons why students fail to answer correctly to summative tests in advanced computer science courses: often the cause is a lack of prerequisites or misconceptions about topics presented in previous courses. One of the ITiCSE 2020 working groups investigated the possibility of designing assessments suitable for differentiating…

Intensive longitudinal data has been widely used to examine reciprocal or causal relations between variables. However, these variables may not be temporally aligned. This study examined the consequences and solutions of the problem of temporal misalignment in intensive longitudinal data based on dynamic structural equation models. First the impact…

Parent rating scales are used at increasing rates across disciplines to track child development and determine diagnoses/needs. This study explored relationships among parental stress and the validity of parents' ratings of their child's behaviors. Participants include children who were referred for assessment of behavioral, social-emotional, and…

Institutional researchers are acutely aware of the systemic inequities pervasive throughout higher education in the United States because the data that we collect, analyze, visualize, and disseminate quantifies and reveals them. As calls for addressing issues of equity have intensified across campuses, the question of how institutional research…

All people deserve the opportunity to earn a better living and build a better life for themselves, their families, and their communities through a postsecondary education. But that opportunity is not available equally to all in the United States, and current postsecondary data sets and collection practices at the federal, state, and institutional…

The American Family Health Study (AFHS) collected family health and fertility data from a national probability sample of persons aged 18-49 between September 2021 and May 2022, using web and mail exclusively. In July 2022, we surveyed AFHS respondents and gauged their willingness to become part of a national web panel that would create novel…

To support and better understand how to scale effectively, in 2020, the Millions Learning project at the Center for Universal Education (CUE) at Brookings joined the Global Partnership for Education's (GPE) Knowledge and Innovation Exchange (KIX), a joint partnership between GPE and the International Development Research Centre (IDRC), to…

High dropout rate exists universally in massive open online courses (MOOCs) due to the separation of teachers and learners in space and time. Dropout prediction using the machine learning method is an extremely important prerequisite to identify potential at-risk learners to improve learning. It has attracted much attention and there have emerged…

The education science (or pedagogy based on scientific principles) of the learning of spectroscopy analysis in terms of critical thinking was examined in a community college setting with high school outreach and senor college transfer students enrolling in courses and/or projects. The most important discovery of science in the 20th Century was the…

Traditionally, assessment professionals use analyses relying upon null hypothesis significance testing (NHST), but those tools have limitations when analyzing small samples or disaggregated data. This study used common NHST analytical techniques, compared their results, and then explored an alternative technique that perhaps allows for a more…