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Nazanin Nezami; Parian Haghighat; Denisa Gándara; Hadis Anahideh – Grantee Submission, 2024
The education sector has been quick to recognize the power of predictive analytics to enhance student success rates. However, there are challenges to widespread adoption, including the lack of accessibility and the potential perpetuation of inequalities. These challenges present in different stages of modeling, including data preparation, model…
Descriptors: Evaluation Methods, College Students, Success, Predictor Variables
Emma R. Dear; Bryce D. McLeod; Nicole M. Peterson; Kevin S. Sutherland; Michael D. Broda; Alex R. Dopp; Aaron R. Lyon – Grantee Submission, 2024
Introduction: Due to usability, feasibility, and acceptability concerns, observational treatment fidelity measures are often challenging to deploy in schools. Teacher self-report fidelity measures with specific design features might address some of these barriers. This case study outlines a community-engaged, iterative process to adapt the…
Descriptors: Measures (Individuals), Data Collection, Observation, Learning Analytics
McLeod, Bryce D.; Jensen-Doss, Amanda; Lyon, Aaron R.; Douglas, Susan; Beidas, Rinad S. – Grantee Submission, 2022
Mental health organizations that serve youth are under pressure to adopt measurement-based care (MBC), defined as the continuous collection of client-report data used to support clinical decision making as part of standard care. However, few frameworks exist to help leadership ascertain how to select an MBC approach for a clinical setting. This…
Descriptors: Mental Health Programs, Measurement, Evidence Based Practice, Youth
Razvan Paroiu; Stefan Ruseti; Mihai Dascalu; Stefan Trausan-Matu; Danielle S. McNamara – Grantee Submission, 2023
The exponential growth of scientific publications increases the effort required to identify relevant articles. Moreover, the scale of studies is a frequent barrier to research as the majority of studies are low or medium-scaled and do not generalize well while lacking statistical power. As such, we introduce an automated method that supports the…
Descriptors: Science Education, Educational Research, Scientific and Technical Information, Journal Articles
Wilhelmina Van Dijk; Cynthia U. Norris; Stephanie Al Otaiba; Christopher Schatschneider; Sara A. Hart – Grantee Submission, 2022
This manuscript provides information on datasets pertaining to Project KIDS. Datasets include behavioral and achievement data for over 4,000 students between five and twelve years old participating in nine randomized control trials of reading instruction and intervention between 2005-2011, and information on home environments of a subset of 442…
Descriptors: Data, Reading Instruction, Intervention, Family Environment
Rachel Abenavoli; Natalia Rojas; Rebecca Unterman; Elise Cappella; Josh Wallack; Pamela Morris – Grantee Submission, 2021
In this article, Rachel Abenavoli, Natalia Rojas, Rebecca Unterman, Elise Cappella, Josh Wallack, and Pamela Morris argue that research-practice partnerships make it possible to rigorously study relevant policy questions in ways that would otherwise be infeasible. Randomized controlled trials of small-scale programs have shown us that early…
Descriptors: Educational Research, Early Childhood Education, Research Design, Preschool Education
Atsushi Miyaoka; Lauren Decker-Woodrow; Nancy Hartman; Barbara Booker; Erin Ottmar – Grantee Submission, 2023
More than ever in the past, researchers have access to broad, educationally relevant text data from sources such as literature databases (e.g., ERIC), an open-ended response from online courses/surveys, online discussion forums, digital essays, and social media. These advances in data availability can dramatically increase the possibilities for…
Descriptors: Coding, Models, Qualitative Research, Focus Groups
Dalrymple, Kirsten A.; Manner, Marie D.; Harmelink, Katherine A.; Teska, Elayne P.; Ellison, Jed T. – Grantee Submission, 2018
The quantitative assessment of eye tracking data quality is critical for ensuring accuracy and precision of gaze position measurements. However, researchers often report the eye tracker's optimal manufacturer's specifications rather than empirical data about the accuracy and precision of the eye tracking data being presented. Indeed, a recent…
Descriptors: Data Collection, Accuracy, Eye Movements, Age Groups
Zhang, Zhiyong; Zhang, Danyang – Grantee Submission, 2021
Data science has maintained its popularity for about 20 years. This study adopts a bottom-up approach to understand what data science is by analyzing the descriptions of courses offered by the data science programs in the United States. Through topic modeling, 14 topics are identified from the current curricula of 56 data science programs. These…
Descriptors: Statistics Education, Definitions, Course Descriptions, Computer Science Education
McLaughlin, Tara W.; Snyder, Patricia A.; Algina, James – Grantee Submission, 2017
The Learning Target Rating Scale (LTRS) is a measure designed to evaluate the quality of teacher-developed learning targets for embedded instruction for early learning. In the present study, we examined the measurement dependability of LTRS scores by conducting a generalizability study (G-study). We used a partially nested, three-facet model to…
Descriptors: Generalizability Theory, Scores, Rating Scales, Evaluation Methods
Beigman Klebanov, Beata; Priniski, Stacy; Burstein, Jill; Gyawali, Binod; Harackiewicz, Judith; Thoman, Dustin – Grantee Submission, 2018
Collection and analysis of students' writing samples on a large scale is a part of the research agenda of the emerging writing analytics community that promises to deliver an unprecedented insight into characteristics of student writing. Yet with a large scale often comes variability of contexts in which the samples were produced--different…
Descriptors: Learning Analytics, Context Effect, Automation, Generalization
Thompson, Aaron M.; Macy, Rebecca J.; Fraser, Mark W. – Grantee Submission, 2011
Advances in statistics provide new methods for analyzing practice data. These advances include person-centered methods (PCMs) that identify subgroups of research participants with similar characteristics. PCMs derive from a frame of reference that is similar to the risk factor perspective in practice. In practice, the delivery of services is often…
Descriptors: At Risk Persons, Risk, Profiles, Data Analysis