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Showing 1 to 15 of 122 results Save | Export
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Joni M. Lakin; Paula Olszewski-Kubilius – Grantee Submission, 2024
We talk about our spatial thinking skills all the time, maybe without knowing that there is an entire domain of cognitive skills that connect these observations. Educators can also recognize these skills in our students. These include students who seem to have an intuitive understanding of computer-aided design and create complex and efficient 3-D…
Descriptors: Gifted Education, Talent Identification, Talent Development, Spatial Ability
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Jon Wai; Joni M. Lakin – Grantee Submission, 2024
Students' talent and potential cannot be served until they are recognized by schools or caregivers. While the field of gifted education has had success in identifying talent among many students with talents in reading and mathematics, those with spatial talents are often overlooked. This article reviews how we might identify spatial talent using…
Descriptors: Spatial Ability, Identification, Talent, Student Evaluation
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Megan N. Imundo; Maria Goldshtein; Micah Watanabe; Jiachen Gong; Nicole Crosby; Tracy Arner; Rod D. Roscoe; Danielle S. McNamara – Grantee Submission, 2025
Academic Status Reports (ASRs) are submitted by an instructor to indicate that a student is succeeding in the course or, more commonly, that the instructor is concerned about their progress or participation in the course (e.g., not attending class or not submitting assignments). ASR notifications are sent to students and may also be shared with…
Descriptors: Undergraduate Students, Academic Achievement, Identification, Early Intervention
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Austin Wyman; Zhiyong Zhang – Grantee Submission, 2025
Automated detection of facial emotions has been an interesting topic for multiple decades in social and behavioral research but is only possible very recently. In this tutorial, we review three popular artificial intelligence based emotion detection programs that are accessible to R programmers: Google Cloud Vision, Amazon Rekognition, and…
Descriptors: Artificial Intelligence, Algorithms, Computer Software, Identification
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Robin Clausen – Grantee Submission, 2024
Early warning systems (EWS) using analytical tools that have been trained against prior years' data, can reliably predict dropout risk in individual students so that educators may intervene early to help avert this from happening. Risk profiles for dropouts aren't always useful since students often do not conform to the profiles. Researchers with…
Descriptors: Early Intervention, Predictor Variables, Potential Dropouts, At Risk Students
Chenchen Ma; Gongjun Xu – Grantee Submission, 2022
Cognitive Diagnosis Models (CDMs) are a special family of discrete latent variable models widely used in educational, psychological and social sciences. In many applications of CDMs, certain hierarchical structures among the latent attributes are assumed by researchers to characterize their dependence structure. Specifically, a directed acyclic…
Descriptors: Vertical Organization, Models, Evaluation, Statistical Analysis
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D. Betsy McCoach; Scott Peters; Anthony J. Gambino; Daniel Long; Del Siegle – Grantee Submission, 2024
Teacher rating scales (TRS) often play a part in service eligibility decisions for gifted services. Although schools regularly use TRS to identify gifted students either as part of an informal nomination process or through behavioral rating scales, there is little research documenting the between-teacher variance in teacher ratings and the…
Descriptors: Gifted Education, Rating Scales, Academically Gifted, Academic Achievement
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Saijun Zhao; Zhiyong Zhang; Hong Zhang – Grantee Submission, 2024
Mediation analysis is widely applied in various fields of science, such as psychology, epidemiology, and sociology. In practice, many psychological and behavioral phenomena are dynamic, and the corresponding mediation effects are expected to change over time. However, most existing mediation methods assume a static mediation effect over time,…
Descriptors: Bayesian Statistics, Statistical Inference, Longitudinal Studies, Attribution Theory
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Samantha Coyle-Eastwick; Melissa Escobar; Jessica Wimmer; Michael Lindsey; Jarius Thompson; Carrie Masia Warner – Grantee Submission, 2024
Social anxiety disorder (SAD) is characterized by significant distress and avoidance surrounding social and performance situations with marked interpersonal and academic impairment. This review paper highlights cultural considerations relevant to the conceptualization, identification and treatment of SAD in Black youth. Research evaluating the…
Descriptors: African Americans, Adolescents, Identification, Anxiety Disorders
Jing Ouyang; Gongjun Xu – Grantee Submission, 2022
Latent class models with covariates are widely used for psychological, social, and educational research. Yet the fundamental identifiability issue of these models has not been fully addressed. Among the previous research on the identifiability of latent class models with covariates, Huang and Bandeen-Roche (Psychometrika 69:5-32, 2004) studied the…
Descriptors: Item Response Theory, Models, Identification, Psychological Studies
Sinharay, Sandip; Johnson, Matthew S. – Grantee Submission, 2021
Score differencing is one of six categories of statistical methods used to detect test fraud (Wollack & Schoenig, 2018) and involves the testing of the null hypothesis that the performance of an examinee is similar over two item sets versus the alternative hypothesis that the performance is better on one of the item sets. We suggest, to…
Descriptors: Probability, Bayesian Statistics, Cheating, Statistical Analysis
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Ruoxuan Li; Lijuan Wang – Grantee Submission, 2024
Causal-formative indicators are often used in social science research. To achieve identification in causal-formative indicator modeling, constraints need to be applied. A conventional method is to constrain the weight of a formative indicator to be 1. The selection of which indicator to have the fixed weight, however, may influence statistical…
Descriptors: Social Science Research, Causal Models, Formative Evaluation, Measurement
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Ting Ye; Ted Westling; Lindsay Page; Luke Keele – Grantee Submission, 2024
The clustered observational study (COS) design is the observational study counterpart to the clustered randomized trial. In a COS, a treatment is assigned to intact groups, and all units within the group are exposed to the treatment. However, the treatment is non-randomly assigned. COSs are common in both education and health services research. In…
Descriptors: Nonparametric Statistics, Identification, Causal Models, Multivariate Analysis
Yuqi Gu; Elena A. Erosheva; Gongjun Xu; David B. Dunson – Grantee Submission, 2023
Mixed Membership Models (MMMs) are a popular family of latent structure models for complex multivariate data. Instead of forcing each subject to belong to a single cluster, MMMs incorporate a vector of subject-specific weights characterizing partial membership across clusters. With this flexibility come challenges in uniquely identifying,…
Descriptors: Multivariate Analysis, Item Response Theory, Bayesian Statistics, Models
Robin Clausen – Grantee Submission, 2023
Research over the monitoring of at-risk youth. Behavioral interventions have been the primary focus of this literature past two decades has focused on one aspect of dropout prevention: early identification an however, there is renewed emphasis placed on attendance and academic risk factors under ESSA. Recognizing a need to promote early…
Descriptors: Identification, Dropouts, At Risk Students, Prediction
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