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Anh Thu Le; Teresa Ober; Ying Cheng – Grantee Submission, 2024
Procrastination in academic contexts is thought to have a negative effect on students' learning and performance. This research sought to provide a comprehensive multi-method and multimodal validation of a self-report measure of procrastination, revealing its intricate associations with behavioral indicators of procrastination, engagement, and…
Descriptors: Time Management, Measures (Individuals), Test Validity, High School Students
Stephanie Owen – Grantee Submission, 2024
The Advanced Placement (AP) program is widely offered in American high schools and has been touted as a way to close racial and socioeconomic gaps in educational outcomes. Using administrative data from Michigan, I exploit variation within high schools across time in AP course offerings to identify the relationship between AP course availability,…
Descriptors: Advanced Placement Programs, Equal Education, Socioeconomic Status, Social Differences
Teresa M. Ober; Alex S. Brodersen; Daniella Rebouças-Ju; Maxwell R. Hong; Matthew F. Carter; Cheng Liu; Ying Cheng – Grantee Submission, 2022
Understanding the extent engagement and math attitudes predict performance in statistics courses could inform educational interventions in this subject area, which has growing demand. We examined direct and indirect associations between course engagement-related constructs, math attitudes, and learning outcomes. Confirmatory factor analysis was…
Descriptors: High School Students, Student Attitudes, Mathematics, Statistics Education

Sarah K. Mason; Matt Hancock; Izzy Thornton – Grantee Submission, 2024
Rural Mississippi schools often face challenges in providing equitable access to Advanced Placement (AP) courses, particularly in STEM fields. This lack of access limits opportunities for high-achieving students to pursue rigorous STEM coursework and related careers. The AP STEM program aims to improve access to AP STEM courses for high-achieving…
Descriptors: Rural Schools, STEM Education, Advanced Placement Programs, Access to Education
Yikai Lu; Teresa M. Ober; Cheng Liu; Ying Cheng – Grantee Submission, 2022
Machine learning methods for predictive analytics have great potential for uncovering trends in educational data. However, simple linear models still appear to be most widely used, in part, because of their interpretability. This study aims to address the issues of interpretability of complex machine learning classifiers by conducting feature…
Descriptors: Prediction, Statistics Education, Data Analysis, Learning Analytics
Teresa M. Ober; Matthew F. Carter; Meghan R. Coggins; Audrey Filonczuk; Cheyeon Kim; Maxwell R. Hong; Ying Cheng – Grantee Submission, 2022
During the Spring 2020 semester, K-12 teachers throughout many parts of the world adapted from face-to-face to online teaching. To better understand these experiences, seven advanced placement (AP) Statistics high school teachers were interviewed following a semi-structured protocol. A collaborative and consensus-driven analysis of transcripts…
Descriptors: Elementary Secondary Education, Educational Technology, COVID-19, Pandemics
Teresa M. Ober; Maxwell R. Hong; Matthew F. Carter; Alex S. Brodersen; Daniella Rebouças-Ju; Cheng Liu; Ying Cheng – Grantee Submission, 2021
We examined whether students were accurate in predicting their test performance two testing contexts (low-stakes and high-stakes). The sample comprised U.S. high school students enrolled in an advanced placement (AP) statistics course during the 2017-2018 academic year (N=209; M[subscript age]=16.6 years). We found that even two months before…
Descriptors: High School Students, Self Evaluation (Individuals), Student Attitudes, High Stakes Tests
Stevenson, Olivia – Grantee Submission, 2021
EMPOWER was an Investing in Innovation (i3) development grant awarded to Cabarrus County Schools by the Office of Innovation and Improvement, U.S. Department of Education. EMPOWER provided social-emotional, academic, and non-cognitive supports in magnet school settings to students from low-income families. Family engagement and teacher…
Descriptors: Magnet Schools, Low Income Students, Literacy, Mathematics Achievement