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Jennifer Hill; George Perrett; Stacey A. Hancock; Le Win; Yoav Bergner – Grantee Submission, 2024
Most current statistics courses include some instruction relevant to causal inference. Whether this instruction is incorporated as material on randomized experiments or as an interpretation of associations measured by correlation or regression coefficients, the way in which this material is presented may have important implications for…
Descriptors: Statistics Education, Teaching Methods, Attribution Theory, Undergraduate Students
Emily Lyons; Almaz Mesghina; Lindsey E. Richland – Grantee Submission, 2022
Gender gaps in mathematics achievement persist in many contexts and when visible, these gaps are paradoxical. Low-stakes measures of mathematics achievement such as grades and study behaviors favor girls, while gaps tend to reverse on assessments/ competitions. We explore whether different impacts of raising performance stakes could be one…
Descriptors: Gender Differences, Achievement Gap, Mathematics Achievement, Grades (Scholastic)
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
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Jordan, Pamela; Albacete, Patricia; Katz, Sandra – Grantee Submission, 2016
Prior research aimed at identifying linguistic features of tutoring that predict learning found interactions between student characteristics (e.g., incoming knowledge level, gender, and affect) and learning. This paper addresses the question: "What do these interactions suggest for developing adaptive natural-language tutoring systems?"…
Descriptors: Intelligent Tutoring Systems, Tutoring, Natural Language Processing, Student Characteristics
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Katz, Sandra; Albacete, Patricia; Jordan, Pamela – Grantee Submission, 2016
This poster reports on a study that compared three types of summaries at the end of natural-language tutorial dialogues and a no-dialogue control, to determine which type of summary, if any, best predicted learning gains. Although we found no significant differences between conditions, analyses of gender differences indicate that female students…
Descriptors: Natural Language Processing, Intelligent Tutoring Systems, Reflection, Dialogs (Language)