Student Grade Data: Grit

Estimating Student Fixed Effects in Explaining Grade Outcomes: The Importance of Grit in Determining Student Performance Grit is a term that has emerged in the literature as a way of describing a person’s persistence over time to overcome challenges and accomplish goals. Past measures of grit have b...

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description Estimating Student Fixed Effects in Explaining Grade Outcomes: The Importance of Grit in Determining Student Performance Grit is a term that has emerged in the literature as a way of describing a person’s persistence over time to overcome challenges and accomplish goals. Past measures of grit have been based on self-reported data. We develop a model of student grit that does not rely on self-reported data by employing a rich data set and fixed effects regression techniques to model student letter grade in a course, a proxy for student learning and success, while controlling for the student’s measured cognitive performance, sociodemographic variables, academic rank, relative high school performance, subject matter of the course, instructor, advisor and an individual student effect, interpreted as grit. We find the individual student effect explains significant variation in student academic performance in the classroom. We also demonstrate a method for predicting student grit using secondary education data. Data represents a cohort of economics majors at a public four-year, full-time selective university located in the midwest. Rows represent a single university course taken by the student. Columns: Grade Grade earned by the student Parent_Work_Income Parent's income, normalized SCHOOL_GPA Student's high school grade point average, normalized ACT_COMPOSITE Student's composite ACT score, normalized STUDENT_RANK Student's college rank when the course was taken (Freshman, Sophomore, etc.) Age Student's age when the course was taken, normalized GENDER_DESC Student's self-identified gender, URM Under-represented minority status Student.22 Student indicators Student.01 Student.36 Student.04 Student.05 Student.23 Student.15 Student.28 Student.27 Student.10 Student.11 Student.12 Student.13 Student.14 Student.30 Student.16 Student.18 Student.31 Student.19 Student.20 Student.03 Student.09 Student.26 Student.21 Student.02 Student.06 Student.32 Student.08 Student.29 Student.07 Student.17 Student.25 Student.33 Student.34 Student.24 Student.35 SubjECON Subject indicators SubjMGT SubjFIN SubjENGL SubjACCT SubjCIS SubjMKT SubjCOMM SubjMATH SubjBIOL SubjBLAW SubjHIST SubjAE SubjPSY SubjMUS SubjELCT SubjPE SubjART SubjPOLS SubjSPAN Instructor.04 Instructor indicators Instructor.08 Instructor.07 Instructor.03 Instructor.16 Instructor.05 Instructor.12 Instructor.02 Instructor.06 Instructor.11 Instructor.13 Instructor.10 Instructor.01 Instructor.14 Instructor.09 Instructor.15 Ad
doi_str_mv 10.17632/c4s6j94546
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Past measures of grit have been based on self-reported data. We develop a model of student grit that does not rely on self-reported data by employing a rich data set and fixed effects regression techniques to model student letter grade in a course, a proxy for student learning and success, while controlling for the student’s measured cognitive performance, sociodemographic variables, academic rank, relative high school performance, subject matter of the course, instructor, advisor and an individual student effect, interpreted as grit. We find the individual student effect explains significant variation in student academic performance in the classroom. We also demonstrate a method for predicting student grit using secondary education data. Data represents a cohort of economics majors at a public four-year, full-time selective university located in the midwest. Rows represent a single university course taken by the student. 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identifier DOI: 10.17632/c4s6j94546
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subjects Academic Learning
Econometrics
Economics
FOS: Economics and business
title Student Grade Data: Grit
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