The National Study of Learning Mindsets, [United States], 2015-2016
- Yeager, David S.
- Students' Perspectives about the Transition to High School (PATHS) (Alternative Title)
- Version 1 (Subtitle)
- Inter-University Consortium for Political and Social Research
- Bill and Melinda Gates Foundation
- National Science Foundation
- Raikes Foundation
- Optimus Foundation
- William T. Grant Foundation
- Spencer Foundation
- Character Lab, Inc
- Bezos Family Foundation
- Houston Endowment
aptitude; education; high school students; high schools; learning; students
AbstractThe National Study of Learning Mindsets (NSLM), also known as Students' Perspectives about the Transition to High School (PATHS), is a longitudinal research study and program evaluation administered by the University of Texas. The purpose of NSLM is to determine if introducing students to a positive growth mindset as they transition to high school will help improve students' academic success during high school and beyond. A growth mindset was defined as a belief amongst students that intelligence can grow and develop, learning is purposeful, hard work can lead to gained skills and accomplished goals, and that these beliefs can maintain motivation in the face of obstacles. The sample consisted of more than 16,000 9th grade students across 76 high schools in 27 states. Number of courses taken, course level, and GPA before and after high school were measured within english, math, social studies, and science. Numerous demographic variables were collected for students, teachers, and schools. Student demographics included gender, race, and age. Teacher demographics consisted of race, teaching methods, and educational background. School characteristics such as location, course offerings, and students' average test scores were assessed.
AbstractThe National Study of Learning Mindsets addresses the following primary research questions: Can the growth mindset intervention improve the grades of lower-performing students in U.S. public schools?; Can the growth mindset intervention motivate students to enroll in challenging math and science courses?; Can the growth mindset intervention reduce group-based inequalities in academic performance in U.S. public schools?; Do the effects of the growth mindset intervention depend on schools' formal resources (e.g., the curriculum and instruction)?; Are the effects of the growth mindset intervention larger in schools or classrooms that are supportive of growth mindset beliefs?;
MethodsStudents were asked to complete two randomly assigned, 25-minute online sessions using their school's computer resources. These sessions included a the growth mindset exercise and a control exercise. In the treatment condition, students read and listened to materials describing scientific evidence about how the brain works and about people's ability to grow intellectual abilities over time. The treatment condition also encouraged students to think about why they might want to grow their brain in order to make a difference on something they personally care about. The students also reflected on how to put these beliefs into practice, for instance, by completing a brief writing assignment providing advice for future ninth graders that might ease their transition to high school based on what the participants had just learned from the intervention. Students in the control group did activities that were closely matched to each of the treatment conditions, but they lacked the growth mindset message.
MethodsThe Grades Long dataset contains information on one record per course grades and participating students' GPA, course level, and classification. The Grades Wide dataset contains information on one record per student grades and how many courses participating students were taking, as well as their GPAs in each course. The Math Teacher Disposition dataset contains administrative variables on what surveys teachers participated in. The School Disposition dataset contains administrative variables on participating schools. The Student Dispostion dataset contains administrative information on students' survey participation as well as weight variables. The Student-Math Teacher Crosswalk dataset contains information on participating students' math classes, whether or not their teachers participated in the survey, and their grades. The Student Survey dataset contains demographic information such as gender, race, and age, as well as variables on participating students' experiences in school from an online survey. The Teacher Survey dataset contains participating teachers' demographic information such as race, their teaching methods, and their education. The Test Scores dataset contains information on number of tests taken and whether or not students met state standards for their given subject. The Grade 10 Math Course Selection dataset contains information on participating students' course selections. The School dataset contains information on participating schools' demographics, such as location, the courses they offer, and average test scores.
MethodsICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Checked for undocumented or out-of-range codes..
- DS0: Study-Level Files
- DS1: Grades Long Data
- DS2: Grades Wide Data
- DS3: Math Teacher Disposition Data
- DS4: School Disposition Data
- DS5: Student Dispostion Data
- DS6: Student-Math Teacher Crosswalk Data
- DS7: Student Survey Data
- DS8: Teacher Survey Data
- DS9: Test Scores Data
- DS10: Grade 10 Math Course Selection Data
- DS11: School Data
Time period: 2015--2016
2015 / 2016
Collection date: 2015--2016
2015 / 2016
cognitive assessment test
- 37353 (Type: ICPSR Study Number)
Is previous version of
Carvalho, Carlos, Feller, Avi, Murray, Jared, Woody, Spencer, Yeager, David. Assessing Treatment Effect Variation in Observational Studies: Results from a Data Challenge. eprint arXiv:1907.07592.Preprint. 2019.
Kudym, Molly R.. Linking Math Teachers' Motivations and Beliefs to Learning Mindsets. Thesis, University of Texas at Austin. 2019.
- ID: https://repositories.lib.utexas.edu/bitstream/handle/2152/78517/KUDYM-THESIS-2019.pdf?sequence=1 (URL)
Update Metadata: 2020-07-27 | Issue Number: 5 | Registration Date: 2019-06-24