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Academic Success Factors in K-12 Education (A Quantitative Analysis)Brianna Chew, UC Riverside
Prior Research• As the gap between rich and poor grows, so does the gap between high and low
performing students• Family structures, such as single parent households, influence how students
perform • Encouraging students to read outside of school and participate in the arts effect
reading and math test scores
Problem• The academic achievement gap continues to grow each year• Lower performing students are less likely to attend college• Adults with no college degree tend to not make as much income to support their
family What demographic and socioeconomic
factors affect student success?
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Measuring Academic Success
Literature Review• Students’ academic success can be measured by SAT
results
• 3-hour long, standardized exam is divided into three subjects - math, reading, and writing.
• SAT predicts college readiness and academic achievement equally amongst all ethnicities
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Hypothesis
• Demographic factors may be correlated to SAT scores
Sample & Methods
• Sample size, n=1,070
• Hotspot Analysis• Logistic Regression• Data Visualization
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Tapestry Segmentation
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Top 3 Schools
Monta Vista High SchoolSanta Clara CountyAverage SAT Score: 2043Percent of Students Scored Above 1500: 75.1%
Mission San Jose High SchoolAlameda CountyAverage SAT Score: 2039Percent of Students Scored Above 1500: 79.8%
Saratoga High SchoolSanta Clara CountyAverage SAT Score: 2009Percent of Students Scored Above 1500: 76.8%
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Centennial High SchoolLos Angeles CountyAverage SAT Score: 1079Percent of Students Scored Above 1500: 1.9%
Fremont High SchoolAlameda CountyAverage SAT Score 1080Percent of Students Scored Above 1500: 0.75%
Roosevelt High SchoolFresno CountyAverage SAT Score: 1115Percent of Students Scored Above 1500: 2.7%
Bottom 3 Schools
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Sample 8
Hotspot Analysis 9
Logistic Regression
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Sample Division
Training Set Validation Set749 Schools 321 Schools
n = 1,070
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Demographic and Socioeconomic VariablesHousehold Spending Potential Indexes• Education Annually• Test Prep & Tutoring Services• Health Insurance• Alcoholic Beverages• Online Entertainment/Games• Books• Cable and Satellite TV Services• Pets• Musical Instruments & Accessories• Elementary/HS School Books/Supplies• School Meals• Social/Recreation/Civic Clubs Member Fee
Demographic Variables• Household Size• Husband-wife Families• Multigenerational Households• Population aged 5-17 who speak Spanish/No English• Diversity Index• Single Father Households with Kids• Single Mother Household with Kids
Socioeconomic Variables• Household Income• Households with Income Below
Poverty Level• Education Attainment: Bachelor's
Degree
1-mile radius ring
Businesses• Number of Educational Services
(NAICS)• Number of Occupations:
Education/Library
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Classification Model
Term Estimate Std Error ChiSquare Prob>ChiSqIntercept 4.48566838 0.9880703 20.61 <.0001*
x1 2017 Average Household Size -0.8208772 0.3628465 5.12 0.0237*x2 Online Entertainment/Games:Ind 0.04542688 0.0202414 5.04 0.0248*
x3 Musical Instruments & Acc:Ind -0.0645742 0.032116 4.04 0.0444*x4 2010 Multigenerational HHs -0.0026234 0.0008054 10.61 0.0011*x5 2017 Education: Bachelor's Degree -0.0005044 7.8779e-5 41.00 <.0001*x6 Social/Recreation/Civic Clubs Member Fee:Ind 0.04725331 0.0165337 8.17 0.0043*x7 2010 Husband-wife Fam: Own Kids <18 0.00177097 0.0003402 27.10 <.0001*x8 Educational Services - Businesses (NAICS) 0.03179732 0.0096527 10.85 0.0010*x9 ACS Pop 5-17 speak Span/No English -0.0178355 0.0087682 4.14 0.0419*x10 2017 Diversity Index -0.0817789 0.0119845 46.56 <.0001*
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Confusion Matrix
Accuracy Rate: 84%Misclassification Rate: 16%
Actual Predicted Count
BINARY RESULT 1 0
1 237/286 = 0.83 70/463 = 0.15
0 49/286 = 0.17 393/463 = 0.85
Validation Datan = 321
Training Datan = 749
Actual Predicted Count
BINARY RESULT 1 0
1 97/131 = 0.74 31/190 = 0.16
0 34/131 = 0.26 159/190 = 0.84
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ROC CurveReceiver Operating Characteristics
Validation SetReceiver Operating Characteristics
Training Set
BINARY RESULT Area
1 0.9129
0 0.9129
BINARY RESULT Area
1 0.8907
0 0.890715
SAT Scores vs.
Income
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SAT Scores vs.
Bachelor’s Degrees
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SAT Scores vs.
Educational Businesses
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SAT Scores vs.
Multigenerational Households
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SAT Scores vs.
Diversity Index
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ConclusionVariables that affect SAT scores are:
Multigenerational Households
Husband-Wife Households
Parental Education
Diversity Index
IncomeEducational Businesses
and ServicesSocial/Rec./Club
Membership Fees
Online Entertainment
Games
Musical Instruments
Language Barriers
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My Interpretation
What can we do now?
• Be more proactive about helping students earlier in their academic careers
• Create and fund programs to help students perform better on exams and academics in general, specifically in the lower socioeconomic areas
• Build a stronger connection between college campuses and high schools
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Acknowledgements
Thank you to both my faculty mentors, Professor Asish Satpathy
and Associate Dean Elaine Wong,
from the UCR School of Business for advising me throughout this project.
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ReferencesAugustine, J. M., & Raley, R. K. (2013). Multigenerational: households and the school
readiness of children born to unmarried mothers. Journal of Family Issues, 34(4), 431–459. http://doi.org/10.1177/0192513X12439177
Barajas, M.S. (2011). Academic achievement of children in single parent homes: A critical review. The Hilltop Review, 5(4). Retrieved from https://scholarworks.wmich.edu/cgi/viewcontent.cgi?referer=https://www.google.com/&httpsredir=1&article=1044&context=hilltopreview
Bethall, T. (2011). Family matters: Multigenerational families in a volatile economy. Retrieved from Generations United.
California Department of Education - Analysis, Measurement, and Accountability Reporting Division DataQuest. (2017). 2015-2016 SAT. Retrieved from https://www.cde.ca.gov/ds/sp/ai/
Dronkers, J. & Velden R. (2013). Chapter 4: Positive but also negative effects of ethnic diversity in schools on educational performance? An empirical test using PISA data. In M. Windzio (Eds), Integration and inequality in educational institutions. (71-98). New York, NY: Springer
Duff, D., Tomblin, J.B., & Catts, H. (2015). The influence of reading on vocabulary growth: A case for a Matthew Effect. US National Library of Medicine. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4610292/
ESRI’s ArcGIS Maps for Office. (2017). ArcGIS Maps [computer software]. United States of America.
ESRI’s ArcGIS Online. (2018). ArcGIS Online [software] Available from https://www.arcgis.com/home/index.html
ESRI 1. (2018). US tapestry: Up and coming families. Retrieved from http://downloads.ESRI.com/ESRI_content_doc/dbl/us/tapestry/7A_UpAndComing_TapestryFlier_G79488_2-18.pdf
ESRI 2. (2018). US tapestry: Set to impress. Retrieved from http://downloads.ESRI.com/ESRI_content_doc/dbl/us/tapestry/11D_SetToImpress_TapestryFlier_G79488_2-18.pdf
ESRI 3. (2018). US tapestry: Old and newcomers. Retrieved from http://downloads.ESRI.com/ESRI_content_doc/dbl/us/tapestry/8F_OldAndNew_TapestryFlier_G79488_2-18.pdf
ESRI 4. (2017). Tapestry segmentation: The fabrics of America’s neighborhoods. Retrieved from http://www.ESRI.com/library/brochures/tapestry-segmentation.pdf
Hodgson, N. (2016). JMP Pro 13. [computer software]. Cary, NC. Reardon, S.F. (2011). The widening academic achievement gap between the rich and the
poor: New evidence and possible explanations. In R. Murnane & G. Duncan (Eds.), Whither Opportunity? Rising Inequality and the Uncertain Life Chances of Low-Income Children. New York: Russell Sage Foundation Press.
Santos-Luiz, (2007). The learning of music as a means to improve mathematical skills. International Symposium on Performance Science. Retrieved from https://www.researchgate.net/publication/266318346_The_learning_of_music_as_a_means_to_improve_mathematical_skills
Vaughn, K., & Winner, E. (2000). SAT scores of students who study the arts: What we can and cannot conclude about the association. Journal of Aesthetic Education. Retrieved from http://www.jstor.org/stable/pdf/3333638.pdf?refreqid=excelsior:0c143f7977e9a3af692452680a47eac9
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