Determining Student Return on Investment in College Edu
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Is college worth the cost? We find out in determining student return on investment in college education
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Determining Student Return on Investment in College Edu
- 1. ↗ 1 Data Driven Decision Making George Jreije, Alex Turgeon, Kyle Elkins, Kai Tay, Daniel Hyland Is College Worth the Cost? Determining Student Return on Investment in College Education
- 2. Introduction ↗ Problem ↗ High Tuition Costs & Uncertain Market Conditions ”What would be the earning potential of a student attending a certain college?” ↗ Solution ↗ Build a prediction model using DOE college data ↗ Identify what factors affect income following college graduation ↗ Evaluate the mean student earnings for specific colleges Introduction | Data | Assumptions | Process | Final Model | Application | Considerations
- 3. ↗ Raw data consisted of 1700 variables ⋄ Time Series Data: 19 years ↗ Consists of 10 main cohort variables ⋄ Cost ⋄ Aid ⋄ Repayment ⋄ Completion ⋄ Earnings ⋄ Identifier ⋄ School Type ⋄ Academics ⋄ Admissions ⋄ Student Introduction | Data | Assumptions | Process | Final Model | Application | Considerations Data Breakdown
- 4. ↗ Assumption ↗ Existing literature and knowledge base ↗ Relevance in major/program, initial loans, college attended ↗ The Fault ↗ Not familiar with dataset ↗ Missing observations ↗ Theoretical approach Initial Assumptions Introduction | Data | Assumptions | Process | Final Model | Application | Considerations
- 5. ↗ Combined 6 years of data into one dataframe ↗ Dropped missing observations ↗ ~48,000 -> ~11,000 (rows) ↗ Dropped variables with missing observations ↗ ~1,700 -> ~300 (columns) ↗ Dropped Categorical variables ↗ ~300 -> 89 (columns) Process | Data Cleaning Introduction | Data | Assumptions | Process | Final Model | Application | Considerations
- 6. Process | Test & Training Introduction | Data | Assumptions | Process | Final Model | Application | Considerations ↗ Split Test & Training set ↗ Training: 2007-2012 ⋄ ~8,500 observations ↗ Test: 2012-2013 ⋄ ~2,700 observations 2007 Training 2012 Test 2013
- 7. Process | Stepwise Introduction | Data | Assumptions | Process | Final Model | Application | Considerations ↗ Regression using stepwise selection (leapseq) ↗ Identify most significant variable ↗ Confirmed our five variables were significant
- 8. Comparison of Models Introduction | Data | Assumptions | Process | Final Model | Application | Considerations ↗ Theoretical Approach: 5 predictors ↗ Student loans ↗ Region ↗ College brand recognition ↗ Major/Program ↗ Type of degree ↗ Data-Driven Approach: 5 predictors ↗ Instructional Expenditure per student ↗ % of aided students with low family income ↗ Average family income of dependent students ↗ % of first-generation students ↗ % of students earning over $25k
- 9. ↗ Ran a multivariate regression on our model ↗ AIC of 143,166 ↗ MSE of 12,672,173 ↗ Test data ↗ Aic of 43,831 ↗ MSE of 12,672,173 ↗ MSE decreased to 59% Final Model | Introduction | Data | Assumptions | Process | Final Model | Application | Considerations
- 10. Application Introduction | Data | Assumptions | Process | Final Model | Application | Considerations ↗ Business- to- Enterprise Application ↗ Need to understand factors that contribute to student success at institutes ↗ Identify areas to improve strengths ↗ Remain competitive in a crowded market ↗ Higher Education ↗ Marketing & Communications ↗ Admissions ↗ Alumni Fundraising and Giving
- 11. Considerations | Data Introduction | Data | Assumptions | Process | Final Model | Application | Considerations ↗ Data ↗ Information Bias ⋄ Schools have a natural incentive to report success stories ⋄ Social work doesn't pay well, therefore earnings numbers may be skewed or suppressed when reported ↗ Inconsistent Source of Data ⋄ School distributed student outcome surveys have a response rate of only 15-20% on average
- 12. ↗ External Factors ↗ Model does not account for external environment and broader economic conditions ⋄ Unemployment Rate ⋄ Emerging Jobs and Shifting Industry Considerations | External Introduction | Data | Assumptions | Process | Final Model | Application | Considerations
- 13. Conclusion ↗ Insights ↗ Being a first-generation student does lead to lower earnings after college. ↗ Higher instructional expenditure on students and family income of dependent students have no effect on the mean earnings six years out. ↗ Not Applicable ↗ Does not account for external factors and time- sensitive data ↗ Improvements ↗ Refine & standardize data gathering method
- 14. Thank you!
Editor's Notes
- George
- George
- George
- George: We thought initial loan would definitely have an impact, and the brand ranking would, which is what the literature said.
- Kyle Talk about how we had used veera to clean our data in the theoretical approach, its a data-blending software online. after cleaning with veera and plugging in the r codes, we realized it wasn’t wirking within r. We had decided halfway through that this would not work, so we talked with professor and he recommended that we start from scratch with the data-driven approach with numerical multi-variate linear regression And here’s what we did afterwards(cue to slide) limmited scaled/ time for categorical variables We had 6 data files representing each year(2007-2013), and we combined it into one dataframe
- Kyle We had 6 data files representing each year(2007-2013), and we combined it into one dataframe We split our test and training sets by which year the data came from. 2007-2012 was our training data
- Kyle We used regression with stepwise selection, the hybrid Which helped identify which variables that was the most significant in predicting mean earnings 6 years out of college Narrowed us to 5 variables
- Kai so the student loans dataset was not on the student level we only had school level data region was actually usable college brand recognition- USA news- created a binary for college recognition for top 150 colleges Major and program is the same as the student loans- don’t know what the student got just what was offered by the school Type of degree- what type of school (years of institution) DATA doesn’t say what we wanted to measure. how much they spent on teaching (pay for prof divided by students)
- Dan
- Alex
- Alex Raw data consisted of 1700 variables spanning 19 years
- Alex
- Alex