Catching students at risk of failure
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Academic success is a huge contributor in the sustainable development of a country, and the rising number of students failing in their classes is a problem Imperial Academy has been trying to address. One of the ways to combat this problem is to identify the factors linked to students at risk of failure and to employ early and appropriate interventions. Using Imperial Academy's data, Team Braving deployed an application that predicts whether a student is likely to fail at least one subject based on identified risk factors. This application allows Imperial Academy to better allocate their resources through targeted implementation of their designed interventions for students at risk of failing.
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Catching students at risk of failure
- 1. Presented by Team #BRAVING Catching Students at Risk of Failure
- 2. “Education is the passport to the future, for tomorrow belongs to those who prepare for it today.” - Malcolm X
- 3. 2 out of 5 “Students who are retained in grade have an increased chance of dropping out of school.” college students do not graduate
- 4. Students Academic Failure Effects Teachers Emotional burden Financial Burden Career Prospects Later Employment Incompetencies Low Teacher Evaluation Retention Rates / Graduation Rates Waste of Talent Waste of Money/ Resources
- 5. Objectives Detecting risk factors to successful learning of students Determining which students are likely to fail, given the risk factors identified Help design appropriate and early interventions for students who may be failing by:
- 6. Enhance learning experience of students Better allocation of resources Improve school's overall performance Benefits
- 7. 1 2 3 Predict the likelihood of a student failing at least one subject during a school year Identify variables that may help explain the likelihood of failing at least one subject during a school year Rank identified variables based on their impact on students failing at least one subject during a school year Analytics Problem Framing
- 8. Business Understanding 1 2 3 Data Visualization 4 Hypothesis Testing 5 Model Development 6 Model Deployment 7 Analytics Process Data Exploration Data Cleaning & Validation
- 9. UNIFORM INPUT TEACHER QUARTILES PASS / FAIL ONE - HOT ENCODING Feature Engineering UNIFORM GENDER INPUTTEACHER QUARTILESPASS / FAILONE - HOT ENCODING
- 10. Challenges Solutions Python, Tableau Inconsistent and Incomplete Constraints Feature Engineering, Hypothesis Testing, Modeling Mentor Consultation, Online Tutorials Agile Method Mentor Consultation, Online Tutorials Constant communication
- 11. Passed all subjectsDid not pass all subjects 1st Year 2nd Year 3rd Year 4th Year 0 25 50 75 100100 75 50 25 % of Total Number of Students Only 55% of 1st Year students passed all subjects in Imperial College. Insights 55% 79% 64% 70% Year Level
- 12. Insights More students passed all subjects during the 2nd Semester. % of Total Number of Students Passed all subjects Did not pass all subjects 0 25 50 75 100 60% 1st Semester 2nd Semester 54% 80% Term
- 13. Passed all subjects Did not pass all subjects More Non-transferees passed all subjects than Transferees. 100 75 50 %ofTotalNumberofStudents 25 0 Transferee Non-transferee Insights 79% 55% Transferee Status
- 14. Passed all subjects Did not pass all subjects 84% of Scholars passed all subjects in Imperial College. 100 75 50 %ofTotalNumberofStudents 25 0 Scholar Non-scholar Insights 84% 50% Scholarship Status
- 15. % of Total Number of Students Passed all subjects Did not pass all subjects 0 25 50 75 100 Insights 73% 61% More Male students passed all subjects than Females. Gender
- 16. Male students did better than females. Test on Two Population Proportions There is a difference in the school performance of transferees and non-transferees. 4.01 x 10 (p-value) -132 9.00 x 10 0.00 -54 Scholars perform better than non-scholars. (p-value) (p-value)
- 17. Initial Model Results 1st year students are more likely to fail 4th year students are less likely to fail Impact of Year Levels 1st Year 2nd Year 3rd Year 4th Year 1.34 0.75 - 0.53 -1.28 1.0 0.5 0.0 -0.5 -1.0 Coefficients positive coefficient: more likely to fail in at least 1 subject negative coefficient: less likely to fail in at least 1 subject
- 18. Students studying in the 1st semester are more likely to fail Impact of Semesters 1st Semester 2nd Semester 0.18 0.790.8 0.6 0.4 0.2 0.0 positive coefficient: more likely to fail in at least 1 subject negative coefficient: less likely to fail in at least 1 subject Coefficients Initial Model Results
- 19. Transferees are more likely to fail Males are less likely to fail Scholars are less likely to fail Impact of Student Status Transferees Males Scholars 0.45 - 0.37 0.6 0.4 0.0 - 0.4 - 0.6 positive coefficient: more likely to fail in at least 1 subject negative coefficient: less likely to fail in at least 1 subject Coefficients - 0.58 0.2 - 0.2 Initial Model Results
- 20. Students with group 1 teachers are more likely to fail Students with group 4 teachers are less likely to fail Impact of Teachers Group 1 Group 2 Group 3 Group 4 0.28 - 1.05 0.25 - 0.25 - 0.50 - 0.75 - 1.00 positive coefficient: more likely to fail in at least 1 subject negative coefficient: less likely to fail in at least 1 subject Coefficients - 0.36 - 0.72 0.00 - 1.25 Teachers Initial Model Results
- 21. Initial Model Results Significance of Risk Factors Significant Not Significant 1st Semester 2nd Semester Transferee Male Gender Scholar Group 1 Teacher Group 2 Teacher Group 3 Teacher Group 4 Teacher 1st year 2nd year 3rd year 4th year
- 22. 1st year 3rd year 4th year 4th Quarter Group 1 Teacher Group 2 Teacher Group 3 Teacher Group 4 Teacher Transferee Male Gender Scholar Updated Model Results Significance of Risk Factors Significant Not Significant
- 23. Model Evaluation INITIAL MODEL Prediction Accuracy 81.69% UPDATED MODEL Prediction Accuracy 81.63%
- 24. Operationalizing the model using an app
- 25. Operationalizing the model using an app
- 26. 40 Target% 20 40 60 80 1000 0 20 60 80 100 Gain Chart Population % Model Random With Model 80% Without Model 40% Model Evaluation
- 27. 8,000 students 32% at risk cost of Php 5,000 Intervention 75% effectiveness cost of Php 1,000 Business Case for Operationalizing the Model
- 28. Scenario A No intervention 2,560 students will likely fail Php 12.8 M Scenario B Intervene all 640 students will likely fail Php 11.2 M 8,000 students intervened Scenario C Targeted Intervention 1,024 students will likely fail Php 8.32 M 3,200 students intervened 26%35% Business Case for Operationalizing the Model 0 students intervened
- 29. Offer non-credit basic subjects to selected freshman students Recommendations Spread teachers based on historical grades Review first semester curriculum
- 30. Conduct transferee experience surveys Gather more student data Recommendations
- 31. Meet Our Team Mary Nathalie Dela Cruz Kelsey Grail Lopez julie.delda@ftwfoundation.org mary.delacruz@ftwfoundation.org kelsey.lopez@ftwfoundation.org Julie Ann Delda Sara Llona sara.llona@ftwfoundation.org Strong Back. Soft Front. Wild Heart.
- 32. References ● http://www.healthofchildren.com/R/Retention-in-School.html ● https://whattobecome.com/blog/college-dropout-rate/ ● https://www.idra.org/resource-center/high-failure-rates-quality-or-ineffectiveness/