2015 DSSG Data Fest: Team High School Graduation

Computation Institute
1 Sept 201504:14

Summary

TLDRImproving high school graduation rates is a significant social issue in the United States, where about 700,000 students fail to graduate on time each year. To tackle this, several US school districts partnered over 14 weeks to create a data-driven solution to identify students at risk. By analyzing data like absences, grades, and demographics, they developed predictive models to prioritize students needing intervention. These models help schools identify risk factors, enabling personalized support to improve graduation outcomes and positively impact students' futures.

Takeaways

  • 🎓 Improving high school graduation rates is a crucial social issue, impacting students' future success.
  • 📊 Every year, approximately 700,000 students in the U.S. do not graduate on time, representing 1 in 5 students.
  • 📉 Students who don't graduate on time face lower life expectancies and expected income compared to those who graduate.
  • 🤝 The project partnered with regionally diverse school districts across the U.S. to develop a data-driven solution to identify students at risk.
  • 🔍 Identifying students at risk of late or non-graduation requires analyzing both dynamic factors (absences, grades) and static data (demographics).
  • 🧠 The team developed predictive models to rank students based on their risk of not graduating on time, helping schools prioritize interventions.
  • 📈 The models demonstrated higher accuracy compared to existing baselines in predicting students at risk, even with limited features.
  • 💡 Schools can use these models to identify different risk factors and drill down to individual student data for more personalized interventions.
  • 🏫 Schools can also compare how their performance and risk factors measure up against other schools in the district.
  • 🌟 The ultimate goal is to enable schools to implement effective interventions that improve graduation outcomes and students' overall life prospects.

Q & A

  • What is the main social issue addressed in the video?

    -The main social issue addressed is improving high school graduation rates in the United States.

  • Why is graduating from high school important for students?

    -Graduating from high school prepares students for higher education, improves their life expectancy, and increases their expected income.

  • How many students in the U.S. fail to graduate from high school on time each year?

    -Approximately 700,000 students do not graduate from high school on time each year in the United States.

  • What is the objective of the project mentioned in the video?

    -The objective is to develop a data-driven solution to identify students at risk of not graduating on time.

  • What kinds of data are used to track student progress in this project?

    -The data includes grade-level information like absences, tardies, grades, and test scores, as well as static demographic information.

  • How does the model developed by the project help schools?

    -The model predicts which students are at risk of not graduating on time and ranks them by the urgency of attention needed, enabling schools to prioritize interventions.

  • What is the goal of developing personalized interventions for students?

    -The goal is to address the specific needs of students at risk of not graduating or graduating late by providing targeted, effective interventions.

  • How does the project evaluate the performance of the predictive models?

    -The models are evaluated by how well they prioritize students at risk, with the ideal model ranking at-risk students above those not at risk.

  • What are some benefits of using this data-driven approach in schools?

    -Schools can identify risk factors, compare performance between schools, and design more effective interventions to improve both graduation rates and students' overall outcomes.

  • What are the next steps for schools using the developed models and data insights?

    -Schools can categorize students by different risk factors, drill down into individual student risks, and compare their performance with other schools to implement more effective interventions.

Outlines

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Keywords

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Highlights

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Transcripts

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相关标签
EducationGraduation RatesData-DrivenAt-Risk StudentsInterventionsSchool DistrictsUS SchoolsStudent OutcomesPredictive ModelsPersonalized Support
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