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Achieving Student Retention Through Data Analysis: A Culture of Evidence at SFCC

This presentation to the Retention Committee in February 2006 discusses the analysis of student enrollment data from Fall 2005 at SFCC. It defines key metrics, such as success rates, non-success rates, and withdrawal rates, and examines factors influencing course completion and overall retention. By analyzing data based on full-time versus part-time status and student demographics, the presentation aims to establish benchmarks and understand how data can contribute to a culture of evidence that enhances student success and graduation rates.

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Achieving Student Retention Through Data Analysis: A Culture of Evidence at SFCC

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  1. Achieving the DreamUsing Data at SFCC Developing a Culture of Evidence Presentation to the Retention Committee February, 2006

  2. Definitions • Data Set – The data consist of the total credit course enrollments for grades (i.e. minus audits and classes dropped or deleted prior to census) in Fall, 2005. N=10,223 • This is a duplicate count. That is, if a single student enrolled in 4 courses in fall semester, they would equate to 4 data points in the overall data set.

  3. Definitions • Success Rate – the percentage of students who complete a credit course with a grade of “A,” “B,” “C,” “P,” “IP,” or “PR.” • Non-Success Rate – the percentage of students who complete a credit course with a grade of “D,” “F,” or “I.” • Withdrawal Rate – the percentage of students who drop a credit course after the census date

  4. Definitions • Full-Time – Students enrolled for 12 credits or more in Fall 2005 • Part-Time – Students enrolled for less than 12 credits in Fall 2005 • Degree-Seeking – Students who have declared a major as of Fall 2005 • Non-Degree-Seeking – Students who have not declared a major as of Fall 2005

  5. Interpreting the Data • What should be an overall benchmark goal? • Common sense versus statistical models • How much difference makes a difference?

  6. Why course completion? • Basic student retention data must account for at least 2 important variables • Student intent to persist • Environmental variables • Course completion data accounts for student intent • Course completion leads to retention which leads to graduation

  7. How the data were analyzed • Student Success Rate • Student Non-Success Rate • Student Withdrawal Rate • Comparing these Rates by: Full-Time/Part-Time Status Student Demographics (Age/Gender/Ethnicity) Developmental English and Math Gateway English and Math Online

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