midfieldr: Data, methods, & metrics for studying student persistence

This talk introduces R users to data and tools for investigating undergraduate persistence metrics using the midfieldr and midfielddata packages. The data are the student records (registrar's data) of approximately 98,000 undergraduates at US institutions from 1987 to 2016. The goal of the talk is to introduce the packages and to share our data, methods, and metrics for intersectional research in student persistence.

Transcript

1. midfieldr
   Data, methods, & metrics for studying student persistence
   Richard Layton
   Russell Long
   Matthew Ohland
   Nichole Ramirez
   Rose-Hulman Institute of Technology
   Purdue University
   Purdue University
   Purdue University
   https://midfieldr.github.io/midfieldr
   useR! Conference, Brisbane, 2018–07–11
   

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2. In education, cross-sectional designs are typical
   group 1
   group 2
   group 3
   different groups
   at one time
   time
   2 / 27
   

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3. Longitudinal studies offer some advantages
   same groups over time
   time
   year 1 year 2 year 3 year 4 year 5 year 6
   3 / 27
   

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4. MIDFIELD is a database for longitudinal studies
   1.6 M undergraduate students at 21 US institutions
   whole-population data from registrars
   1987–present
   4 / 27
   

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5. MIDFIELD data are curated in four categories
   students courses
   terms degrees
   MIDFIELD : 1.6 M students
   5 / 27
   

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6. R package midfielddata provides a stratified sample
   students courses
   terms degrees
   midfielddata : 98 000 students
   6 / 27
   

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7. Each observation is a unique student
   students courses
   terms degrees
   student ID
   institution
   term
   major
   transfer
   sex, race, age
   us citizen
   home zip code
   SAT, ACT
   midfielddata : 98 000 students
   midfieldstudents
   98,000 observations
   19 Mb of memory
   7 / 27
   

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8. Each observation is one term for one student
   students courses
   terms degrees
   student ID
   institution
   term
   major
   transfer
   sex, race, age
   us citizen
   home zip code
   SAT, ACT
   student ID
   institution
   term
   major
   level
   standing
   co-op
   credit hours
   GPA
   midfielddata : 98 000 students
   midfieldterms
   729,000 observations
   82 Mb of memory
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9. Each observation is one course for one student
   students courses
   terms degrees
   student ID
   institution
   term
   major
   transfer
   sex, race, age
   us citizen
   home zip code
   SAT, ACT
   student ID
   institution
   term
   major
   level
   standing
   co-op
   credit hours
   GPA
   student ID
   institution
   term
   course
   --section
   --hours
   --type
   --grade
   --instructor
   midfielddata : 98 000 students
   midfieldcourses
   3.5 M observations
   348 Mb of memory
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10. Each observation is a unique student
    students courses
    terms degrees
    student ID
    institution
    term
    major
    transfer
    sex, race, age
    us citizen
    home zip code
    SAT, ACT
    student ID
    institution
    term
    major
    level
    standing
    co-op
    credit hours
    GPA
    student ID
    institution
    term
    course
    --section
    --hours
    --type
    --grade
    --instructor
    student ID
    institution
    term
    major
    degree
    midfielddata : 98 000 students
    midfielddegrees
    98,000 observations
    10 Mb of memory
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11. midfielddata provides the data
    midfielddata
    midfieldstudents
    midfieldterms
    midfieldcourses
    midfielddegrees
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12. midfieldr provides the tools
    midfielddata
    midfieldstudents
    midfieldterms
    midfieldcourses
    midfielddegrees
    library(midfielddata)
    library(midfieldr)
    cip_filter()
    ever_filter()
    grad_filter()
    race_sex_join()
    multiway_order()
    etc.
    midfieldr
    12 / 27
    

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13. Both packages are currently available on GitHub
    https://midfieldr.github.io/midfieldr 13 / 27
    

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14. Why R?
    Increase accessibility to the data
    Support a collaborative community of researchers
    Share our methods and metrics
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15. 15 / 27
    

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16. Which is stickier: Engineering or Stats/Applied-Math?
    stickiness =
    N graduates of a program
    N students ever enrolled in the program
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17. We start with the programs’ Classification of
    Instructional Programs (CIP) codes
    midfieldr
    cip_filter()
    midfieldr
    1584 observations
    cip
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18. cip_filter() helps us find the codes we want
    27-series: Applied math and stats
    #> # A tibble: 4 x 2
    #> cip4 cip4name
    #>
    #> 1 2701 Mathematics
    #> 2 2703 Applied Mathematics
    #> 3 2705 Statistics
    #> 4 2799 Mathematics and Statistics, Other
    14-series: Engineering
    #> # A tibble: 1 x 2
    #> cip2 cip2name
    #>
    #> 1 14 Engineering
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19. ever_filter() identifies all students ever enrolled
    in these two programs
    midfieldr
    ever_filter()
    midfielddata
    730 000 observations
    midfieldterms
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20. ever_filter() identifies all students ever enrolled
    in these two programs...nearly 20,000
    #> # A tibble: 19,404 x 2
    #> id program
    #>
    #> 1 MID25783162 Engineering
    #> 2 MID25783166 Engineering
    #> 3 MID25783167 Engineering
    #> 4 MID25783178 Engineering
    #> 5 MID25783197 Engineering
    #> 6 MID25783199 Engineering
    #> 7 MID25783257 Engineering
    #> 8 MID25783259 Engineering
    #> 9 MID25783275 Engineering
    #> 10 MID25783388 Engineering
    #> # ... with 19,394 more rows
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21. race_sex_join() adds student race and sex
    variables to the data frame
    98 000 observations
    midfieldr
    race_sex_join()
    midfielddata
    midfieldstudents
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22. We group and summarize these data by program,
    race, and sex
    #> Observations: 28
    #> Variables: 4
    #> $ program "Engineering", "Engineering"
    #> $ race "Asian", "Asian", "Black", "
    #> $ sex "Female", "Male", "Female",
    #> $ ever 302, 998, 734, 1273, 177, 57
    Numbers are a little high because "possible graduation in
    6 years" not yet accounted for.
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23. grad_filter() identifies the students graduating
    from these two programs
    midfieldr
    grad_filter()
    midfielddata
    98 000 observations
    midfielddegrees
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24. Again, we group and summarize these data (7500
    graduates) by program, race, and sex
    #> Observations: 27
    #> Variables: 4
    #> $ program "Engineering", "Engineering"
    #> $ race "Asian", "Asian", "Black", "
    #> $ sex "Female", "Male", "Female",
    #> $ grad 129, 445, 276, 395, 55, 206,
    24 / 27
    

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25. We join the graduates to the ever-enrolleds and
    compute stickiness for programs with > 5 enrolled
    stickiness <- left_join(ever_enrolled,
    graduated) %>%
    filter(ever > 5) %>%
    mutate(stickiness = grad / ever)
    Graph using conventional ggplot2 functions.
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26. Statistics and Applied Math
    Engineering
    0.1 0.2 0.3 0.4 0.5 0.6
    Black Male
    Hispanic Male
    Native American Female
    Black Female
    Native American Male
    Hispanic Female
    White Female
    White Male
    Asian Female
    Asian Male
    Black Male
    Hispanic Male
    Native American Female
    Black Female
    Native American Male
    Hispanic Female
    White Female
    White Male
    Asian Female
    Asian Male
    Stickiness
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27. To find out more...
    R packages
    https://midfieldr.github.io/midfieldr
    MIDFIELD Project
    https://engineering.purdue.edu/MIDFIELD
    midfield@purdue.edu
    Support provided by the US National Science Foundation, Grant 1545667
    Expanding Access to and Participation in the Multiple-Institution Database
    for Investigating Engineering Longitudinal Development
    27 / 27
    

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