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A tartalmat a Jared Tippets and Eric Kirby, Jared Tippets, and Eric Kirby biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Jared Tippets and Eric Kirby, Jared Tippets, and Eric Kirby vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.
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S2 - E32 - Persistence Scorecards (Data)

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Manage episode 448292069 series 3405647
A tartalmat a Jared Tippets and Eric Kirby, Jared Tippets, and Eric Kirby biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Jared Tippets and Eric Kirby, Jared Tippets, and Eric Kirby vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.

In volume one of ASCEND to Higher Retention Rates, we discussed the value and power of having all new students complete a pre-arrival questionnaire (Tippets & Kirby, 2022). This allows us to capture lots of important insights about a student’s background, motivations, and commitment to higher education. Using the data from this survey, and combining it with other data collected from the application process, our peer mentors create “scorecards” for each student to help us prioritize our outreach and support.

Many institutions have purchased sophisticated data analytic software that ingests data to predict the likelihood of a student persisting and graduating. These are fabulous tools, but they are expensive and sometimes complicated to use. And, sometimes the predictions from these tools don’t match the outcomes or what feels right when working with the students.

As an alternate approach to predicting the likelihood of a student retaining, we have our peer mentors use the available data we have to create scorecards for each student that results in an individual score for each student. We have found the following data points to be powerful predictors of persistence and use these scores to help guide our outreach and support to students in the early weeks of their first semester:

  • high school GPAs
  • first-generation status
  • ACT/SAT scores
  • intent to transfer
  • race/ethnicity
  • residency status
  • living on campus or off campus

In the end, the method doesn’t have to be exact or overly complex. The key is to look at student data and use that data to identify students who will need some additional support and care. Then, go to work.

  continue reading

137 epizódok

Artwork
iconMegosztás
 
Manage episode 448292069 series 3405647
A tartalmat a Jared Tippets and Eric Kirby, Jared Tippets, and Eric Kirby biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Jared Tippets and Eric Kirby, Jared Tippets, and Eric Kirby vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.

In volume one of ASCEND to Higher Retention Rates, we discussed the value and power of having all new students complete a pre-arrival questionnaire (Tippets & Kirby, 2022). This allows us to capture lots of important insights about a student’s background, motivations, and commitment to higher education. Using the data from this survey, and combining it with other data collected from the application process, our peer mentors create “scorecards” for each student to help us prioritize our outreach and support.

Many institutions have purchased sophisticated data analytic software that ingests data to predict the likelihood of a student persisting and graduating. These are fabulous tools, but they are expensive and sometimes complicated to use. And, sometimes the predictions from these tools don’t match the outcomes or what feels right when working with the students.

As an alternate approach to predicting the likelihood of a student retaining, we have our peer mentors use the available data we have to create scorecards for each student that results in an individual score for each student. We have found the following data points to be powerful predictors of persistence and use these scores to help guide our outreach and support to students in the early weeks of their first semester:

  • high school GPAs
  • first-generation status
  • ACT/SAT scores
  • intent to transfer
  • race/ethnicity
  • residency status
  • living on campus or off campus

In the end, the method doesn’t have to be exact or overly complex. The key is to look at student data and use that data to identify students who will need some additional support and care. Then, go to work.

  continue reading

137 epizódok

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