Lesson
Materials

In this case study, we use the Postsecondary Data Partnership dashboards to better understand the profile of transfer-in students and their short-term and long-term outcomes.

Transcript
In this case study, we use the Postsecondary Data Partnership dashboards to better understand the profile of transfer-in students and their short-term and long-term outcomes.

By exploring the institution's Retention and Persistence PDP dashboard, Thomas, our institution’s provost, notices that the transfer-in student population’s retention rate has declined nearly 12 percentage points between 2012-13 and 2017-18.

He takes these data to the President and secures funding to start a transfer student success program in hopes of reversing this trend.​

He recruits Angela, the Director of Academic Advising, to lead this new initiative and asks her for recommendations on ways to better support transfer-in students.​

Angela is excited about leading this new program, but before she can make recommendations, she needs more information like:​

  • How many students transfer to our institution each term?
  •  ​

  • Are most transfer-in students ready for college-level courses?​
  • How many credits do transfer-in students earn per year?​
  • And what percentage of transfer-in students complete a credential within three years of enrolling at our institution?​

Because Angela has reviewed the training materials for the Postsecondary Data Partnership and has access to the institution's dashboards, she feels confident that she can find the answers to her questions. Let’s follow along as she explores the dashboards.​

Angela’s first question is, “How many transfer-in students enroll in our institution each term?”​

From the PDP home page, ​she clicks on the “Enrollment” dashboard to get started.​

Angela sees that her institution enrolled over 16,000 students in 2018-19. Of those, 4,719 are new transfer-in students.​

In order to better understand this population, Angela filters this dashboard by clicking on “Enrollment Type”, deselects “All”, selects “Transfer-In”, then clicks “Apply”. Now this dashboard only shows transfer-in students.​

Because she will be developing new programs, she needs to understand in which term do most transfer-in students enroll. To answer this question, she adds the “Cohort Term” dimension to the dashboard. Hovering over the 2018-19 data point, she learns that 69.8% enter in the fall, 21.2% in the spring, and 9% in the summer, and only one student entered in the winter term.​

She exits out of the dashboard and returns to the home page.​

Angela is ready to explore her next question. ​“Are transfer-in students ready for college-level courses?”​

The Enrollment dashboard can answer this question too. Clicking this icon takes her back to the dashboard.   ​

Before she begins, she makes sure that Enrollment Type is set on “Transfer-In”. ​

There are two metrics in this dashboard that she can review. Those are Math Prep and English Prep. First, she adds the “Math Prep” dimension which reports the breakdown of transfer-in students who are, or are not, ready for college-level math. She also filters out the “Unknown” data since it won’t help her.  ​

Looking at the line chart and hovering over the 2018-19 data point, Angela sees that 36.7% of transfer-in students enter the institution not prepared to take college-level math.  ​

She repeats this process for English Prep. First, she adds back in the “Unknown” students in the Math Prep filter so that she doesn’t accidentally miss students. Then, she changes the dimension to “English Prep” and filters out the “Unknown” data in the English Prep filter.​

Hovering over the 2018-19 data point, she sees that 17.8% of transfer-in students are not ready to take college-level English.  ​

She exits out of the dashboard and returns to the home page.​

Angela is ready to explore her next question. ​“How many credits do transfer-in students earn per year?”​

​The Credit Completion Ratio Institution-Level dashboard should give her the information to answer this question. Clicking on that link takes her to the dashboard. Before she looks at the charts, she clicks on Enrollment Type to filter to transfer-in students only.​

She looks at the lower right chart and sees that the transfer-in student credit completion ratio for 2018-19 is 80%. Hovering over that bar, she sees that transfer-in students, on average, attempted 16.9 credits/year and completed 13.9 credits/year.​

From an earlier question, Angela knows that a high percentage of transfer-in students are not prepared to take college-level math in their first-year of enrollment. She wonders if that lack of preparedness could impact students’ credit completion ratio. ​

Clicking on “Select Dimension”, she selects Math Prep. Then, she removes the “Unknown” category using the Math Prep filter. ​

Hovering over the 2018-19 data point in the line chart, she sees that transfer-in students, who are prepared for college-level math, complete 80% of the credits they attempted. And on average, they complete 14.4 credits per year.​

In comparison, transfer-in students who are not ready for college-level math, complete 12.9 credits they attempt for a credit completion ratio of 71.9%, on average.​

She exits out of the dashboard and returns to the home page.​

Now, Angela is ready to explore her last question which is “What percentage of transfer-in students complete a credential within three years of enrolling at our institution?”​ ​

​The Outcomes Institution-Level dashboard should give her the information to answer this question. Clicking on that link takes her to the dashboard.

Before she looks at the charts, she sets up her filters. First, she clicks on the enrollment type to filter to transfer-in students only.

Second, she changes the outcomes timeframe to three years.

And third, she selects 2016-17 from the part to whole chart. Now, she's ready to review the data.

Hovering over the 2016-17 data point in the line chart, Angela sees that 24.3% of students entered during the 2016-17 academic year have completed a credential. That's down two percentage points from the 2011-12 cohort. Looking at the part to whole chart, she sees that of the 2016-17 transfer-in student cohort:

25.1% are still enrolled at the institution
17.8% completed their associate degree at the institution
33.1% is no longer enrolled in college and left before completing their credential
13% transferred to a 4-year institution and are still enrolled
4.4% transferred to a 2-year institution and are still enrolled
3.6 transferred and completed a bachelor's degree
And the remaining 2.9% transferred and completed an associate degree.

Angela has gathered a lot of information about her institution’s transfer-in students and she’s ready to summarize what she’s learned.​

Her institution enrolled nearly 5,000 transfer-in students in 2018-19. Of those students: ​

  • 70% entered in the fall term, 21% entered in spring, and 9% entered in summer; 
  • 37% entered the institution not prepared to take college-level math and 18% were not ready for college-level English

Across all transfer-in students, the average number of credits completed per year was 17 while those who were not ready for college-level math completed just 13 credits/year​

And, within three years of enrolling at the institution, 43% are still enrolled in college either at the institution or at another institution, 24% completed a credential, and 33% have left college without completing a credential.​

Angela drafts the following recommendations and shares them with the Provost.  ​

Programs must be fully staffed in fall to support the large in-flux of transfer-in students, but staffing can be reduced in spring and summer​.

She wants to meet with math and English faculty to discuss their recommendations on ways to better support transfer-in students who are not prepared for college-level courses​.

She would like to develop a peer mentoring program that would connect prior transfer-in students with current transfer-in students to help them connect to the institution.​

She would also like to start a “30-to-Finish” campaign to encourage transfer-in students to accumulate at least 30 credits per year for on-time completion.

She would like to reach out to transfer-in students who have left college without completing a credential to discuss options for them to re-enroll.​

And she would like to reach out to prior transfer-in students, who have transferred out, to determine if they could finish a credential at this institution using reverse transfer.​

In summary, Angela was able to learn a wealth of information about transfer-in students by accessing her institution’s PDP dashboards.​ What other information could Angela explore? And how could she use that information to better support student success?​​

We encourage you to use the steps outlined in this tutorial to explore your institution’s transfer-in student population. Thank you for joining us.​

X