IR Data Series #3 – Guided Pathways Scorecard: College-Level Math

Greeting EvCC community and beyond! Last fall, I wrote a couple of blogs about our efforts related to data and tracking our progress with Guided Pathways (refresh your memory here and here). I had planned to post throughout the fall quarter about different aspects of our baseline data for Guided Pathways. It turns out that my ability to remember to write blog posts is not that great. Nevertheless, here I am, jumping back on the horse with the first of a few posts examining different data points.

Before I review the first measure (earning college-level math credit in the first year), I want to point out that nearly all of the data I am examining through these posts is available on Tableau, which is a data visualization platform that allows users to drill down into data to answer questions related to the data. To date, 90 faculty and staff members at EvCC have been trained and have access to the Tableau dashboards that IR has designed in our attempt to democratize data. In order to gain access, EvCC employees must attend a training session to learn the ins and outs of the software and how to use it. Please reach out to me if you have not yet been trained and would like to gain access to these dashboards. Now, onto baseline measures.

College-level Math and English are key gatekeeper courses for students to earn a degree from EvCC. I focus specifically on college-level Math for this post. By college-level Math, I am referring to any math course over 100-level, as well as Philosophy 120: Introduction to Logic, which counts toward the Basic Quantitative Skills requirement. One of our baseline measures for tracking progress with Guided Pathways is the percentage of new students who complete a college-level math course within their first year. A substantial portion of our new students place below college-level math, and these students can spend a lot of time and potentially financial aid dollars trying to complete these courses and earn their college-level math credit. Part of our Guided Pathways efforts include a curricular redesign to facilitate these students in particular reaching and completing these courses sooner.

At the data summit this past summer, I shared that only 23% of our new degree-seeking students from 2011-2017 passed college-level math within their first year. This in and of itself points to a need to improve and facilitate student success in math. However, our disaggregated data reveal equity gaps that are potentially more troubling. Specifically, compared to the college-wide average, we find that students from historically underrepresented groups pass at much lower rates within their first year; 19% of Pacific Islander, 17% of Latinx, 11% of Black, and 8% of Native American students passed college-level math within that first year. Additionally, only 12% of our Pell-eligible students earned these credits within their first year. We exhibit clear inequities in supporting our students in earning this credit. Guided Pathways in and of itself is a means for improving these numbers but will also necessitate targeted interventions and support to try to close these gaps.

In addition to the information from the data summit, we recently performed an analysis in preparation for the upcoming Guided Pathways winter retreat examining student pass rates based on where they placed for their initial placements in math. Not surprisingly, students are more likely to pass college-level math if they place into college-level math; only 14% of students who place into pre-college-level math earn college-level credit within one year compared to 64% of students who place directly into college-level math. When we extend out to three years, only 32% of pre-college-placed students have earned that math credit, while 73% of those who place into college-level math have earned it. Again, our curricular redesign of our pre-college math sequences is intended to facilitate students moving to college-level math sooner, hopefully contributing to improvements in these rates.

Exploring the EvCC Guided Pathways Scorecard dashboard can help you identify other trends in our students’ pass-rates of college-level math within their first year. What do you see in the data? What do you think we can do to improve these rates and better serve our students?

IR Data Series #2 – Equity and Data in Guided Pathways

Welcome to the second installment of the IR data series related to Guided Pathways. By now, you have likely seen and/or heard a presentation about the equity framework informing EvCC work and its dimensions: aspiration, access, achievement, economic progress, and engagement. While the limited space of this blog post prohibits me from reviewing the underlying concepts of and explicating these dimensions, I think it is important to discuss what infusing equity means from a data and measurement standpoint.

Essentially, infusing equity means disaggregating data and trying to identify trends and patterns among different student and pathway characteristics. This is not a perfect way to examine equity; quantitative data and numbers don’t tell us everything, and inequities are rooted in broader systems. However, examining seven years’ worth of patterns (which we did for the data summit) helps to reveal inequitable patterns. It is important to interrogate these patterns and what they reveal about our institutions. Doing so helps us determine how our institution can work to achieve greater equity. The focus in doing this is on EvCC’s capacity and performance, not on the abilities of certain groups of students. This is an important point of emphasis. Identifying inequities and performance gaps identifies the ways in which we are underserving different groups of students, not on deficiencies of these students. It is up to EvCC to ensure that all students are successful; therefore, the equity gaps we uncover are a reflection on our ability to serve and educate all students.

Throughout this series, I will disaggregate our data by race, sex, age, and socioeconomic status. There is inherent imprecision in doing so as categories can be reductionist and labels are charged. However, when dealing with broader patterns, we must do this to identify how we are serving and underserving our students. This focus on equity is also a priority at the state level, as new recommendations for Student Achievement Initiative (SAI) focus on closing equity gaps for historically underrepresented and underserved racial and ethnic groups (African American or Black, Hispanic or Latinx, American Indian or Alaska Native, and Pacific Islander or Native Hawaiian), as well as low-income students. The focus on these groups also aligns with our own internal goals and strategies for Strategic Enrollment Management.

Finally, I want to share a helpful description that I think encapsulates what we want to see in our data if we make progress toward equity: “As data on academic achievement and other student outcomes are disaggregated and analyzed, one sees high comparable performance for all identifiable groups of learners, and achievement and performance gaps are virtually non-existent” (Intercultural Development Research Association). The data I will share in the coming weeks will exhibit gaps, and the imperative of our work moving forward is to increase performance for all students while closing these gaps.

Data Guiding our Guided Pathways Efforts – IR Data Series #1

Greetings from the Institutional Research (IR) office! I am Sean Gehrke, the director of IR here at EvCC, and throughout the fall quarter I am going to be contributing posts to the Guided Pathways (GP) blog regarding data we are using and tracking as part of our GP efforts. These posts are a fantastic opportunity for the EvCC community to see the baselines we are working from as we undergo this important institutional transformation.

If you are unfamiliar with Guided Pathways, I recommend you read up on it here and here.

The impetus for these blog posts was the GP Data Summit we hosted on August 8, 2017 in Jackson Conference Center. At the summit, I engaged more than 50 staff, faculty, and students around the baseline data we have collected related to GP. I may be biased, but I think we had invigorating conversations about student success and equity gaps that our data revealed. I’d like to think that these conversations served as an additional call-to-action for the individuals in the room to engage in GP so that we can improve our outcomes for all students.

In the summit, we reviewed data from two different sources: a) a dashboard provided by the State Board of Community and Technical Colleges (SBCTC), and b) a scorecard curated by the IR office with local measures of student success. The SBCTC dashboard provides broad, long-term measures that help give us some key baselines (e.g., completion, transfer, employment). We will be able to see the impact from our GP efforts on this dashboard in the years to come. The EvCC GP Scorecard is curated with internal IR data and offers our first ability to disaggregate both long-term and short-term measures by pathway and other student characteristics. We will be using the EvCC GP Scorecard to track our progress from the beginning of our efforts with GP. The measures on the GP scorecard include the following:

  • Enrollment patterns
  • Average credits earned in a) first quarter and b) first year
  • Enrollment in college-level math and English in the first quarter
  • Pass rates in college-level math and English in the first year
  • Retention rates (fall-to-winter and fall-to-fall)
  • Four-year (200%) completion rates
  • Average and median credits per degree

During the fall quarter, I will be sharing our scorecard data on these measures, as well as some trends found on the SBCTC dashboard. For now, I wanted to take this time to welcome you to the series. When I post next week, I will touch on how equity is infused into our GP data work before posting about our data in the coming weeks.