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A First Look at Equity in eLearning

As EvCC has continued its Guided Pathways efforts over the past year, equity has been frequently discussed as essential to  helping students make informed decisions about their education and future careers. In a post on the Guided Pathways blog last spring, Samantha Reed discussed some of the ways that increased awareness of equity considerations can help programs identify gaps in outcomes, thereby creating openings for change that will help us “make sure our institution serves all our students equitably.” More recently, Director of Institutional Research Sean Gehrke has been posting on using data to identify equity gaps. Equity was also a topic of discussion at the summer meeting of our system’s eLearning Council, where we noted as a clear priority the need for more research on “equity gaps in applying technology to learning” and “structural barriers to access to technology-mediated instruction.”

Prompted by some of these ongoing conversations, I decided to do a little initial investigating of my own to see where there might be obvious equity gaps in the context of eLearning at EvCC. The real work of examining equity is difficult and potentially requires multiple types of data in order to get meaningful analytical purchase on its many dimensions. So as a somewhat easier starting point, I posed a fairly simple question: “Are there significant differences between student populations in face-to-face and online courses at EvCC?” Granted, that’s probably a diversity question rather than an equity question–but it creates necessary space for considering those more challenging equity issues in online learning. Once we have a better sense of who might be missing from online courses, we can take up the questions of why they’re missing and how their absence may by symptomatic of systemic inequities.

To answer my question, I turned to our institutional Enrollment, Headcounts, and FTE Tableau dashboard (thank you, Sean!) and starting crunching some numbers.

To eliminate a number of factors that would complicate the analysis, I focused only on transfer students and I chose to exclude Running Start students. I mention this at the outset to acknowledge that the results might look very different if non-transfer students and/or Running Start were included. Both of those categories should also be part of a conversation about equity, but for this first look I wanted to keep things as simple as possible.

First, I looked at face-to-face enrollments according to a few broad racial/ethnic categories. Here’s what I found:

Line chart showing face-to-face enrollment by race/ethnicity in the academic years from 2013 to 2016. White students account for more than 50% of enrollments in all years, following by Hispanic, Asian, African American, and Native American. These demographic categories each account for less than 11% of total enrollments.

This told me what percentage of total enrollments in face-to-face courses were students belonging to a given racial/ethnic designation. As you can see, just over 50% of total enrollments in the 2016-17 academic year are identified as White, while only 1.2% are identified as American Indian/Alaska Native. (It’s worth emphasizing again that this isn’t annual headcount or FTE; it’s literally just a count of every enrollment but a transfer, non-Running Start student at the college.)

For comparison, I compiled the same information from our online courses:

Line chart showing online enrollment by race/ethnicity in the academic years from 2013 to 2016. White students account for more than 55% of enrollments in all years, though the trend is slightly downward. Hispanic, Asian, African American, and Native American students account for less than 10% each of enrollments, though the trend is slightly up for these groups.

What’s apparent when comparing this chart to the one above is that the general trends are nearly identical in both, but that White enrollments make up a higher percentage of total enrollments in online courses (55.2%) than they do in face-to-face courses (50.3%). While that’s not a huge difference, it seems like something worth exploring further.

My next step was to consider whether particular racial/ethnic groups appear to be over- or underrepresented in online courses, relative to enrollments in face-to-face classes.  I calculated the ratio of online to face-to-face enrollments, looking once again at the same racial/ethnic categories. This provides another way to see whether there are patterns that might suggest inequities related to online and distance learning environments.

Line chart showing the ratio of online enrollments to face-to-face enrollments by race/ethnicity in the academic years from 2013 to 2016. White students students have a consistent ratio of about 1.125. African American, Asian, Hispanic, and Native American students have ratios between .6 and .8, with some fluctation between years.

This chart deserves a little explanation. Essentially, if the ratio of online to face-to-face enrollments equals 1, then the percentage of enrollments for a given racial/ethnic category are the same in both course modes. That doesn’t mean the total number of enrollments are the same. It just means that demographic group accounts for the same percentage of total enrollments in both course modes. For example: let’s say we have 100 enrollments in face-to-face courses, and 10 of them are African American. Let’s also say we have only 50 enrollments in online courses, 5 of which are African American. If that were true, African American students would account for 10% of the enrollments in both course modes, giving us an online to face-to-face ratio of exactly 1. But if only 3 out 50 enrollments in online courses were African American, then the ratio would be just 0.6.

In other words, the chart above confirms that Whites account for a disproportionately large number of online enrollments, while African American, Asian, Hispanic and Native American enrollments are disproportionately small in comparison to face-to-face courses. (That last phrase is worth emphasizing: this does not tell us about over- or under-representation of actual students, since we’re looking only at raw enrollment numbers.)

However, the starkest difference between enrollments in face-to-face and online courses isn’t to be found by looking racial/ethnic groups, but rather at gender identity. Here’s what enrollments in face-to-face courses look like broken down by gender:

Line chart showing face-to-face enrollments by gender in the academic years from 2013 to 2016. Male students account for slightly less than 50% of enrollments; female students account for 46%. Women did account for more enrollments than men in 2013-14.

That looks pretty good, right? Males account for more enrollments than females, but not by a huge margin.

Line chart showing online enrollments by gender for the academic years 2013 to 2016. Women consistently account for just under 60% of enrollments, while men account for slightly less than 40%.

But this looks like a startling large gap in online courses, with women accounting for nearly 60% of total enrollments. And, indeed, looking once more at the online to face-to-face ratio confirms that this gap is real, and quite large:

Line chart showing the ratio of online to face-to-face enrollments by gender. Women consistently see a ratio of 1.25, while men are consistently just above .75.

What conclusions can be drawn from this? This sort of enrollment analysis doesn’t provide many answers, but it does generates a number of new and productive questions that can lead to a deeper conversation about equity in distance learning. A chart like the one above makes me wonder whether women account for more online enrollments because:

  • there are more online courses in the programs that women are already more likely to select, or fewer offered in male-dominated programs
  • women perceive the online course mode to be more suitable for their interests or needs, or men perceive the opposite to be true
  • there are gender-specific differences in levels of full-time versus part-time enrollment, thereby skewing an enrollment-based analysis

If explored further, each of these conjectures has the potential to reveal sources of inequity. Of course, just because differences exist does not mean they are inherently inequitable. There’s nothing wrong with women choosing to enroll in online courses at higher rates than men — provided that is, in fact, an intentional choice. But it’s another matter entirely if these differences are the product of systemic disparities (e.g., programs that enroll men don’t offer enough online courses) or lack of aspiration (e.g., men don’t think they can succeed in online learning environments), to list just two possibilities. And these same types of questions could also be applied to the differences in online enrollments among various racial/ethnic categories, or even among groups identified by other characteristics (military veterans, first-generation students, disabled students).

All of which is to say that this initial foray into eLearning enrollment data has whetted my appetite to look more closely at some of these questions. If you have thoughts about equity in eLearning, or want to join me in exploring this topic, join the conversation in the discussion below.

[Edited 6/14/2018: Typo correction]