In the last 7 years, our percentage of students taking “only face-to-face” courses vs. students taking “face-to-face and online courses” has flip-flopped (58% to 27% for only F2F; 25% to 52% taking both F2F and online). The completely online (non-local) students have kept fairly steady at 17-20% of the overall student body. This shift is not necessarily due to student choice, and there is a lot of controversy on campus about the “role of online learning.” The high number of local students taking online courses is often perceived as a detriment to campus life and student learning experiences. Some of these assumptions may be true, but the perception also leads to a negative view of online courses overall, even though we know that 20% of our students intentionally choose to be completely online (likely due to timebound or placebound circumstances). We think there needs to be a better understanding of all of these “student modality groups,” their demographic breakdowns, their actual locations, possibly their enrollment patterns, and their achievement. We want to try to figure out “who our students are” and the actual “role of online learning” at our institution. Are there certain populations that seem to need online coursework? Are there certain populations for whom online coursework seems to create achievement barriers? In short: yes, we have determined that our institution should focus on developing a process for gathering and regularly reviewing such evidence.

Questions we are seeking to answer:

1. What is the student modality breakdown (face-to-face, online, mixed)?
2. How has it changed over time?
3. What are the key demographic representation rates in these groups? (gender, race, age, low income, first-generation, location/remoteness, full-time/part-time, admit types – i.e., first-time, tribal transfer)
4. What are the achievement gaps between modality groups (retention, GPA, gateway DFW, overall course pass rate)
5. What access issues seem to exist, based on an interpretation of qualitative and quantitative data? Where should resources be directed for academic support and/or opening more appropriate access?

• It takes a lot of time and back-and-forth with the actual data to formulate questions. This made certain phases of the fellowship reflection difficult because we weren't ready for those questions yet. We had to start playing with the data to generate interesting avenues for analysis.
• MSUN doesn't systematically collect some of the data addressed in this fellowship, such as veteran status and disability status. Other institutions do....so why don't we? We struggled with these questions for a bit.
• Having institutional research (IR) on the team makes data access easy but that comes with its own challenges: analysis paralysis, distraction with other data points not originally considered, etc. At some points we got too ambitious.
• Verifying assumptions about the data takes a lot of time (e.g., What does a null value in the course grade field mean?) As a result, data cleaning when addressing new questions takes more time than expected.
• Over-disaggregation quickly makes data irrelevant (small institution).
• Access is difficult to understand, evaluate, and quantify.