In our Personal Experience Insights series, members of the Everspring Learning Design department share first-hand accounts of creating online learning content and meaningful takeaways from their professional experiences.
Teelina House has been a Director of Learning Design at Everspring since 2021. She has more than 20 years of experience in education as a program manager, instructional designer, and project manager.
Imagine managing a program without seeing the big picture or having robust data to make informed decisions. How would you be able to answer critical questions applicable to any program, such as the following:
- What kinds of training or resources do faculty need?
- Which courses would benefit from revision?
- Are courses adhering to university standards or mandates?
- What factors impact retention rates?
As a Director of Learning Design at Everspring supporting several online higher education programs, my responsibilities include managing the overall health of a program. Once courses are developed and launched, the ongoing focus shifts to program and course maintenance. This maintenance is heavily informed by data.
Of course, there is a wide range of data sources that program managers and administrators use to keep tabs on the state of their programs: enrollment trends, customer relationship management (CRM) system data, student activity and feedback, market research, and graduation rates. I use all of these types of sources to understand the ins and outs of a program and investigate actions that can improve it. However, I’ve observed that these sources cannot provide every side of the story. For one, they tend to provide more information about what students are bringing into the program and what they are leaving with after the program than what they are experiencing during the program. Secondly, these sources offer little information about why some things are happening: why a student might stay in a program or leave it, or if they passed or failed a course because of timing, content, or technical issues. Once I had identified and gathered this data, I realized that some key information simply couldn’t be represented by these sources.
Fortunately, there are also underutilized options that I rely on for actionable information, such as data from faculty support or teaching and learning services. By analyzing the support trends for a program, I can provide internal and external stakeholders with meaningful feedback that goes beyond the faculty lens, ultimately impacting critical aspects of program health from marketing to student retention. These sources often provide faculty perspectives (e.g., “I can’t figure out how to send an announcement!”) that illuminate sources of issues that show up only generically in student feedback (e.g., “The instructor didn’t communicate with us that much.”).
Support services will vary across institutions, but all schools tend to have one or more support resources: a central IT department, a teaching and learning center, or, at a smaller scale, a go-to staff member consistently answering emails about faculty or student issues. Tracking course-related faculty or student issues handled by these support bodies may be an excellent source of data, particularly if you are able to determine who is submitting requests, how many, and on which topics. You may also have more complex sources of data, such as third-party integrations, accessibility compliance checkers, or other integrated tools.
Once you have gathered the data, your analysis may indicate opportunities for improvement. Faculty and student support trends can reveal needs for additional training or resources, which courses might benefit from revisions, where compliance with standards or best practices may be an issue, or even opportunities to improve retention via program structure changes. When reviewing the overall health of a program, I leverage this data to explore these kinds of topics. Below are some of the most common questions I answer with this data, along with potential sources of insight on those questions so that you can do the same.
What Kinds of Training or Resources Do Faculty Need?
Perhaps most obvious, faculty support request data often highlights faculty support needs. This can suggest where training and resource time should be invested. For example, if a specific instructor frequently seeks assistance, I may suggest a targeted training on best practices for course facilitation or navigating the learning management system prior to the start of the next term. If there are specific questions that recur each term, the creation and distribution of a resource or a reminder email may be appropriate. You may also be able to tap into these insights by reviewing faculty requests for support from IT or campus teaching and learning centers, as well as any curriculum committee or student feedback available.
Which Courses Would Benefit From Revision?
Faculty support data can point to courses that could benefit from an update. We recommend an iterative review of courses for updating. In many cases, it is more advantageous to complete a course-wide review and revision than to make a series of small edits—not only is it more efficient to undertake updates all at once, but it also helps preserve consistency and accessibility within a course. If I receive a large number of minor update requests for a specific course, this could signal a need to reexamine the course as a whole. To determine which of your courses could benefit from revision, you may also consider reviewing data from your registrar, such as drop rates, pass rates, which instructor’s sections fill fastest (or which fail to fill), and any records you have of previous revisions or updates.
Are Courses Adhering to University Standards or Mandates?
Ensuring that courses meet university standards, such as accessibility, inclusion, or AI guidelines, requires monitoring a variety of data sources. The same types of data that inform general revision needs can also highlight opportunities to remediate content, diversify examples or required materials, and align with established standards. Along with faculty support data, student and faculty feedback, formal course reviews, and accessibility checker reports can all play critical roles in this process.
What Factors Impact Retention Rates?
Student retention is often a focal point for programs I support. Collecting and analyzing data from multiple sources can help inform efforts to increase retention. For example, considering not only course-specific data, such as student evaluations, but also faculty support request data can provide you with a more complete picture of the factors that might be impacting retention, such as technical issues, outdated content, or training needs. Data may also inform course schedule changes. If students are frequently withdrawing during a particular term, I review the courses offered and support requested during that term to determine if a change in the course schedule may be beneficial.
In short, adding data sources to your toolbox that you can compare, cross-reference, and put into action can be a powerful program management tool. Your institution may or may not have robust records of these kinds of interactions already. However, almost any program can find and foster both conventional and less expected sources of data to inform a wide range of programs, serving as a valuable resource for enhancing your educational offerings. By analyzing this information, you can identify areas for improvement, adapt to evolving educational needs, and ultimately create a more enriching experience for your students.