 Welcome to a new video. This video is going to cover the process for collecting and reporting student savings data from the use of OER, as well as other free or affordable course materials. I'd like to thank Jeff Galant, Program Director for Affordable Learning Georgia, for inspiring this video and providing a base for me to work from. So let's get into it. There are three major steps to OER data collection. Collecting data, synthesizing and organizing data, and presenting and sharing your results. The first piece of this is, obviously, collecting the student savings data. However, this data doesn't have one standard format or process for collection. Depending on your institution, you might partner with your campus store to get textbook adoption information. You may work with an Office of Institutional Research to get information like student enrollment numbers, or you may solicit this information through department chairs or faculty directly. But your process may differ based on who you have access to and how your OER work is perceived across campus. The most important part of this information gathering phase is to get accurate and comprehensive course adoption data. This can be difficult, particularly if you don't have a close relationship with your campus store or administration. Still, you should do all that you can to ensure that your data is complete. Here are some tips for getting accurate data. First, leverage collaborators across campus. One reason why I know I have fairly accurate course adoptions data is because I have a close partnership with Iowa State's campus store. However, you can leverage other partners on your campus to similar effect. Integrating reporting into existing workflows like textbook adoption reporting can be incredibly impactful. Second, you can look at multiple means of reporting. This might mean partnering with multiple groups across campus or just providing a fail-safe for faculty. Which brings me to number three. You can provide additional options for faculty to report their OER adoptions to catch anyone who might have fallen through the cracks near other processes. What I do is provide a standing survey form where instructors can send in their OER adoption information directly. That was a lot of information, so let's look at something a little to the side of this question. What kind of data should you track? Of course, you want to get an idea of how many courses are using OER, but maybe there are other types of affordable course materials you're interested in learning about as well. For example, you may be interested in learning about the use of other no-cost course materials like library-licensed ebooks or low-cost course materials like course packs. As you approach this work, it's worthwhile to ask yourself, which of these types of content do you want to gather data on and how can you do that effectively? Like I said before, the how will depend on your circumstances, so I'll leave that question open-ended for this video. Once you have your data, you need to synthesize and organize it. You can use a standard number, base savings on historical course costs or base savings on estimated current course material costs when you're estimating student savings for the use of OER and other no-cost course materials. Personally, I use a standard number at my institution. There are pros and cons of using a standard number to estimate savings. Pros include that it simplifies and streamlines your data synthesis and that it fixes cases where equivalent texts are not available. Cons are that it over or under inflates actual savings, though a true average should even out once you have a high adoption number and deciding on what number to use as your standard savings numbers can be contentious. Here are a few examples of estimated average saving numbers. $100 used by Oregon in the OAN, $116.94 put together by Spark with the help of David Wiley and $134 estimated by U.S. Pergs in 2018. Each of these are supposed to approximate an average textbook cost, though their data sources vary. The links to additional information are provided in the description below. When you're using a standard number, collecting and reporting savings is as easy as getting student enrollment numbers and multiplying them by the cost of the average textbook cost your institution uses. The formula here checks if you've verified that a course uses OER before multiplying the student enrollment numbers by estimated savings. This sort of formula is particularly useful for long-running OER initiatives where you expect the same course to be using OER each year, and you need to verify with the instructor that this is, in fact, true. Besides student saving numbers generally, there are other data points to consider collecting for your OER initiative. Other data to collect might include the number of students supported by no-cost course materials, the total number of no or low-cost courses in place, and percent of all courses, student retention and academic achievement in no and low-cost courses, and average cost for degree completion in various majors. Reporting in this sort of extra data, particularly learning outcomes, can become even more useful when you disaggregate data to showcase the impact of no-cost course materials on students from different socioeconomic backgrounds. For example, do students who qualify for Pell Grants have the same or more positive responses to OER than their peers on average? Research like the 2018 Colvard Watson and Park study out of Georgia have used data like this to great effect. Which brings us to our final section, reporting data. Once you've pulled together your data into some semblance of normalcy, you have to report it somehow. You can report in a few ways. You can use internal reports, public reports and websites, and course-marking initiatives, or other living markers of impact. These are all options available to you. A good example of a website showcasing OER is Penn State Berks OER Dashboard, set up by Corey Weatherington as part of his Spark Open Education Leadership Capstone in 2019. This site captures the impact and number of OER courses that cross his campus and features them in a live Tableau dashboard. Another option might be to utilize course markings or public markings for class sections using no-cost and low-cost course materials. These are becoming a common way of improving cost transparency for students in higher ed. You can learn more about the hows and whys of course markings in the book featured here and linked in the description of the video. Course markings can be as simple or as complex as your technical systems allow. In some cases, as in this example from UT Erlington, you can select courses with free educational materials from a drop-down menu. In other examples, institutions include a visual marker or icon on sections using OER. There are a lot of options for highlighting and sharing the impact of OER and other no-cost course materials on your campus. Collecting this data can be incredibly useful, but reporting it responsibly is just as, if not more important. Sometimes in an effort to showcase and promote our work, we can be a bit lax in the privacy and support for our students. Ensure that if you are sharing any sort of de-identified or anonymized student data, you're sharing this data with a purpose and it is not an easy matter to re-identify these students. Only share what you need to to get across your point. Remember, we're doing this work for our students, not the sake of showcasing ourselves and our work. Their needs should be at the heart of what we do. Thanks for watching. This video has not covered everything about collecting and reporting student savings data, but I hope it's been useful for you and the region's conversation more in the comments below or elsewhere online.