How Faculty Can Use Courseware Data to Inform Their Teaching Practices
The student learning data generated by digital courseware can be a useful resource for instructors, but many faculty are unsure how to start accessing and interpreting it — or even whether that data is valuable.
Chad Marchong, associate director of learning analytics and research at the Center for Excellence in Teaching, Learning, and Online Education at Georgia State University, says that courseware data offers opportunities and insights that instructors may not be aware of.
The data can inform how an institution designs new support programs, how an instructor revises a course after the semester, or how faculty revise the current week’s lesson plan during the semester. Marchong says, “A lot of our work with faculty is making them aware that if they have access to these tools or data, they can make decisions sooner.”
Improving Class Performance with Analytics
Marchong, who recently joined the CourseGateway Product Advisory Board, says a large number of faculty typically know what they want from the data — which individual students are struggling, and which assignments and assessments may not be working for most students — but aren’t quite sure how to see the data in a courseware’s format.
“These resources aren’t used by a majority of our faculty because they’re already used to seeing this data elsewhere,” he says. “In a traditional classroom, you see the faces of the students, look at the gradebook, understand the writing patterns, and see where students need support. As you transition to online, particularly in an asynchronous environment, it gets harder to see those patterns and that engagement.”
While data like quiz scores and completion times may be easily accessible, Marchong says faculty members are often surprised by the robust tools and unique ways that these programs can compile data. New reporting formats also create new perspectives or insights for faculty.
Many courseware products let users build “automations” — automated processes — to generate specific reports and to distribute messages at key moments. For example, targeted messages can be set up to be sent to students after earning a specific grade or after finishing a specific reading or practice activity. These automations and the messages can be established before the first day of class and can be ported over and revised in later terms.
An important thing for faculty to consider when choosing a courseware or LMS is data management, which can be simply defined as the systems of intaking, organizing, and storing data. Since the promise of modern courseware is that it reduces the data management burden for individual faculty, part of selecting courseware is to ensure that it delivers on this promise so that faculty actually get data they can use.
Marchong warns about using multiple courseware programs in the same course or section. “Everyone assumes that the technology should be managing data for them, and it does,” he says. “However, if you’re using several tools, you have to pull that data together, which can be challenging. When instructors try to merge tools that don’t go together, it can really put them at a disadvantage. We encourage our faculty to put grades in one place.”
Using Data to Paint a Story
Not all the data generated by a courseware tool is relevant to an instructor, but understanding the purpose it serves can be helpful.
For faculty to understand the breadth of data available, Marchong suggests thinking of it like a story beginning with a single student. From this individual, a range of data points can be gleaned: academic background, classroom performance, and demographic information. In isolation, each point suggests some context about a student’s experience at an institution, but by examining the patterns between the data points, educators get a fuller understanding of a student’s story.
Data from a student’s performance before the start of a course can provide an instructor with a valuable baseline to measure the student’s growth. If that data isn’t accessible to faculty, using formative assessment can achieve similar results by allowing faculty to identify a baseline and then monitor progress.
Courseware generates more data than a single faculty user might need, so one important skill is determining which data is relevant to a course. For this reason, Marchong is a big advocate for data literacy. Additionally, faculty who maintain a level of data literacy will be able to discern whether their data is being skewed.
“It’s important to understand where the data is originating from, how it’s being analyzed, and the way it's being presented to you,” Marchong says.
Addressing Equity Gaps with Analytics
Colleges and universities increasingly want to examine data about racially and ethnically minoritized students, but they often do so in a way that erases the varied experiences of those students. A 2022 report from Every Learner Everywhere, Toward Ending the Monolithic View of “Underrepresented Students”: Why Higher Education Must Account for Racial, Ethnic, and Economic Variations in Barriers to Equity, found that overreliance on aggregated data obscures the experience of minoritized students and can actually reinforce barriers to equity. By bundling all Black, Latino, Indigenous, and Asian-American students into one category called “underrepresented students” (often also bundled with poverty-affected and first-generation students), institutions miss opportunities to design more equitable learning.
Marchong echoes this argument, saying, “The one thing we don’t want to do is allow the data to make judgments about how courses should be taught. We know what the student body looks like in general, and we know which students enroll in what sort of classes. We can take that knowledge into consideration, but we want to ensure that equity is being served across the board in the way that courses are being developed and informed.”