NSSENSSENSSE
The Faculty Survey of Student Engagement (FSSE) is designed to parallel the National Survey of Student Engagement (NSSE) and to add context to NSSE results by allowing comparisons of student and instructional staff views on undergraduate engagement. As such, FSSE results are most meaningful when paired with current or prior-year NSSE participation. In this guide, we offer seven tips to consider when adding context to NSSE with FSSE.
Starting in 2026, FSSE offered institutions a more customizable survey experience with a shortened core survey and the option to choose 4-6 content focused modules of items from a large bank of item sets.
Some content modules on FSSE were written to closely parallel items on the NSSE survey. For example, NSSE asks students how much their coursework emphasizes various higher-order learning activities. In the FSSE Effective Educational Practice content module, instructional staff similarly respond about how much the coursework in their courses emphasizes those same higher-order learning activities.
Other content modules, such as FSSE’s Teaching Professional Development content module, asks instructional staff about their participation in professional development activities and how important it is to instructional staff to receive help with aspects of their teaching. Although these items are not directly parallel with any specific items on NSSE, learning about the ways instructional staff need and do develop in their teaching is important to understanding students’ experiences in their courses.
FSSE’s instructional staff perspectives can help explain why certain student results appear in NSSE. A helpful starting point is to identify the NSSE findings that are important to your institution and that you want to explore further. To guide that process, think about:
Once you have chosen a focus area on NSSE, find the potentially related or parallel measures on FSSE. As with our example in the previous tip, if you are disappointed that your students say their coursework frequently emphasizes memorization, you can examine instructional staff responses to see if both groups agree or disagree and tailor resulting conversations as needed.
Gap analyses can be a valid way to examine NSSE and FSSE results, but you should use them with caution.
A common mistake people make is subtracting NSSE and FSSE percentages directly and using the resulting numerical gaps to determine whether results are inherently good or bad. While NSSE and FSSE questions are often related, they have several “apples-to-oranges” differences that limit direct comparison, including:
Since the items are not always measuring the same thing, the numerical gaps in these analyses can be misleading. Gap analyses work best when items closely align in wording, responses, and framing.
Another caution is that gap analyses can sometimes hide important issues. People often focus only on whether the gap is small or large without examining the scores themselves. A quick interpretation might lead someone to think that a small gap implies agreement between students and instructional staff on an issue and therefore there is no problem. But, for example, if 21% of students engage in collaborative learning activities and 24% of instructional staff encourage collaborative learning in their courses, the gap is small, but the low scores may still suggest a problem if your institution is trying to prioritize collaborative learning.
In other cases, a large gap may be good. For instance, if 87% of students feel their institution emphasizes support for students’ overall wellbeing, and only 34% of instructional staff believe their institution should increase emphasis on this type of support, this could suggest that students already feel adequately supported and there is less need for additional emphasis.
It is essential to consider context when using gap analyses to ensure results are not oversimplified or misrepresented. Additionally, note that you should not examine NSSE Engagement Indicators and FSSE Scales using gap analyses as the two scores are not intended to be comparable to one another. Gap analyses, with caution, should only be used for item-level comparisons.
Unlike gap analyses, quadrant analyses can use both questions that are closely parallel and those that cover comparable content. When conducting quadrant analyses, avoid analyzing the entire survey at once. Instead, start small by choosing one topic, content area, or set of items to avoid getting overwhelmed.
Once you have selected the items or content areas you want to analyze, independently determine whether the NSSE and FSSE results are relatively high or low within your institutional context and according to your institutional goals and mission. In other words, look at each item and ask yourself whether the results are where you want them to be. Are you pleased or disappointed? Do the numbers feel strong or concerning? Use those types of judgments to classify the results as high or low for both student responses on NSSE and instructional staff responses on FSSE.
After determining whether each result is high or low, plot the items in the corresponding quadrant below and consider some of the questions within that quadrant. A benefit of quadrant analyses is that they encourage deeper conversations about your results, rather than simply comparing percentages like gap analyses.
| - | - | NSSE | NSSE |
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Low Results | High Results | ||
FSSE | Low Results |
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FSSE | High Results |
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Disciplines may be the largest factor in differing engagement within colleges and universities. Norms for instructional practice vary greatly amongst instructional staff in different disciplinary areas. Although institution-level results can be useful for starting conversations, examining engagement at the disciplinary level may be more practical for making changes. Instructional staff may also have more buy-in if you tailor the results to their discipline. It can be difficult for instructional staff to make sense of results when they aren’t reflective of the contexts in which they are most familiar.
If you’re doing more advanced statistical modeling with your NSSE results, you might consider adding FSSE data. Although you cannot directly match students to instructional staff with NSSE and FSSE data, aggregated FSSE scores grouped by relevant characteristics such as disciplinary area or teaching mode can be matched to students by major field or course mode. This data from instructional staff could be used as second-level, independent, or control variables in statistical models. Aggregate FSSE results can be used as a proxy for your institution’s educational culture and environment in student analyses and provide additional context for understanding variation in student engagement.
Although not directly related to student experiences, the conditions under which instructional staff work can affect their ability to do their best work to support student success. The core FSSE survey asks instructional staff about the time they spend on scholarly activities and whether they have the support and resources to do their best work.
These variables can help add context to instructional staff responses on FSSE and may suggest areas that your institution can help to support instructional staff in their work. For example, an institution might be concerned about the low frequency of collaborative learning for senior engineering majors. Instructional staff may echo this but seeing that instructional staff in engineering strongly believe that the classrooms they teach in are not conducive to quality teaching can provide valuable evidence for how an institution can help instructional staff make changes to the way that they teach.
Additionally examining the time that instructional staff spend on scholarly activities such as teaching, research, advising, and service can provide valuable clues to the priorities of instructional staff. It may be difficult, for example, to ask instructional staff to spend more time with advisees given the amount of time they spend on service. This might signal institutional leaders to conduct an audit of service commitments to see where time could be saved and spent on other activities.
Importantly, disaggregating instructional staff time, support, and resources can also help institutions find and confront areas of inequity amongst instructional staff. Uncovering the hidden labor and unequal distribution of responsibilities of subgroups of instructional staff can help create a supportive atmosphere for instructional staff to do their best work.
