As our community of 350+ partners continues to grow, greater opportunity emerges to provide meaningful insights on aggregated intelligence across active student records. This type of analytic agility is allowing us to identify the next-generation benchmarks and trends intended to empower our partner institutions and the broader higher education field. These signals illuminate new opportunities to drive improvement, often dispelling myths and conventional wisdom, so that we can collectively improve student success outcomes by taking data-inspired action.
Let’s look back on some of the major benchmarks discovered in previous issues from our the Community Insights Reports.
With better intelligence about the long-term impact of each course grade, institutions can provide both tactical and strategic responses and outreach. For example, if you know an English Composition class grade is critical to likelihood to graduate, an institution can provide scaffolding in the way of direct learning assistance and tutoring for students in real time. These are ways that institutions can support the academic journey and not generalize or miss the mark.
Engagement Matters
For early-term students, engagement — specifically, LMS activity — is highly predictive of student success. This holds true across all institution types and sizes. We took a deeper dive into what specific engagement activity mattered the most, and we found the following derived variables provided the deepest intelligence:- Attendance
- LMS Grades
- Course Material
- Discussion Boards
It’s Not Just Failure
We also found that academic failure isn’t the only reason causing departure from institutions. Most institutions that we included in our findings are losing very large numbers of students with high GPAs. On average, 44% of students who leave without a degree do so with GPAs in the 3.0 to 4.0 range. This applies across institutions types and sizes. If the institutions were to design their student outreach around flags for students at GPAs of 2.0 or below, they would have overlooked a large segment of students who not only needed outreach, but stood to benefit greatly from it.Grades Are Strong Signals
In another report, we shared findings on the signals found from grades, and how we can use that information to make decisions at the institutional level. With the long-term impact of each course grade, institutions can provide both tactical and strategic responses and outreach. We have broken these benchmarks into four types of grade signals.
With better intelligence about the long-term impact of each course grade, institutions can provide both tactical and strategic responses and outreach. For example, if you know an English Composition class grade is critical to likelihood to graduate, an institution can provide scaffolding in the way of direct learning assistance and tutoring for students in real time. These are ways that institutions can support the academic journey and not generalize or miss the mark.
Part-Time Students Are Here
Post-traditional and nontraditional students are a growing population, and many of these students are also Part-time students. In many cases, these students are not accounted for or are seen as the first priority for campus-wide student success initiatives or institution and policy-driven reporting. This becomes more apparent as retention rates for part-time students lag behind those attending full-time. We found the average gap between full-time and part-time persistence rates for institutions included in this analysis to be 12.03 percentage points, with large variation between specific institutions. Some had persistence rate gaps as large as 31.8 percentage points for, while others had gaps as small as less than one percentage point. Additionally, persistence may be improved with increased course loads for some students.There is significant opportunity to improve persistence by recommending some students take one more course – this holds especially true for those who take one course and those taking two. Capitalizing on this opportunity will rely on knowing which students have a strong probability of finishing, and then determining the right nudge that considers their particular life and logistics.Figure 1: Propensity (left) and prediction score probability density functions before (top) and after matching. As you can see, there were significant differences between pilot and control, which disappeared after matching.
Figure 1: Propensity (left) and prediction score probability density functions before (top) and after matching. As you can see, there were significant differences between pilot and control, which disappeared after matching
