Like we always say though, data predicts populations and not individuals. There are lots of good questions waiting to be capitalized on so that we can identify other connections with success that we can implement for our students!

#Teaching #CollegeAffordability #StudentSuccess #EducationData

Our students have tons of pressures surrounding their choices though, namely financials (current and future). This is also only a single metric, self-reported, and uncontrolled in this preliminary analysis for other factors and drivers (at least in this installment since we’ll see those next).

Once again, this one makes sense without running the numbers if you’ve led a class, but having data to share with students, educators, and leadership always helps. My advice is simple, although I understand that life is complicated: choose a major that you enjoy.

The only data that I can pull from our LMS is "% of objects accessed" which gives me a decent but often incomplete picture of that side of engagement since most do end up clicking nearly all of them :/

Hello! You're correct that it associates with the other positive metrics, GPA included. The main GPA caveat is that 2nd years typically have less classes, which means GPAs can be a bit more jumpy towards extremes. We we did a Cox Regression, and Attendance and Hours emerged as sig. factors :)

But like we say, data predicts populations and not individuals. This is also a single metric. I also promise that my scores aren't that cruel since we ultimately normalize to the top score.

I'm happy to discuss any details below, and feel free to share with anyone that could find the data helpful!

So let's confirm what most of us already assume: yes, populations that attend class at/more than 90% of the time perform significantly better than those that do not (N = 252).

It's not exactly a Nobel finding, but having numbers to share with students, educators, and leadership can help.

The true question is: how do they do it? And how can we help more students do the same? The Prefect lab practice sessions that we implemented with success are one method, but bigger changes have small beginnings. We're excited to keep closing this gap.

Stats/Data predict populations, not individuals, and the real magic is in the stories of those who beat the odds. Cancer patients with tough genetic mutations and prognoses who still find a way to remission come to mind.