
26:36
It would be good to know how “job focused” is defined.

28:24
“That other stuff” can offer a clear focus on employability.

33:28
but all this noncredit data is headcount, not FTE

42:33
Are military benefits in self paid?

46:46
Do you have a cost estimate?

48:38
Fantastic overview. Our college:state participated in the survey and found this to not only be a helpful process to go through, but also to have this data/story in national context.

49:00
Great presentation. Thank you! I was struck by the state variation in non-credit students as a % of total. Could you talk more about what might be behind that?

49:03
Did the issue of stackable certificates (non-credit) or stackable credentials (credit side) emerge as a strategic interest?

49:25
+1

49:34
Great study! One clarification question: The one slide about whether students could "leverage their prior learning" -- what is this referring to exactly? Did the question ask specifically about CPL/PLA opportunities, or was the question more about whether there were articulations between the noncredit and credit side of the house?

51:36
+1 @Becky Klein-Collins

01:09:07
We've done some research and I think the chicken and the egg thing is true regarding stackables. Lots of adults don't know what they don't know. We as institutions do not market stackability and therefore adults don't know it exists. When they are presented with the concept, interest is there. There's a bit more on credit certificates stacking into degrees. I do agree that noncredit to credit pathways being stackable as aspiration. Not a question to the speaker. Just my random thoughts.

01:10:37
Thanks for the great discussion...unfortunately I have to jump off for another call. Take care.

01:12:31
I also need to drop off, thanks as always to NCRN for these great webinars

01:14:39
This is great stuff. Thank you to NCRN and the presenters. I need to drop off now.

01:16:13
Will the headcount involve people in both credit and noncredit

01:16:59
remedial presents a problem. Academic depts. offer remedial English, math, etc that don't count toward the degree but do count for financial aid as part of load and do get reported to IPEDS as enrollment.

01:19:14
When in doubt, press <esc> ;-)

01:21:11
Have you thought about having a category in which the courses are related instruction as part of an apprenticeship?

01:23:45
Thanks for all the hard work!! well appreciated in the field!!

01:23:48
Echoing Bob Lerman -- it would seem that you need to have information on the proportion credit/non-credit for students who are primarily credit, but partly non-credit & primarily non-credit, but partly credit.

01:24:28
The categories and criteria seem to make sense. Thank you. I think the bigger challenge is the incentive and ability for the institution to collect the data. One example is Land Grant X is so decentralized that there are hundreds of noncredit programs offered to professionals and the adult learner. When my team found this data and organized it, Land Grant X was surprised. They didn't realize they had so much going on. They also didn't realize that many academic units were in a sense offering similar programs and literally competing one another. Again, just an observation. Credit data is captured through institutional research and other ways, but noncredit data is the wild west. Many institutions don't have the central repository. Again, not a question, just an observation from my perspective.

01:28:28
gotta run, have a good day all!!

01:29:43
Hear, hear. This work is so exciting — thanks to you both for the meaningful progress you’ve made with this work!

01:29:43
Thank you all and I apologize for getting here late and leaving early. I appreciate everything you do and the nuggets I am learning. Unfortunately, we are having our virtual conference on marketing today and over the next few days. I have to drop off. Wish you all well.

01:29:52
Thank you, Tamar, for the additional attention on noncredit. And Tara, for taking these important steps toward noncredit data in IPEDS--so, needed!

01:30:16
Thank you so much for sharing your work with us!

01:31:03
Thanks for this overview of critical work. Shared data definitions may be dry, but without it we’re toast.

01:31:05
Thank you both for sharing such important information with us!

01:31:40
thank you!

01:31:51
Great session. Thank you all!

01:31:54
Than you all!

01:32:02
*Thank