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Mark,

 

We conduct the following types of data analysis.

 

·         For tutoring

o   Demographics (composition of the population served compared to the campus population)

o   By subject

§  Clients served

§  Hours of tutoring dedicated to the subject

§  Correlation of ACT sub-scores and high school GPA to performance, for the campus

§  Performance of students served compared to students not served, disaggregated by ACT sub-scores and high school GPA

§  Fall-to-Fall persistence (we only use the term retention when referring to FTFT Freshmen who are in the cohort)

o   By Department

§  Clients served who were enrolled in courses in the department

§  Clients served who have a major in the department

§  Percent of workload dedicated to the department

§  Percent of fill: the number of seats in sessions used compared to the number of seats available

o   For the university

§  Non-duplicated clients served

§  ROI

·         For SLA supported course (SLA is similar to SI, but it has teeth)

o   By course supported

§  Clients served

§  Correlation of ACT sub-scores and high school GPA to performance, for the campus

§  Performance of students enrolled in SLA-supported sections compared to students enrolled in regular sections, disaggregated by ACT sub-scores, class, and high school GPA

·         Grade distribution

·         D,F,W rate

·         Fall-to-Fall persistence

§  Comparative data on student performance for start time of the lecture

§  We have one additional analysis we can run upon request by a Department Chair, Dean or the Provost, but I am the only person in our department that will ever run the report, and I only deliver it, in person, to the requestor. It is also understood that I never talk about the data in this report to anyone other than the requestor. This report looks at performance, disaggregated by professor. In addition to providing the data on student outcomes, it highlights the name of any data value that is more than two standard deviations from the mean (green of better than expected, red for worse)

o   For the university

§  Non-duplicated clients served

§  ROI

 

 

Martin Golson

Director, Academic Support

Certified Learning Center Professional – Level 4

Austin Peay State University

 

(931) 221-6553

 

"Luck is what happens when preparation meets opportunity." - Seneca

 

 

 

 

 

From: Open Forum for Learning Assistance Professionals [mailto:[log in to unmask]] On Behalf Of Mark Manasse
Sent: Wednesday, March 13, 2019 3:24 PM
To: [log in to unmask]
Subject: [External] Research and Apportionment

 

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Good afternoon!

 

I have two different types of questions:

 

·         All: When researching your own learning assistance programs, what quantitative analysis do you conduct beyond utilization, success, retention, and persistence questions?

·         For my California Community College folks specifically: Are you collecting apportionment?  If so, I would love to hear from you about how that is going. What changes (if any) do you foresee on your campus due to updated California Ed Code?  What is the approximate individual headcount for your campus/semester (10,000ish? 25,000ish?), and what approximate percentage (10%ish? 25%ish?) of that group does your learning assistance program serve? How much funding are you generating?

 

Feel free to send me a private email or respond to the group if you prefer.

 

Mark Manasse, Ph.D.

Tutoring Program Coordinator

Mesa Tutoring and Computing Centers

San Diego Mesa College

[log in to unmask]

619-388-2215

 

 

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