“FYE in 180 Days”
Sauk Valley Community College
Jon Mandrell,
Dean of Instruction, SVCC
Steve Nunez,
Dean of Institutional Research, SVCC
Dr. Andrea Wirgau,
Assistant Director, DCCL
Ferris State University
Background
•2-Year Community College
•Located in Dixon, IL
(90 miles west of Chicago)
•4000 students annually
•700 new students in the fall
•300 in the spring
Where we stood…
College success course required
Could be taken anytime in 2 years
Taught by counselors – no faculty
A change in format and
curriculum was a common cry
Jan 11
January 18
February
March
April
May
June
Campus invites Dr. David Trites to present on
retention at the campuswide in-service training
“The Vital Few”
Student success is not an accident – it is the
result of intentional activities taken by the
college
The Vital Few at SVCC
1. Mandatory FYE class in first semester
2. Mandatory Orientation
3. No late registration for new students (once class has met, the
student cannot register)
4. Full time retention coordinator (student success coordinator)
5. Course based placement/pre-requisites and developmental
6. Tutoring/additional support for course repeaters.
7 Require a college success program for all remedial students
8. Interactive web site
9. More Bridge programs
10 Partnership with local employers for curriculum and placement
11. More productive designated faculty hours
12. More full time faculty
One Week Later….
• Proposal developed by CAO
• President grants approval
• Diverse Committee Created
• Model Schools Targeted to
Visit
• Conferences Targeted
February 2013
Visit four colleges with elite
programs
Attend Gardner Conference
Establish framework of FYE
(Mandated Orientation and
College Success Course)
Create course outcomes
February
March
•
April
May
June
July
Framework Established
• Late Registration Removed
• Create two subgroups to work on
orientation and college success
course
• Build syllabus and outline
April 2013
Weekly Meetings
Subcommittee Recommendations
•Curriculum and textbook
•Faculty development
•Policy and procedures
•Assessment
April 2013:
Mandatory Orientation
First-time, degree-seeking students
•Send orientation assignments
to students
-15 sessions offered in summer
3-4 hours long
•Sessions provide and overview to:
•Services
•Tours of campus
•Campus log-ins
•Faculty panels
April 2013
FYE 101
Course title established
•One curriculum is set
•16 weeks
•Built into Moodle
- Allows data to be tracked in all
sections of FYE 101
Don’t forget to pick a logo! (Warning:
This can get complicated!)
February
March
April
May
June
July
August
Policies and procedures created
-Absences, withdrawing
Course approved by State
Instructor training days planned
Instructor recruitment begins…
February
March
April
May
June
July
Continue campus visits
Establish class sizes
(30 max)
Build sections (24)
Continue recruiting
instructors!
August
August 2013
Instructor development
Full-time and adjunct faculty
Anyone with a Master’s currently
employed by college
The more diverse, the better!
February
March
April
May
Send invites for mandated
orientation
Have a mock orientation
Adjust!
Host orientation June - August
June
July
August
February
March
April
May
June
Continue refining FYE course
Continue to educate public on new
mandates!
Keep everyone informed
-Address concerns
July
August
After It Begins..
• Frequent contact with instructors
• Feedback and sharing – online and
face-to-face
• Listen to students
“Do I have to take this??”
“This class is worthless.”
“Is this just a way to make money?”
• Make FYE data accessible
• Monitor student progress and grades
FYE Student Demographics
Numbers
556 first-time, degree seeking students
Gender
56.5% female
43.5% male
Age
86% 23 years or younger (“traditional”)
14% 24 years or older (“nontraditional”)
Ethnicity
85% Caucasian
9% Hispanic
3% African American
Degree seeking type
69.4% A.S. or A.A. (transfer)
30.6% A.A.S. or certificate (CTE)
Enrollment status
66% full-time
34% part-time
Average number of college classes
3.82 (excluding FYE)
HS academic record
2.88 HS GPA
19.9 ACT composite
Impact on Enrollment
➔ Mandates “should” negatively affect enrollment
➔ At SVCC, a 1% drop in credit hours would shrink
tuition/fee revenue by $50,000
➔ Must have “courage” to do what’s right
➔ Fall 2013 “10th day” enrollment numbers
◆ headcount was down 0.4%
◆ Illinois community college headcount down 2.2%
Retention Data
% Completed Course
PSY 100 (2010-2013) >>>> 94.9 - 95.4%
FYE (2014) >>>> 77.7%
Fall to Spring Retention
PSY 100 (2010-2013) >>>> 76.5 - 82.2%
FYE (2014) >>>> 77.7%
Fall GPA
PSY 100 (2011-2012) >>>> 2.71 GPA for completers
FYE (2013) >>>> 2.83 GPA for completers
http://www.discovercarlisle.co.uk/business/profile/
Can FYE provide an “early alert?”
➔ Common wisdom suggests that early identification of atrisk students can help the college intervene earlier and
improve retention.
➔ Ways to predict at-risk students?
◆ analyze their high school transcript
◆ review early semester data from FYE
◆ use both!
Early alert variables
➔ How is success (or failure) measured?
◆ Fall Completion Rate (%) (FCR)*
● FCR = # credits passed / # credits attempted
◆ Fall Semester GPA* (FGPA)
http://blog.oup.com/2012/02/turning-data-into-dates/
*Excludes FYE
➔ What variables might correlate to success (failure)?
◆ High school GPA (HSGPA)
◆ ACT composite score (ACTc)
◆ Class attendance in FYE (1st four weeks) (CA)
◆ Average grade in FYE (1st four weeks) (AG)
Predicting Fall Completion Rate
using linear regression
Independent Variables
Dependent Variable
Statistical Significance
R2 (variance
accounted for)
HS Model
HSGPA*
FCR
p<0.001
0.147
(15%)
FYE Model
CA & AG
FCR
p<0.001
0.392
(39%)
Combined Model
CA, AG, and HSGPA
FCR
P<0.001
0.388
(39%)
*ACTc was not significant and not included within the model.
Google Images
Predicting Fall Completion Rate
cross tabulations (1)
# classes
attended
sample
size
FCR
%
CA
n
100%
80-99%
60-79%
1-59%
0%
0-1
5
0
0
0
20%
80%
2
18
27.8%
0
0
16.7%
55.6%
3
70
38.6%
4.3%
16%
18.2%
22.9%
4
375
62.1%
3.8%
15.3%
12.7%
6.1%
Google
Images
Predicting Fall Completion Rate
cross tabulations (2)
Average grade on
first 4 FYE
assignments
AG
sample size
FCR
%
n
100%
80-99%
60-79%
1-59%
0%
A
241
77.2%
3.3%
11.2%
5.6%
1.7%
B
50
62.0%
4%
16%
14%
4.0%
C
84
51.2%
2.4%
28.8%
17.6%
0%
D
30
53.3%
3.3%
26.6%
11.1%
6.7%
Predicting Fall GPA (FGPA)
Independent
Variables
Dependent
Variable
Statistical
Significance of
model
R2 (variance
accounted for)
HS model
HSGPA & ACTc
Fall GPA
p<0.001
0.289
(29%)
FYE model
AG*
Fall GPA
p<0.001
0.288
(29%)
Combined model
HSGPA, ACTc, & AG
Fall GPA
P<0.001
0.411
(41%)
*CA was not significant and not included in any models.
Conclusion & Advice
•Start somewhere – whatever fits your
institution best
•Visit others
•Attend conferences
•Set up a data accessible FYE
program
•FYE data may be used as an early
alert
•Continually reassess and improve