Title of Presentation - Innovative Educators

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Developing and Implementing a
Web-Based Early Alert System
Dale R. Tampke – Dean, Undergraduate Studies,
University of North Texas
[email protected]
Where we’re headed today…
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Our Context - UNT
Early Alert as a Concept
Project Scope (the tech-y part)
Building Advocacy
Functionality
 End-user
 Responder
Data from 2011-12 (and what we’ve learned so far)
System improvements
University of North Texas - UNT
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Main campus –
Denton, TX
Enrollment
total
headcount
 23,756
undergraduates
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 97
Bachelor’s
 101 Master’s
 48 Doctoral
 35,754
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1105
 ACT 23.4
Faculty
 988
FT
 519 PT
Moderately selective
 SAT
11 Colleges/Schools
Degrees
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Median Class Size 28
A bit more about UNT
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Gender
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Ethnicity
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Female (56.0%)
White (62.2%)
African American (13.2)
Latino (12.8)
Asian (5.5)
Native American (0.7)
Non-resident Alien (4.7)
Over 80% from <100 mi
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25% Pell eligible
49% first-generation
Students admitted into
colleges and schools
Mandatory two-day
summer orientation
FTIC retention rate –
75.6% (2011 cohort)
Six-year graduation rate
– 49.4% (2005 cohort)
Please respond to the following:
Describe your institution:
A.
B.
C.
D.
E.
Public or Private
Two-year or Four-year
Small (999 and below), Medium (1,000 –
4,999), Large (5,000 – 24,999),
Mega (25,000 and up)
Residential or commuter
Urban or rural
The Early Alert concept
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Grounded in literature on undergraduate retention
 Student
behavior can predict attrition
 Early intervention can change outcomes
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First efforts were course-centered
 Poor
performance
 Excessive absences
(Think “mid-term” grades)
Early Alert progresses…
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Expansion to campus-wide availability
 Include
psycho-social concerns
 Web front end
 E-mail back end
 Authentication varies
 Integration varies
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A common issue:
How many faculty use the system?
Our idea:
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Integrate with student information system
We could build it ourselves
Start with a focus on faculty (make it easy for them)
Designate a central receiver of the data
Expand beyond “academic” issues
Have a ready referral
Begin a personal, caring conversation
Here’s a question:
What stakeholders would you need to
include to implement an Early Alert system
on your campus?
Building Advocacy
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Include stakeholders
 Students
(8 from office staffs)
 Faculty (12 from Arts and Sciences)
 Academic Advisors (10 from all colleges)
 Student Services (15 areas)
 IT
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Get feedback at the conceptual stage
Be ready to adopt a good idea
Create a faculty test group
Things to ask (examples)
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Issues that affect student performance
User access to the system
Information a user would need to know about a
student
How and whether to inform the student of the alert
Security and permissions
Real time or batch processing
Reporting (programmed, ad hoc, or both?)
Aspects of the system
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Secure – authentication required
Campus wide access
Easy for faculty to use
Menu-driven
 Minimal information about the student needed
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Ability to inform referred student via e-mail
Timely
Real-time ad hoc query capability
Nightly reporting
Completed in six weeks by one programmer
A question…
What student issues would be included in a
drop-down menu on an Early Alert system
at your campus?
Reasons for Referral
(what’s on the drop down menu)
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Poor class attendance
Poor performance on
quizzes/exams
Poor performance on writing
assignments
Does not participate in class
Difficulty completing
assignments
Difficulty with reading
Difficulty with math
Sudden decline in academic
performance
Concerns about their major
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College adjustment issues
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Financial problems
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Physical health concerns
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Mental health concerns
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Alcohol or substance use
concerns
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Roommate difficulty
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Disruptive behavior
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Absent from work
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Student needs veterans
assistance
Other concerns (text box)
How Early Alert works
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EARS 1.0 (early alert referral system) is available
from the on-line class roll
Instructors of record receive an e-mail reminding
them of EARS at the beginning of the term
Accessed through the faculty portal (The “Faculty
Center”)
Nightly report delivered to a central office (Student
Academic Readiness Team – START)
Follow up within one day of receiving
Other features
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Relationship to student

Professor, instructor
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Teaching assistant, teaching fellow
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Academic Advisor
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Mentor
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Department administrator
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Campus Employer

Club, organization advisor
“I have had a conversation with the student”
Send a copy of the referral to the student (via e-mail)
Another question…
How would access to alert records be
determined on your campus? Consider
academic advisors, student services staff,
faculty, clerical staff, others?
Accessing Early Alert
From the Faculty Center in the Student Information System
To the class roster…
From the class roster…
To the Early Alert form…
After the referral is made…
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Review report every morning
Real-time e-mail prompt to sender
 Morning report
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Includes following information
Demographics
 Student ID
 Faculty member’s name
 Course
 Reason(s) for referral
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Follow-up – Routing alerts
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First responders – Routine referrals
Residence hall staff
 Course Achievement Assistants (peers)
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More serious issues
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Academic Readiness Advisors
 Academic Advisors
 CARE team
 Counseling, Health Center
EARS is not designed for urgent situations
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More follow-up – The student
experience
Caring conversation (no scolding)
 Emphasize mattering
 Resources
 Self-efficacy
 Focus on academic success
 Follow-up2 (we need to get better at this)
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EARS Data from UNT
Descriptive data from academic year 2010-11
Alert frequency during the term
Fall 2011: Alerts by Week (n=546)
133
101
59
49
53
49
30
14
A28-S3
11
S4-10
S11-17
S18-24
S25-O1
O2-8
O9-15
29
7
O16-22 O23-29 O30-N5
4
N6-12
2
2
N13-19 N20-26 N27-D3
1
2
D4-10
D11-17
Alert frequency during the term
Spring 2012: Alerts by Week (n=776)
172
147
107
82
59
57
33
31
24
17
J15-21
11
J22-28
7
J29-F4
F5-11
13
F12-18 F19-25 F26-M3 M4-10 M11-17 M18-24 M25-31
13
1
A1-7
1
1
A8-14 A15-21 A22-28 A29-M5 M6-12
First reasons for alerts
2011-12: Alerts by Reason
605
575
116
26
Attendance Issues
Academic Issues
Behavioral Issues
Other Issues
Demographic data
Alerts by Ethnicity: 2011-12
(n=1322)
Af-Amer
Am-Ind
As-Pac
Hispanic
Non-Res
Other
309, 23%
12, 1%
669, 51%
58, 4%
231, 18%
11, 1%
32, 2%
White
Gender
Alerts by Gender: 2011-12
(n=1322)
Male
52%
Female
48%
Annual Totals
Annual Alert Totals
(2008-present)
1322
1400
1200
1000
882
920
800
600
618
553
400
200
0
2008-9
2009-10
2010-11
2011-12
2012 (fall only)
Outcomes data
Analysis from Fall 2008 (pilot year)
Outcomes
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Literature suggests early intervention impacts:
 Student
success
 Student persistence/progression
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Fall GPA
Spring re-enrollment
Use a within-group comparison
No useful “control” group
Findings
Success and Persistence
 Fall GPA – 1.39
 Cumulative GPA –
1.94
 Persistence – 70.2%
Course Grade
Distribution
 A’s – 3.4%
 B’s – 5.9%
 C’s – 11.9%
 D’s – 12.3%
 F’s – 43.0%
 I’s – 1.3%
 Drops – 21.7%
Contact types (frequencies)
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Faculty
 E-mail
notice only – 42.0%
 Personal – 8.2%
 Both – 3.5%
 None – 46.3%
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Academic Readiness
 E-mail
notice only – 65.9%
 Personal (phone, response from student, meeting) – 34.1%
Outcomes by contact type
Fall GPA
Persistence
(% re-enrolling)
Faculty
E-mail only 1.19
62.6
Personal 2.17
85.7
Both 2.07
77.8
None 1.39
73.7
E-mail only 1.26
67.9
Personal 1.64
74.7
START
Some statistics
Personal Contact
Mean Term GPA
Significance
Faculty
Yes (n=25) 2.15
No (n=213) 1.30
F=11.894, p<.001
START
Yes (n=60) 1.63
No (n=158) 1.26
F= 5.436, p<.021
Outcomes by Contact Type by Reason
(Attendance)
Attendance (n=144)
Fall GPA
Persistence
(% re-enrolling)
Faculty
E-mail only 0.83
53.1
Personal 1.96
100.0
Both 1.77
80.0
None 1.34
71.2
E-mail only 1.06
62.3
Personal 1.48
73.7
START
Outcomes by Contact Type by Reason
(Performance)
Performance (n=74)
Fall GPA
Persistence
(% re-enrolling)
Faculty
E-mail only 1.90
83.3
Personal 1.88
100.0
Both 2.48
100.0
None 1.52
80.0
E-mail only 1.58
82.4
Personal 1.88
85.0
START
System Improvements
EARS 2.0
Making the system better – EARS 2.0
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Available to all staff via web portal
Immediate e-mail communication
 To
referrers
 To service providers
 To students
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Real-time referral based on alert type
Improved outcome tracking using workflow
Batch uploads (at-risk students)
From the staff portal…
New responder screen…
Responder notes…
Responders can add an
infinite number of “Alert
Notes” to track
conversations /
referrals they have
made for each student.
Each note will be time /
date stamped and
include Advisors’ EUID
and name.
Assessment data…
Advisor /
Responder
contacts student
Advisor / Responder
creates notes / adds
additional notes.
Advisor / Responder
“completes” Alert only
if student completes
prescribed
intervention.
COTS Early Alert Offerings
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SunGard Course Signals (Purdue) - http://www.sungardhe.com/signals/
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Hobson’s Early Alert system - http://www.hobsons.com/products/earlyAlert.php
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Starfish Early Alert - http://www.starfishsolutions.com/sf/solutions/earlyalert.html
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Datatel Retention Alert - http://www.datatel.com/products/products_a-z/student-retentionsoftware.cfm
EducationDynamics Early Alert - http://www.educationdynamics.com/RetainStudents/Early-Alert-Systems.aspx
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EBI MAPWorks - http://www.map-works.com/
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Sinclair Community College -http://www.sinclair.edu/support/success/ea/
What we’ve learned
1.
2.
3.
4.
5.
6.
7.
8.
9.
Including faculty in the design was critical
Linking to class roll, self-populating made it easier for faculty
to use
Faculty generally focus on course-related issues
Personal faculty contact is the most effective follow-up
E-mail contact by itself is not effective
Some positive effect on success and persistence based on
type of contact
Timing of alert has no apparent effect on success or
persistence
Tracking confirmed contacts needs improvement
EARS is not a “large class” solution
Resources
Bowen, E., Price, T., Lloyd, S., & Thomas, S. (2005). Improving the quantity and
quality of attendance data to enhance student retention. Journal of Further and
Higher Education, Vol. 29 (4), 375-385.
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Eimers, M. (2000). Assessing the impact of the early alert program. AIR 2000
Annual Forum Paper. (ERIC Document Reproduction Service No. ED446511)
Retrieved February 28, 2009, from ERIC database.
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Fischman, J. (2007, October 29). Purdue uses data to identify and help
struggling students. Chronicle of Higher Education Online, Retrieved May 15, 2009
from http://chronicle.com/daily/2007/10/530n.htm.
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Geltner, P., & Santa Monica Coll., CA. (2001). The characteristics of early alert
students, Fall 2000. (ERIC Document Reproduction Service No. ED463013)
Retrieved February 28, 2009, from ERIC database.
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Hudson, W. (2006). Can an early alert excessive absenteeism warning system
be Effective in retaining freshman students? Journal of College Student
Retention, Vol. 7(3-4), 217- 226.
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More references
Kelly, J. & Anandam, K. (1979). Computer enhanced academic alert and
advisement system. (ERIC Document Reproduction Service No. ED216722) Retrieved
February 23, 2009, from ERIC database.
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Richie, S. & Hargrove, D. (2005). An analysis of the effectiveness of telephone
intervention in reducing absences and improving grades of college freshmen. Journal
of College Student Retention, Vol. 6(4), 395-412.
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Tampke, D. (2013). “Developing, implementing, and assessing an early alert
system,” Journal of College Student Retention, 15 (1), in press.
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The Hanover Research Council. (May 2008). Intrusive advising and large class
intervention strategies: A review of practices. Washington, DC: Author.
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Wasley, P. (2007, February 9). A secret support network. Chronicle of Higher
Education, 53(23), A27.
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Thank you for your participation!
Dale R. Tampke
Dean, Undergraduate Studies
University of North Texas
[email protected]
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