1. Early Alert System (EAS) in Online Education: Student Demographic Profile
Helen Zaikina-Montgomery, Ph.D.
University of Phoenix
Scott Burrus, Ph.D.
Meryl Epstein, Ed.D.
Elizabeth Young, Ed.D.

2. Overview and Purpose of Study
Examine the demographic characteristics of students who receive Early Alerts (EAs) in courses
Compare demographics of students who receive EAs to students who do not receive EAs
Existing research on course intervention strategies, such as EA identifies a need to better understand demographics of students who struggle in course work
[Ortagus, 2017]

3. EAS in the Present Study Context
Implemented in 2007 with the goal of increasing course completion by alerting the student’s Academic Counselor to contact the student & discuss concerns.
Updated January 2016 to automatically alert the student’s Academic Counselor (AC) if the student did not submit an assignment or participate in the online discussions in the course.
Current EAS Process
Faculty files EA through classroom or EA issued by LMS
Academic Counselor is notified of issue
Academic Counselor reaches out to student

4. Supporting Research Overview
Student retention and graduation rates are a topic of institutional concern and academic examination
Due to a difference in modality of delivery, online courses are structured differently than traditional on-ground or blended courses
Online courses require students to possess more intrinsic motivation and higher levels of organizational and self-management skills
Through a mutually collaborative ecosystem,
[Allen, Seaman, Poulin, & Taylor, 2016; Braxton, 2002; Eaton, 2011; McElroy & Lubich, 2013]

5. Supporting Research Overview
National Postsecondary Student Aid Study (NPSAS) shows that being married, being a parent, and a full-time employee were positively associated with online course enrollment.
NPSAS data showed that minority students were less likely to engage in online education than their non-minority peers
Effective interventions for students in online courses need to be well-matched to the online learning environment and to the demographic characteristics of those who are most likely to struggle in their course work
[Allen, et al., 2016; Donnelly, 2010; McElroy & Lubich, 2013; NPSAS, 2012; Ortagus, 2017]

6. Questions guiding this Research
What is the demographic profile of students who received an Early Alert?
How do students on who received an Early Alert differ demographically from students who did not receive an Early Alert?

7. Data Source
Collected from the university Office of Business Analytics and Operations Research.
Included records from students who were issues an early alert either by the course instructor or the learning management system (LMS) sometime during their course
Included courses with start dates between November 24, 2015 and January 26, A total of 26,573 student records were accessed and used in the study of which 2.4% (n = 640) were students who received an Early Alert

8. results

9. What is the demographic profile of students who received an Early Alert?
Demographic Characteristic
EAS Students (N = 640)
Non-EAS Students (N = 25,933)
Age in years M (SD)
33.9 (8.59)
35.1 (9.15)
Number of people in family M (SD)
2.69 (1.41)
2.83 (1.50)
GPA M (SD)
2.57 (0.59)
3.10 (0.62)
Total transfer credits into the university M (SD)
14.17 (19.38)
15.8 (20.44)
Gender N (%)
Male
173 (27.4)
8,045 (31.3)
Female
458 (72.6)
17,677 (68.7)
Marital status N (%)
Single
379 (63.5)
12,935 (54.7)
Married
145 (24.3)
7,686 (32.5)
Separated
36 (6.0)
1,193 (5.0)
Divorced
37 (6.2)
1,819 (7.7)

10. Demographic Characteristic
EAS Students (N = 640)
Non-EAS Students (N = 25,933)
Have dependents N (%)
Yes
86 (14.5)
3,475 (14.8) No
507 (85.5)
19,944 (85.2)
Military status N (%)
Military (past or current)
140 (21.9)
5,446 (21.0)
Not Military
500 (78.1)
20,487 (79.0)
Passed course N (%)
268 (41.9)
21,351 (82.3)
372 (58.1)
4,582 (17.7)
Course grade N (%) A
10 (1.6)
5,890 (22.7) A-
4,150 (16.0) B+
14 (2.2)
2,052 (7.9) B
18 (2.8)
2,011 (7.8) B-
28 (4.4)
2,196 (8.5) C+
26 (4.1)
1,297 (5.0) C
31 (4.8)
1,331 (5.1) C-
67 (10.5)
1,243 (4.8) D+
22 (3.4)
614 (2.4) D
42 (6.6)
554 (2.1) D-
30 (4.7)
546 (2.1) F
108 (16.9)
1,240 (4.8) W
234 (36.6)
2,800 (10.8)

11. Demographic Characteristic
EAS Students (N = 640)
Non-EAS Students (N = 25,933)
Form of payment N (%)
Student Direct Pay
27 (11.2)
2,313 (8.9)
Financial Aid
558 (87.2)
22,638 (87.3)
Military
10 (1.6)
797 (3.1)
Scholarship (non-EAS only)
0 (0.0)
185 (< 1.0)
Program level N (%)
Associate
172 (27.1)
6,546 (25.5)
Undergraduate
383 (60.4)
16,262 (63.3)
Graduate
79 (12.5)
2,875 (11.2)

12. Female students more likely to receive an EA than male students
How to students on who received an Early Alert differ demographically from students who did not receive an Early Alert?
Female students more likely to receive an EA than male students
Younger students and students who were single were more likely to have an early alert
Students who received an EA were less likely to pass the course in which early alert was filed, three times more likely to withdraw from the course, and four times more likely to fail the course

13. Cramer’s V ɸc (p-value)
How to students on who received an Early Alert differ demographically from students who did not receive an Early Alert?
Chi square tests applied to EAS status related to categorical variables
Variable
Chi-square df
p-value
Phi ɸ (p-value)
Cramer’s V ɸc (p-value)
Gender
4.276* 1
.039
-.031 (.039)
Marital status
23.047** 3
.000
.031 (.000)
Dependents
.052
.820
-.001 (.820)
Military status
.288
.592
.003 (.592)
Passed course **
.159 (.000)
Course grade ** 13
.187 (.000)
Form of payment
4.362 2
.076
.088 (.076)
Program level
2.365
.307
.009 (.307)
* = Values significant at the .05 level
** = Value significant at the level

14. Conclusions & Future research
Students who receive EA tend to be more academically “at risk” than non-EA students
Some of the expected factors (those that add more responsibility, such as being married and having dependents) were not associated with EA status
Additional data and further analysis, including hierarchical linear modeling to account for potential nested effects should be conducted
Universities can continue to develop interventions for “at risk” students keeping in mind factors from this study

15. Questions & Discussion