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.

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]

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

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]

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]

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?

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, 2016. 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

results

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)

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)

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)

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

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 673.994** .159 (.000) Course grade 930.036** 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 .0001 level

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

Questions & Discussion