Course Engagement in an Accelerated On-Line RN-to-BSN Program

Background: Since 2003, enrollment in RN-to-BSN programs across the US has increased annually (AACN, 2017). In 2017, 747 RN-to-BSN programs were available nationwide. These programs are offered in a variety of formats, with more than 600 delivered partially or completely online. Given the growing number of online RN-to-BSN programs, it is imperative for nurse educators to better understand the online course behaviors of nursing student enrollees. Online behaviors may include early participation in each online course, timely assignment submissions, and time spent in the online learning environment. Understanding online course usage and its association with retention and persistence in this population may result in the identification of “at-risk” students and timely interventions to promote persistence and success (Cipher, Urban, Boyd, & Mancini, 2018). Little is known about the behavior of nursing students within learning management systems or how these data could be used to predict academic retention, progression, or success with RN-to-BSN students (Cipher, Mancini, & Shrestha, 2017). There is a need to better understand the online behaviors of students within their programs’ learning management systems.

Method: To better understand the online course engagement behaviors of students who select an online RN-to-BSN program and the factors that predict their success, we undertook a retrospective associational analysis of 307 students enrolled in the [University withheld] accelerated online (AO) RN-to-BSN program.

Student demographic characteristics (gender, race, age, prior degree status, financial aid), learning management system (LMS) variables (time to first login, number of missing assignments, and total hours spent in online learning for the entire program) and progression to graduation were collected for AO RN-to-BSN student cohorts who enrolled during the Spring of 2014. For each course, time to first login was defined as the time elapsed between the official start date of the course and the time that the student logged into the course for the first time. Because the courses were available to students prior to the official start date, students could have logged in prior to the start date, on the day of the start date, or after the start date. The LMS also captured and reported the total hours spent engaged with the LMS for each student’s entire set of online courses.

These students were followed to completion that resulted in either graduation, discontinuation, or failure. Discontinuation was defined as the failure to enroll in courses for an entire calendar year (365 days). No currently enrolled or progressing students were included.

Generalized linear mixed models (GLMM) were computed to examine the predictive associations between engagement in online courses and outcomes. The GLMM models accounted for multiple occurrences of students within courses. Significance was evaluated at a .10 alpha level, per the recommendations of Hosmer and Lemeshow (2000) regarding exploratory logistic regression models.

Results: The multilevel data consisted of 307 students who took eight courses. Of the 307 students, most were female (86.6%), and the mean age was 36.5 ± 8.8 years. The ethnic breakdown of the sample was primarily white (59.2%), followed by Black/African-American (15.7%), Hispanic/Latino (12.6%), and Asian (5.9%). Most of the students enrolled in the program with an associate’s degree as their highest level of education (67.4%), and 20.5% had a previous bachelor’s degree in another field. Over half of the sample (51.2%) received financial aid for all or part of the program.

Most of the students (91.2%) graduated from the program, and of those, 100% graduated on time (6 semesters or less). The remaining students either discontinued (8.5%) or failed out of the program (.3%). Descriptive statistics for each course indicate that many students (more than 50%) logged into the LMS prior to the official start date (the median time to login for all classes was 1 day prior to start date). Missing assignments were rare (the median for all classes was 0 missing assignments). The number of total program hours spent in the LMS was 671.6 ± 384.1 for all students, and 685.7 ± 398.8 for only those students who graduated.

Generalized linear mixed models (GLMM) were computed to examine the association between time to first login, number of missing assignments, total hours spent in online learning for the entire program and progression to graduation. Covariates were age at enrollment, gender, and ethnicity. Analyses indicated that the total hours spent in online learning significantly predicted the likelihood of graduation, after controlling for age, gender, and ethnicity (p = .07). Students who graduated spent significantly more time within the LMS system than those who did not graduate (M = 686 versus 272 hours, respectively). Time to first login (p = .68) and number of missing assignments (p = .99) did not significantly predict graduation after controlling for age, gender, and ethnicity.

Conclusion: These results indicate that the time that students spend in their online courses can predict program success. With the recent rapid increase in online nursing programs, it is important that we more fully understand the online educational experiences of students in online campus RN-to-BSN programs. This case study found that on average, students demonstrated punctual behaviors in their online courses. Results revealed that time spent engaging in online courses is a predictor of success, even when adjusting for demographics. The creation of an early-identification process for at-risk students who exhibit low levels of online engagement would have the potential to enhance educational outcomes even further.