The Relationship Between Student Engagement and Outcomes for Online MSN Students

Saturday, 29 July 2017

Patricia A. Riccio, PhD, MSN, BSN
Joanne Farley Serembus, EdD
College of Nursing and Health Professions, Drexel University, Philadelphia, PA, USA


This study sought to examine the relationship between student engagement and student outcomes for online MSN students.

One of the many challenges with nursing education in the online setting is to obtain a scientific measure of how nursing students optimize their learning. The basis for this research is to evaluate student engagement and subsequent learning outcomes from two core courses in the online Master of Science in Nursing (MSN) program at Drexel University- a Research Methods and Biostatistics course and a Health Policy and Politicscore course. One key essential outcome for graduate students is the ability to integrate new learning experiences with vital critical thinking skills which is a focus of these online courses.

While faculty design these courses to apply critical thinking, students vary in their ability to engage in the online environment. Online learning can be isolating for students leading to attrition. Minimal instructor interaction and lack of course information are some of the most common reasons for attrition (Hannum, Irvin, Lei, & Farmer, 2008). Reducing attrition rates among online education programs is a major concern and impacts the success of higher education institutions. Engagement serves as a foundation to successful student retention initiatives. The more engaged a student is, the more likely he or she will remain enrolled in a particular course or in the institution as a whole (Lundberg & Sheridan, 2015).

Alexander Astin (1975;1985; 1988; 1993) conducted the seminal work in what he termed student involvement. He defined student involvement as the amount of physical or psychological activity or student energy devoted to the academic experience. This construct evolved over time to the term engagement (Kuh, 2003). Student engagement is viewed as the level of interest students show towards the subject matter being taught; their interaction with the content, instructor, and peers along with their motivation to learn and progress through the course. Student engagement pertains to the time and physical energy that students expend on activities in their academic experience. Building community by engaging learners in their learning tasks is one of the first necessary steps toward successful online learning (Cooke, 2016). So in essence, the theory of involvement (engagement), according to Astin (1975; 1985; 1993), described students as highly involved if: 1) they interacted with faculty more, 2) participated actively with fellow students on a more frequent basis, and 3) devoted more time to studying. Likewise, according to Astin (1985) those students who neglected studying, had less frequent interactions with faculty and with fellow students were considered to be on the opposite end of high levels of involvement.

Cochran, Campbell, Baker, & Leeds (2014) found that students make decisions such as withdrawal based on the engagement they feel in the online environment. Students satisfied with their online education named a number of factors: faculty activity in discussion boards, faculty e-mailed announcements and prompt response to their questions, along with overall faculty availability via e-mail and phone, faculty respect for students, Videos and audio recaps of the lessons and assignments helping to improve their understanding of course material. (Price, Whitlatch, Maier, & Bundi, 2016). Faculty need to identify strategies to engage, motivate, and support students in online courses.

Analysis of data related to student engagement can be obtained from the learning management system (LMS). Collecting and analyzing such data is known as the field of learning analytics. Oblinger (2012, p. 11) defines learning analytics as focusing on “students and their learning behaviors, gathering data from course management and student information systems in order to improve student success”. Using learning analytics has the potential to enable faculty to increase their understanding of their students’ learning needs and to influence student learning and progression. This would benefit students as well as, the institution’s retention and success rate (de Freitas, 2015; Slade & Prinsloo, 2013).



A secondary analysis of Blackboard Learn Course Analytics was conducted using quantitative analyses. The sample for this study included 300-geographically diverse online students who completed two online graduate Nursing courses: 1) A Research Methods and Biostatistics course and 2) a Health Policy and Politics course during the 2015-2016 academic years. To maintain confidentiality of all data from the faculty researchers, an honest broker was used to abstract all data using “course analytics” from files stored in Blackboard Learn. This included all identifiers located in all of the data files in course analytics. Specifically, the variables included in this examination included: course access, total minutes, total interactions and total submissions- all completed identifiers within course analytics in Blackboard Learn. Demographic data were stored in Blackboard Learn but de-identified as well. Abstracted data were managed and organized within Blackboard Learn and once identifiers were excluded from these data, they were downloaded directly into an analysis program.



Using descriptive statistics, demographic data were analyzed using frequency and percentage distributions to describe the sample. Two way AVOVA and multivariant regression analyses were used to examine the relationships between the input variables present prior to entering the courses at Drexel University (GPA and Selectivity) with engagement (total course access and total minutes, total interactions and total submissions) occurring while students were in the course, with outcomes in the course- grades- which were obtained at the end of the course.

Preliminary results from the Research Methods and Biostatistics and the Health Policy and Politicscourses described quite similar results. Those students who received a higher grade in these courses, spent fewer amounts of time in the course, had fewer course accesses and fewer interactions with fellow students and faculty.


These findings are consistent with Astin’s seminal work on student involvement (engagement). Given the continued growth in online learning, reports of high attrition, and an overall interest in seeing students succeed, the investigators set forth to better understand student traits and characteristics most successful in the online environment. Faculty play a vital role in student engagement, retention, and long-term program sustainability.