Doctoral Students: Online, On Time, On to Graduation

Sue Adragna, Ph.D. Education Programs Chair


Kelly Gatewood, Program Chair

Instructional Design and Technology Ph. D. Program

Abstract: As post-secondary institutions continue to expand online offerings, increased numbers of students are enrolling in online doctoral programs.  The results of this study can guide the development of retention strategies for students who are at risk of academic failure and who might ultimately drop from online doctoral programs.

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The expansion of online programs and student enrollment continues through post-secondary education at all levels, including the doctoral level.  The number of online students increased by approximately 1 million to 5.6 million in fall 2009, demonstrating an increase of 21% (Bollinger & Halupa, 2012).  Although doctoral online programs are gaining popularity, student persistence remains comparable to the 50% undergraduate retention rates.  It is unclear which factors contribute to student persistence at the doctoral level; however, faculty are deemed integral contributors for the support of doctoral students.  Yet, despite mentoring, increased academic support systems, and implementation of retention strategies, student retention at the doctoral level remains nearly 50% (Stallone, 2009).

Bollinger and Halupa (2012) noted that students have been opting for online education because the courses tend to fit their busy lifestyles.  They analyzed 84 first course doctoral students in four areas related to anxiety and satisfaction and explored student anxiety expressed over the use of the computer, the Internet, and online course delivery, as the literature supported each of these areas as potential areas of anxiety for students.  Anxiety provoking experiences reported by the students included their lack of information literacy that highlighted their inability to navigate the Internet and locate appropriate resources.  Bollinger and Halupa (2012) noted a correlation between higher levels of satisfaction and reduced anxiety.  Enhanced student orientation, utilizing student-centered approaches, and planned interventions to lessen student apprehension were recommended.  Faculty lack the online experience and literacy skill sets in their interactions with doctoral students in online doctoral programs to generate increased student satisfaction.  Although the study did not directly examine faculty/student time in the online course room, the relationship between satisfaction and anxiety may relate and therefore pertain to this study and issues of student retention.

The doctoral curricula has been studied to identify how to strengthen and guide individuals in doctoral programs (Kumar, Dawson, Black, Cavanaugh & Sessums, 2011) with the application of research-based knowledge and the link of context-based knowledge to enhance and improve the practice (Shulman, Golde, Conklin, Bueschel & Garabedian, 2006).  Stallone (2009) assessed four characteristics associated with doctoral student retention: (a) persistence, (b) cultural diversity, (c) psychological characteristics, and (d) college engagement.  It was noted in Stallone’s (2009) research that psychological factors are the most identified cause for student attrition.  The human quality factors related to cultural diversity sensitivity are what assisted the student in achieving doctoral success.  Kumar et al. (2011) reported 94% of the doctoral students agreed that their expectations were met during the initial year of their doctoral training, with most students identifying faculty members’ support as the key ingredient of doctoral student persistence.

Understanding the necessary skills and relevant experiences faculty would need for successful online doctoral studies is significant for administrators who seek to increase online doctoral persistence.  Green et al. (2009) reported previous studies had identified motivating factors that enhanced faculty retention to be flexible hours, innovative pedagogy, acquiring new technological skills, and expanding faculty career opportunities.  The authors also reported unfavorable aspects that included added time and effort required to teach online courses, lack of monetary compensation, limited organizational support structures, faculty inexperience, and the faculty member’s lack of technological skills.  The greater problem was in the perceived lack of vision by administration for online education.  Green et al. informed this study as it related to population demographics, such as faculty years of experience teaching online, gender and others.

Encouraging factors, as noted by these researchers included mentoring, continual training, collaboration with on-site faculty, and enhanced engagement within organizational community of the college.  Lovitts (2009) discussed students not being prepared to make the transition from student to independent scholar.  For example, in the first years of doctoral programs, students begin to deal with isolation.  Further development was based upon the connections made that support or understands the student.  The researchers concluded that online faculty would benefit from assistance in instructional design, added training, and early mentorship for new online faculty to reduce online faculty turnover.

Seaman (2009) found the faculty delivering online courses to be both experienced and novice, part-time and full-time.  The researcher reported the top ranking concern among faculty surveyed was that online course preparation required more time than conventional classroom delivery.  The data also indicated they needed assistance with support and incentives.  Only one third of the surveyed faculty had taught an online course, and even fewer were currently online instructors at the time of the inquiry.  The faculty paradoxically expressed some concerns about online programming while most had at some time recommended it as a viable option to students.  The contradictory nature of the faculty responses was reflective of the distant role of administration to the unique support needs on online programming at their colleges.  Seaman (2009) reported faculty who had never taught an online course view that the online student outcome was inferior as compared to faculty that had taught an online course found student outcome as good or superior to traditionally taught courses.  All faculty surveyed identified the lack of support services for online programming (Seaman, 2009).

Mentoring, according to Columbaro (2009), had all of the benefits in the virtual environment and few of the historical limitations.  Columbaro (2009) contended that exemplary professors could mentor doctoral students and prepare them for professional challenges in the real world.  She explained that mentorship was essential to describing the relational quality of the professor and student and preparing them for professional placement.  The students who were unengaged in mentoring needed to be motivated, a significantly different problem.  Online faculty had few incentives to reinforce student productivity.

Understanding student productivity in the online environment was addressed by experienced online faculty in several different ways.  Meyer and McNeal (2011) in a qualitative study interviewed 10 online faculty to determine what methods maximized student productivity in the online environment.  Faculty reported pedagogical methods that increased student productivity were creating relationships, student engagement, timely responding, planned intervals for communication, assignment reflection, well organized course structure, applied technology, adaptable, and having the utmost in expectations for the student.

Literature indicates student persistence is negatively impacted by anxiety, and was positively affected by faculty presence that contributed to student satisfaction (Baltes et al., 2010; Bollinger & Halupa, 2012; Kumar et al., 2011; Stallone, 2009).  Faculty status, training, incentives, and experience contributed significantly to both faculty and student retention (Green, Alejandro, & Brown, 2009; Lee et al., 2009; Seamon, 2009).  The literature review has indicated that course room time has needed intensive instructional design that was best accomplished collaboratively with other faculty and modeled after institutional mentoring practices (Columbaro, 2009; Meyer & McNeal, 2011).

Gaps in the research of online doctoral student’s persistence have common features.  None of the studies indicated that actual measurement of time spent online, frequency of faculty contacts, and correlation to course outcomes.  These measures would provide good markers of student progress, persistence, and engagement throughout the doctoral level course.  If such indicators could be benchmarked, it is reasonable to use them throughout a given course by experienced online instructors as flags warranting potential intervention.

In online doctoral programs, the completion of the coursework has become a challenge and concern.  The purpose of this research was to determine whether a correlation exists between faculty and student time spent in online doctoral course rooms and student persistence.

Research Questions

The following questions guided this study: (a) Is there a statistically significant correlation between faculty time in the Educational Leadership and Instructional Design and Technology doctoral online course rooms and doctoral student persistence?  (b) Is there a statistically significant correlation between student time in the Educational Leadership (EDL) and Instructional Design and Technology (IDT) doctoral online course rooms and doctoral student persistence?


The study was quantitative, using archived data–expo facto–to determine whether a correlation existed between the dependent variable, student persistence, and the independent variables, faculty and student time spent in doctoral online course rooms.  The data was collected from the Educational Leadership (EDL) and Instructional Design and Technology (IDT) 3-credit courses from years 2009 to 2012, at a Level 6, not-for-profit institution in South Florida.  The IDT program began in 2012.


Students enrolled in the EDL and IDT PhD program represent diverse backgrounds and locations.  Thirty states are represented, as are China, Ghana, and Puerto Rico.  Racial distribution is equally diverse with 46% of the students being White, 38% African American, 11% Hispanic, 1% native Hawaiian, and 4% Other.  Ages of the students range from 27-81 years and 67% are female and 33% are male.  Fifty-five percent of the students are married, 25% are single, 17% are divorced, and 3% are separated.  Although the main research questions were concerned only with the relationship between faculty/student time in courses and student retention, addition analysis was completed on demographic information and any statistically significant items of interest.

Faculty teaching in the EDL and IDT online doctoral programs have varied online teaching experience ranging from 1-10 years.  All hold a terminal degree in the content area related to the courses they teach.


Archived data consisted of 1782 records of students who took online doctoral classes in EDL and IDT programs.  Students could have taken more than one course; therefore, individual students were pulled out to identify better the number of courses that each student took.  The data were aggregated (collapsed) to a single, individual case to determine the average amount of time each student spent in all course work.  This was done so that we had independence.  To conduct an independent sample T-test, the assumptions of that statistical test–independent samples–needed to be met.

Because the same students appear more than once in the data set, with most students having taken multiple courses, a correlation was not initially conducted because the cases were not independent of one another.  For example, student A would be highly correlated with student A. Student A could appear in the data as many as 17 times in the data set, which makes students highly correlated with themselves.  In addition, students who spent a lot of time in their first class all the way through to their 17th class were going to be very similar to themselves.  There were 179 persisters and 69 students who dropped, weighting the data to favor persisters, which violates our assumption of independence.  The data was then collapsed so that each student appears in the data only once.  An average was determined.  The learning platform (LMS) provides a total number of minutes students and faculty spend online in the course room.  The total time spent in all classes was divided by the number of courses taken.  Persisters’ time in courses was compared to students who dropped out of the program, allowing the independent assertion to be made.  The instructors’ minutes spent in the course and the students’ time spend in the course created a mean called I average time and S average time.  Instructor time spent off-line was not calculated.  The files were each collapsed to create unique, aggregated file that consisted of 260 students.  A total of 260 students took between 1 and 17 courses.  Of those 260 students, 63 dropped at some point and 197 persisted (coded as 1 and 0 respectively).  An independent sample T-test was selected for the analysis because it allows for comparisons between two variables, two dichotomous groups, and to find out if those who were coded as 1, persisters, and those who were coded as 0, non-persisters, were significantly different in the amount of time that they spent in their courses on average.


An independent-samples t-test was conducted to compare the average amount of time individual instructors spend in an online doctoral program spent in course rooms and the persistence or non-persistence of their students in the program.  These results were highly significant as indicated by the alpha level was 0.001suggesting counter intuitively, those students who did not persist had on average instructors who spent significantly longer amounts of time in the courses than those who persisted.  A significant difference exists in the scores for Instructor Average Amount of Time in Courses and (M = 4.2, SD = 1.3) and students who persisted and those who did not (M = 9516, SD = 2628); t (257) = 4.565, p = 0.000.


An independent-samples t-test was conducted to compare the average amount of time individualstudents in an online doctoral program spent in course rooms and their persistence in the program.  There was no significant difference in the scores for Student Average Amount of Time in Courses (M = 4397, SD = 3048) and students who did not persist and those did (M = 5187, SD = 3049); t (257) = -1.780, p = 0.076.  These results suggest that the time students spend in online courses does not play a role in persistence.  Specifically, our results suggest that there is no statistically significant difference between persisting students and non- persisting students in the average amount of time they spend in their online courses at the .05 alpha level.  It was shown to be significant at an alpha level of .1, suggesting that those who dropped out of the program spent significantly less time, on average, than those who persisted.

Additional Analysis

None of the additional variables of interest, including faculty gender, faculty full time vs part time status, faculty years of experience, or average time faculty spent in the course per student were correlated with student persistence.  The proportion of students who persisted (PropPerst was our Dependent variable) was computed by taking the total number of students a particular faculty had had in an online course during the study period (ranging from 5 to 213 students) and calculating the proportion of students that persisted compared to those that dropped from the program (range .6 or 60% persisted to 1 or 100% of that faculty’s students persisted).


Table 2


Additional Variables


EDL and IDT doctoral course rooms revealed a statistically significant correlation between the faculty time in course rooms and students who did not persist.  Interestingly, the results do not reflect the current thinking that faculty are productive and available while logged on.  Meyer and McNeal’s (2011) study indicated faculty effectively used access to content, their faculty role, increased their interaction, encouraged student effort, required real world applications, and stressed time usage consistently over their course, regardless of their discipline.  Although both studies seem to be contradictory, the question still remains: what do faculty do when logged on?  Some faculty might grade lengthy papers while logged on and others might download all papers and grade while offline.  Others might be answering phone calls and e-mail while logged on.  The data indicated that the more experienced faculty spent more time logged into their online classes.  Seidman (2005) asserted faculty members have the most influence on the attitudes of students, and therefore, the “greatest effect on retention” (p. 223).  The current study indicates that faculty time online alone is not a factor in student persistence.  The results of this study can guide the development of retention strategies for students who are at risk of academic failure and who might ultimately drop from online doctoral programs.

            The findings of this study revealed that student time online in EDL and IDT online courses at the doctoral level was not a significant factor in student persistence and time was not a predictor for students who might drop out.  A limitation of this study was the inability to gauge how students were using time when logged into the EDL and IDT online course rooms.  Some students might prefer to download materials and work offline, which results in fewer minutes counted as “online” compared to students who are logged in while reading, writing, or just away from their computers.  Another consideration is the computer expertise of students.  Because many students have not taken online courses and/or have been out of school for many years, learning to maneuver in the course room and in the programs needed to complete assignments might have added to the login time.  Time logged in doctoral online courses is only one piece of the retention puzzle.  Other factors mitigate student decisions to dropout (see Table 2).


Retention rates in PhD programs have gained increased attention (Cassuto, 2010).  The focus has been on the dissertation stage, not the coursework (Cassuto, 2010).  All students, regardless of interventions and best practices offered, are at risk of dropping out.  Although connectedness to the faculty and the university have a positive influence on retention rates (Seidman, 2005), the student time logged into the EDL and IDT doctoral programs was not a factor in persistence.  However, faculty time logged in had a negative impact—more time, higher dropout rates.  Suggestions for future research might include a qualitative study exploring what faculty and students do while logged into online course rooms.


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