Effects of Course Interactivity on Learner Persistence, Motivation, and
Achievement in Online Courses
Anna Martin
Kent State University
Doctoral Student of Educational Psychology & Instructional Technology
dejulioa@kent.edu
Abstract: Online attrition rates are a pressing issue for organizations offering online courses. This
paper focuses on the most commonly cited factors associated with online course attrition rates; in
particular, both the individual characteristics and the external factors of attrition rates will be
discussed. Additionally, specific examination of the internal factors associated with persisting or
dropping out will be analyzed. This is of great importance as the demand for online education
continues to grow and the factors associated with online attrition have not been highly
generalizable due to differences in the instructional design of courses and the use of varying online
course platforms.
Introduction
A growing number of educational organizations are interested in the use of online learning formats. This
increase has been influenced by demand to create more equitable opportunities for all learners. A recent report has
indicated that over 6.7 million students have taken at least one online course, and about 32% of all students will take
at least one online course (Allen & Seaman, 2013). Distance education has the opportunity to offer many benefits to
learners, however there are still a number of unresolved issues related to student learning that need to be further
investigated. As the number of online course offerings for universities have increased, the issues of student
retention and persistence have raised major concerns for both students and their universities (Lee & Choi, 2010).
Online attrition rates vary from school to school and course-to-course; however, such learning formats have
much greater dropout rates than traditional learning formats. Wojciechowski & Palmer (2005) discuss these attrition
rates by siting prior research indicating that in many instances online learning attrition produces dropout rates
nearing 50%, and when compared to traditional courses face-to-face courses attrition rates tend to be 20% greater.
These attrition rates have implications for educators because such high rates of attrition may give the appearance
that their online programs are poorly implemented, in addition to reducing the confidence amongst students
attempting to complete coursework online; this could lead to students avoiding online courses in the future, with the
mindset that they may learn more in traditional classrooms and not be set back by the potential of being part of these
attrition statistics (Lee & Choi, 2011).
Student persistence in online courses was linked to a multitude of internal (e.g. age, gender, education,
employment status) and external factors (e.g. scheduling conflicts, family issues, financial problems, managerial
support, personal issues) that influenced online attrition rates; the identification of such factors is important because
the rate of attrition may indicate that not every individual is a good fit for online courses (Wojciechowski & Palmer,
2005). Although factors influencing attrition rates have been previously explored, Park & Choi (2009) explain that
there is little consensus regarding these factors, which may be due to the fact that there is a limited amount of
empirical studies evaluating the characteristics associated with online attrition.
Because motivation is the driving force that pushes students to learn, this has become a focal point in the
discussion of factors effecting online attrition (Park & Choi, 2009). Both intrinsic and extrinsic motivations play a
role in a student’s drive for learning (Bruning, Schraw & Norby, 2010). It is important for educators to understand
the differences between these types of motivation: intrinsic motivation does not require a reward for task
completion, whereas extrinsic motivation is directly linked to an outside contingency that pushes the student to act
(Bruning, Schraw & Norby, 2010; Lai, 2011). Although differences in the quantities and qualities of motivation for
individual students exists, research has found that intrinsic motivation is linked to greater amounts of academic
achievement, due to their desire for learning in and of itself, rather than for the outside reward (Lai, 2011). When
intrinsic motivation is present, students are more likely to work hard and overcome challenges without losing their
will to learn and developing “road blocks” that would interfere with future learning (Bruning, Schraw & Norby,
2010). Intrinsic motivation has, therefore, been the type of motivation that researchers have explored as it relates to
persistence in online courses.
In addition, a growing problem is that there are also a variety of platforms or learning management systems
being used between universities, along with various levels of interactivity within the online courses offered. The
majority of existing research compares online courses to traditional courses and provides little empirical evidence on
the way in which the factors affecting online attrition differ within varying online course and instructional designs.
Therefore, further analysis and understanding of online dropout rates could contribute to increased student retention
in online courses.
Purpose Statement and Research Questions for Future Study
As a result of the existing gaps in the research this study will examine whether or not internal factors (i.e.
course interactivity) impact learner persistence, situational interest and achievement in online courses. More
specifically three-research questions will be tested. These questions include: (1) Do high and low levels of
interactivity impact student persistence in online courses? (2) Do high and low levels of interactivity impact
Situational-Interest (SI) in online courses? (3) Do learners in courses with high and low interactive qualities show
significant differences in learning before and after the course?
Motivational Framework in Online Attrition
With an understanding that students achieve greater success in the classroom when they are intrinsically
motivated to learn, this study will be approached from the Four-Phase Model of Interest Development (Hidi &
Renninger, 2006). This model explains the development and deepening of interest as a factor of motivation through
four phases. This progressive model moves individuals from triggered-situational interest to well-developed
individual interest. Interest is an important indicator within this study as it has been closely linked to intrinsic
motivation (Krapp, 2005) and an ability to help learners persist when challenged (Hidi & Renninger, 2006).
Moreover, interest has also had positive effects on academic achievement across individuals and contexts (Hidi &
Harackiewicz, 2000). The four-phase model of interest development suggests that interest is a psychological state
and has both cognitive and affective components where the cognitive components are related to activities associated
with learner engagement and the affective component being the emotions associated with student engagement
(Krapp, 2005, Hidi & Renninger, 2006). Additionally, interest development depends upon the interactions learners
have with both content and context (Hidi & Renninger, 2006).
Hidi & Renninger (2006) explain the two types of interest that play a key role in student motivation as
being “situated” and “individual” interest. Situated interest is the type of interest that is triggered by stimulus within
the environment, whereas individual interest is more internally determined and tends to bring the learner back to the
content across time (Hidi & Renninger, 2006). The four phases of interest development include; triggered
situational interest, maintained situational interest, emerging individual interest, and well-developed individual
interest. Triggered situational interest can be explained as the type of interest typically triggered by an
environmental or external stimulus, which might be promoted through instructional practices. Maintained
situational interest follows triggered situational interest and is interest that is sustained through environmental
opportunities that provide learners with meaningful ways to engage in content. Emerging individual interest on the
other hand begins the process of a learner’s desire or predisposition to reengage in the content because they have
made positive associations with it. Finally, well-developed individual interest is the furthering of the previous
phase, where interest becomes more likely that a learner will reengage in content because they have placed
additional value on it over time; additionally, this type of interest can be deepened through the types of learning
opportunities utilized during this phase.
Of great importance for this particular study is the impact differing levels of interactivity have on
situational interest in online courses. Situational interest focuses on the environmental conditions that have positive
or negative effects on student attention and affect (Hidi & Harackiewicz, 2000). This aspect of interest is vital to
learning especially when there has been little, if any individual interest established between the individual and the
content, which could lead to increased engagement and ultimately a greater motivation to learn (Hidi &
Harackiewicz, 2000). In direct relation to this study, situational interest has also been found to be affected by the
presentation of course materials (Hidi & Harackiewicz, 2000). Although limitedly explored and compared in online
settings, research has demonstrated that situational interest can be motivated by the design of educational activities
and the strategies used for learning (Hidi & Renninger, 2006; Cordova & Lepper, 1996). In Cordova & Lepper’s
(1996) study, researchers found that the contextualization, personalization and choice provided within learning
environments utilizing computers to teach arithmetic to elementary students had significant impacts their
engagement and their willingness to engage in more challenging environments in the future.
In Hidi & Renninger’s (2006) model of interest development situational interest, specifically, triggered
situational interest and maintain situational interest leads to greater individual interest, where greater value is placed
on learning the content itself and voluntary repeat engagement in this learning is frequent. Triggered situation
interest is typically externally supported and can be triggered by the established environmental learning conditions
and strategies utilized (Hidi & Renninger, 2006). Maintained situational interest is elicited by triggered situational
interest, this phase is also predominately externally supported and meaningful learning activities help to sustain this
interest in the content and could potentially lead to the development of individual interest (Hidi & Renninger, 2006).
These understanding will aid in the examination of the conditions within online courses, and the way in
which situational interest can be triggered and specifically externally supported through the environmental
conditions established in the online course. These conditions may be built within the online learning management
system being used and/or the designed amount of interactivity within the course. With a better understanding of the
way in which students are motivated in online courses, universities will have a better chance of curbing high attrition
rates through increased persistence.
Factors Potentially Influencing Online Attrition Rates
At this point, research on online learning attrition has not been highly generalizable; however, the most
commonly cited reasons for the increase in attrition amongst online offerings include individual learner
characteristics and external and internal factors. Individual characteristics include factors like a learner’s age,
gender, and level of education. External factors account for the support structures available to learners, such as
family and instructor support, while internal factors account for the personal attributes of students, such as student
motivation and instructional design (Park & Choi, 2009).
The individual characteristics associated with student dropout rates are the personal characteristics students
bring with them to the classroom; these personal characteristics precede the class itself. Park & Choi (2009)
conducted a study to evaluate the way in which these factors effect a student’s decision to drop out of an online
course at a large Midwestern university. In this study, researchers found no statistical difference between students’
individual characteristics and their likelihood to drop out (p = .147); however, even without a statistically significant
difference, this does not mean these characteristics should be entirely ignored (Park & Choi, 2009; Park, Lee, &
Kim, 2013). These factors should not be ignored entirely because individual characteristics can influence success or
the degree of that success in online courses, however when they are indicators of success they have been typically
linked to prior knowledge and preparation. In a study tracking a large data set of 51,017 students at 34 different
community colleges in Washington State, Xu & Jaggars (2013) compared the students’ individual characteristics
and course delivery format to gain a better understanding of the effect they had on course persistence and learning
outcomes. Researchers found that across the board all subgroups had negative coefficients for online learning and
persistence; however males, Blacks, and those with a lack of college preparation were more likely to dropout or
have greater negative impacts on their course grade (Xu & Jaggars, 2013).
External factors involving the support provided within the family as well as by the instructor are also
commonly cited factors associated with increased attrition in online courses. Although examining the attrition
process for face-to-face courses, Bean & Metzner (1985) begin the discussion of the differences between traditional
and nontraditional students and the factors affecting their decisions to persist or dropout. A meta-analysis was used
to develop a model of this attrition process, in which they found the greatest difference between these two groups of
students was the impact external environmental variables had on nontraditional students (Bean & Metzner, 1985).
Recent empirical research in online attrition rates associated with external factors still tends to be
inconsistent across studies. Park & Choi (2009) conducted a study examining 147 learners in online job-related
courses at a large Midwestern University. The goal of this study was to determine how persistent learners differ
from dropouts in online courses in regard to their individual, external, and internal factors. The external factors of
family and organizational support demonstrated statistically significant results between groups (p < .05).
Alternatively, in a similar study examining the same five factors associated with student persistence and attrition
(i.e. support from family and work, academic locus of control, academic self-efficacy, time and environment
management skills, and metacognitive self-regulation skills), Park, Lee, & Kim (2013) examined the way in which
169 students majoring in education in a Korean university differed in persistence. In regard to the external factors
evaluated (i.e. level of family and work support) a MANOVA revealed no statistically significant differences (p =
0.997).
Student Motivation and Online Attrition Rates
Student motivation is one of, if not the most, commonly cited factor associated with online persistence and
dropout rates. This is primarily because while every student can potentially be motivated by different intrinsic and
extrinsic factors, not all students are motivated in the same way. Due to the varying levels of motivation between
students, educators are constantly looking for learning strategies that will motivate students to persist in online
environments that tend to have higher rates of attrition than traditional environments. Motivation may potentially be
one of the most important characteristics to examine because it is a characteristic that can be obstructed by the other
individual characteristics or other both internal and external factors (Giani & O’Guinn, 2010).
Lee & Choi, leaders in recent research regarding online attrition rates, have frequently examined the
internal factors associated with the increased dropout rates for such courses. In a recent study involving 282
students, Lee & Choi, examined four variables, including internal academic locus of control (ALOC), learning
strategies, flow experience, and student satisfaction. Structural Equation Modeling (SEM) found statistically
significant effects for the variables of internal ALOC and retention (p = .004), satisfaction and retention (p = .003),
internal ALOC and satisfaction (p = .003), flow and satisfaction (p < .01), and learning strategies and flow (p =
.041) (Lee & Choi, 2013). The examination of a student’s locus of control is vital in understanding student
motivation, as a strong locus of control has been linked to greater “self-efficacy” or attitude towards their ability to
complete a task. In this instance, the researchers found that having an internal locus of control led to increased
satisfaction with online courses and a decreased chance of dropping out; however, a major piece missing from this
particular study is actual retention data, as only the intent of students to persist was measured.
Lee, Choi, and Kim (2013) further researched the connections between student motivation and attrition by
investigating five factors: “(1) time and study environment management, (2) metacognitive self-regulation, (3)
academic self-efficacy, (4) academic locus of control and (5) support from family and work” (p. 322). Examination
of such factors took place in an online course offered to undergraduate students in education involving 169
participants at a Korean university. The research provides additional information to the field by breaking down
important characteristics associated with student motivation and their impacts on attrition in online courses. Most
significantly, this study concluded in an overall analysis of significant results that academic locus of control (p <
0.05) and metacognitive self-regulation (p = 0.012) were most influential in determining persistence and attrition
between groups (Lee, Choi, & Kim, 2013). Although the population examined in this study is not highly
generalizable to U.S. students, the researchers make an important contribution to online attrition rates by providing
evidence for practical application.
Additionally, limited research exists on the components of the motivational variable of student interest and
the effects it could have on student engagement and persistence in online learning environments. In a study
conducted by Sun & Rueda (2012) they examined 203 students’ levels of situational interest, self-efficacy and selfregulation and their effects on student engagement in online and blended courses in the schools of Gerontology and
Engineering. Interestingly, interest was the only motivational variable with significance related to emotional
engagement (p < .001), however, interest at the bivariate level affected all three types of engagement, which also
included behavioral and cognitive engagement. Therefore, it is important for educators to include instructional tools
to improve student interest, however these tools alone may not have an effect on students behavioral and cognitive
engagement, thus making instructional design particularly important (Sun & Rueda, 2012). This is one of the few
studies examining situational interest in relation to online courses, however additional research is necessary to
determine its effect on instructional design and attrition.
Instructional Design and Online Attrition Rates
Further elaboration on the design of online courses is necessary, as student motivation is often linked to
instructional design (Roblyer & Wiencke, 2010). The level of student engagement in learning is a necessary factor
for motivation. Student engagement deals with the level at which students are attentive and focused on the learning
task. This factor is extremely important in fostering a students desire to be active participants in the learning
process. Student engagement is key to motivation and academic success, although individual students vary in their
quantities and qualities of motivation given the context. (Saeed & Zyngier, 2012). Helping to encourage intrinsic
motivation tends to increase the likelihood of students willingness to persist in challenging situation and has been
linked to greater amounts achievement (Giani & O’Guinn). Additionally, students tend to have greater amounts of
academic success when they have an implicit desire to learn and the context in which learning occurs is safe and
allows for students to learn from their errors (Giani & O’Guinn). To a degree, all of these characteristics can be
controlled by the teachers themselves through deliberate content and course design, which ultimately may motivate
students and reduce attrition in online courses.
Wentzel & Wigfield (1998) examined existing research regarding academic and social motivation and their
influence on student performance. In their summation they explain instructors can play a major role in the social
indicators of student motivation through the deliberate creation of instructional practices and the way in which they
create interpersonal interactions within their courses (Wentzel & Wigfield, 1998). A key piece of student
motivation has been linked to the socialization that takes place with the students’ teachers and peers. For instance,
clear communication about academic expectations and goals can positively impact student learning. The ability of
teachers to differentiate instruction and make it relatable to the differing abilities and interests of students also plays
a role in the level of motivation a student may experience in an individual class.
These factors have been most commonly associated with traditional courses but can be applied to the
internal factors most frequently cited with the rise in online attrition. For instance, in Park & Choi’s (2009) study
found that satisfaction (p < .05) and relevance (p < .05) were statistically significant between those learners
choosing to dropout or persist. More recently, Lim, Morris, and Kupritz (2014) compared a blended course design
to a completely online-based course in order to identify differences in learning outcomes and in instructional and
learner factors. Interestingly, out of the 126 undergraduates from the University of Tennessee participating in the
study, there were no significant differences between course format and learning outcomes, however the most
significant and applicable finding in this study indicated that both groups showed the most important factors
influencing their learning was having “clear and concise learning content” (p. 32), along with the level of support
provided in the course” (p. 31). Researchers additionally found that 76% of participants perceived group and
individual projects as being the most helpful instructional activities, while another 47% of participants perceived
discussion activities as being of the most helpful aspects of these online course formats. These results correspond
with previous research that indicates effective instructional design has the potential to increase student motivation
and success in online courses.
Similarly, Kui (2013) examined how student motivation and peer feedback might impact learning behavior
in online course discussions. Kui’s study focuses on the correlation between student motivation, peer feedback and
student posting and non-posting activity in an online course. Kui found that students’ posting and non-posting
behaviors in online courses were significantly linked to a student’s sense of confidence and, therefore, their
likelihood to engage in online courses leading to increased success. This was evident in the significant effects the
motivational and peer feedback variables had on student’s online discussion behaviors (e.g. number of poses, length
of post, number of topics read, number of ratings given, number of logins without posting, length of logins without
posting; p < .00). Similarly, the study indicated that peer feedback also had significant effects on students’
motivation, which was indicated in multiple regression analyses identifying significance for intrinsic motivation (p =
.001), autonomy (p < .01), competence (p < .001) and relatedness (p < .01). This is significant because it implies
that teachers can have an impact on student motivation and engagement through their design and organization of the
online course (Kui, 2013).
Many of these characteristics have been condensed into a rubric that characterizes the differences between
“high” and “low” interactive online courses. Roblyer & Ekhaml (2000), developed a rubric that can be used by both
students and instructors to help categorize the level of interactivity in an online course through the evaluation of four
specific variables related to the social goals of interaction, the instructional goals of interaction, the types and uses of
technologies, and the impact of interactivity changes in learner behaviors. These variables have been based on the
prior definition of the term interaction as it relates to online courses, which Roblyer & Wiencke (2010) explain is
based on three factors:
First, it is apparent that interaction is achieved through a complex interplay of social, instructional,
and technological variables. Second, though influenced by all these factors, the aspect of
interaction acknowledged to be most meaningful to instructors and designers in student
engagement in the learning process. Third, student engagement can be increased when learning is
structured around collaborative experiences (p. 86).
An updated rubric more relevant to current characteristics in online learning has identified that high
interactive courses on the rubric tend to receive a score of 18-25 points, moderately interactive courses tend to
receive a score of 10-17 points, and courses with low interactivity receive scores between 1-9 points on the rubric
(Roblyer & Wiencke, 2010). Because of the physical distance inherent in distance education courses this rubric will
be used to identify whether or not differences in instructional design impact online learning motivation and
persistence in online environments.
Conclusions and Implications
Limitations
Research is clear in that there are specific factors that influence online attrition rates, yet the research is
unclear in determining which factors are most significantly affecting student attrition. In addition, the largest gap in
the research appears to be the fact that online learning studies that examine student attrition tend to be compared to
face-to-face courses, which ignores the differing designs of other completely online courses within and across
schools. As noted by Park & Choi (2009), “[o]nline learners can easily lose motivation unless the course is designed
to stimulate their active participation and interaction and meet their expectations. Therefore, an online course needs
to be designed in ways that guarantee learners’ satisfaction and be relevant to learners’ needs” (p. 216). Because of
the limited amount of research focused on the relations between student motivation, online instructional design, and
attrition rates, further investigation is necessary in this field.
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