Internet Learning Volume 6, Number 2, Fall 2017/Winter 2018 | Page 25

Internet Learning Journal
dent motivation, achievement emotions,
and self-efficacy as factors that
influence achievement in online mathematics
courses. With the goal of determining
why some students succeed
in online mathematics classes while
others do not, the researchers found
that achievement emotions (i.e. boredom,
anger, and enjoyment) were the
most significant predictors of student
achievement (Kim et al., 2014). The
findings suggest that self-efficacy can
be moderated by emotional experiences
and that a focus on improving students’
motivational experiences could
lead to increased achievement (Kim et
al., 2014).
In a similar study, Hodges and
Kim (2010) used email to deliver
self-regulation strategies to students
and sought to determine if a relation
exists between achievement and the use
of self-regulation strategies or self-efficacy.
Zimmerman (as cited in Hodges
& Kim, 2010) lists the three components
of self-regulation as behavioral,
environmental, and personal. Studying
college students enrolled in an asynchronous,
online mathematics class,
the researchers grouped the students
into three categories where one group
received self-regulation strategies with
personalized email messages, one group
received the same emailed strategies
without personalization, and the third
group did not receive any strategies
(Hodges & Kim, 2010). The personalized
email messages embedded self-regulation
strategies to help students plan,
set goals, and self-monitor their learning
(Hodges & Kim, 2010). They found
that although the email messages did
not lead to a positive change in self-efficacy
or self-regulation, there was a positive
relation between students’ self-efficacy
and achievement (Hodges & Kim,
2010). Given that the course was a university
requirement and not in the majors
of most students, there may have
been a lack of self-efficacy among the
students which led to a lack of implementation
of the self-regulation strategies
(Hodges & Kim, 2010).
Term Length
While studies abound on topics such as
learning preferences (Bonk et al., 2015),
motivation (Kim et al., 2014), persistence
(Kranzow, 2013), grit (Duckworth,
Peterson, Matthews, & Kelly,
2007; Smilie & Smilie, 2017), and the
aforementioned achievement (Hodges
& Kim, 2010; Kavitha & Sundharavadivel,
2012; Vilardi & Rice, 2014)
in online education, the literature on
term length in the online classroom is
limited (Rodrigue et al., 2016). Even
more limited is the literature on the
role of term length in online mathematics
classes. Term length has been the
subject of research in the face-to-face
classroom with findings relevant to the
online setting. Both Murphy (2010) and
Anderson and Anderson (2012) examined
the impact of accelerated terms
on student achievement in quantitative-based
classes. Murphy (2010) used
a content-specific exam to compare the
achievement of Master of Business Administration
(MBA) students in 8-week
and 16-week microeconomics classes
and found a minimal difference in
achievement between the two groups.
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