Journal of Computing in Higher Education (2022) 34:321–342
The relationship amongmotivation, self‑monitoring,
self‑management, andlearning strategies ofMOOC
MeinaZhu1 · MinYoungDoo2
Accepted: 23 October 2021 / Published online: 2 November 2021
© The Author(s) 2021
In massive open online learning courses (MOOCs) with a low instructor-student
ratio, students are expected to have self-directed learning abilities. This study inves-
tigated the relationship among motivation, self-monitoring, self-management, and
MOOC learners’ use of learning strategies. An online survey was embedded at the
end of three MOOCs with large enrollments asking for learners’ voluntary partici-
pation in the study. The survey results from 470 participants indicated that motiva-
tion positively inﬂuenced self-monitoring, self-management, and learning strategies.
In addition, self-monitoring and self-management did not aﬀect the utilization of
learning strategies. This underscores learners’ motivation and the need to encour-
age them to adopt appropriate learning strategies for successful learning. The results
also revealed that self-monitoring positively aﬀected self-management. The ﬁndings
highlight the critical need to enhance self-monitoring skills to further promote self-
management skills in MOOCs. In addition, self-monitoring and self-management
did not encourage learners to use related learning strategies in this study. This study
should be extended to investigate practical ways to encourage MOOC learners to
adopt learning strategies.
Keywords Self-directed learning· Learning strategies· Motivation self-monitoring·
* Meina Zhu
Min Young Doo
1 Learning Design andTechnology, Wayne State University, 365 Education Building, 5425
Gullen Mall, Detroit, MI48202, USA
2 Department ofEducation, College ofEducation, Kangwon National University, Chuncheon-si,
Massive open online courses (MOOCs), which were ﬁrst introduced in 2008,
give open access to learners around the world (Milligan & Littlejohn, 2016), with
more than 900 universities providing over 11,000 MOOCs (Shah, 2019). Class
Central (a website that tracks MOOC learning platforms) indicated that MOOCs
have rapidly and signiﬁcantly increased since March 2020 due to the enforcement
of social distancing rules during the COVID19 pandemic (Rindlisbacher, 2020;
Schaﬀhauser, 2020). Students who were not allowed to attend face-to-face classes
often sought learning opportunities in MOOCs as an alternative to traditional
courses. For example, students enrolled in over 10 million courses in Coursera in
a 30-day period in 2020, representing a 644%increase (Schaﬀhauser, 2020).
MOOCs have diﬀerent characteristics than traditional online courses, includ-
ing tuition, credits, and the number of students who enroll (Pappano, 2012).
MOOCs are usually free or have a low-cost fee for certiﬁcates or degrees com-
pared to traditional education. In addition, a large number of students typically
enroll in a MOOC with as many as 100,000 students per class. Consequently,
student-instructor interaction is limited in MOOCs. In addition, students have
more control over their own learning in MOOCs, including self-directed learning
strategies and a ﬂexible time and place to learn. However, the sudden transition
to more learning control from the instructor to the learners poses challenges for
learners (Fournier etal., 2014). In particular, learners need self-directed learning
(SDL), where they take responsibility for their own learning (Lee etal., 2020).
Brookﬁeld (2013) stated that self-directed learners could select the topic,
learning strategies, and amount of content they want to learn as well as how to
evaluate their own learning. SDL has been identiﬁed as a critical skill in diverse
education settings (Hiemstra, 1994; Owen, 2002) and is an essential feature for
lifelong learning (Dynan et al., 2008; Hyland & Kranzow, 2011; Sze-yeng &
Hussain, 2010). Although SDL beneﬁts learners in many ways (Sze-yeng & Hus-
sain, 2010), including improving academic performance (Cleary & Zimmerman,
2004), the typically low instructor-to-student ratio in MOOCs underscores the
importance of MOOC learners’ SDL (Kop, 2011; Kop & Fournier, 2010; Rohs &
Another pivotal component for successful online learning is whether to adopt
appropriate learning strategies for learning. Kovanović et al. (2015) and Shen
etal. (2013) noted that students tended to underuse appropriate learning strate-
gies and tools for learning in online learning environments. Thus, it is critical to
explore whether students’ learning strategies are aﬀected by students’ SDL skills.
While SDL is a requirement for MOOC learners’ successful learning, and
appropriate learning strategies are important in online learning, limited research
has examined student SDL skills and learning strategies in MOOCs. There-
fore, the purpose of the present study is to examine the structural relationship
among learning strategies in MOOCs and three key features of SDL: motiva-
tion, self-monitoring, and self-management. The research question guiding this
study is, “To what extentdo MOOC learners’ motivation, self-monitoring, and
The relationship amongmotivation, self‑monitoring,…
self-management skills predict their use of learning strategies?” The research
ﬁndings and implications are expected to encourage MOOC learners to utilize
learning strategies for successful learning.
The development ofself‑directed learning
Tough (1971) ﬁrst proposed that SDL was rooted in adult education (Merriam etal.,
2007). There are two interpretations of SDL: as learners’ personal attributes and
as a learning process (Brockett & Hiemstra, 1991). Researchers who have empha-
sized the personal attributes interpretation included Guglielmino (1978), Long
(1991), and Merriam (2001). Long (1991) identiﬁed independence, self-eﬃcacy,
metacognitive awareness, intrinsic motivation, deep learning, and priority in learn-
ing as requirements for SDL. More recently, Sze-Yeng and Hussian (2010) added
learner autonomy, which is the ability to control the learning process through per-
sonal responsibility as a personal attribute for SDL. Speciﬁcally, learners have the
freedom to choose their behaviors (Deci & Ryan, 2008), which motivates them to
engage in their own learning (Skinner etal., 2008). Similarly, Brookﬁeld (2013)
stated that self-directed learners should choose the topics they want to learn, the
learning strategies to use, the amount of time to learn, and how they want to evalu-
ate the results of their learning. The criticism of the personal attribute perspective is
that it underestimates the inﬂuence of the external environment on learning (Ainoda
etal., 2005) and thus may lead to a misunderstanding that SDL is solely determined
by personal attributes.
The interpretation of SDL as a learning process is used in the present study.
Knowles (1975) described SDL as “a process in which individuals take the initia-
tive, with or without the help of others, in diagnosing their learning needs, formulat-
ing learning goals, identifying human and material resources for learning, choosing
and implementing appropriate learning strategies and evaluating learning outcomes”
(p. 18). Similarly, Brookﬁeld (1986) viewed SDL as a process that allows learners
to work independently or collaboratively to plan, implement, and evaluate their own
Garrison (1997) described three interrelated aspects of SDL: (1) self-monitoring,
(2) self-management, and (3) motivation (see Fig.1). Self-monitoring is related to
learners’ cognitive and metacognitive processes. According to Garrison (1997), self-
monitoring refers to learners’ responsibility for the construction of personal learn-
ing, including cognitive and metacognitive processes. The self-management aspect
focuses on the external environment and activities aﬀecting the learning process.
Learners should be able to manage their learning time as well as learning resources
and support. The third aspect, motivation, is a predictor of learners’ behaviors and
learning performance (Williams & Deci, 1996; Williams etal., 1997). It consists
of initiating motivation (e.g., encouraging learning initiatives) and task motivation
(e.g., learning persistence).
These three dimensions of SDL are interrelated (Garrison, 1997). For instance,
the Zhu etal. (2020) conﬁrmed that MOOC learners’ cognitive and meta-cognitive
activities (i.e., self-monitoring) impacted their self-management. Abd-El-Fattah
(2010) also investigated the relationship among motivation, self-monitoring, and
self-management with 119 undergraduates in a face-to-face learning setting. The
results of path analysis indicated that the three dimensions were interrelated, and
motivation mediated the relationship between self-management and self-monitoring.
Given that MOOCs require learners to be self-directed learners for successful learn-
ing outcomes, it is necessary to further examine the relationship among motiva-
tion, self-monitoring, and self-management for MOOC learners to provide practical
implications for MOOC instructors and learners.
SDL intraditional online courses
In an online learning environment, student’s learning motivation and engagement in
the learning process are important for their success (Wang etal., 2013). SDL skills are
related to the cognitive presence (Garrison, 2003), which, in turn, supports knowledge
construction in the online learning process (Hartley & Bendixen, 2001). Considerable
research has found that SDL positively inﬂuences online learners’ academic achieve-
ment (Broadbent & Poon, 2015; Broadbent, 2017; Richardson etal., 2012; Wang etal.,
2013). The eﬀects of SDL on online learning have also been conﬁrmed in mobile
online learning (Zheng et al., 2018) and collaborative online learning (Kuo et al.,
Fig. 1 Self-directed learning model (Garrison, 1997)
The relationship amongmotivation, self‑monitoring,…
2015). These ﬁndings indicate that SDL is pivotal to the success of learning in many
diﬀerent types of online learning (Serdyukov & Hill, 2013).
Given the importance of SDL, online learners should have the skills to plan, moni-
tor, and manage their learning (Ally, 2004). Lin and Hsieh (2001) and Owston (1997)
asserted that to be successful in an online learning environment, learners should have
the ability to set their learning goals and pace. Therefore, it is critical for online learn-
ers to be responsible for controlling their learning (Hartley & Bendixen, 2001; Hsu &
Shiue, 2005), follow course schedules (Discenza etal., 2003), be good at self-manage-
ment (Hill, 2002; Roper, 2007), and actively participate in class activities (Garrison
Self‑directed learning inMOOCs
MOOCs’ open access oﬀers new learning opportunities for learners around the world
for free or at a low cost from universities (Veletsianos etal., 2015), non-proﬁt organiza-
tions (Jagannathan, 2015; Zhang etal., 2020), and corporate entities (Bersin, 2013).
More than 11,000 MOOCs have been oﬀered by more than 900 universities around
the world (Shah, 2019). MOOCs provide an opportunity for learners to gain knowl-
edge and skills (e.g., Barak etal., 2016; Barak & Watted, 2017; Evans etal., 2016;
Hew & Cheung, 2014) using diverse delivery modes (e.g., instructor-led and self-paced
MOOCs) (Zhu & Bonk, 2019).
MOOCs are diﬀerent from traditional online courses in terms of the purpose of
enrollment, the number of enrolled learners, open access to content, and how students
and instructors interact. The average number of learners enrolled in a MOOC is 8000
(Chuang & Ho, 2016), which is much larger than traditional online courses. Given this
low instructor-learner ratio, the interaction between instructors and students is very
limited. MOOCs also have low completion rates (Jordan, 2013; Lewin, 2012; Reich,
2014), ranging from 7 to 10% (Daniel, 2012; Jordan, 2014).
The critical factors impacting learners’ success in online courses include self-direc-
tion, responsibility, and motivation (Grow, 1991; Schrum & Hong, 2002) as well as
learners’ cognitive and metacognitive performance (Barnard et al., 2008; Kitsantas
etal., 2008; Zimmerman, 1989, 2008). Some researchers have recently examined how
to improve these SDL skills in MOOC learners. For example, the Zhu (2021) explored
how to enhance MOOC learners’ self-management skills, such as how to promote
learning goals, time management, resources and support management, and navigating
in MOOCs. Similarly, Onah etal. (2021) explored learners’ self-directed learning abili-
ties in MOOCs and found that goal setting and time management were highly related
to their self-regulation skills. However, more work is needed to investigate the impact
of these SDL factors on MOOC learners’ successful learning (El-Gilany & Abusaad,
2013; Kop & Fournier, 2010; Terras & Ramsay, 2015; Zhu etal., 2020).
Learning strategies inMOOCs
Much research has examined the eﬀects of learning strategies on learning out-
comes (Alario-Hoyos etal., 2017; Halawa etal., 2014; Littlejohn & Milligan,
2015; Schunk, 2005). A learning strategy refers to “any thoughts, behaviors,
beliefs or emotions that facilitate the acquisition, understanding or later transfer
of new knowledge and skills” (Weinstein etal., 2000, p. 227). Learning strate-
gies include self-regulated learning (SRL) (Schunk, 2005; Zimmerman, 2002),
which is the process that students initiate and maintain cognitive activities
towards achieving their learning goals (Zimmerman, 1989). From an SDL per-
spective, students are expected to control and regulate their own learning using
various strategies, such as cognitive, meta-cognitive, and learning resource
usage strategies, leading to successful learning outcomes (Pintrich etal., 1993;
Zimmerman, 2002). SRL is a sub-concept of SDL (Loyens etal., 2008; Saks &
Leijen, 2014). Lin etal. (1999) explained the importance of SDL, especially in
a technology-enhanced learning environment. Several studies have also shown
that SRL is a reliable predictor of students’ learning outcomes in online learning
environments (Halawa etal., 2014; Kizilcec etal., 2017; Lin etal., 2017; Little-
john & Milligan, 2015).
MOOC learners face unique challenges when learning on their own in an
online MOOC environment. Many MOOC learners seem to use inappropriate
or insuﬃcient learning strategies (Winne & Jamieson-Noel, 2003) or they do
not leverage learning resources to support their learning (Ellis etal., 2005; Lust
etal., 2013). Lust etal. (2013) investigated learners’ capacity to use learning
resources to support their learning and found that only 3% eﬀectively leveraged
the learning resources. They also struggle to ﬁnish the courses. The substantially
low completion rate of MOOCs, ranging from 7 to 10% (Daniel, 2012; Jordan,
2014), demonstrates the signiﬁcance of self-regulation for MOOC learners. Lit-
tlejohn etal. (2016) explained that this lack of self-regulation is partially due
to the restricted interaction with instructors and peers in the MOOC learning
environment. Pintrich and de Groot (1990), Zimmerman (1990), and Kim etal.
(2019) have also emphasized the importance of self-directed learning strate-
gies in open resource education because successful learners are motivated to use
self-regulatory strategies, including planning, monitoring, and adaptation.
Alario-Hoyos etal. (2017) also found that MOOC learners need time manage-
ment skills to improve self-regulation based on the results of their study with
over 6000 MOOC learners. To address time management skills, Yen etal. (2018)
developed a self-regulated digital learning framework to facilitate self-regulated
learning in online learning environments. The framework has eight features: (1)
learning plans (e.g., goal settings or time management); (2) recording and shar-
ing about learning progress; (3) assessment (e.g., reﬂection, learning outcomes);
(4) human feedback; (5) machine feedback; (6) visualization (e.g., a concept
map); (7) scaﬀolding/prompts; and (8) agents. These learning strategies related
to self-regulated learning are expected to enhance learning achievement in an
online learning environment. Based on the signiﬁcance of SDL for success in
MOOC learning, this study investigates the relationship among motivation, self-
monitoring, self-management, and the use of learning strategies.
The relationship amongmotivation, self‑monitoring,…
The theoretical framework of this study is Garrison’s (1997) SDL model, which
explains that motivation aﬀects self-monitoring and self-management. Self-monitor-
ing and self-management inﬂuence each other. SDL is expected to aﬀect the use of
learning strategies of MOOC learners. Thus, this study examines the relationship
among motivation, self-monitoring, and self-management and their eﬀects on learn-
ing strategies in MOOC learning environments (see Fig.2). The research question
guiding this study is, “To what extentdo MOOC learners’ motivation, self-monitor-
ing, and self-management skills predict their use of learning strategies?” Six hypoth-
eses were tested for this study:
H1: Motivation positively aﬀects self-monitoring.
H2: Motivation positively aﬀects self-management.
H3: Motivation positively aﬀects learning strategies.
H4: Self-monitoring positively aﬀects self-management.
H5: Self-monitoring positively aﬀects learning strategies.
H6: Self-management positively aﬀects learning strategies.
The participants of this study were MOOC learners who were enrolled in three
MOOCs. The ﬁrst course was a Duke University physiology course with 265,107
students enrolled in Coursera. It took students approximately 31h to complete the
10-week course. The course videos were in English with Simpliﬁed Chinese subti-
tles. It was rated 4.7/5 by 2224 participants from the beginning of the course oﬀer-
ing until February 2020, when this study’s data collection was completed. The sec-
ond course was an Arizona State University English course with 36,746 enrolled
students in Coursera. It took students approximately ﬁve hours per week for six
months to complete the course. Students watched course videos recorded in Eng-
lish, and students selected subtitles in Arabic, Ukrainian, Simpliﬁed Chinese sub-
titles, Portuguese, Russian, Spanish, Persian, or Tamil. It was rated 4.9/5 by 14,135
participants from the beginning of the course oﬀering until February 2020. The
third course was a math course in FutureLearn oﬀered by the Davidson Institute of
Fig. 2 The research model of this study
Science Education in Israel. It took students approximately four hours per week for
three weeks to complete the course.
An optional link to an online survey (see Appendix) was inserted into the pages
of three MOOCs in the physiology, English, and math courses from November 2018
to February 2020. A total of 470 survey responses were received from students in
the three MOOCs. Although the response rate is low, it provides a representative
cross-section of enrolled students. The demographic information is presented in
The previous MOOC experience of the survey participants ranged from none to
more than ﬁve courses: none (N = 136, 28.9%), one (N = 96, 20.4%), two (N = 54,
11.5%), three (N = 38, 8.1%), four (N = 19, 4.0%), and ﬁve or more (N = 127, 27.0%).
The online survey had four demographic questions and 33 5-point Likert scale ques-
tions. The demographic information asked about (1) gender, (2) educational level,
(3) current employment status, and (4) MOOC learning experience. The primary
variables of this study are self-management (9 items), motivation (i.e., desire for
learning) (8 items), and self-monitoring (i.e., self-control) (9 items). These varia-
bles, as self-directed learning scales, were developed by adopting instruments from
Fisher and King (2010) and Williamson (2007) to MOOC learning environments.
Although there are diverse instruments to measure individual variables such as moti-
vation, Fisher and King’s (2010) instrument has been veriﬁed to speciﬁcally meas-
ure SDL as a whole. Learning strategies items measured the learners’ perceptions
of discussion, peer-assessment, simulations, interactive technologies, and interaction
with instructors. Williamson’s scale originally included 12 items; however, some
of the items were excluded because they were not applicable to a MOOC learning
Table 1 Demographic
information Category Sub-categories Numbers (percentage)
Gender Male 40.2% (N = 189)
Female 59.1% (N = 278)
Education High schools 24.7% (N = 116)
Bachelor’s degree 36.6% (N = 172)
Master’s degree 22.33% (N = 105)
Doctoral degrees 8.7% (N = 41)
Others 7.7% (N = 36)
Employment Full-time employees 36.4% (N = 171)
Currently unemployed 25.5% (N = 120)
Part-time employees 14.3% (N = 67)
Others (e.g., retired,
between jobs, and
23.9% (N = 112)
Students (42.7%) Full-time students 30.6% (N = 144)
Part-time students 12.1% (N = 57)
The relationship amongmotivation, self‑monitoring,…
environment (e.g., “1 ﬁnd ’role play’ is a useful method for complex learning” or “1
ﬁnd learning from case studies useful”).
Example statements of self-management are “I set strict time frames for learning
in this MOOC” and “I am disorganized while learning in this MOOC” (reversed
coded). Sample items of motivation include “I enjoy learning new informa-
tion through this MOOC” and “I have a need to learn from this MOOC.” Exam-
ple questions of self-monitoring include “I am responsible for my own learning in
this MOOC” and “I am aware of my own limitations when I take this MOOC.” The
items for learning strategies (7 items) were developed based on Williamson’s (2007)
scale of learning strategies in the self-directed learning process. Learning strategies
questions included the ability to choose appropriate learning strategies and transfer
of knowledge, such as “I participated in course discussions in this MOOC” and “I
am able to relate the knowledge I learned in MOOCs with my work or life.”
The reliability of the variables captured on the questionnaire is in Table2 with
Cronbach’s alphas and related information for the four latent variables. Cronbach’s
alphas for the latent variables were higher than 0.70, except for learning strategies.
While Cronbach’s alphas for learning strategies were 0.602 in this study, William-
son’s (2007) Cronbach’s alphas were 0.73 for the original scale. Nunnally and Bern-
stein (1994) suggest that Cronbach’s alphacoeﬃcients should be higher than.70.
However, Nunnally’s (1967) original work and other researchers (i.e., (Sirakaya-
Turk etal., 2017; Ho, 2006) suggested that Cronbach’s alpha coeﬃcient above 0.6 is
also acceptable for explanatory studies.
To examine the hypotheses in this study, we applied structural equation modeling
(SEM), a multivariate analysis method consisting of conﬁrmatory factor analysis
and path analysis. SEM is used to examine the relationship between latent constructs
and measurement variables (Kline, 2010). Considering the number of measurement
variables (33 items) and sample size (n = 470), items were parceled into two bins to
achieve better modeling results based on the results of exploratory factor analysis
Table 2 Research instruments Variables Number
Self-management 9 .76 Fisher and King (2010)
Self-monitoring 9 .80 Fisher and King (2010)
Motivation 8 .71 Fisher and King (2010)
Learning strategies 7 .60 Williamson (2007)
(Little etal., 2002; Matsunaga, 2008). Harman’s single-factor test was conducted to
detect common method bias. The total variance for a single factor was 21.90%, indi-
cating no common method variance in the data (Podsakoﬀ etal., 2003).
The analysis was conducted with SPSS (Version 27.0) and Amos (Version 26.0).
To assess discrepancies between the proposed model and the data, we used several
ﬁt indices for analyses: comparative ﬁt index (CFI), Tucker-Lewis index (TLI),
root mean square error of approximation (RMSEA), standardized root mean square
residual (SRMR), and a chi-square test. CFI and TLI values greater than 0.90 are
considered a good ﬁt between a proposed model and the data. In terms of RMSEA, a
value of 0.05 indicates a close ﬁt, 0.08 is a fair ﬁt, and 0.10 is a marginal ﬁt (Browne
& Cudeck, 1993; MacCallum etal., 1996). For SRMR, Hu and Bentler’s (1999) cut-
oﬀ value is 0.08 for SRMR.
Conﬁrmatory factor analysis (CFA) was performed using maximum likelihood
prior to testing the hypotheses (see Table3). Based on the convergent validity guide-
lines by Fornell and Larcker (1981) and Hair etal. (2006), factor loading values for
individual items should be higher than 0.5. The CFA results for all factor loadings
were over 0.6, and the measurement model indicated a good ﬁt for the data. Conver-
gent validity was examined using average variance extracted (AVE) and composite
reliability (CR). The AVE values were over 0.5, and all CR values of the constructs
were over 0.7. The results conﬁrmed that the overall CFA, including factor loadings,
AVE, and CR values of the data, were all satisfactory.
Table 3 Results of conﬁrmatory factor analysis
Latent variable Measurement variable Factor loading
AVE (> .5) CR (> .7)
Motivation Motivation 1 .65 .73 .85
Motivation 2 .72
Self-monitoring Self-monitoring 1 .97 .85 .92
Self-monitoring 2 .55
Self-management Self-management 1 .65 .67 .80
Self-management 2 .75
Learning strategies Learning strategies 1 .54 .74 .85
Learning strategies 2 .85
Table 4 Discriminant validity assessment
Measures Motivation Self-monitoring Self-management Learning strategies AVE CR
Motivation (ρ2) – .66 (.43) .60 (.36) .63 (.40) .73 .85
Self-monitoring (ρ2) – .66 (.43) .50 (.25) .85 .92
– .48 (.23) .67 .80
– .74 .85
The relationship amongmotivation, self‑monitoring,…
To assess the discriminant validity, the square root of the correlations for each
latent variable and AVE value were compared. The AVE values for the latent vari-
ables, as shown in Table4, were greater than the square root of the correlation. The
results indicated that the discriminant validity was acceptable.
The statistical signiﬁcance of the path coeﬃcient among the latent variables in
the research model was examined using the ﬁtness index.
As indicated in Table 5, the research model indicated a fair ﬁt to the data
(χ2 = 46.80; df = 14; CMIN/df = 3.34; TLI = 0.93; CFI = 0.97; SRMR: 0.04;
RMSEA = 0.07) (Browne & Cudeck, 1993; MacCallum et al., 1996). The results
imply that the hypothesized model is fair to explain the relationship among the vari-
ables in data.
Descriptive data for the four latent variables (i.e., motivation, self-monitoring, self-
management, and learning strategies) are summarized in Table6. The correlations
between the four latent variables are statically signiﬁcant at p < . 001.
Table7 summarizes the descriptive statistics, including the means, standard devi-
ations, and correlations among the measurement variables. The skewness and kurto-
sis of each measurement were computed as an indicator of normal distribution. The
minimum/maximum values were from − 2 to 2, so the normal distribution assump-
tion was considered tenable (George & Mallery, 2010).
To test the six hypotheses, we examined the statistical signiﬁcance of the path
coeﬃcient among the variables. The results indicated that H1, H2, H3, and H4
Table 5 Results of the ﬁtness of the research model (n = 470)
χ2p df TLI CFI SRMR RMSEA (90%
Structural model 46.80 .001 14 .93 .97 .04 .07 (.05 ~ .08)
Fit criteria > .90 > .90 < .08 < .08
Table 6 Descriptive data
**p < .001; *p < .05
Latent variables Mean SD Correlation
Motivation 3.99 .48 1
Self-monitoring 4.14 .45 .51** 1
Self-management 3.79 .54 .40** .53** 1
Learning strategies 3.58 4.79 .50** .36** .33** 1
were accepted (t > 1.96, p < 0.05) as displayed in Table8. Motivation had a signiﬁ-
cant inﬂuence on self-monitoring (β = 0.65, t = 6.57), self-management (β = 0.30,
t = 2.90), and learning strategies (β = 0.49, t = 4.01). Thus, H1, H2, and H3 were sup-
ported. Self-monitoring positively inﬂuenced self-management (β = 0.46, t = 4.94)
Table 7 Correlation between measurement variables
**p < .001; *p < .05
Variables 1 2 3 4 5 6 7 8
Motivation 1 1 .47** .44** .31** .26** .28** .18** .32**
Motivation 2 1 .43** .21** .23** .37** .30** .40**
Self-monitoring 1 1 .53** .46** .45** .22** .42**
Self-monitoring 2 1 .29** .27** .09** .18**
Self-management 1 1 .51** .18** .21**
Self-management 2 1 .28** .36**
Learning strategies 1 1 .46**
Learning strategies 2 1
Mean 4.49 3.83 4.04 4.46 4.08 3.59 3.50 3.65
SD .53 .60 .52 .52 .74 .65 .63 .51
Skewness − .99 − .02 .05 − .53 − .92 .03 .19 .00
Kurtosis 1.07 − .17 − .45 − .46 1.26 .18 − .32 .71
Table 8 Path coeﬃcient estimates
**p < .001; *p < .05
Hypothesis B β SE t-value
H1: Motivation → Self-monitoring .43 .65** .07 6.57
H2: Motivation → Self-management .35 .30* .12 2.90
H3: Motivation → Learning strategies .39 .49** .10 4.01
H4: Self-monitoring → Self-management .82 .46** .17 4.94
H5: Self-monitoring → Learning strategies .10 .08 .89 .37
H6: Self-management → Lear ning strategies .11 .16 .10 1.67
Fig. 3 The results of hypothesis testing
The relationship amongmotivation, self‑monitoring,…
but not learning strategies (β = 0.08, t = 0.37), which indicated that H4 was sup-
ported, and H5 was rejected. Self-management did not inﬂuence the use of learning
strategies (β = 0.16, t = 1.67), indicating that H6 was not supported. The results of
testing the research model are shown in Fig.3.
As shown in Table9, indirect eﬀects of motivation on self-management through
self-monitoring were observed at p < 0.05. However, the indirect eﬀects of motiva-
tion and self-monitoring on learning strategies were not statistically signiﬁcant.
The importance of MOOCs as a learning paradigm has increased with rising student
enrollments. MOOC learners are expected to be self-directed learners and to adopt
appropriate learning strategies for their learning outcomes. Considering the open-
access characteristics of MOOCs, the intention and motivation of MOOC learners
are diverse (e.g., auditing students, sporadic students, serious learners who complete
MOOCs). Kovanović etal. (2019) classiﬁed MOOC learners into three categories
based on their study strategies: limited users, selective users, and broad users. This
study aimed to understand the relationship among motivation, self-monitoring, and
self-management and their eﬀects on the use of MOOC learners’ learning strategies.
This study applied Garrison’s SDL model to a MOOC learning environment to
test the six hypotheses. The results indicated that motivation positively inﬂuenced
self-monitoring (H1) and self-management (H2), and self-monitoring positively
aﬀected self-management (H4). The ﬁndings also indicated that motivation facili-
tated self-monitoring, self-management, and the adoption of learning strategies.
Among the three components of Garrison’s SDL model, only motivation had posi-
tive eﬀects on learning strategies in SDL. The results of this study conﬁrmed the
research ﬁndings by Kovanović etal. (2019) and Alario-Hoyos etal. (2017). In par-
ticular, the current study conﬁrmed Kovanović etal.’s (2019) ﬁndings that MOOC
learners’ intention and motivation to take a MOOC course determine their use of
learning strategies. Many researchers have examined the importance of learners’
Table 9 Direct and indirect eﬀects
**p < .001; *p < .05
Hypothesis Total Eﬀects Direct Eﬀects Indirect eﬀects
H1: Motivation → Self-monitoring .65* .65* –
H2: Motivation → Self-management (through self-
.60* .30* .30*
H3: Motivation → Learning strategies (through self-
.64* .49* .15
H4: Self-monitoring → Self-management .48* .48* –
H5: Self-monitoring → Learning strategies (through self-
.15 .08 .07
H6: Self-management → Lear ning strategies .16 .16 –
motivation and learning strategies in MOOCs. Alario-Hoyos etal. (2017) found that
MOOC learners were generally motivated, and learners’ learning strategies could be
enhanced. Building on these previous ﬁndings, this study revealed that motivation
could predict students’ learning strategies such as online interactions with instruc-
tors and teaching assistants, participation in online discussions, assessment, and
transferring knowledge to their work and life (H3).
Learning strategy refers to factors that support the acquisition, understanding,
and transferring of knowledge and skills. Learning strategies can predicate online
students’ learning outcomes (Halawa etal., 2014; Kizilcec etal., 2017; Littlejohn &
Milligan, 2015) and can be an eﬀective approach for self-regulated learning (SRL)
strategies (Schunk, 2005; Zimmerman, 2002). In SDL, students should use diverse
learning strategies to direct, control, and regulate their own learning (Pintrich etal.,
1993; Zimmerman, 2002). Both SRL and learning strategies are required in educa-
tion, especially in a technology-enriched learning environment (Lin et al., 1999).
Researchers have reported that learning strategies are reliable predictors of students’
learning outcomes in online learning environments (Halawa et al., 2014; Kizil-
cec etal., 2017; Littlejohn & Milligan, 2015). Thus, research is needed to exam-
ine learners’ diverse motivations and strategies to improve learners’ motivation and
enhance their learning strategies. MOOC instructors should examine students’ initial
motivation as the learning initiative and regularly monitor their task motivation dur-
ing the program to check their learning persistence. This study also demonstrated
that motivation is critical to learning in MOOCs in terms of encouraging learners to
adopt learning strategies for successful learning. In addition, the results showed that
motivation is a prerequisite element in SDL (Fournier etal., 2014). In particular, this
study found that motivation had indirect eﬀects on self-management through self-
monitoring as well as direct inﬂuence on self-management.
In the current study, self-monitoring and self-management did not inﬂuence the
adoption of learning strategies. Therefore, we do not expect that those who have
self-monitoring or self-management skills will adopt eﬀective learning strategies
for their learning (H5, H6). More research should be conducted to investigate the
relationship between self-monitoring, self-management, and learning strategies. One
plausible reason for this research ﬁnding could be explained by the items measuring
learning strategies. Considering the importance of instructional methods and media
in MOOCs, we included questions in the measurement scale of learning strategies
asking about students’ perceptions of the instructional methods and media as well as
their cognitive or meta-cognitive skills, (i.e., abilities to choose learning strategies
and the capabilities to transfer knowledge). The ﬁndings may have been inﬂuenced
by combining these two diﬀerent levels of learning strategies, the perceptions of
instructional methods and media, and cognitive or meta-cognitive skills for learning.
The data were collected from three MOOCs which were taught in English (with
selected subtitles), thus limiting the generalizability of our research ﬁndings to stu-
dents who enrolled in MOOCs in local languages (i.e., other than English) in other
The relationship amongmotivation, self‑monitoring,…
countries. More participants from diverse MOOCs may increase the generalizability
of this study. Second, we did not distinguish between students from diﬀerent subject
areas or the various lengths of the MOOCs. It would be interesting for future studies
to explore whether the subject area or the length of a MOOC inﬂuences students’
self-directed learning skills. Third, the survey participants were self-selected volun-
teers, which could introduce bias, given that volunteers may have more SDL skills.
Fourth, the data of this study were collected from a self-reported questionnaire, and
Harman’s single-factor test conﬁrmed that there was no common method variance in
our data (i.e., total variance for a single factor was 21.90). Therefore, to strengthen
and verify the research ﬁndings, various data collection methods such as interviews
and log data are strongly recommended in the future.
Coronavirus (COVID-19) has given students who were used to learning in tradi-
tional classrooms opportunities to experience online learning as an alternative
learning method. Online learning, including MOOCs, is expected to become more
widespread across the globe. MOOCs allow students more autonomy, ﬂexibility, and
independence in learning. However, it requires that students are self-directed learn-
ers and can choose and adopt appropriate learning strategies for successful learning
outcomes. The motivation of MOOC learners varies and inﬂuences their learning
outcomes through self-directed learning skills and learning strategies. Kovanović
etal. (2019) noted that providing learning tools and activities is not suﬃcient to
enhance learning for MOOC students. Researchers and instructors need to pay more
attention to MOOC learners’ motivation and encourage them to adopt appropriate
learning strategies for successful learning.
Appendix: Questionnaires used inthestudy
Self-management 1. I prefer to plan my own learning in this MOOC
2. I am self-disciplined while learning in this MOOC
3. I have good management skills (e.g., time, learning resources, etc.) in this MOOC
4. I set speciﬁc times to study in this MOOC
5. I set strict time frames for learning in this MOOC
6. I am able to keep my learning routine in this MOOC separate from my other
7. I can apply a variety of learning strategies in this MOOC
8. I am disorganized while learning in this MOOC
9. I am conﬁdent in my ability to search for information related to learning content
in this MOOC
Motivation 1. I have a need to learn from this MOOC
2. I want to learn new information through this MOOC
3. I enjoy learning new information through this MOOC
4. I enjoy the various challenges of this MOOC
5. I critically evaluate new ideas in this MOOC
6. I need to know the deeper reasons of the facts in this MOOC
7. I learn from my mistakes in this MOOC
8. When presented with a problem I cannot resolve, I will ask for assistance through
diﬀerent means provided by this MOOC
Self-monitoring 1. I am responsible for my own learning in this MOOC
2. I am in control of my learning in this MOOC
3. I have high learning standards when I take this MOOC
4. I prefer to set my own learning goals in this MOOC
5. I evaluate my own performance in this MOOC
6. I have high beliefs in my learning abilities in this MOOC
7. I can ﬁnd information related to learning content for myself when I take this
8. I am able to focus on answering or solving a problem in this MOOC
9. I am aware of my own limitations when I take this MOOC
Learning strategies 1. I participated in course discussions in this MOOC
2. Peer-assessment is eﬀective in this MOOC
3. Interacting with the instructor or teaching assistant is more helpful than just
learning alone in this MOOC
4. Learning through simulations is helpful in MOOCs
5. Interactive educational technology (e.g., automatic feedback, online games,
learner polling, discussion forums, etc.) enhances my learning in this MOOC
6. I am able to choose my own learning strategies in this MOOC
7. I am able to relate the knowledge I learned in MOOCs with my work or life
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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published
maps and institutional aﬃliations.
Meina Zhu is Assistant Professor in the Learning Design and Technology program in the College of Edu-
cation at Wayne State University. She received her Ph.D. degree in the Instructional Systems Technology
program at Indiana University Bloomington. Her research interests include online education, self-directed
learning, STEM education, and learning analytics. She taught graduate-level courses: Mobile Learning
Technologies, Video, simulation, and games for learning, Interactive Course Design, User experience
design for learning, etc. She can be reached at firstname.lastname@example.org.
Min Young Doo is Assistant Professor in the Department of Education in the College of Education at
Kangwon National University, Korea. Her research interests include instructional design, online learning,
ﬂipped learning, and human resource development. She can be reached at mydoo@Kangwon.ac.kr.
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