The relationship among motivation, self-monitoring, self-management, and learning strategies of MOOC learners

Abstract

In massive open online learning courses (MOOCs) with a low instructor-student ratio, students are expected to have self-directed learning abilities. This study investigated 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 participation in the study. The survey results from 470 participants indicated that motivation 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 encourage 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.

Full text

Vol.:(0123456789)
Journal of Computing in Higher Education (2022) 34:321–342
https://doi.org/10.1007/s12528-021-09301-2
1 3
The relationship amongmotivation, self‑monitoring,
self‑management, andlearning strategies ofMOOC
learners
MeinaZhu1 · MinYoungDoo2
Accepted: 23 October 2021 / Published online: 2 November 2021
© The Author(s) 2021
Abstract
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·
Self-management· MOOCs
* Meina Zhu
meinazhuiu@gmail.com
Min Young Doo
mydoo@Kangwon.ac.kr
1 Learning Design andTechnology, Wayne State University, 365 Education Building, 5425
Gullen Mall, Detroit, MI48202, USA
2 Department ofEducation, College ofEducation, Kangwon National University, Chuncheon-si,
Korea
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Introduction
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 etal., 2014). In particular, learners need self-directed learning
(SDL), where they take responsibility for their own learning (Lee etal., 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 &
Ganz, 2015).
Another pivotal component for successful online learning is whether to adopt
appropriate learning strategies for learning. Kovanović et al. (2015) and Shen
etal. (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 extentdo MOOC learners’ motivation, self-monitoring, and
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The relationship amongmotivation, 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.
Literature review
The development ofself‑directed learning
Tough (1971) first proposed that SDL was rooted in adult education (Merriam etal.,
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 etal., 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
etal., 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
learning.
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 etal., 1997). It consists
of initiating motivation (e.g., encouraging learning initiatives) and task motivation
(e.g., learning persistence).
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These three dimensions of SDL are interrelated (Garrison, 1997). For instance,
the Zhu etal. (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 intraditional online courses
In an online learning environment, student’s learning motivation and engagement in
the learning process are important for their success (Wang etal., 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 etal., 2012; Wang etal.,
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)
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The relationship amongmotivation, 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 etal., 2003), be good at self-manage-
ment (Hill, 2002; Roper, 2007), and actively participate in class activities (Garrison
etal., 2004).
Self‑directed learning inMOOCs
MOOCs’ open access offers new learning opportunities for learners around the world
for free or at a low cost from universities (Veletsianos etal., 2015), non-profit organiza-
tions (Jagannathan, 2015; Zhang etal., 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 etal., 2016; Barak & Watted, 2017; Evans etal., 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
etal., 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 etal. (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 etal., 2020).
Learning strategies inMOOCs
Much research has examined the effects of learning strategies on learning out-
comes (Alario-Hoyos etal., 2017; Halawa etal., 2014; Littlejohn & Milligan,
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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 etal., 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 etal., 1993;
Zimmerman, 2002). SRL is a sub-concept of SDL (Loyens etal., 2008; Saks &
Leijen, 2014). Lin etal. (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 etal., 2014; Kizilcec etal., 2017; Lin etal., 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 etal., 2005; Lust
etal., 2013). Lust etal. (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 etal. (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 etal.
(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 etal. (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 etal. (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.
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The relationship amongmotivation, self‑monitoring,…
Methods
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 extentdo 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.
Participants
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 31h 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
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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
Table1.
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%).
Instruments
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
others)
23.9% (N = 112)
Students (42.7%) Full-time students 30.6% (N = 144)
Part-time students 12.1% (N = 57)
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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 Table2 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 alphacoefficients should be higher than.70.
However, Nunnally’s (1967) original work and other researchers (i.e., (Sirakaya-
Turk etal., 2017; Ho, 2006) suggested that Cronbach’s alpha coefficient above 0.6 is
also acceptable for explanatory studies.
Data analysis
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
of items
Cron-
bach’s
alpha
Reference
Self-management 9 .76 Fisher and King (2010)
and Williamson
(2007)
Self-monitoring 9 .80 Fisher and King (2010)
and Williamson
(2007)
Motivation 8 .71 Fisher and King (2010)
and Williamson
(2007)
Learning strategies 7 .60 Williamson (2007)
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(Little etal., 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 etal., 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 etal., 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 Table3). Based on the convergent validity guide-
lines by Fornell and Larcker (1981) and Hair etal. (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
(> .5)
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
Self-management
(ρ2)
– .48 (.23) .67 .80
Learning strategies
(ρ2)
– .74 .85
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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 Table4, 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.
Results
Descriptive analysis
Descriptive data for the four latent variables (i.e., motivation, self-monitoring, self-
management, and learning strategies) are summarized in Table6. The correlations
between the four latent variables are statically significant at p < . 001.
Table7 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%
confidence
interval)
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
1234
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
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were accepted (t > 1.96, p < 0.05) as displayed in Table8. 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
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333
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The relationship amongmotivation, 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 Table9, 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.
Discussion
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 etal. (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 etal. (2019) and Alario-Hoyos etal. (2017). In par-
ticular, the current study confirmed Kovanović etal.’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-
monitoring)
.60* .30* .30*
H3: Motivation → Learning strategies (through self-
management)
.64* .49* .15
H4: Self-monitoring → Self-management .48* .48* –
H5: Self-monitoring → Learning strategies (through self-
management)
.15 .08 .07
H6: Self-management → Lear ning strategies .16 .16 –
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motivation and learning strategies in MOOCs. Alario-Hoyos etal. (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 etal., 2014; Kizilcec etal., 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 etal.,
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 etal., 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 etal., 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.
Limitations
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
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335
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The relationship amongmotivation, 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.
Conclusion
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
etal. (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 inthestudy
Variables Items
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
commitments
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
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Variables Items
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
MOOC
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
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as
you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com-
mons licence, and indicate if changes were made. The images or other third party material in this article
are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the
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not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission
directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen
ses/ by/4. 0/.
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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 meinazhuiu@gmail.com.
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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