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Investigating self-directed learning and technology readiness in blending learning environment

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Abstract Blended Learning (BL) creates a ‘rich’ educational environment with multiple technology-enabled communication forms in both face-to-face and online teaching. Students’ characteristics are closely related to the learning effectiveness in the BL environment. Students’ ability to direct themselves in learning and to utilise learning technologies can affect student learning effectiveness. This study examined the impacts of self-directed learning, technology readiness, and learning motivation on the three presences (social, teaching, cognitive) among students undertaking subjects in BL and non-BL (NBL) settings. The results indicated that the BL environment provides good facilitation for students’ social involvement in the class. Student technology readiness plays a stronger role in impacting the teaching presence in a BL environment than NBL environment. These findings imply that a proper BL setting creates a cohesive community and enhances collaborations between students. Prior training of learning technologies can potentially enhance students’ teaching presence.
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R E S E A R C H A R T I C L E Open Access
Investigating self-directed learning and
technology readiness in blending learning
environment
Shuang Geng
1
, Kris M. Y. Law
2*
and Ben Niu
1
* Correspondence: Kris.law@deakin.
edu.au
2
School of Engineering, Deakin
University, Geelong, Australia
Full list of author information is
available at the end of the article
Abstract
Blended Learning (BL) creates a richeducational environment with multiple technology-
enabled communication forms in both face-to-face and online teaching. Students
characteristics are closely related to the learning effectiveness in the BL environment.
Studentsability to direct themselves in learning and to utilise learning technologies can
affect student learning effectiveness. This study examined the impacts of self-directed
learning, technology readiness, and learning motivation on the three presences (social,
teaching, cognitive) among students undertaking subjects in BL and non-BL (NBL)
settings. The results indicated that the BL environment provides good facilitation for
studentssocial involvement in the class. Student technology readiness plays a stronger
role in impacting the teaching presence in a BL environment than NBL environment.
These findings imply that a proper BL setting creates a cohesive community and
enhances collaborations between students. Prior training of learning technologies can
potentially enhance studentsteaching presence.
Keywords: Blended learning, Self-directed learning, Technology readiness, Motivation,
Community of inquiry
Highlights
Blended Learning (BL) has been advocated in higher education section.
This study investigates the impacts of self-directed learning, technology readiness,
and learning motivation on studentsperception of three presences (social, teaching,
cognitive).
Results show that students in the BL group achieve significantly higher social presence
than students in the NBL group.
Self-directed learning has significant and direct impacts on the cognitive presence
of students in the BL setting.
Student technology readiness plays a stronger role in impacting the teaching presence
in BL environment than NBL environment.
Social presence has significant impacts on the other two presences.
© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International
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indicate if changes were made.
Geng et al. International Journal of Educational Technology in Higher Education
(2019) 16:17
https://doi.org/10.1186/s41239-019-0147-0
Introduction
Blended Learning (BL) creates a rich educational environment enabling various forms
of communication by combining face-to-face learning with technologically enhanced
learning so that instruction and learning occur both in the classroom and online (Collis
& Moonen, 2012). A blended learning course lies between a continuum anchored at
opposite ends by entirely face-to-face and fully online learning environments (Rovai &
Jordan, 2004). The effective integration of the face-to-face and Internet Technology
(IT) components determines the quality of course design so that blended learning is
not just an add-on to the existing dominant approach or method (Garrison & Kanuka,
2004). In the higher education context, the interaction and sense of engagement in a
community provide the conditions for free and open dialogue, critical debate, nego-
tiation and agreement, which are the hallmark of effective education (Garrison &
Cleveland-Innes, 2005). The Community of Inquiry (CoI) framework is widely used in
online learning research and pedagogy for enriching studentslearning experience
(Annand, 2011). The three presences in the CoI framework, social presence, cognitive
presence, and teaching presence integrally promote social and intellectual interactions
among participants and materials and, thereby, fruitful learning outcomes (Annand,
2011; Garrison, Anderson, & Archer, 2000). The three presences also offer a convenient
instrument with three dimensions to assess the studentsperceptions of the learning
experience and reflect their learning effectiveness.
In the online learning scenarios, where the structure of an online curriculum is
mostly automatic (Khan, 2009), students have more flexibility in deciding when, how
and with what content and activities they engage (Milligan & Littlejohn, 2014). This
flexibility requires students to monitor and adjust their behaviour and actions con-
cerning the specific learning context (Zimmerman, 2000). Students are aware of their
learning responsibility in themselves instead of an external source, such as a teacher
(Demir, 2015). A self-directed learner tends to actively engage in the learning processes,
such as acquiring information, planning and evaluating the learning activities. Active
learning strategies can increase studentsparticipation and improve the learning
process and performance (Freeman et al., 2014; Yilmaz, 2016). However, not much
empirical evidence is available in the extent literature regarding the impact of
self-direct learning in the blended learning setting.
Technology readiness is another critical dimension connected with studentslearning
in the blended learning environment. The emergence of various computer technologies
enables the usage of multimedia content and multimedia communication (Horton,
2006) for education, and provides anywhere, anytime access to the learning content.
Existing studies have been focused on studentsadoption of learning technologies and
the determinant factors, for instance, personal innovation, perceived usefulness,
performance expectancy, effort expectancy, social influence, perceived playfulness,
self-management of learning, using the Technology Acceptance Model (TAM) and
Unified Theory of Acceptance and User of Technology (UTAUT) (Liu, Li, & Carlsson,
2010; Wang, Wu, & Wang, 2009). Studentstechnology readiness refers to their
propensity to embrace new technologies for accomplishing goals in learning (Parasuraman,
2000). Studies on e-learning readiness found that studentslevel of e-learning readiness can
influence of level of success in e-learning (Moftakhari, 2013;Piskurich,2003). Today, most
university students are digital natives and use technology well (Prensky, 2001). However,
Geng et al. International Journal of Educational Technology in Higher Education (2019) 16:17 Page 2 of 22
the utilisation of learning technologies combined with traditional in-class teaching is still a
developing teaching approach for university instructors and students, and predictors of
learning effectiveness remain unclear (Hao, 2016).
Besides, though there are some research works on technology enhanced learning, there
exists no well agreed results. Studies have shown different results which contains positive
relationship, negative relationships, and no significant relationships between using the
internet for course material and student learning outcome (Gulek & Demirtas, 2005;
Shouping & Kuh, 2001;Sana,Weston,&Cepeda,2013).
To fill this gap, it necessary to explore the impact of technology readiness and
individual behaviour on academic performance in the blended learning context. While
motivation is one of the success factors for online learning (Lim, 2004), for its signi-
ficant impact on learner attitudes and learning behaviour in traditional educational
environments (Fairchild, Jeanne-Horst, Finney, & Barron, 2005).
The purpose of this paper is two-folded. First, grounded on the Theory of Planned
Behavior (TPB) which posits individual behaviour is driven by behaviour intentions,
and social cognitive theory, this study explores the deeper connections between
self-directed learning, technology readiness, and student motivation, to understand
their integral effects on the three presences of CoI (teaching, cognitive and social), thus
expanding the literature in blended learning research and examining its influencing
factors which have not been sufficiently explored.
Second, considering the lack of studies addressing the interdependencies in different
settings, our study compares the interdependences in blended learning and non-blended
learning, with the aim to provide empirical evidence and insights for instructors to adopt
a proper instructional strategy in online, and offline teaching.
The subsequent sections of this paper discuss the related literature supporting the
proposed research model. The research hypotheses and data collection method are then
presented. The results and findings are reported, and conclusions are drawn.
Literature review
Blended learning
Blended Learning (BL) integrates face-to-face learning with online learning and enables
asynchronous teaching and learning (Graham, 2013). Littlejohn and Pegler (2007)used
strongand weakblends to indicate the various amounts of e-learning. Through a
variety of online learning technologies, such as online discussion forums, BL enables
communication among learners and between learners and teachers. Effective integration
of traditional classroom teaching with e-learning provides support to asynchronous and
cooperative learning among students. Achieving a balance between classroom and online
learning is necessary as students still value the face-to-face opportunities to receive
feedback in BL setting (Vanslambrouck, Zhu, Lombaerts, Philipsen, & Tondeur, 2018).
There have been a number of studies carried out on the adoption of effective educational
technology (Findik & Ozkan, 2013;Mtebe&Raisamo,2014). Graham et al. (2013) identifies
that strategy, structure, and support are three key factors for BL adoption. Challenges to
the design of effective BL course have been classified by Boelens, De Wever, and Voet
(2017) into four types, which includes incorporating teaching flexibility, facilitating students
interaction, facilitating learning process, and fostering affective learning climate. Porter,
Geng et al. International Journal of Educational Technology in Higher Education (2019) 16:17 Page 3 of 22
Graham, Bodily, and Sandberg (2016) found that innovation adoption strategy affects how
institutional strategy, structure, and support decisions facilitate or impede BL adoption.
Besides innovation adoption strategy, institution blocks, teachers and students also deter-
mine the key factors promoting successful BL. Institutional blocks including organisational
readiness, adjacent technical resources, motivated faculty, professional development for
teachers and studentsmaturity and readiness for blending learning are all concerns
(Tabor, 2007;Vaughan,2007). Therefore, the learning experience of students in BL
courses is presumed to be influenced by a different set of factors from traditional classes.
Theory of planned behaviour and social cognitive theory
The Theory of Planned Behavior (TPB) argues that individual attitude toward the behaviour
can determine individual behaviour (Ajzen, 1991). Social Cognitive Theory (SCT) explains
human behaviour through three interacting determinants: cognitive, affective and biological
events; environment, and behaviour (Compeau & Higgins, 1995). TPB and SCO are widely
applied in studies to explain individual behaviour related to technology use (Barnard-Bark,
Burley, & Crooks, 2010; Compeau & Higgins, 1995).
Personal factors such as own intentions and attitudes are the main focus of this study.
Studentsself-directed learning here refers to studentsperceptions of their independent
learning, their sense of responsibility in their learning and their initiative in learning.
Self-directed learning shares some common features with self-regulated learning. Broad-
bent (2017) found that self-regulated learning has different predictive value among online
learners and BL learners. Technology readiness refers to individual attitudes toward new
technologies. Studentsperceptions in CoI to a certain extent reflect the learning effective-
ness or learning experience of students in a course. Based on TPB and SCT, self-directed
learning and technology readiness are postulated to be able to differently drive the
studentslearning behaviour, with different learning experience and perceptions of CoI.
Community of inquiry
A community is essential to support collaborative learning. The framework of the
Community of Inquiry (CoI) developed by Garrison et al. (2000) provides necessary
guidance for the employment of instructional technologies to support the BL environ-
ment. There are three dimensions in CoI framework, which include social presence,
cognitive presence, and teaching presence. Social presence represents the ability of
learners to behave socially and emotionally. The student group cohesiveness and inter-
action is strongly correlated with the learning outcomes (Hwang & Arbaugh, 2006;
Williams, Duray, & Reddy, 2006), which are essential in a BL design. Cognitive presence
refers to the extent that learners can absorb meaning in the process of reflection and
discourse. Cognitive presence involves practical inquiry (Garrison & Arbaugh, 2007),
interaction and critical thinking skills of the participants (Duphorne & Gunawardena,
2005). Teaching presence refers to the design, facilitation and direction of student
learning and thinking processes (Garrison et al., 2000). Studentssense learning commu-
nity and satisfaction are influenced by pedagogical design of BL course (Shea, Li, Swan, &
Pickett, 2005), and particularly teaching presence (Arbaugh, 2007).
These three presences are closely interconnected (Akyol & Garrison, 2008;Sheaet
al., 2010). Teaching presence makes the student become more actively thinking about
Geng et al. International Journal of Educational Technology in Higher Education (2019) 16:17 Page 4 of 22
the learning content and involvement in student learning discussion, thus improves
cognitive and social presences (Ke, 2010). Social presence can also predict student cog-
nitive presence (Archibald, 2010). However, the interrelationships are dynamic in a dif-
ferent learning setting between the three presences and requires further exploration.
Self-directed learning and learning effectiveness
Self-directed learning (SDL) refers to the psychological processes of learners that
purposively direct themselves to gain knowledge and understand how to solve problems
(Long, 1994). Self-directed learners usually more actively participate in learning tasks
such as reading online learning material, completing classroom tasks, planning and
evaluating milestones of learning. High-level self-management is important in SDL and
learners to need to adopted different strategies in dealing with various problems (Lee &
Teo, 2010). Similar to self-regulated learning, SDL also emphasises on goal setting and
choice making, which are crucial to student collaborative learning (Gilbert & Driscoll,
2002). The difference between SDL and self-regulated learning lays in their required
skills. The constructs of SDL are at the macro level, and constructs of self-regu-
lated learning belong to micro-level (Jossberger, Brand-Gruwel, Boshuizen, & Wiel,
2010).
Self-directed learners tend to search the online learning platform for resources.
Research on self-directed learning with technology (SDLT) (Teo et al., 2010) revealed
that studentsperceptions of collaborative learning can enhances students SDL. Student
SDL processes contribute to the use of Internet communication technology for colla-
borative learning (Lee, Tsai, Chai, & Koh, 2014).Theroleofself-regulatedlearning dis-
cussed in the CoI framework was found to be positively related to students
perceivedCoIandaffectiveoutcomes(Cho,Kim,&Choi,2017;Garrison&Akyol,
2015). Learners that are skilled at SRL will visit course materials more frequently
(Kizilcec, Pérez-Sanagustín, & Maldonado, 2017). Despite that existing studies
reveals of impact of SDL on learning effectiveness, how SDL enhances or under-
mine studentsperception of CoI remains unexplored.
Technology readiness and learning effectiveness
Technology-readiness refers to ones willingness to leverage new technologies in
performing tasks (Parasuraman, 2000). Web-based technologies, though well estab-
lished, still face the challenge of being readily accepted when introduced to a new
application setting. Compared to traditional classroom learning, studentsreadiness to
accept and utilise web-based learning resources varies across individuals. Students
attitude toward technology-based applications reflects their technology readiness in
the learning scenarios. Cheon, Lee, Crooks, and Song (2012) found that college
studentsattitude positively influences their intention to adopt mobile learning. For
the blended learning context, using online learning sources is compulsory. Otherwise,
it will be not possible to get the desired learning result.
As mentioned earlier, the use of learning technologies has different impacts on
studentslearning outcomes which may be caused by contextual and cognitive factors
(Hong, Hwang, Liu, & Chen, 2014). BL environment was found to increase student
attendance and learning satisfaction in science education (Stockwell, Stockwell,
Geng et al. International Journal of Educational Technology in Higher Education (2019) 16:17 Page 5 of 22
Cennamo, & Jiang, 2015). Moreover, using online course material can enhance student
intellectual development (Shouping & Kuh, 2001). On the other hand, some students
reported that their course grades decrease as they spend too much time on online
course material. These diverse research results reveal the interest and importance of
exploring the readiness for learning technologies and its influences on studentspercep-
tions and behaviours. Parasuraman (2000) developed and validated a measurement
scale, called the Technology Readiness Index (TRI) for technology readiness, which
consisted of 28 items, clustered into four categories: optimism, innovativeness, dis-
comfort, and insecurity. These four categories integrally reflect the individual attitude
toward new technologies in the learning process.
Learning motivation
Learning motivation is the process whereby goal-directed activity is instigated and
sustained, and it is reflected in personal investment and in cognitive, emotional, and
behavioural engagement in learning activities (Fredricks, Blumenfeld, & Paris, 2004).
Research on studentslearning reveals that self-efficacy and goal settings are highly related
to learning motivation (Che-Ha, Mavondo, & Mohd-Said, 2014; Law & Breznik, 2017;
Law, Lee, & Yu, 2010;Ngan&Law,2015). Motivation is an essential factor in the comple-
tion of both online and in-class learning activities. Although various educational research
emphasises on learning motivation, its relationships between self-directed learning and
technology readiness have not been sufficiently explored in the blended learning setting.
Research questions
The BL environment offers a different setting with multiple media for teaching, commu-
nication, discussion and evaluation. The evidence from the existing literature highlights
the importance of self-monitored learning behaviours and technology readiness in the
online learning environment. The balance between online learning and in-class learning is
relatively hard to achieve and one of the challenges for BL course design. Exploring the
impacts of self-directed learning and technological readiness on studentsmotivation and
perceptions of CoI can deepen the understanding of blended learning course pedagogy
design. The comparison between blended learning and non-blended learning course
students can further provide insights into special needs and behaviours of students in a
blended learning environment. Therefore, the research questions of this study are:
Q1. Is there difference between students attending BL courses and student attending
traditional classroom course in their perception of CoI?
Q2. Do self-directed learning and technology readiness have equal influences on students
motivation and perception of CoI between BL and traditional classroom course?
Methodology
Research hypotheses
A concept model is proposed, as illustrated in Fig. 1, which presents hypothesised
relationships between self-directed learning, technology readiness, motivation and
studentsperceptions of CoI. The conceptual model is also applied to examine the
Geng et al. International Journal of Educational Technology in Higher Education (2019) 16:17 Page 6 of 22
differences of interrelationships between BL student groups and non-BL student
groups. In this section, hypotheses are developed in response to our research questions.
Impacts of self-directed learning
Self-directed learners actively engage in the learning process and can adopt proper
learning strategies according to the learning setting. A technology-rich learning
environment can provide students with great opportunities and abilities to be self-
directed in their learning (Fahnoe & Mishra, 2013). The blended learning teaching
context offers students opportunities to interact with instructors and classmates
face-to-face through discussion and self-controlled access to multimedia learning
content. Self-directed aspects of learning (the choice of what, when, and how long
to study) have significant repercussions in the effectiveness of userslearning
efforts (Tullis & Benjamin, 2011). Facing uncertainties in the online learning
context, students need to adjust or formulate their own best learning strategies. It
is anticipated that highly self-directed learners are involved in learning activities
online more actively by asking questions and joining in discussions, thus have a
stronger sense of CoI than students with low self-directedness. Self-directed
students also have a stronger willingness to achieve learning goals. Therefore, we
put forward the following hypotheses:
H1.Student self-directed learning readiness positively correlates with students
perception of CoI
H2. Self-directly learning positively correlates with learning motivation
Impacts of technology readiness
Students with higher levels of technology readiness hold a positive attitude toward
technological learning media and innovative platforms for communication. Students
with a sense of discomfort and insecurity in adopting technologies may take a longer
Learning
motivation
Self-directed
learning
readiness
Technology
readiness
Cognitive
presence
Social
presence
Teaching
presence
Perceptions of CoI
Fig. 1 Conceptual structural model
Geng et al. International Journal of Educational Technology in Higher Education (2019) 16:17 Page 7 of 22
time to become efficient users of online learning platforms. The blended learning
context requires students to complete the online learning tasks together with
in-class learning activities. Student factors such as self-efficacy in using the computer,
motivation toward t-learning are efficient to fulfil the online course prerequisites, (Demir,
2015;Hao,2016;Moftakhari,2013). Previous studies have evaluated studentsreadiness
for specific learning technologies or platforms (Cheon et al., 2012;Shouping&Kuh,
2001). Studentsattitude toward the broad collection of new technology products includes
optimism, innovativeness, discomfort, and insecurity (Parasuraman & Colby, 2015).
Students with optimism and innovativeness toward learning technologies are more willing
to adopt the online learning strategy than students with discomfort and insecurity.
Therefore, we put forward following these hypotheses:
H3. Student Technology readiness positively correlates with studentsperception
of CoI
H4. Technology readiness positively correlates with learning motivation
Impacts of learning motivation
It is believed learning motivation can influence studentsattitudes and behaviour in
educational environments (Fairchild et al., 2005). In the online learning context,
strongly motivated students are more likely to watch videos and read the online
learning material compared to students who are less motivated. Thus, motivation is
mainly related to student learning effectiveness in the blended learning setting.
Therefore, our fifth hypothesis is:
H5. Student learning motivation correlates with studentsperception of CoI
Relationships between three presences
Students, who behave more socially and emotionally in mediated communication, can
interact with group members more efficiently, thus enhance the group cohesiveness.
Socially communication can also facilitate the communications between teachers,
platforms and students. In the interactions, students can develop critical thinking skills
to deal with various types of opinions and reflect on the learning content. Therefore,
we hypothesise that:
H6. Social presence positively correlates with cognitive and teaching occurrences
H6a. Social presence positively correlates with teaching presence
H6b. Social presence positively correlates with the cognitive presence
Blended learning context for engineering students
Various BL models have been reported to be useful in previous studies (Dziuban &
Moskal, 2001; Martyn, 2003). Blended learning undergraduate courses were designed
for engineering students in a university in Hong Kong. Engineering students are
expected to be more adaptive to practical situations according to their abilities (Law &
Geng, 2018). Individual differences in these learning attributes pose challenges to
engineering education whose aim is to provide instruction about real-worldengineering
Geng et al. International Journal of Educational Technology in Higher Education (2019) 16:17 Page 8 of 22
design and operations, provide training in critical and creative thinking skills, provide
graduates who are conversant with engineering ethics and connect between technology
and society (Felder, Woods, Stice, & Rugarcia, 2000). The engineering management
subjects, including innovation and entrepreneurship, and the business process are
developed with a mixture of collaborative learning, project-based learning (PBL),
team/peer learning, and independent learning. An online learning management sys-
tem (LMS), as shown in Fig. 2, was adopted and integrated with face-to-face
in-class teaching. The primary learning objectives of these engineering management
subjects are to prepare engineering students with basic understanding of engineering
management concepts and the relevant techniques, tools and skills, while the appli-
cation of knowledge and team skills are also emphasised. The LMS offers students
online learning materials, and chapter-end exercises according to the predefined
course outline. Students can, therefore, learn at their own pace. Videos of real-life
case studies are also available on the LMS, to provide students with further elabo-
ration on the learnt concepts.
Elements in this course, include classroom teaching, E-learning, workshop in class
(the practice of learnt knowledge, and peer learning on specific topics such appli-
cation of assessing and analytical techniques), group projects (peer learning, use of
knowledge, sharing of experience, and reflective learning). This arrangement of
blended components in the course is to keep students motivated and on-track,
while they can learn interactively (interactivity) in the classroom, collaboratively in
workshops and in group projects with peers. They would also be able to develop
good communication with both peers and teachers in person, or via various reflec-
tions. An agreed assessment plan also shows the learning progress of the students,
as well as to pinpoint the areas for improvement in the learning journey. To
summarise, the assessment consists of Individual assignments, tests and in-class
activities (50%), Group, peer learning projects (30%), Reflections on individual and
peer learning (20%).
Fig. 2 Interface of the LMS used for the blended learning subject
Geng et al. International Journal of Educational Technology in Higher Education (2019) 16:17 Page 9 of 22
Data collection
Instrument
A questionnaire was developed corresponding to the factors in our model (see Fig. 1),
using 5-point Likert scales (1 = strongly disagree; 5 = strongly agree). The questionnaire
has two parts, and the first part contains four scales from existing studies:
1) The learning motivation scale was used in Law and Geng (2018) on student
innovativeness and handedness.
2) Self-directed learning with technology (Lee et al., 2014; Teo et al., 2010) for measuring
young studentsperceptions of self-directed learning with the support of technology.
3) Technological readiness index (Parasuraman, 2000; Parasuraman & Colby, 2015)
measures peoples propensity to embrace and use new technologies in four
dimensions: optimism, innovativeness, discomfort, and insecurity.
4) Modified CoI instrument consists of teaching presence, social presence, and
cognitive presence (Arbaugh et al., 2008).
The second part consists of the personal particulars of respondents, such as gender, age,
discipline, and year of study. A pilot study was carried among ten volunteer students, to
confirm the validity of the questionnaire, before data collection. For the data collection,
we invited voluntary participation from students who were in the course. The data collec-
tion was carried out near the end of the semester. There were 96 valid samples received
from the Blended Learning (BL) student group out of 102 responses and 111 valid
samples received from Non-BL (NBL) student group out of 121 responses.
Measurement model estimation
Partial Least Squares (PLS) (Henseler & Sarstedt, 2013) was adopted to estimate the
proposed model (Fig. 1). The unidimensionality of six blocks of constructs (learning
motivation, self-directed learning readiness, technology readiness, social presence,
cognitive presence, teaching presence) and the results contained in the outer model
were firstly tested. Cronbachs alpha, Dillon-Goldsteims rho, Composite reliability and
AVE were used to check the unidimensionality.
As presented in Table 1, the Cronbachs alpha, Dillon-Goldsteims rho, Composite
reliability for all the constructs are above 0.70 (Sanchez, 2013). The AVE values are above
the threshold of 0.50 (Fornell & Larcker, 1981) among all the constructs. Therefore, the
construct validity of the measurements fulfil the requirement.
Table 1 Unidimensionality of constructs
Latent variable MVs Cronbachs
alpha
Dillon-Goldsteins
rho
Composite
reliability
AVE
Learning motivation (LM) 4 0.754 0.757 0.843 0.574
Self-directed learning
readiness (SDL)
3 0.703 0.713 0.834 0.626
Technology readiness (TRD) 4 0.814 0.825 0.878 0.643
Social presence (SP) 4 0.786 0.790 0.862 0.611
Cognitive presence (CP) 6 0.858 0.859 0.894 0.586
Teaching presence (TP) 5 0.842 0.850 0.888 0.613
Geng et al. International Journal of Educational Technology in Higher Education (2019) 16:17 Page 10 of 22
The outer weights, loadings and communality measures shown in Table 2demonstrate
the convergent validity as item loadings are higher than threshold (Factor loadings> 0.7,
communalities > 0.5). The discriminant validity condition was also fulfilled as square root
of the AVE for each construct is larger than its correlation with other construct as shown
in Table 3(Chin, 1998; Fornell & Larcker, 1981).
Results
Student demographics
A total of 102 engineering students participating in BL courses and 121 enginee-
ring students participating in non-BL courses filled in the questionnaire and pro-
vided a total of 207 valid answers. An overview of these participants is presented
in Table 4.
The results obtained using One-way ANOVA indicate that gender, year and age of
the students do not influence the results of six constructs. Therefore, participants were
treated as a single group in our analysis.
Table 2 The outer model estimation
Latent variable Manifest Variable Outer weight Factor loadings Communality
Learning motivation (LM) LM1 0.295 0.752 0.566
LM2 0.313 0.784 0.615
LM3 0.333 0.731 0.534
LM8 0.380 0.762 0.581
Self-directed learning
readiness (SDL)
SD4 0.400 0.781 0.610
SD6 0.481 0.826 0.682
SD9 0.380 0.765 0.585
Technology readiness (TRD) TRD1 0.282 0.720 0.518
TRD3 0.285 0.826 0.682
TRD4 0.314 0.805 0.648
TRD5 0.363 0.851 0.724
Social presence (SP) SP2 0.295 0.712 0.507
SP4 0.316 0.757 0.573
SP5 0.334 0.828 0.686
SP6 0.333 0.823 0.677
CP1 0.240 0.766 0.587
Cognitive presence (CP) CP2 0.208 0.756 0.572
CP3 0.202 0.801 0.642
CP4 0.222 0.802 0.643
CP5 0.206 0.733 0.537
CP6 0.229 0.731 0.534
Teaching presence (TP) TP1 0.212 0.708 0.501
TP2 0.258 0.766 0.587
TP3 0.239 0.782 0.612
TP4 0.280 0.836 0.699
TP5 0.283 0.816 0.666
Geng et al. International Journal of Educational Technology in Higher Education (2019) 16:17 Page 11 of 22
Difference between BL and NBL groups
The measurement item mean scores for learning motivation (LM), self-directed
learning (SDL), technology readiness (TRD), social presence (SP), cognitive presence
(CP), and teaching presence (TP), and their standard deviation among all participating
students in both BL and NBL group are presented in Table 5.
As seen from Table 5, the BL group students have higher mean scores for LM, TP, SP
than the NBL group. The NBL group has higher mean scores for SDL and CP than the
BL group. However, the difference between the mean scores of the BL and NBL groups
are minimal. Thus we performed statistical analysis to test the significance of the
difference. Independent sample t-testing at a significance level of 0.05 was carried
out. The results obtained are shown in Table 6.
The t-test results show that students participating in BL courses have sig-
nificantly higher levels of social presence than students attending non-BL classes
(p< 0.010). This result supports our hypothesis H1a. Students from NBL groups
show higher levels of technology readiness than students from the BL group (p<0.050).
No significant differences were found for cognitive and teaching presences
between the BL and NBL student groups. Thus hypotheses H1b and H1c are not
supported.
Table 3 Correlation between constructs
Cognitive
presence(CP)
Learning
motivation
(LM)
Self-directed
learning readiness
(SDL)
Social
presence
(SP)
Teaching
presence
(TP)
Technology
readiness
(TRD)
Cognitive
presence (CP)
0.765
Learning motivation (LM) 0.520 0.758
Self-directed
learning
readiness (SDL)
0.469 0.531 0.791
Social presence
(SP)
0.664 0.600 0.405 0.782
Teaching
presence (TP)
0.617 0.583 0.429 0.697 0.783
Technology
readiness (TRD)
0.315 0.410 0.417 0.323 0.423 0.802
Note: The boldface figures in the diagonal represent the square root of the AVE figures. They should be higher than the
correlation figures
Table 4 Demographic details of respondents
Blended Non-Blended Total
Gender Male 66 Male 64 130
Female 30 Female 47 77
Age 20 or below 47 20 or below 11 58
Above 20 49 Above 20 100 149
Year 2
nd
year 32 2
nd
year 2 34
3
rd
year 54 3
rd
year 16 70
4
th
year 10 4
th
year 93 103
Total 96 111 207
Geng et al. International Journal of Educational Technology in Higher Education (2019) 16:17 Page 12 of 22
Table 5 Overall statistical results
Mean Score S.D. BL Mean S.D. NBL Mean S.D.
Learning motivation 3.630 0.532 3.636 0.541 3.624 0.526
LM1 3.780 0.748 3.725 0.773 3.826 0.727
LM2 3.807 0.719 3.814 0.714 3.802 0.726
LM3 3.561 0.785 3.608 0.785 3.521 0.786
LM4 3.623 0.760 3.676 0.747 3.579 0.772
LM5 3.637 0.799 3.696 0.755 3.587 0.833
LM6 3.395 0.868 3.402 0.824 3.388 0.907
LM7 3.650 0.813 3.588 0.813 3.703 0.813
LM8 3.565 0.840 3.559 0.765 3.570 0.902
Self-directed learning 3.496 0.584 3.426 0.524 3.555 0.627
SD1 3.520 0.915 3.441 0.929 3.587 0.901
SD2 3.260 0.802 3.294 0.752 3.231 0.844
SD3 3.238 0.712 3.196 0.745 3.273 0.683
SD4 3.466 0.793 3.412 0.825 3.512 0.765
SD5 3.574 2.766 3.324 0.760 3.785 3.684
SD6 3.552 0.769 3.431 0.777 3.653 0.750
SD7 3.601 0.837 3.569 0.802 3.628 0.867
SD8 3.507 0.782 3.490 0.728 3.521 0.828
SD9 3.570 0.725 3.490 0.754 3.636 0.695
SD10 3.668 0.715 3.608 0.692 3.719 0.733
Technology readiness 3.569 0.515 3.562 0.587 3.574 0.448
TRD1 3.794 0.778 3.716 0.813 3.860 0.745
TRD2 3.798 0.704 3.784 0.766 3.810 0.650
TRD3 3.592 0.771 3.559 0.896 3.620 0.649
TRD4 3.462 0.868 3.500 0.941 3.430 0.804
TRD5 3.507 0.848 3.431 0.939 3.570 0.762
TRD6 3.399 0.879 3.324 0.903 3.463 0.857
TRD7 3.619 0.743 3.608 0.810 3.628 0.685
TRD8 3.363 0.848 3.559 0.815 3.198 0.843
Social presence 3.535 0.520 3.559 0.579 3.515 0.467
SP1 3.534 0.709 3.500 0.728 3.562 0.694
SP2 3.700 0.640 3.755 0.681 3.653 0.602
SP3 3.592 0.716 3.647 0.779 3.546 0.658
SP4 3.668 0.709 3.618 0.797 3.711 0.625
SP5 3.404 0.810 3.422 0.789 3.388 0.830
SP6 3.314 0.805 3.412 0.813 3.231 0.793
Cognitive presence 3.542 0.576 3.518 0.611 3.562 0.548
CP1 3.543 0.733 3.539 0.792 3.546 0.683
CP2 3.498 0.753 3.382 0.784 3.595 0.714
CP3 3.556 0.797 3.510 0.829 3.595 0.770
CP4 3.583 0.754 3.529 0.792 3.628 0.720
CP5 3.525 0.776 3.490 0.767 3.554 0.785
CP6 3.520 0.770 3.598 0.721 3.455 0.806
CP7 3.570 0.845 3.578 0.826 3.562 0.865
Geng et al. International Journal of Educational Technology in Higher Education (2019) 16:17 Page 13 of 22
PLS modelling results
PLS modelling was carried out among the BL group students and the NBL group
students separately. The relationship between learning attributes is graphically
presented in Fig. 3a and b. The statistical testing results are reported in Tables 7
and 8. All path coefficients between the latent variables in the models are positive,
which indicate the positive relationships between each pair of connected factors.
Both direct and indirect relationships are examined in the structural model, and
the results are also included in Tables 7and 8. Although PLS path modelling does
not provide a widely acceptable global model fit (Chin, 1998; Sarstedt, Ringle, &
Gudergan, 2017), we can still assess the model fit by using the Standardized Root
Mean Square Residual (SRMR) and Chi-square methods to the degree of freedom
(x
2
/df). If SRMR value is less than 0.10 or 0.08, the model fitness is considered as
good(Sarstedtetal.,2017). If x
2
/df is less than 5 and larger than 2 when the
sample size is larger than 200, the modelling result is considered to be satisfactory
(Hafiz & Shaari, 2013). For both structure models (BL and NBL) in this study, the
SRMR values are less than 0.08 (BL_SRMR = 0.066, NBL_SRMR = 0.079). The x
2
/df
values are also within satisfactory range (NBL_ x
2
/df =2.385, BL_ x
2
/df =4.246).
Justification of the hypotheses
An overview of the statistical test results of the hypothesised relationships is presented
in Table 9. The different results between the BL and NBL student groups are
highlighted.
Table 5 Overall statistical results (Continued)
Mean Score S.D. BL Mean S.D. NBL Mean S.D.
Teaching presence 3.611 0.861 3.733 1.032 3.507 0.671
TP1 3.713 0.799 3.706 0.765 3.719 0.829
TP2 3.579 0.828 3.696 0.742 3.479 0.886
TP3 3.498 0.890 3.627 0.832 3.388 0.925
TP4 3.704 2.843 3.618 0.821 3.446 0.885
TP5 3.561 0.780 3.627 0.730 3.504 0.818
Table 6 BL group and NBL group mean comparison
Variable Blended Non-Blended Mean
diff.
F-
value
Sig.
Mean score Mean score
Self-directed learning readiness 3.426 3.555 0.129 0.003 0.956
Technology readiness 3.562 3.574 0.0.12 6.100 0.014*
Social presence 3.559 3.515 0.044 9.363 0.002**
Cognitive presence 3.518 3.562 0.044 1.377 0.242
Teaching presence 3.733 3.507 0.226 1.175 0.280
Learning motivation 3.636 3.624 0.012 0.255 0.614
ns Not significant
** p< 0.010, *p< 0.050
Geng et al. International Journal of Educational Technology in Higher Education (2019) 16:17 Page 14 of 22
Learning
motivation
Technology
readiness
Cognitive
presence
Social
presence
Teaching
presence
0.343***
0.473***
0.360***
0.544***
0.520***
0.567***
0.177*
Self-directed
learning
readiness
Learning
motivation
Self-directed
learning
readiness
Technology
readiness
Cognitive
presence
Social
presence
Tea chi ng
presence
0.113n.s.
0.506***
0.175*
0.449***
0.516***
0.131n.s.
0.292**
0.187**
a
b
Fig. 3 aPLS result for engineering students in the BL group. bPLS result for engineering students in the
NBL group
Table 7 Structural path coefficients for the BL group
Cognitive
presence (CP)
Social
presence (SP)
Teaching
presence (TP)
Learning
motivation (LM)
Direct Indirect Direct Indirect Direct Indirect Direct Indirect
Learning motivation (LM) 0.258*** 0.473*** 0.246***
Self-directed learning readiness (SDL) 0.343*** 0.146*** 0.268*** 0.139*** 0.567***
Social presence (SP) 0.544*** 0.520***
Technology readiness (TRD) 0.046n.s. 0.084n.s. 0.084*** 0.043n.s. 0.177*
ns Not significant
*** p<0.001, *p<0.050
Geng et al. International Journal of Educational Technology in Higher Education (2019) 16:17 Page 15 of 22
Discussion
The structural models of the BL and NBL groups reveal different patterns of inter-
relationships between the learning attributes and the three presences. Both
modelling results highlight the critical roles of self-directed learning, technology
readiness, and learning motivation in influencing the learning effectiveness in both
BL and NBL settings, and imply how the BL setting can be further modularised
for various themes and educational purposes.
Table 8 Structural path coefficients for NBL group
Cognitive
presence (CP)
Social
presence (SP)
Teaching
presence (TP)
Learning
motivation (LM)
Direct Indirect Direct Indirect Direct Indirect Direct Indirect
Learning motivation (LM) 0.455*** 0.506*** 0.187** 0.679***
Self-directed learning
readiness (SDL)
0.113n.s. 0.101n.s. 0.172n.s. 0.137n.s. 0.131n.s.
Social presence (SP) 0.449*** 0.516***
Technology readiness (TRD) 0.184* 0.312** 0.175* 0.248** 0.292**
ns Not significant
*** p< 0.001, ** p< 0.010, *p< 0.050
Table 9 Hypotheses testing results
Hypotheses BL group NBL Group
H1.Student self-directed learning readiness positively correlates
with studentsperception of CoI
H1a.Student self-directed learning readiness positively correlates
with student teaching presence
Not support Not support
H1b.Student self-directed learning readiness positively correlates
with student cognitive presence
Support Not support
H1c.Student self-directed learning readiness positively correlates
with student social presence
Not Support Not
Support
H2.Self-directly learning positively correlates with learning motivation Support Not support
H3.Student Technology readiness positively correlates with students
perception of CoI
H3a.Student Technology readiness positively correlates with
student teaching presence
Support Support
H3b.Student Technology readiness positively correlates with
student cognitive presence
Not support Not Support
H3c.Student Technology readiness positively correlates with
student social presence
Not support Not Support
H4.Technology readiness positively correlates with learning
motivation
Support Support
H5.Student learning motivation correlates with students
perception of CoI
H5a.Student learning motivation correlates with students
perception of teaching presence
Not support Support
H5b.Student learning motivation correlates with students
perception of cognitive presence
Not support Not Support
H5c.Student learning motivation correlates with students
perception of social presence
Support Support
H6. Social presence positively correlates with the cognitive and
teaching presences
H6a. Social presence positively correlates with teaching presence Support Support
H6b. Social presence positively correlates with the cognitive presence Support Support
Geng et al. International Journal of Educational Technology in Higher Education (2019) 16:17 Page 16 of 22
The three presences in BL settings compared to NBL settings
Students in the BL group achieve significantly higher social presence than students in
the NBL group. This result indicates that the BL setting surpassed traditional
face-to-face teaching setting in socially involving students. The BL course setting
provides an open communication environment for students, which allows the students
to express themselves socially and emotionally through communication (Garrison et al.,
2000). Students can interact with each other and with teachers through online learning
platforms besides traditional face-to-face discussion. Social presence provides the
cohesion to sustain studentsparticipation and focus. It also creates a sense of be-
longing, supporting freedom of expression. Therefore, a proper BL setting creates a
cohesive community and enhances collaborations between students. The results also
support that students in blended courses have higher levels of sense of community
than complete online course (Rovai & Jordan, 2004). The BL setting offers more
all-rounded learning facilitation to assist with studentsdifferent learning scenarios.
From the results of our study, social presence positively enhances teaching presence
and cognitive presence, as shown in the structural models (Fig. 3a and b), confirming
the close interrelationships among the presences (Akyol & Garrison, 2008; Garrison
Cleveland-Innes, & Fung, 2010; Shea et al., 2010). Social presence is found to have a
direct effect on the cognitive presence (Shea & Bidjerano, 2009), whereas teaching pre-
sence does not have a direct relationship with the cognitive presence in the BL setting.
Cognitive presence allows students to have reflect on their interpretations (Garrison et
al., 2000). The communication among student group members during collaborative ac-
tivities contribute to studentssystematic and critical thinking, which is the hallmark of
effective higher education. Instructor expertise, instructor support, and students
self-efficacy influence student satisfaction (Diep, Zhu, Struyven, & Blieck, 2017). In the
BL setting, where instructional technologies are in use, the roles of instructors to
organise the course, facilitate the discourse, direct the cohesion are overwhelmed by
the technology-enhanced learning media. This explains the weakened influence of the
teaching presence on cognitive presence.
Attributes determining learning effectiveness in BL and NBL settings
Self-directed learning and cognitive presence
Self-directed learning has significant and direct impacts on the cognitive presence of
students in the BL setting, while it does not have a direct impact on the cognitive
presence in the NBL setting. In the BL setting, students are expected to direct themselves
in learning on the online platforms, whereas teachers in the face-to-face NBL setting lead
them. Enhancing student ability to control and to direct for understanding helps students
learn more actively in exploring course content and ideas. The BL setting allows students
to construct and confirm meaning through reflection on their own. In the NBL setting,
teachers play the role of directing, explaining, and pace controlling, which makes the
learning effectiveness less dependent on student self-directed behaviour.
Self-directed learning, technology readiness and learning motivation
Self-directed learning and technology readiness have a positive influence on learning
motivation in BL, whereas in the NBL learning environment only technology readiness
Geng et al. International Journal of Educational Technology in Higher Education (2019) 16:17 Page 17 of 22
influences learning motivation. The results imply that students who are more
self-directed and with active attitudes toward technology-based products are more
motivated in adopting online learning strategies and achieving their learning goals. In
the NBL setting, learning motivation is influenced by technology readiness, but not
self-directed learning. This implies that web-based learning technology can be a com-
plementary extension of the traditional classroom teaching for inducing self-directed
learning effects which in return, can influence learning motivation. It is therefore
meaningful to integrate and optimise online and offline course design to reduce
studentsdifficulty in adopting the learning technologies, with the aim of enhancing
student learning motivation.
Learning motivation, teaching presence and social presence
Learning motivation is found positively influencing the social presence in both the BL
and NBL teaching environments, where learning motivation represents the personal
goal orientation that a student brings to a course of study (Lynch & Dembo, 2004).
Students with stronger learning motivation will engage more in the learning process and
discuss more with group members for the idea discussion and content understanding.
This explains the positive influence of learning motivation on teaching presence in both
the BL and NBL setting.
Technology readiness and teaching presence
Technology readiness plays a more important role in influencing teaching presence in
the BL learning environment than the NBL learning environment while both are
statistically significant. Studentsintention to adopt web-based learning technologies
determines studentsattitude to learning behaviour and perceived behavioural control.
Students who are readier to adopt the web-based learning approach understand the
online and offline course design better and are more aware of teaching presence while
teaching presence is critical to the course and facilitation design. Our results, therefore,
provide implications that course designers need to consider technology readiness when
adopting BL teaching approach, for more effective teaching presence.
Conclusions and future study
In this study, we investigate the roles of self-directed learning, technology readiness,
and student motivation in BL and NBL settings and their impacts on students per-
ception of the three presences from the CoI framework. The results show that the BL
environment is better than the NBL environment in providing learning facilitation. The
results from structural modelling imply that self-directed learning plays a vital role in
influencing the cognitive presence, while in the NBL environment it does not. Course
designers and instructors shall recognise the value of fostering studentsself-directed
learning in a more flexible learning context. The impact of social presence on the other
two presence indicates the importance of emotionally and socially engaging students in
the learning process in both online and offline learning scenarios. Technology readiness
has a stronger positive influence on teaching presence in the BL setting compared to
the NBL setting. Prior training or briefing of learning technologies or platforms would
potentially improve studentsperception of teaching presence.
Geng et al. International Journal of Educational Technology in Higher Education (2019) 16:17 Page 18 of 22
Limitation of study
Though the sample size was not that big due to the restricted enrolment number for
the BL classes and was only offered to a selected group of students of the same
background for the better control of the experiment. Given the above constraints, and
with a systematic controlled setting, the sample sizes of 102 and 121 of BL and non-BL
students respectively, is considered acceptable for providing insights for the specific study.
We expect to extend the study to more selected BL classes further. Due to the
resource limit of this study, other types of evidence, for instance, the studentssystem
usage data, are not incorporated here.
Contributions
The findings in our study reveal the impacts of self-directed learning, technology
readiness and learning motivation on the learning effectiveness in the blended learning
environment and the non-blended learning environment. This study expands the
literature in blended learning and its influencing factors which have not been sufficiently
explored. By comparing the interdependences in different learning settings, our study
provides empirical evidence and insights for educators for proper instructional strategy
adoption in both online and offline teaching, to enhance the perceived social, teaching,
and cognitive presences leading to improved learning outcomes.
Acknowledgements
The publication of this paper is supported by the Natural Science Foundation of China (Grant Nos. 71571120).
Professor Ben Niu is the second corresponding author of this paper.
Funding
This study is supported by the Natural Science Foundation of China (Grant Nos. 71571120).
Availability of data and materials
No data is available.
About the authors
Dr. Shuang Geng is currently a Postdoctoral Researcher at School of Management, Shenzhen University. She obtained
her PhD degree at the System Engineering and Engineering Management department of City University of Hong
Kong. Her research interests include workplace knowledge recommendation, organizational learning, and project
management in the context of Chinas electronics manufacturing industry, designing and developing online learning
systems for higher education, and tracking and analyzing educational data. Her research papers appear in Project
Management Journal,The Organizational Learning,Knowledge Management: An International Journal,International
Journal of Technology and Design Education.
Dr. Kris Law is currently an Associate Professor at the School of Engineering, Deakin University, Australia. Prior to her
joining Deakin University, she was a lecturer at the Department of Industrial and Systems Engineering, Hong Kong
Polytechnic University. She currently also holds a Docentship (adjunct professorship) in the Department of Industrial
Engineering and Management, Oulu University in Finland. Her expertise lies in Organizational Learning and Development,
Technology and Innovation Management, Technology-based Entrepreneurship, Project Management and Engineering
Education.
Dr. Law undertook a post-doctoral research scholarship and was a visiting researcher at the Graduate Institute of
Industrial Engineering, National Taiwan University (20092011).
Professor Ben Niu is currently working at Management Science department at School of Management, Shenzhen
University. He used to be Visiting Professor of Arizona State University, Hong Kong University, Hong Kong Polytechnic
University, China The Academy of Sciences, Victoria University of Wellington, New Zealand. He has been granted 5
national natural science funds, published more than 100 academic papers, and published 3 books. His research interests
include big data analysis and processing, learning recommendation systems, entrepreneurship education, financial
engineering and business intelligence, swarm intelligence theory and application, image processing, feature extraction,
artificial intelligence.
Authorscontributions
KMYL carried out the empirical investigation and SG wrote the first draft of the manuscript. KMYL and SG participated
in designing the empirical investigation protocol, structure and review the manuscript. BN participated in finalizing the
draft. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Geng et al. International Journal of Educational Technology in Higher Education (2019) 16:17 Page 19 of 22
PublishersNote
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Author details
1
College of Management, Shenzhen University, Shenzhen, China.
2
School of Engineering, Deakin University, Geelong,
Australia.
Received: 20 December 2018 Accepted: 25 April 2019
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... Previous literature by Nimavat et al. (2021) supported our findings, elaborating that with convenient access to recorded material along with a comfortable learning environment, undergraduates experienced more significant academic achievement during the pandemic. This is further substantiated by Geng et al. (2019), who showed that based on the Theory of Planned Behaviour (TPB), remote learning enables students to learn at any time, encouraging self-directed learning. TPB proposes that an individual's behaviour is determined by his intentions, beliefs and subjective norms (Ajzen, 1991). ...
... This can be achieved by ensuring their remote learning infrastructure is periodically updated with an extensive array of modern teaching materials presented in different interactive mediums that cater to the changing individual needs of undergraduates (Coman et al., 2020;Van Wart et al., 2020). The attitudes and subjective norms of undergraduates of online education depend on its success in the long term that is if online education successfully sustains their continuity of learning and academic progress, they will be more inclined to use online education (Geng et al., 2019). ...
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... 8 This is an explicit problem as findings in the literature indicate the need for metacognitive skills (Flavell 1979) and that students have the potential to develop these skills with the necessary support (Callender, Franco-Watkins & Roberts 2016;Schraw 1998). Moreover, Geng, Law and Niu (2019) also referred to the limited empirical research in the field of BL in support of self-direct learning. ...
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The existing literature suggests that self-regulated learning (SRL) strategies are relevant to student grade performance in both online and blended contexts, although few, if any, studies have compared them. However, due to challenges unique to each group, the variety of SRL strategies that are implicated, and their effect size for predicting performance may differ across contexts. One hundred and forty online students and 466 blended learning students completed the Motivated Strategies for Learning Questionnaire. The results show that online students utilised SRL strategies more often than blended learning students, with the exception of peer learning and help seeking. Despite some differences in individual predictive value across enrolment status, the key SRL predictors of academic performance were largely equivalent between online and blended learning students. Findings highlight the relative importance of using time management and elaboration strategies, while avoiding rehearsal strategies, in relation to academic subject grade for both study modes.
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