ChapterPDF Available

Abstract

This chapter begins by looking broadly at learning as a process of knowledge construction and the increasing role of digital technologies in this process within tertiary education contexts. This is followed by an introduction to online learning along with definitions, discussion of foundational online learning concepts and contemporary pedagogical approaches used in online learning environments. Next, the reasons why motivation is an essential consideration in online teaching and learning contexts are explored. Then, existing research into motivation to learn in online environments is discussed in light of contemporary theoretical motivation frameworks. Finally, self-determination theory (SDT)—an intrinsic-extrinsic theory of motivation—is discussed in detail. In particular, the continuum of human motivation that outlines a range of different types of extrinsic motivation and the underlying psychological concepts of autonomy, competence and relatedness that SDT is built on are discussed. In doing so, justification for the use of SDT as the conceptual framework for this work is provided.
Chapter 1
The importance of motivation in online learning
Abstract This chapter begins by looking broadly at learning as a process of knowledge construction and the
increasing role of digital technologies in this process within tertiary education contexts. This is followed by an
introduction to online learning along with definitions, discussion of foundational online learning concepts and
contemporary pedagogical approaches used in online learning environments. Next, the reasons why motivation
is an essential consideration in online teaching and learning contexts are explored. Then, existing research into
motivation to learn in online environments is discussed in light of contemporary theoretical motivation
frameworks. Finally, self-determination theory (SDT) – an intrinsic-extrinsic theory of motivation – is discussed
in detail. In particular, the continuum of human motivation that outlines a range of different types of extrinsic
motivation and the underlying psychological concepts of autonomy, competence and relatedness that SDT is
built on are discussed. In doing so, justification for the use of SDT as the conceptual framework for this work is
provided.
Keywords e -l e a r n i n g , o n l i n e l ea r n i n g , m ot i v a t i o n , s e l f - e f f i c ac y , i n t e re st ,
g o a l o r i e n ta t i o n , s e l f- d e t e r m i n a ti o n , i n t r i ns ic , e x t r i ns ic , a u t o n o m y
1.1 Motivation and online education
Paris and Turner (1994) describe motivation as the ‘engine’ of learning. Motivation can influence what we learn,
how we learn and when we choose to learn (Schunk & Usher, 2012). Research shows that motivated learners are
more likely to undertake challenging activities, be actively engaged, enjoy and adopt a deep approach to
learning and exhibit enhanced performance, persistence and creativity (Ryan & Deci, 2000b). Given the
important reciprocal relationship between motivation and learning (Brophy, 2010), it is not surprising that
motivation has been actively researched across a wide range of traditional educational settings (Schunk, Meece,
& Pintrich, 2014). Despite this, studies that explore motivation to learn in online contexts are limited in both
number and scope, as others have noted (Bekele, 2010).
Of the research that is available, there has been a tendency to adopt a limited view of motivation that does not
acknowledge the complexity and dynamic interplay of factors underlying and influencing motivation to learn
(Brophy, 2010). Instead, designing motivating learning environments has received attention (Keller, 2010).
Alternatively, motivation has been viewed a relatively stable personal characteristic and studies have focused on
identifying lists of traits of successful learners (Yukselturk & Bulut, 2007). Comparative studies between online
and on-campus students are common using this approach (Wighting, Liu, & Rovai, 2008) and findings indicate
that online students are more intrinsically motivated than their on-campus counterparts.
However, higher dropout rates associated with online courses compared to similar face-to-face ones (Park &
Choi, 2009) lend support to the view that motivation is more complex than the above studies suggest. Feelings
of isolation (Paulus & Scherff, 2008), frustrations with the technology (Hara & Kling, 2003) and time
constraints due to other responsibilities (Keller, 1999) have all been identified as factors influencing students’
decisions to withdraw from online courses. However, poor motivation has also been identified as a decisive
factor in contributing to the high dropout rates (Artino, 2008; Keller, 2008). Therefore, student motivation is
considered a crucial factor for success in online learning environments (Artino, 2008; Keller, 2008) and is a
primary reason for the current study. Collectively, these factors point to the need to reconsider motivation to
learn in technology-rich environments. But before turning our attention to motivation it is important to start by
defining what is meant by online learning.
1.2 Online learning
Today, there are a plethora of terms to describe the application of digital technologies in learning including
distance, online, open, flexible, blended, flipped, mixed and MOOCs (Massive Open Online Courses). To help
make sense of these terminologies, Bullen and Janes (2007) have conceptualised a continuum of technology use
1
ranging from face-to-face to fully distance environments. E-learning is a common term used to describe
anything on this continuum that incorporates digital technologies in the learning process (Nichols, 2008).
1.2.1 Definition
Online learning has its roots in distance education. A. W. Bates (2005) points out that the terms ‘online learning’
and ‘e-learning’ are used interchangeably, but makes the distinction that e-learning can encompass any form of
technology while online learning refers specifically to using the internet and the web. The term “fully online” is
used by Bates (2005, p. 9) to distinguish distance courses where students must have access to an internet capable
device to undertake the course. Ally (2008) also highlights that there are many definitions of online learning that
reflect the diversity of practice and technologies in use. He goes on to define it in the following way:
… the use of the internet to access materials; to interact with the content, instructor, and other
learners; and to obtain support during the learning process, in order to acquire knowledge, to
construct personal meaning, and to grow from the learning experience (p. 5).
Given the lack of consensus of terminology, the term online learning is used in this book. It is taken to
encompass the definition offered by Ally and incorporates the fully online distinction used by Bates that makes
cognisant the distance context of courses. In other words, online learning described here is taken to be a form of
distance education mediated by technological tools where learners are geographically separated from the
instructor and the main institution.
1.2.2 Learner autonomy and control in online learning
While it not the intention here to offer a comprehensive review of the history of distance education, or the place
of online learning within it, it is important to discuss two theoretical concepts that have been influential in the
overall development of the field and continue to influence our understanding of learning and motivation in
contemporary online contexts. These concepts are transactional distance that encompasses the notions of
structure, dialogue and autonomy suggested by Moore (1990); and the alternative concept of learner control
(Garrison & Baynton, 1987). Similar concepts exist within contemporary motivation literature, particularly
those associated with self-determination theory (Deci & Ryan, 1985) – the motivational framework that
underpins this investigation.
Moore (1990) coined the phrase transactional distance to define the psychological separation frequently
experienced by students, as a result of the spatial and/or temporal separation between learners and instructors in
a distance learning context. From this perspective, the relative amount of structure and dialogue inherent in the
learning activity determines the degree of ‘distance’ experienced by the learner (Dron, 2007). Structure refers to
the design of the course and expresses the flexibility or rigidity of the teaching methods, objectives and
assessment practices (Moore, 1993). Dialogue refers to the degree of interaction with the instructor and is
associated with the communication medium (Moore & Kearsley, 2005). In Moore’s theory, low dialogue and
structure equate to high transactional distance and vice versa (Garrison, 2000). However, the theory points out
that high dialogue and structure are difficult to achieve simultaneously (Dron, 2007). The theory also
incorporates a third concept, learner autonomy. The greater the transactional distance (i.e. low structure and
dialogue), the more responsibility is placed on the learner (Moore & Kearsley, 2005). In this model, Garrison
(2003) argues that autonomy is associated with independence and self-directed learning. While Moore points out
that the transactional distance model does not imply that autonomous learners do not require teachers, he does
suggest that they require less dialogue and minimal structure when compared with less autonomous learners
(Moore, 2007).
Other researchers in the field have argued that the term autonomy has suffered from the lack of clear definition
(Garrison, 2000; Garrison & Baynton, 1987). Garrison and Baynton (1987) argue that a richer, more inclusive
concept is that of learner control, as it helps to address the confusion associated with the role of independence in
distance education. In this conceptualisation, “control is concerned with the opportunity and ability to influence,
direct, and determine decisions related to the education process” (p. 5). This can only be achieved by striking a
balance between independence (being free to make choices without restrictions or outside influences); power
later referred to as competence (the capability to be responsible for and take part in the learning process); and
support (the resources, including the teacher, available to the learner throughout the learning process). In this
model, support from the teacher enhances greater control on the part of the learner; it does not take away from
it. Baynton (1992) tested this model via confirmatory factor analysis and found that the subsequent three main
factors mirrored the proposed dimensions.
2
The work of other researchers has also influenced our understandings of choice, control and autonomy in
distance education, most notably Candy (1991). Candy focused on self-direction and distinguished two different
types: self-direction as 1) a personal characteristic; and 2) the degree of control a learner has in determining his
or her learning path. This is an important distinction because it recognises that autonomy is both a personal and
situational variable. In other words, the degree of autonomy a person expresses can vary from situation to
situation.
Dron (2007) has built on the work of previous theorists and developed a conceptual model called transactional
control. Transactional control has to do with choice and attempts to explain the dynamics of transactional
distance. In this model, structure is equivalent to teacher control, dialogue relates to negotiated control, and
autonomy relates to learner control. In other words, control is seen as a continuum from learner control at one
end to teacher control at the other, which is determined by the choices made throughout the learning trajectory.
While the concepts of autonomy, independence, control and agency have been central to the development of
distance education theory, other theories have also been influential.
1.2.3 Contemporary theories of learning
With the advent of the internet and communication technologies that enable interaction between and among
student groups, contemporary learning theories increasingly inform teaching and learning practices in online
contexts (T. Anderson & Dron, 2011; McLoughlin & Lee, 2008). In particular, constructivist and social
constructivist perspectives of learning have gained prominence in online education research and literature (Ally,
2008; Dyke, Conole, Ravenscroft, & de Freitas, 2007).
Constructivism sees the student at the centre of the learning process and actively involved in the construction of
knowledge (Dalgarno, 2001). Learning from this perspective places emphasis on authentic activities,
collaboration, learner control or agency, reflection, active engagement and intrinsic motivation (Herrington &
Oliver, 2000). There are several strands of constructivism. Two which figure prominently are cognitive
constructivism and social constructivism (Dyke, et al., 2007).
Individual cognitive constructivism has grown out of the foundational work of Piaget (1977) and is a theory that
views the learner as agentic (i.e., the ability of an individual to make choices and act on those choices) and
learning as an active process of individual meaning-making. Favoured approaches tend to be task-oriented,
hands-on and self-directed (Dyke, et al., 2007). Examples of cognitive constructivist methods include: active
learning, problem-based learning and inquiry learning (Kirschner, Sweller, & Clark, 2006). Researchers
(Lindgren & McDaniel, 2012) have recognised that digital technologies present new opportunities for
supporting learner agency most notably by personalising the learning experience, allowing the student to
choose, assemble and construct their own representations of knowledge in their own way (Conole, 2010).
The foundations of social constructivist theory can be found in Vygotsky’s cultural-historical theory (1978) and
the writings of Dewey (1916). Social constructivism conceptualises learning as participation in shared activities
where the context and the situated nature of learning are integral considerations. Social constructivist theory also
acknowledges the importance of motivation and the crucial part contextual factors play in the fostering of
motivation among learners (McInerney & Van Etten, 2004). From this perspective, knowledge is distributed
among members of a community, and learning involves individuals’ abilities to participate successfully in
community practices (Wenger, 1998). Language is a central tool for learning and co-construction of knowledge
(Dyke, et al., 2007). It can be argued that the recent emergence of the theory of connectivism, that views
learning as a process of developing networks of information, resources and people (Siemens, 2005), is a logical
development of social constructivist theory in a digitally-mediated world.
The situated, social and constructed nature of learning has been recognised in the online learning literature
(Howland, Jonassen, & Marra, 2012). Principles such as mediation, zone of proximal development,
internalisation, cognitive apprenticeship and distributed intelligence have been adopted to underpin the design
and development of online learning environments (Dyke, et al., 2007). Particular emphasis has been placed on
the development of online learning communities (Harasim, 2012) where opportunities for collaboration and
interaction are realised through the use of various digital communication tools (Haythornthwaite & Andrews,
2011). While there is a focus on the socially-mediated nature of learning in the sections that follow, this does not
negate the importance of individual constructions of knowledge. Learner interactions with course content in
particular, frequently occurred at an individual level in the online learning contexts.
3
1.2.4 The role of interaction in online learning
Interaction has been used in online learning to denote anything from clicking on a link to interpersonal dialogue
among many participants (Nichols, 2008). However, for the purposes of this investigation, a useful starting point
is the work of Moore (1989). Moore identified three types of interaction in earlier generations of distance
education, namely: learner-instructor, learner-content, and learner-learner interaction. Hillman, Willis, and
Gunawardena (1994) added a fourth type, namely learner-interface interactions.
Learner-instructor interaction refers to exchanges that occur between learners and the teacher and are
characterised by attempts to motivate and interest the learner. They also provide a mechanism for feedback
allowing clarification of misunderstandings. Thach and Murphy (1995) identified seven types of learner-
instructor interactions in distance education settings: 1) establishing learning outcomes/objectives; 2) providing
timely, useful feedback; 3) facilitating information presentation; 4) monitoring and evaluating student progress;
5) facilitating learning activities; 6) facilitating discussions; and 7) determining learning needs and preferences.
More recently, Garrison, Anderson, and Archer (2000) have developed the concept of teaching presence as part
of the community of inquiry model. Teaching presence explicates the teaching role in online environments
which encompasses design and organisation, facilitating discourse and direct instruction (Garrison, 2011).
Teaching presence and the effective facilitation of learner-instructor interactions, particularly via online
dialogue, has continued to be an area of active research (Garrison, 2011; Mishra & Juwah, 2006; Rovai, 2007).
From this, guidelines for facilitating effective practice have emerged that build on those of Thach and Murphy
(1995). For example, Rovai (2007) explicates design and facilitation guidelines for effective online discussions
based on research and experience. They include ways of encouraging learner motivation, incorporating
opportunities for learner choice, and clarification of expectations as well as developing and nurturing a strong
sense of community. Mishra and Juwah (2006) highlight the importance of establishing a purpose and context
for discussions, clarifying the relevance of discussions by making links to learning outcomes and the importance
of encouraging learners to participate through the provision of appropriate support.
Learner-content interaction describes the intellectual process that occurs between the learner and the resources
associated with the topic of study (Moore, 1989). Learner-content interactions occur when learners access such
things as textual and graphical representations of the subject matter (Hirumi, 2006). With the increasing
availability of technology, learners can now choose from a huge variety of information at any time or from any
place. But in order to interact with content, learners need to be able to access relevant and appropriate resources
which frequently, requires guidance from the teacher (T. Anderson, 2006). Availability of adequate resources has
also been shown to be important from a motivational perspective (Reeve, Deci, & Ryan, 2004).
Learner-learner interactions highlight processes that take place between peers undertaking a course together
(Moore, 1989). This can include processes such as sharing information and understandings, working together to
interpret and complete activities, solving problems, and sharing opinions or personal insights. Technology-
mediated communication technologies, for example, provide learners with opportunities to collaborate and
actively participate in knowledge co-construction via online discussion (Hirumi, 2006).
Juwah (2006) argues that for learners to participate and have positive peer interactions, they need know how to
effectively use the digital tools and must understand how to learn. This includes having the necessary
prerequisite, prior knowledge and an understanding that successful learning requires self-regulation. Even with
the necessary skills, peer interactions in technology-mediated environments are complex and cover a range of
intellectual (e.g., reviewing, conceptualising), social/ emotional and instructional interactions (e.g., critiquing).
Much of what is known today about what is required for effective peer interactions to occur in technology-
mediated environments has emerged from the analysis of asynchronous discussion transcripts (De Wever,
Schellens, Valcke, & Van Keer, 2006). Garrison et al. (2000) developed the community of inquiry model that
posited that interactions must consist of three core elements for effective peer learning to occur. They are:
cognitive presence – the degree to which the participants can construct meaning through ongoing
communication; social presence – the ability of participants to present themselves as ‘real’ to other community
members; and teaching presence – the design and facilitation of the learning experience.
Learner-interface interaction refers to a learner’s ability to use the required technological tools in order to
interact and communicate with the instructor, other students and the course content (Hillman, et al., 1994). A
learner’s belief in their ability to use the necessary technological tools to learn online has also been found to be
related to performance (Moos & Azevedo, 2009).
4
Online communities Rovai and Lucking (2003, p. 6) state that “interaction is the primary mechanism through
which community is built and sustained”. Interaction between learners and the development of learning
communities has gained considerable attention (T. Anderson, 2006; Harasim, 2012; Rovai, 2000) because it has
been identified as a crucial factor in creating and sustaining online communities (Haythornthwaite & Andrews,
2011).
The development of a supportive network among learners can foster motivation to learn, commitment to group
goals, encourage the co-construction of knowledge (Bonk & Khoo, 2014), and has been shown to be
significantly related to perceived cognitive learning (Rovai, 2002). However, building such a network is not
straightforward. Interaction is an essential element of a supportive community but will not occur by simply
providing the technological tools to learners (Garrison, 2011). Course structure (T. Anderson, 2008), class size
(Vrasidas & McIsaac, 1999), prior experience (Juwah, 2006), social presence (Lin, Lin, & Laffey, 2008),
instructor immediacy (Shea, Swan, & Pickett, 2005), use of self-disclosure (Cutler, 1995), collaborative learning
(Boekaerts & Minnaert, 2006), group facilitation (Jones & Issroff, 2007), personal agency (B. Anderson, 2006),
and the ability of learners to meet their peers’ affective needs within small group settings (B. Anderson &
Simpson, 2004), have all been found to influence student interaction and their sense of being part of an online
community.
The discussion to this point has identified that the adoption of social constructivist principles that encompass the
concepts of collaboration, interaction, and dialogue are important underpinnings in the development of
successful online learning communities. Developing and sustaining a sense of online community is also
important in fostering motivation among learners (Bonk & Khoo, 2014). In the section that follows, attention
turns to the existing body of research that has investigated the motivation of learners in online environments.
1.3 Motivation to learn in online environments
The characteristics of independence, self-direction and intrinsic motivation have long been associated with
distance learners (Moore, 1989). Intrinsic motivation has also been identified as an important characteristic of
online learners (Shroff, Vogel, Coombes, & Lee, 2007). Findings from comparative studies between online and
on-campus students (Huett, Kalinowski, Moller, & Huett, 2008; Shroff & Vogel, 2009; Wighting, et al., 2008)
also suggest that online learner are more intrinsically motivated compared with their on-campus counterparts at
both undergraduate and postgraduate level.
But as Martens, Gulikers, and Bastiaens (2004) argue, online learners are often required to be more intrinsically
motivated because the learning environment typically relies on intrinsic motivation and the associated
characteristics of curiosity and self-regulation to engage learners. In fact, the technology itself is viewed by
some as inherently motivating because it provides a number of qualities that are recognised as important in the
fostering of intrinsic motivation, namely challenge, curiosity, novelty and fantasy (Lepper & Malone, 1987).
The novelty factor tends to wear off as users become accustomed to the technology (Keller & Suzuki, 2004) and
intrinsic motivation can wane. Frustration with technical problems can also reduce intrinsic motivation.
While the intrinsic motivation of learners is an important consideration, contemporary research studies
exploring motivation in these environments is limited in both number and scope (Bekele, 2010). Recent concern
over attrition rates in online courses (Lee, Choi, & Kim, 2013), particularly from new technology-mediated
environments such as MOOCs (Liyanagunawardena, Adams, & Williams, 2013), highlights the need for greater
understanding of the complexity of factors that influence motivation to learn in online contexts.
1.3.1 What is motivation?
Brophy (2010, p. 3) defines motivation as “a theoretical construct to explain the initiation, direction, intensity,
persistence, and quality of behaviour, especially goal-directed behaviour”. Motivation involves goals that
provide the impetus for purposeful action with an intended direction. Whether physical or mental, activity is an
essential part of motivation. Inherent in this definition is the notion that motivation is a process rather than an
end result. This has implications in terms of measurement of motivation. That is, because it cannot be observed
directly it must be inferred from actions such as choice of tasks, persistence, effort and achievement, or from
what individuals say about themselves(Schunk, et al., 2014). Contemporary views link motivation to
individuals’ cognitive and affective processes such as thoughts, beliefs, goals and emotions and emphasise the
situated, interactive relationship between the learner and the learning environment that is facilitated or
constrained by various social and contextual factors (Schunk, et al., 2014).
5
1.3.2 Why is motivation important?
Motivation has been described as the ‘engine’ of learning (Paris & Turner, 1994) and can influence what, when,
how we learn and is a significant factor in performance (Schunk & Usher, 2012). It has been shown to play an
important role in determining whether a learner persists in a course, the level of engagement shown, the quality
of work produced, and the level of achievement attained. Understanding the nature of motivation and the ways
in which personal histories, social factors, experiences and circumstances may influence the motivation of
learners, therefore, has important practical implications for those involved in online teaching and learning.
While few would disagree that motivation is an important factor in learning, the complexity and multifaceted
nature of the construct has resulted in the development of several theories (Schunk, et al., 2014). These can be
broadly conceptualised in terms of a general expectancy – value model of motivation (Brophy, 2010). The
expectancy component is concerned with learners’ beliefs about whether they are able to perform a task
(Bandura, 1997). The value component relates to beliefs a learner holds about the task itself (Eccles & Wigfield,
2002). In addition, comprehensive reviews of the motivation literature have resulted in the development of
several motivation design models. These include Keller’s (2010) ARCS model and Ginsberg and Wlodkowski’s
(2000) motivational framework for culturally responsive teaching. Keller’s model, in particular, has been
frequently used as a conceptual framework for the development of online learning environments that enhance
learner motivation.
1.3.3 Motivation, the learning environment and the learner
Different perspectives have been adopted when exploring motivation to learn in online environments. The two
that feature most prominently are motivation from the perspective of instructional design and motivation viewed
as a trait of the learner. The first perspective concentrates on the design of the learning environment and the
factors considered necessary to provide optimum learner motivation (Keller & Deimann, 2012; Zaharias &
Poylymenakou, 2009). The second perspective views motivation as a relatively stable personal characteristic of
the learner (Wighting, et al., 2008; Yukselturk & Bulut, 2007). But as we begin to understand more about the
nature of motivation in online contexts, a third situated perspective is emerging that acknowledges the dynamic
and responsive nature of motivation to different situations (Hartnett, St. George, & Dron, 2011; Rienties et al.,
2012). Throughout the remainder of the chapter, research from all three perspectives is presented. The various
motivational theories that underpin different research investigations are also discussed.
1.3.1.1 Motivation from a learning design perspective
The first perspective adopted when examining motivation in online learning settings has been to concentrate on
the design of the environment to elicit student motivation. Several instructional design models have been put
forward, some of which consider learner motivation as a component of a broader design approach, and others
which focus exclusively on motivation (see for example Chan & Ahern, 1999). By far the most frequently used
instructional design framework for the development of motivating online learning environments is Keller’s
ARCS model (Keller, 1987). The framework was developed as a means of influencing learner motivation by
using a systematic approach to instructional design. The attention, relevance, confidence and satisfaction
(ARCS) categories serve as guidelines for systematically developing instructional strategies that capture learner
attention, establish relevance of what is being taught, encourage learner confidence, and provide a sense of
satisfaction via intrinsic and extrinsic rewards (Keller, 2010). Though not originally developed for it, the ARCS
model has been used as a design approach for instruction in online learning contexts (Keller, 2008; Keller &
Deimann, 2012) and has underpinned a variety of other studies (ChanLin, 2009; Hodges & Kim, 2013; Paas,
Tuovinen, van Merriënboer, & Darabi, 2005).
These kinds of instructional design approaches have been very important in developing our understanding of
motivation in online learning environments. However, they are not sufficient on their own to explain the
complex processes that occur as they often do not take into account learner differences. Even though the full
application of the ARCS design process incorporates an analysis of the motivation of learners (Keller, 2010), the
model itself is often applied in a more prescriptive way (ChanLin, 2009; Hodges & Kim, 2013). Such
approaches concentrate on the view that it is the designer and developer who make the material motivating and
frequently reflect earlier behaviourist theories of motivation that assume that behaviour is caused by events or
stimuli external to the person (Hickey & Granade, 2004). Contemporary motivation literature suggests that it is
a complex mix of these as well as other factors that contribute to a learner’s motivation in any given situation
(Brophy, 2010).
1.3.1.2 Motivation from a learner trait perspective
6
The second and predominant method for investigating motivation has been to conceptualise various motivation
constructs as learner characteristics or traits. The impetus for conducting much of this research has been in an
attempt to identify factors that contribute to higher attrition rates (Lee, et al., 2013). Conversely, other studies
have attempted to identify characteristics that predict learner success (Yukselturk & Bulut, 2007).
Moos and Marroquin (2010) contend that research investigating motivation in technology rich environments
should be guided by fundamental and well-established theories of motivation. These include, self-efficacy
theory (Bandura, 1997); goal orientation theory (Murayama, Elliot, & Friedman, 2012); interest theory (Hidi,
Renninger, & Krapp, 2004); and intrinsic–extrinsic motivation theory, in particular self-determination theory
(Ryan & Deci, 2000a). Of these, self-efficacy theory has been used most frequently.
Self-efficacy: Social cognitive theory proposes that motivation influences both learning and performance
(Schunk & Usher, 2012) and focuses on how people acquire knowledge, skills, beliefs and strategies through
their interactions with and observations of others. Bandura’s (1986) social cognitive theory is central to this area
of motivational research. It is based on the premise that there is a reciprocal interactive relationship among
personal factors, behaviours and environmental influences. A focal point of this theory is the notion of self-
efficacy, defined as the belief that one is capable of learning or performing at a certain level in order to attain
particular goals. Self-efficacy, unlike similar constructs such as self-concept, is focused on an individual’s
beliefs about their performance capabilities for a particular task within a particular context that has yet to be
undertaken.
Bandura (1997) proposed that individuals use information from a number of sources in order to judge self-
efficacy. These include actual experiences (successes, failures), vicarious experiences (model observation),
attributions, verbal persuasion, and physiological/affective states. Actual experience plays a major role in
assessing self-efficacy for a task, with success generally raising self-efficacy and failure lowering it. Ability and
effort attributions affect self-efficacy with positive ability attributions enhancing self-efficacy more than effort
attributions (Schunk, et al., 2014).
Observing similar peers successfully completing a task can convey to the observer that they too have the
capabilities for success where model similarity is an important factor. Having a trusted person tell you that you
have the ability to succeed is a further important source of information. Physiological symptoms such as
increased heart rate or sweating can act as a signal of anxiety, indicating a lack of skills or ability. Alternatively,
it may be interpreted as positive anticipation suggesting confidence in the ability to succeed.
Self-efficacy has been linked to factors influencing goal setting and goal performance (Schunk & Usher, 2012)
and has been shown to be a major motivational factor that affects students’ task choices, effort, persistence and
achievement (see Brophy, 2010). Research has consistently shown that self-efficacy is a strong predictor of
performance and student motivation (Schunk, et al., 2014).
Self-efficacy has also been highlighted as an important predictor of successful outcomes and satisfaction in
online learning environments (Kuo, Walker, Belland, & Schroder, 2013). Academic self-efficacy (Artino, 2008;
Lynch & Dembo, 2004) and efficacy to learn online (Shen, Cho, Tsai, & Marra, 2013) have both been found to
be significantly related to a number of factors. These include: use of high level learning strategies (Moos &
Azevedo, 2009; Wang & Wu, 2008); critical thinking and metacognitive learning strategies (Artino & Stephens,
2006); persistence (Hart, 2012); satisfaction (Artino, 2007, 2008); participation (Kuo, et al., 2013); and
academic performance (Hodges, 2008). However, several studies exploring self-efficacy to learn online did not
predict student achievement outcomes (Bell, 2007; Xie, DeBacker, & Ferguson, 2006). Prior successful
experience with online learning has also been found to be important for learners to feel efficacious about future
learning in similar contexts (R. Bates & Khasawneh, 2007). Furthermore, learner self-efficacy may fluctuate as
they come to understand the challenging nature of learning in technology-rich environments (see Moos &
Marroquin, 2010).
Goal orientation: A second conceptual framework commonly used to support studies investigating motivation
to learn in online contexts, is goal orientation theory. Goal orientation theory explores learners’ reasons for
engaging in achievement behaviour, in particular the beliefs that result in different approaches to and
engagement in achievement situations (Murayama, et al., 2012).
Although there are numerous types of goal orientations, the two that have been studied most extensively are
learning (mastery or task-involved) goals and performance (ego-involved) goals (Schunk, et al., 2014). Learners
who adopt a learning goal orientation tend to focus on learning for understanding, developing new skills, and
7
improving or developing competence where the standard for judging the achievement or otherwise is internal to
the learner. In contrast, a performance goal orientation tends to focus on demonstrating competence or ability
where the standard for measurement is in comparison to others (Murayama, et al., 2012).
While earlier research focused on the differences between learning and performance goals, more recent work
recognises that performance goal orientation can be further categorised into performance-approach (wanting to
demonstrate competence in relation to others) and performance-avoid (wanting to avoid looking incompetent)
orientations. This research also suggests that performance-approach goals can be potentially positive for
learning and, when combined with learning goals, can lead to optimal motivation (Harackiewicz, Barron,
Pintrich, Elliot, & Thrash, 2002). What is also clear from the research is that a performance-avoidance
orientation is negatively related to various learning outcomes (Brophy, 2010).
Studies in online learning environments have found that students who adopt a performance orientation are more
likely to contribute to assessed activities (Bures, Abrami, & Amundsen, 2000) and focus on administrative tasks
(Dawson, Macfadyen, & Lockyer, 2009) in comparison to learners who adopted a learning goal orientation.
Furthermore, research has shown positive relationships between learning goal orientation and increased
participation in discussions related to learning and sharing (Dawson, et al., 2009), metacognitive strategy use
and performance (C.-H. Chen & Wu, 2012), and learners’ overall satisfaction (Kickul & Kickul, 2006). A small
body of research has investigated approach and avoid goal orientations, for example, a learning-approach
orientation has been shown to be a predictor of achievement (Crippen, Biesinger, Muis, & Orgill, 2009). In
addition, Moos and Marroquin (2010) highlight the fact that the type of strategies learners use differ depending
on their goal orientation, while Ng (2012) found that the positive effects of both learning and performance goal
approach orientations are supported by learners’ control beliefs. Apart from a few exceptions (Ng, 2008, 2009),
studies that have considered the adoption of multiple simultaneous goals by learners in online contexts are rare.
Interest, a concept closely related to intrinsic motivation, is a distinct motivational construct evident in some
online motivational research. Research in traditional educational contexts has consistently shown that the level
of an individual’s interest has a significant influence on their learning (Hidi & Renninger, 2006). Interest is a
concept that has been characterised in a number of ways, but is most often viewed as a psychological state that
“involves focused attention, increased cognitive functioning, persistence, and affective involvement” (Hidi,
2000, p. 311). Interest is always content specific (Krapp, 2002) and two types of interest have frequently been
associated with this psychological state, namely individual and situational interest (Hidi & Harackiewicz,
2000). Individual interest is seen as a relatively stable disposition or motivational orientation towards certain
activities. Situational interest is engendered in response to particular conditions within the environment and
tends to be less enduring (Hidi & Ainley, 2008).
Rather than being seen as opposites, situational and individual interest are considered distinct constructs that
can interact and influence each other. While researchers have highlighted the importance of individual interest
on learning and motivation (Hidi & Renninger, 2006), research has also focused on situational interest as a way
for educators to foster student involvement and motivation in specific activities (Hidi & Harackiewicz, 2000).
In their four-phase model of interest, Hidi and Renninger (2006) describe two different types of situational
interest, triggered and maintained. Triggered situational interest tends to be short-lived. Maintained situational
interest follows on from the triggered state and is usually sustained over longer periods of time.
Triggered situational interest has been linked to learning environments that include group work and use of
computers (Hidi & Renninger, 2006; Lepper & Malone, 1987). Maintained situational interest has been linked
to a variety of conditions such as personal relevance and utility value (Hidi & Renninger, 2006), collaborative
work as well as authentic and meaningful activities (Blumenfeld, Kempler, & Krajcik, 2006; Boekaerts &
Minnaert, 2006).
Studies of online learning have shown that higher engagement occurs when learners a) are personally interested
in the topic (Schallert & Reed, 2003) and b) have a pre-existing individual interest in computers (Sansone,
Fraughton, Zachary, Butner, & Heiner, 2011). Additionally, personal interest is enhanced in autonomy-
supportive online environments (Moos & Marroquin, 2010); a learner’s level of topic interest has been linked to
mathematics learning (Renninger, Cai, Lewis, Adams, & Ernst, 2011) and reading comprehension (Akbulut,
2008) in online environments; and situational interest has been shown to increase with the inclusion of
conceptual scaffolding (Moos & Azevedo, 2008). However, researchers have highlighted the need to account for
novelty effects frequently seen in technology-rich contexts where learner interest diminishes over time (see
Moos & Marroquin, 2010).
8
Intrinsic – extrinsic motivation: is another motivational construct that has been used to investigate learner
motivation in online environments. “Intrinsic motivation is defined as the doing of an activity for its inherent
satisfactions rather than for some separable consequence” (Ryan & Deci, 2000a, p. 56). Intrinsic motivation
often results from the challenge, interest or fun an individual derives from an activity. In contrast, “extrinsic
motivation is a construct that pertains whenever an activity is done in order to attain some separable outcome”
(Ryan & Deci, 2000a, p. 60). In other words, intrinsic motivation is associated with undertaking an activity for
the enjoyment or interest inherent in it. Extrinsic motivation is associated with a source outside the activity
itself, such as undertaking a course of study to improve future career prospects. Research suggests that
individuals who are intrinsically motivated are more likely to undertake challenging activities; be actively
engaged and enjoy learning; adopt a deep approach to learning; and exhibit enhanced performance, persistence,
and creativity (Amabile, 1985; Brophy, 2010; Ryan & Deci, 2000b).
Several studies have explored students’ reasons for engagement in online environments from an intrinsic –
extrinsic motivation perspective (e.g., Rentroia-Bonito, Jorge, & Ghaoui, 2006; Shroff & Vogel, 2009; Xie, et
al., 2006). Huang and Liaw (2007) found that learners’ perceptions of autonomy were predictive of both
intrinsic and extrinsic motivation. A study by Martens et al. (2004) examined the intrinsic motivation of
psychology and technology undergraduates undertaking authentic computer tasks. They found that high levels of
intrinsic motivation were not necessarily indicative of higher levels of achievement. Instead, intrinsic motivation
was associated with greater exploration of the learning environment. Results of research by Rienties, Tempelaar,
Van den Bossche, Gijselaers and Segers (2009) revealed that difference in learner motivation influenced the type
of discourse contributions with intrinsically motivated learners being central and prominent contributors. While
this body of research adds to our understanding of motivation, it is important to note that there has been the
tendency to focus predominantly on intrinsic motivation (Martens, et al., 2004; Rovai, Ponton, Wighting, &
Baker, 2007; Shroff & Vogel, 2009). In doing so, current views that individuals can be simultaneously
intrinsically and extrinsically motivated to a greater or lesser degree over time in any given context, are
neglected (Paris & Turner, 1994).
1.3.1.3 Motivation from a situational perspective
Although fewer in number, studies have been conducted that do acknowledge a more contemporary situated
‘person in context’ perspective (Turner & Patrick, 2008). For example, using self-efficacy theory, studies have
shown that receiving elaborated and timely feedback significantly enhanced student self-efficacy (Artino, 2007,
2008; R. Bates & Khasawneh, 2007; Wang & Wu, 2008). Collective efficacy, “people’s shared beliefs in their
collective power to produce the desired results” (Bandura, 2000, p. 75), is a related construct that has been
shown to have positive effects on discussion behaviour and group performance in computer supported
collaborative learning environments (Wang & Lin, 2007a, 2007b).
Using goal orientation theory, Matuga (2009) found that goal orientation changed from a performance to
learning orientation over time, within the context of an online science course. In a related study, Whipp and
Chiarelli (2004) found that instructor support, peer support and course design all influenced learner interest
within a web-based course. Xie et al. (2006) identified contextual factors that increased student intrinsic
motivation (e.g., clearly stated guidelines, well-designed discussion topics and instructor involvement and those
that decreased it (e.g., lack of instructor and peer feedback).
1.4 Self-determination theory as a framework for studying online motivation
Arguably one of the more well-known theories of motivation is intrinsic–extrinsic motivation. An influential
theory that explains this motivation concept is self-determination theory (SDT) (Deci & Ryan, 1985). Self-
determination theory is a contemporary theory of situated motivation that is built on the fundamental premise of
learner autonomy. SDT argues that all humans have an intrinsic need to be self-determining or autonomous, as
well as competent and connected, in relation to their environment.
Connell (1990) defines autonomy as “the experience of choice in the initiation, maintenance and regulation of
activity and the experience of connectedness between one’s actions and personal goals and values” (pp. 62-63).
When autonomous, students attribute their actions to an internal locus of causality and experience a sense of
freedom and choice over their actions. Competence is defined as “the need to experience oneself as capable of
producing desired outcomes and avoiding negative outcomes” (Connell & Wellborn, 1991, p. 51). Relatedness
“encompasses the need to feel securely connected to the social surround and the need to experience oneself as
worthy and capable of … respect” (Connell & Wellborn, 1991, pp. 51-52).
SDT states that if the environmental conditions are such that they support an individual’s autonomy, competence
9
and relatedness needs, then a learner’s inherent intrinsic motivation will be promoted (Ryan & Deci, 2000a).
When intrinsically motivated, outside incentives are unnecessary as the reward lies in the doing of the activity
(Ryan & Deci, 2000b). In contrast, students who are extrinsically motivated undertake activities for reasons
separate from the activity itself (Ryan & Deci, 2000a); for example gaining good grades, avoiding negative
consequences, or because the task has utility value such as passing a course in order to earn a degree.
Ryan and Deci (2000a) recognised that learners will not be intrinsically motivated at all times and in all
situations. SDT explains extrinsic motivation processes in terms of external regulation, as the reasons for
undertaking the task lie outside the individual. However, the degree to which an activity is perceived as
externally regulated can vary and therefore different types of extrinsic motivation exist. The taxonomy of human
motivation details a continuum of regulation that incorporates amotivation (lack of motivation) at one end
through to intrinsic motivation at the other, with different types of extrinsic motivation sitting between the
extremes. The various forms of extrinsic motivation highlight a shift in the degree to which externally motivated
behaviour is autonomously determined. They range from externally controlled with little or no self-
determination, to more internal control and self-regulation where a learner engages in an activity because of its
significance to their sense of self.
Research has shown that intrinsic and extrinsic types of motivation can and do co-exist (Lepper, Henderlong
Corpus, & Iyengar, 2005). It is the degree to which a student is intrinsically or extrinsically motivated that is
important, with more self-determined students experiencing positive learning outcomes even when extrinsically
motivated (Reeve, et al., 2004; Reeve, Jang, Hardre, & Omura, 2002). Furthermore, autonomous forms of
motivation have also been shown to have a potential buffering effect on less self-determined types of motivation
(Ratelle, Guay, Vallerand, Larose, & Senécal, 2007; Sheldon & Krieger, 2007).
According to this taxonomy, an amotivated individual lacks intention because he/she feels incompetent or has
low self-efficacy. They feel that whatever they do it will not affect the outcome, or they place low value on the
task being undertaken. Within the four patterns of extrinsic motivation, external regulation refers to individuals
who are responsive to threats of punishment or the offer of rewards. This is the type of extrinsic motivation most
often contrasted with intrinsic motivation, especially in earlier research. Introjection refers to students who
engage in a task because they feel they should due to the expectations of others and feel guilty if they do not
participate. Even though the feelings are internal, the individual is not self-determining as they are being
controlled by their feelings (Ryan & Deci, 2002). The third level of extrinsic motivation, called identification, is
associated with individuals who engage in the task because it has personal value to them. The locus of causality
is internal in the sense that the individual has chosen the goal or identifies with it and is aware of its importance.
But the motivational pattern is still considered extrinsic in the sense that it is the utility value (a means to an
end), personal importance and/or relevance of the task rather than the task itself that determines the behaviour.
The final level within the extrinsic motivation types is integration, where learners engage in the activity because
of its significance to their sense of self. Both identified and integrated types of motivation share some of the
qualities of intrinsic motivation (Ryan & Deci, 2000a) and have similar consequences for learning and
motivation. This has important implications as it highlights how educators can assist learners to appreciate the
importance and value of learning activities even when they are not intrinsically interesting. More recently, Deci
and Ryan (2012) have described the continuum of human motivation in terms of two meta-theoretical concepts,
namely controlled and autonomous motivation to differentiate between externalised and internalised types of
extrinsic motivation. External and introjected regulations are viewed as types of controlled extrinsic motivation
while identified and integrated regulations are considered types of autonomous motivation in conjunction with
intrinsic motivation. For a diagrammatic representation of the continuum see Ryan and Deci (2000a).
Research in traditional learning situations shows that autonomy support within the learning context leads to
more self-determined forms of motivation among learners (Deci & Ryan, 2008; Guay, Ratelle, & Chanal, 2008;
Reeve, 2009; Reeve, Ryan, Deci, & Jang, 2008; Van Etten, Pressley, McInerney, & Liem, 2008). Examples
include: providing rationales for tasks, the use of non-controlling language, and the provision of relevant and
meaningful instructional activities that align with students’ personal interests.
Conversely, external regulation such as deadlines, directives and compliance requests serve to undermine self-
determined types of motivation (Deci & Ryan, 2008; Guay, et al., 2008; Ryan & Deci, 2000a; Vallerand,
Pelletier, & Koestner, 2008; Van Etten, et al., 2008). Rewards can have a similar effect if used in order to control
behaviour such as task engagement, completion or performance (Deci, Koestner, & Ryan, 1999). Choice has
also been shown to be supportive of learners’ autonomy needs (Katz & Assor, 2007; Patall, Cooper, &
Robinson, 2008). However, it is the perception of choice, or lack of it, rather than the actual choices that is
10
critical in terms of self-determination (Reeve, Nix, & Hamm, 2003).
Support for the competence needs of learners is also necessary to facilitate motivation (Schunk & Zimmerman,
2006). The provision of structure (Connell & Wellborn, 1991) has been shown to be important in supporting
competence needs and facilitating self-determined types of motivation. Structure includes explicit, detailed
information that clarifies expectations without seeking to control behaviour; provision of informational feedback
given in a timely manner; and responsiveness to student questions, comments and suggestions, (Deci & Moller,
2005; Reeve, et al., 2004; Reeve, et al., 2008).
The fact that high structure within the learning activity can co-exist and be seen as mutually supportive, rather
than conflicting with the autonomy needs of learners, is something that has been previously noted in the general
motivation literature (Jang, Reeve, & Deci, 2010; Reeve, 2009). In addition to structure supporting learner
competence, learning activities designed to be optimally challenging, that is where the challenge of the task is
high and reasonably well-matched to learners’ skill levels (Csikszentmihalyi, 1985), encourage feelings of
capability and more self-determined motivation.
The more an individual experiences having their autonomy, competence and relatedness needs met within a
relationship, the more connected and trusting they feel towards that person (Ryan, La Guardia, Solky-Butzel,
Chirkov, & Kim, 2005). In line with this, teacher involvement in terms of the amount of time invested, care
taken, and attention given, have also been shown to be powerful motivators (Brophy, 2010). Inclusion, which
encompasses respect and connectedness, has also been identified as one of the basic conditions necessary for
encouraging and supporting motivation across diverse groups of learners (Ginsberg & Wlodkowski, 2000).
Conversely, difficulties in relationships with teachers and other learners have been associated with a
corresponding undermining of autonomy needs (Martens & Kirschner, 2004).
Criticism of self-determination theory centres around the argument that the fundamental assumptions on which
it is based adopt a distinctly Western perspective and may not be universal (McInerney & Van Etten, 2004). In
particular, the assumption that autonomy is a universal human need is questioned within collectivist cultures
(Markus & Kitayama, 1991). However, research in non-Western cultures supports SDT, although with slightly
differing emphasis on autonomy and relatedness (for a summary see Reeve, et al., 2004). Several researchers
(Reeve, et al., 2004; Ryan & Deci, 2006) point out that this criticism often stems from the misunderstanding of
the concept of autonomy where it is frequently equated with individualism and separateness. Research has
shown that autonomy and relatedness are compatible constructs (Ryan & Deci, 2006).
Several online studies have utilised self-determination theory as a theoretical basis (Giesbers, Rienties,
Tempelaar, & Gijselaers, 2013; Hartnett, et al., 2011; Rienties, et al., 2012). For example, Chen, Jang and
Branch (2010) showed that adressing the autonomy, competence and relatedness needs of learners is likely to
enhance online engagement, achievment and course satisfaction. Collectively, other research studies have
demonstrated that feedback, the instructor’s role in online discussions, choice, competence, challenge, interest,
relevance and collaboration all influenced student intrinsic motivation to learn in the various online learning
contexts. Few studies, however, draw on multiple perspectives (i.e., of both instructors and students) or
examined more self-determined forms of extrinsic motivation. This has resulted in a tendency by some
researchers to characterise online distance learners as intrinsically motivated (Rovai, et al., 2007). The study by
Hartnett et al. (2011) is distinctive in highlighting the complex, multifaceted, situation-dependent nature of
motivation in online contexts using SDT as an analytic tool.
1.5 Summary
With advances in technology that have enabled greater connectivity among learners contemporary learning
theories, in particular social constructivism, have increasingly informed teaching and learning practices in
online distance learning contexts. Constructivist principles that encompass concepts of collaboration, interaction
and dialogue, where the context and situated nature of learning are integral considerations, have been shown to
be important underpinnings in the development of successful online learning communities. Motivation has been
identified as a key factor in developing and sustaining a sense of community as well as learning and
achievement in online contexts.
The review of the literature has highlighted the limited number and scope of studies that have explored
motivation to learn in online learning settings. Moreover, the majority of existing studies have either adopted a
behaviourist approach, focusing on the environment, or a cognitive perspective that concentrates on the
characteristics of the learner. Both overlook the dynamic and responsive nature of motivation to learn.
Contemporary theories of motivation have been used to underpin some research. However, they have generally
11
been applied in limited ways. Studies that have used a situated approach do exist, but are also limited in terms of
the breadth of social and contextual motivational influences explored and their use of narrow conceptualisations
of motivation. An example of this has been the tendency to view intrinsic and extrinsic motivation as opposites
and to focus exclusively on intrinsic motivation in studies using self-determination theory as a conceptual
framework. Taken together, these issues highlight the need for research that explores motivation from a
contemporary situated perspective, in ‘real-life’ online settings that includes consideration of a broad range of
social and contextual influences.
References
Akbulut, Y. (2008). Predictors of foreign language reading comprehension in a hypermedia reading
environment. Journal of Educational Computing Research, 39(1), 37-50. doi: 10.2190/EC.39.1.c
Ally, M. (2008). Foundations of educational theory for online learning. In T. Anderson (Ed.), Theory and
practice of online learning (2nd ed., pp. 3-31). Retrieved from
http://www.aupress.ca/index.php/books/120146
Amabile, T. M. (1985). Motivation and creativity: Effects of motivational orientation on creative writers.
Journal of Personality and Social Psychology, 48(2), 393-399. doi: 10.1037/0022-3514.48.2.393
Anderson, B. (2006). Writing power into online discussion. Computers and Composition, 23(1), 108-124. doi:
10.1016/j.compcom.2005.12.007
Anderson, B., & Simpson, M. (2004). Group and class contexts for learning and support online: Learning and
affective support in small group and class contexts. International Review of Research in Open and
Distance Learning, 5(3), Retrieved from http://www.irrodl.org/index.php/irrodl/index
Anderson, T. (2006). Interaction in learning and teaching on the educational semantic web. In C. Juwah (Ed.),
Interactions in online education: Implications for theory and practice (pp. 141-155). London:
Routledge.
Anderson, T. (2008). Teaching in an online context. In T. Anderson (Ed.), Theory and practice of online
learning (2nd ed., pp. 343-366). Retrieved from http://www.aupress.ca/index.php/books/120146
Anderson, T., & Dron, J. (2011). Three generations of distance education pedagogy. International Review of
Research in Open and Distance Learning, 12(3), 80-97. Retrieved from
http://www.irrodl.org/index.php/irrodl/article/view/890/1826
Artino, A. R. (2007). Online military training: Using a social cognitive view of motivation and self-regulation to
understand students' satisfaction, perceived learning, and choice. Quarterly Review of Distance
Education, 8(3), 191-202.
Artino, A. R. (2008). Motivational beliefs and perceptions of instructional quality: Predicting satisfaction with
online training. Journal of Computer Assisted Learning, 24(3), 260-270. doi: 10.1111/j.1365-
2729.2007.00258.x
Artino, A. R., & Stephens, J. M. (2006). Learning online: Motivated to self-regulate? Academic Exchange
Quarterly, 10(4), 176-182.
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ:
Prentice-Hall.
Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman.
Bandura, A. (2000). Exercise of human agency through collective efficacy. Current Directions in Psychological
Science, 9(3), 75-78. doi: 10.1111/1467-8721.00064
Bates, A. W. (2005). Technology, e-learning and distance education (2nd ed.). New York: RoutledgeFalmer.
Bates, R., & Khasawneh, S. (2007). Self-efficacy and college students’ perceptions and use of online learning
systems. Computers in Human Behavior, 23, 175-191. doi: 10.1016/j.chb.2004.04.004
Baynton, M. (1992). Dimensions of "control" in distance education: A factor analysis. The American Journal of
Distance Education, 6(2), 17-31. doi: 10.1080/08923649209526783
Bekele, T. A. (2010). Motivation and satisfaction in internet-supported learning environments: A review.
Educational Technology & Society, 13 (2), 116-127.
Bell, P. D. (2007). Predictors of college student achievement in undergraduate asynchronous web-based courses.
Education, 127(4), 523-533.
Blumenfeld, P. C., Kempler, T. M., & Krajcik, J. S. (2006). Motivation and cognitive engagement in learning
environments. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 475-488).
Cambridge, NY: Cambridge University Press.
Boekaerts, M., & Minnaert, A. (2006). Affective and motivational outcomes of working in collaborative groups.
Educational Psychology, 26(2), 187-208. doi: 10.1080/01443410500344217
Bonk, C. J., & Khoo, E. (2014). Adding some TEC-VARIETY: 100+ activities for motivating and retaining
learners online. Bloomington, IN: Open World Books.
Brophy, J. (2010). Motivating students to learn (3rd ed.). New York, NY: Routledge.
12
Bullen, M., & Janes, D. P. (Eds.). (2007). Making the transition to e-learning: Strategies and issues. Hershey,
PA: Information Science Publishing.
Bures, E. M., Abrami, P. C., & Amundsen, C. C. (2000). Student motivation to learn via computer conferencing.
Research in Higher Education, 41(5), 593-621. doi: 10.1023/A:1007071415363
Candy, P. C. (1991). Self-direction for lifelong learning: A comprehensive guide to theory and practice. San
Francisco: Jossey Bass.
Chan, T. S., & Ahern, T. C. (1999). Targeting motivation – Adapting flow theory to instructional design. Journal
of Educational Computing Research, 21(2), 151-163.
ChanLin, L.-J. (2009). Applying motivational analysis in a web-based course. Innovations in Education &
Teaching International, 46(1), 91-103. doi: 10.1080/14703290802646123
Chen, C.-H., & Wu, I. C. (2012). The interplay between cognitive and motivational variables in a supportive
online learning system for secondary physical education. Computers & Education, 58(1), 542-550. doi:
10.1016/j.compedu.2011.09.012
Chen, K.-C., Jang, S.-J., & Branch, R. M. (2010). Autonomy, affiliation, and ability: Relative salience of factors
that influence online learner motivation and learning outcomes. Knowledge Management & E-
Learning: An International Journal 2(1), 30-50.
Connell, J. P. (1990). Context, self, and action: A motivational analysis of self-system processes across the life-
span. In D. Cicchetti & M. Beeghly (Eds.), The self in transition: Infancy to childhood (pp. 61-98).
Chicago: University of Chicago Press.
Connell, J. P., & Wellborn, J. G. (1991). Competence, autonomy and relatedness: A motivational analysis of
self-system processes. In M. R. Gunnar & L. A. Sroufe (Eds.), Self processes and development: The
Minnesota symposia on child development (Vol. 23, pp. 43-77). Hillsdale, NJ: Lawrence Erlbaum.
Conole, G. (2010). Personalisation through technology-enhanced learning In J. O'Donoghue (Ed.), Technology-
supported environments for personalized learning: Methods and case studies (pp. 1-15). Hershey, PA:
IGI Global.
Crippen, K. J., Biesinger, K. D., Muis, K. R., & Orgill, M. K. (2009). The role of goal orientation and self-
efficacy in learning from Web-based worked examples. Journal of Interactive Learning Research,
20(4), 385-403.
Csikszentmihalyi, M. (1985). Emergent motivation and the evolution of the self. In D. A. Kleiber & M. L.
Maehr (Eds.), Advances in motivation and achievement (Vol. 4, pp. 93-119). Greenwich, Conn.: JAI
Press.
Cutler, R. (1995). Distributed presence and community in cyberspace. Interpersonal Computing and
Technology: An Electronic Journal for the 21st Century, 3(2), 12-32.
Dalgarno, B. (2001). Interpretations of constructivism and consequences for computer assisted learning. British
Journal of Educational Technology, 32(2), 183-194. doi: 10.1111/1467-8535.00189
Dawson, S., Macfadyen, L., & Lockyer, L. (2009). Learning or performance: Predicting drivers of student
motivation. Proceedings of the ascilite conference (pp. 184-193). Auckland, New Zealand. Retrieved
from http://www.ascilite.org.au/conferences/auckland09/procs/all-abstracts.html
De Wever, B., Schellens, T., Valcke, M., & Van Keer, H. (2006). Content analysis schemes to analyze transcripts
of online asynchronous discussion groups: A review. Computers & Education, 46(1), 6-28. doi:
10.1016/j.compedu.2005.04.005
Deci, E. L., Koestner, R., & Ryan, R. M. (1999). A meta-analytic review of experiments examining the effects of
extrinsic rewards on intrinsic motivation. Psychological Bulletin, 125(6), 627-668. doi: 10.1037/0033-
2909.125.6.627
Deci, E. L., & Moller, A. C. (2005). The concept of competence: A starting place for understanding intrinsic
motivation and self-determined extrinsic motivation. In A. J. Elliot & C. S. Dweck (Eds.), Handbook of
competence and motivation (pp. 579-597). New York: The Guilford Press.
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York:
Plenum Press.
Deci, E. L., & Ryan, R. M. (2008). Self-determination theory: A macrotheory of human motivation,
development and health. Canadian Psychology, 49(3), 182-185. doi: 10.1037/a0012801
Deci, E. L., & Ryan, R. M. (2012). Motivation, personality, and development within embedded social contexts:
An overview of self-determination theory. In R. M. Ryan (Ed.), The Oxford handbook of human
motivation (pp. 85-107). Oxford, UK: Oxford University Press.
Dewey, J. (1916). Democracy and education. New York: Mcmillan.
Dron, J. (2007). Control and constraint in e-learning: Choosing when to choose. Hershey, PA: Information
Science.
Dyke, M., Conole, G., Ravenscroft, A., & de Freitas, S. (2007). Learning theory and its application to e-
learning. In G. Conole & M. Oliver (Eds.), Contemporary perspectives in e-learning research: Themes,
methods and impact on practice (pp. 82-97). London: Routledge.
13
Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53,
109-132. doi: 10.1146/annurev.psych.53.100901.135153
Garrison, D. R. (2000). Theoretical challenges for distance education in the 21st Century: A shift from structural
to transactional issues. International Review of Research in Open and Distance Learning, 1(1).
Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/2/22
Garrison, D. R. (2003). Self-directed learning and distance education. In M. G. Moore & W. G. Anderson (Eds.),
Handbook of distance education (pp. 161-168). Mahwah, N.J: Lawrence Erlbaum Associates.
Garrison, D. R. (2011). E-learning in the 21st century: A framework for research and practice (2nd ed.). New
York, NY: Routledge.
Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer
conferencing in higher education. The Internet and Higher Education, 2(2), 87-105. doi:
10.1016/S1096-7516(00)00016-6
Garrison, D. R., & Baynton, M. (1987). Beyond independence in distance education: The concept of control.
The American Journal of Distance Education, 1(3), 3-15. doi: 10.1080/08923648709526593
Giesbers, B., Rienties, B., Tempelaar, D., & Gijselaers, W. (2013). Investigating the relations between
motivation, tool use, participation, and performance in an e-learning course using web-
videoconferencing. Computers in Human Behavior, 29(1), 285-292. doi: 10.1016/j.chb.2012.09.005
Ginsberg, M. B., & Wlodkowski, R. J. (2000). Creating highly motivated classrooms for all students: A
schoolwide approach to powerful teaching with diverse learners. San Francisco: Jossey-Bass.
Guay, F., Ratelle, C. F., & Chanal, J. (2008). Optimal learning in optimal contexts: The role of self-
determination in education. Canadian Psychology, 49(3), 233-240. doi: 10.1037/a0012758
Hara, N., & Kling, R. (2003). Students’ distress with a web-based distance education course: An ethnographic
study of participants' experiences. Turkish Online Journal of Distance Education, 4(2). Retrieved from
http://tojde.anadolu.edu.tr/tojde10/articles/hara.htm
Harackiewicz, J. M., Barron, K. E., Pintrich, P. R., Elliot, A. J., & Thrash, T. M. (2002). Revision of
achievement goal theory: Necessary and illuminating. Journal of Educational Psychology, 94(3), 638-
645. doi: 10.1037/0022-0663.94.3.638
Harasim, L. (2012). Learning theory and online technologies. New York, NY: Routledge.
Hart, C. (2012). Factors associated with student persistence in an online program of study: A review of the
literature. Journal of Interactive Online Learning, 11(1), 19-42.
Hartnett, M., St. George, A., & Dron, J. (2011). Examining motivation in online distance learning environments:
Complex, multifaceted and situation-dependent. International Review of Research in Open and
Distance Learning, 12(6), 20-38. Retrieved from
http://www.irrodl.org/index.php/irrodl/article/view/1030
Haythornthwaite, C., & Andrews, R. (2011). E-learning theory and practice. London: Sage.
Herrington, J., & Oliver, R. (2000). An instructional design framework for authentic learning environments.
Educational Technology Research and Development, 48(3), 23-48. doi: 10.1007/BF02319856
Hickey, D. T., & Granade, J. B. (2004). The influence of sociocultural theory on our theories of engagement and
motivation. In D. M. McInerney & S. Van Etten (Eds.), Research on sociocultural influences on
motivation and learning: Big theories revisited (Vol. 4, pp. 223-247). Greenwich, CT: Information Age
Hidi, S. (2000). An interest researcher's perspective: The effects of extrinsic and intrinsic factors on motivation.
In C. Sansone & J. M. Harackiewicz (Eds.), Intrinsic and extrinsic motivation: The search for optimal
motivation and performance (pp. 309-339). San Diego, CA: Academic Press.
Hidi, S., & Ainley, M. (2008). Interest and self-regulation: Relationships between two variables that influence
learning. In D. H. Schunk & B. J. Zimmerman (Eds.), Motivation and self-regulated learning: Theory,
research, and applications (pp. 77-109). New York: Lawrence Erlbaum.
Hidi, S., & Harackiewicz, J. M. (2000). Motivating the academically unmotivated: A critical issue for the 21st
century. Review of Educational Research, 70(2), 151-179.
Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist,
41(2), 111-127. doi: 10.1207/s15326985ep4102_4
Hidi, S., Renninger, K. A., & Krapp, A. (2004). Interest, a motivational variable that combines affective and
cognitive functioning. In D. Y. Dai & R. J. Sternberg (Eds.), Motivation, emotion, and cognition:
Integrative perspectives on intellectual functioning and development (pp. 89-115). Mahwah, NJ:
Lawrence Erlbaum Associates.
Hillman, D. C., Willis, D. J., & Gunawardena, C. N. (1994). Learner-interface interaction in distance education:
An extension of contemporary models and strategies for practitioners. The American Journal of
Distance Education, 8(2), 31-42. doi: 10.1080/08923649409526853
Hirumi, A. (2006). Analysing and designing e-learning interactions. In C. Juwah (Ed.), Interactions in online
education (pp. 46-71). London: Routledge.
Hodges, C. B. (2008). Self-efficacy in the context of online learning environments: A review of the literature
14
and directions for research. Performance Improvement Quarterly, 20(3-4), 7-25.
Hodges, C. B., & Kim, C. (2013). Improving college students' attitudes toward mathematics. TechTrends:
Linking Research & Practice to Improve Learning, 57(4), 59-66. doi: 10.1007/s11528-013-0679-4
Howland, J. L., Jonassen, D., & Marra, R. M. (2012). Meaningful learning with technology (4th ed.). Boston,
MA: Pearson.
Huang, H.-M., & Liaw, S.-S. (2007). Exploring learners' self-efficacy, autonomy, and motivation toward e-
learning. Perceptual & Motor Skills, 105(2), 581-586. doi: 10.2466/PMS.105.6.581-586
Huett, J. B., Kalinowski, K. E., Moller, L., & Huett, K. C. (2008). Improving the motivation and retention of
online students through the use of ARCS-based e-mails. American Journal of Distance Education,
22(3), 159-176. doi: 1080/08923640802224451
Jang, H., Reeve, J., & Deci, E. L. (2010). Engaging students in learning activities: It’s not autonomy support or
structure, but autonomy support and structure. Journal of Educational Psychology, 102(3), 588-600.
doi: 10.1037/a0019682
Jones, A., & Issroff, K. (2007). Learning technologies: Affective and social issues. In G. Conole & M. Oliver
(Eds.), Contemporary perspectives in e-learning research: Themes, methods and impact on practice
(pp. 190-202). London: Routledge.
Juwah, C. (2006). Interactions in online peer learning. In C. Juwah (Ed.), Interactions in online education (pp.
171-190). London: Routledge.
Katz, I., & Assor, A. (2007). When choice motivates and when it does not. Educational Psychology Review,
19(4), 429-442. doi: 10.1007/s10648-006-9027-y
Keller, J. M. (1987). Development and use of the ARCS model of instructional design. Journal of Instructional
Development, 11(4), 2-10. doi: 10.1007/BF02905780
Keller, J. M. (1999). Using the ARCS motivational process in computer-based instruction and distance
education. New Directions for Teaching & Learning, Summer(78), 39-47.
Keller, J. M. (2008). First principles of motivation to learn and e3-learning. Distance Education, 29(2), 175-185.
doi: 10.1080/01587910802154970
Keller, J. M. (2010). Motivational design for learning and performance: The ARCS model approach. New York:
Springer.
Keller, J. M., & Deimann, M. (2012). Motivation, volition, and performance. In R. A. Reiser & J. V. Dempsey
(Eds.), Trends and issues in instructional design and technology (3rd ed., pp. 84-95). Boston, MA:
Pearson.
Keller, J. M., & Suzuki, K. (2004). Learner motivation and e-learning design: A multinationally validated
process. Journal of Educational Media, 29(3), 229-239.
Kickul, G., & Kickul, J. (2006). Closing the gap: Impact of student proactivity and learning goal orientation on
e-learning outcomes. International Journal on E-Learning, 5(3), 361.
Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An
analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based
teaching. Educational Psychologist, 41(2), 75-86. doi: 10.1207/s15326985ep4102_1
Krapp, A. (2002). An educational-psychological theory of interest and its relation to SDT. In E. L. Deci & R. M.
Ryan (Eds.), Handbook of Self-Determination research (pp. 405-427). Rochester, NY: The University
of Rochester Press.
Kuo, Y. C., Walker, A. E., Belland, B. R., & Schroder, K. E. E. (2013). A predictive study of student satisfaction
in online education programs. The International Review of Research in Open and Distance Learning,
14(1), 16-39.
Lee, Y., Choi, J., & Kim, T. (2013). Discriminating factors between completers of and dropouts from online
learning courses. British Journal of Educational Technology, 44(2), 328-337. doi: 10.1111/j.1467-
8535.2012.01306.x
Lepper, M. R., Henderlong Corpus, J., & Iyengar, S. S. (2005). Intrinsic and extrinsic motivational orientations
in the classroom: Age differences and academic correlates. Journal of Educational Psychology, 97(2),
184-196. doi: 10.1037/0022-0663.97.2.184
Lepper, M. R., & Malone, T. W. (1987). Intrinsic motivation and instructional effectiveness in computer-based
education. In R. E. Snow & M. J. Farr (Eds.), Aptitude, learning and instruction (Vol. 3: Conative and
affective process analyses, pp. 255-286). Hillsdale, NJ: Lawrence Erlbaum Associates.
Lin, Y.-M., Lin, G.-Y., & Laffey, J. M. (2008). Building a social and motivational framework for understanding
satisfaction in online learning. Journal of Educational Computing Research, 38(1), 1-27. doi:
10.2190/EC.38.1.a
Lindgren, R., & McDaniel, R. (2012). Transforming online learning through narrative and student agency.
Journal of Educational Technology & Society, 15(4), 344-355.
Liyanagunawardena, T. R., Adams, A. A., & Williams, S. A. (2013). MOOCs: A systematic study of the
published literature 2008-2012. International Review of Research in Open & Distance Learning, 14(3),
15
202-227. Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/1455/2531
Lynch, R., & Dembo, M. (2004). The relationship between self-regulation and online learning in a blended
learning context. International Review of Research in Open and Distance Learning, 5(2). Retrieved
from http://www.irrodl.org/index.php/irrodl/article/view/189/799
Markus, H. R., & Kitayama, S. (1991). Culture and the self: Implications for cognition, emotion, and
motivation. Psychological Review, 98(2), 224-253. doi: 10.1037/0033-295X.98.2.224
Martens, R. L., Gulikers, J., & Bastiaens, T. (2004). The impact of intrinsic motivation on e-learning in authentic
computer tasks. Journal of Computer Assisted Learning, 20(5), 368-376. doi: 10.1111/j.1365-
2729.2004.00096.x
Martens, R. L., & Kirschner, P. A. (2004). Predicting intrinsic motivation. Association for Educational
Communications and Technology (pp. 621-630). Washington, DC: Association for Educational
Communications and Technology.
Matuga, J. M. (2009). Self-regulation, goal orientation, and academic achievement of secondary students in
online university courses. Educational Technology & Society, 12(3), 4-11. Retrieved from
http://www.ifets.info/
McInerney, D. M., & Van Etten, S. (2004). Big theories revisited: The challenge. In D. M. McInerney & S. Van
Etten (Eds.), Research on sociocultural influences on motivation and learning: Big theories revisited
(Vol. 4, pp. 1-11). Greenwich, CT: Information Age.
McLoughlin, C., & Lee, M. J. W. (2008). The three P's of pedagogy for the networked society: Personalization,
participation, and productivity. International Journal of Teaching and Learning in Higher Education,
20(1), 10-27.
Mishra, S., & Juwah, C. (2006). Interactions in online discussions. In C. Juwah (Ed.), Interactions in online
education (pp. 156-170). London: Routledge.
Moore, M. G. (1989). Three types of interaction. American Journal of Distance Education, 3(2), 1-6. doi:
10.1080/08923648909526659
Moore, M. G. (1990). Recent contributions to the theory of distance education. Open Learning, 5(3), 10-15. doi:
10.1080/0268051900050303
Moore, M. G. (1993). Theory of transactional distance. In D. Keegan (Ed.), Theoretical principles of distance
education (pp. 23-38). London: Routledge.
Moore, M. G. (2007). The theory of transactional distance. In M. G. Moore (Ed.), Handbook of distance
education (2nd ed., pp. 89-108). Mahwah, N.J: Lawrence Erlbaum.
Moore, M. G., & Kearsley, G. (2005). Distance education: A systems view (2nd ed.). Belmont, CA: Wadsworth.
Moos, D. C., & Azevedo, R. (2008). Exploring the fluctuation of motivation and use of self-regulatory processes
during learning with hypermedia. Instructional Science, 36(3), 203 - 231. doi: 10.1007/s11251-007-
9028-3
Moos, D. C., & Azevedo, R. (2009). Learning with computer-based learning environments: A literature review
of computer self-efficacy. Review of Educational Research, 79(2), 576-600. doi:
10.3102/0034654308326083
Moos, D. C., & Marroquin, E. (2010). Review: Multimedia, hypermedia, and hypertext: Motivation considered
and reconsidered. Computers in Human Behavior, 26, 265-276. doi: 10.1016/j.chb.2009.11.004
Murayama, K., Elliot, A. J., & Friedman, R. (2012). Achievement goals and approach-avoidance motivation. In
R. M. Ryan (Ed.), The Oxford handbook of human motivation (pp. 191-207). Oxford, UK: Oxford
University Press.
Ng, C. (2008). Multiple-goal learners and their differential patterns of learning. Educational Psychology, 28(4),
439-456. doi: 10.1080/01443410701739470
Ng, C. (2009). Profiling learners' achievement goals when completing academic essays. Educational
Psychology, 29(3), 279-295. doi: 10.1080/01443410902797988
Ng, C. (2012). The role of self-efficacy, control beliefs and achievement goals on learning among distance
learners. In J. L. Moore & A. D. Benson (Eds.), International perspectives of distance learning in
higher education (pp. 233-252). Shanghai: InTech.
Nichols, M. (2008). E-learning in context - #1. ePrimer series. Retrieved from Ako Aotearoa website
http://akoaotearoa.ac.nz/project/eprimer-series/resources/files/e-learning-context-1-eprimer-series
Paas, F., Tuovinen, J. E., van Merriënboer, J. J. G., & Darabi, A. A. (2005). A motivational perspective on the
relation between mental effort and performance: Optimizing learner involvement in instruction.
Educational Technology Research & Development, 53(3), 25-34. doi: 10.1007/BF02504795
Paris, S. G., & Turner, J. C. (1994). Situated motivation. In P. R. Pintrich, D. R. Brown & C. E. Weinstein
(Eds.), Student motivation, cognition, and learning: Essays in honor of Wilbert J. McKeachie (pp. 213-
237). Hillsdale, NJ: Lawrence Erlbaum.
Park, J.-H., & Choi, H. J. (2009). Factors influencing adult learners' decision to drop out or persist in online
learning. Educational Technology & Society, 12(4), 207-217. Retrieved from http://www.ifets.info/
16
Patall, E. A., Cooper, H., & Robinson, J. C. (2008). The effects of choice on intrinsic motivation and related
outcomes: A meta-analysis of research findings. Psychological Bulletin, 134(2), 270-300. doi:
10.1037/0033-2909.134.2.270
Paulus, T., & Scherff, L. (2008). "Can anyone offer any words of encouragement?" Online dialogue as a support
mechanism for preservice teachers. Journal of Technology and Teacher Education, 16(1), 113-136.
Piaget, J. (1977). The origin of intelligence in the child. (M. Cook, Trans.). Harmondsworth, England: Penguin
Books.
Ratelle, C. F., Guay, F., Vallerand, R. J., Larose, S., & Senécal, C. (2007). Autonomous, controlled, and
amotivated types of academic motivation: A person-oriented analysis. Journal of Educational
Psychology, 99(4), 734-746. doi: 10.1037/0022-0663.99.4.734
Reeve, J. (2009). Why teachers adopt a controlling motivating style toward students and how they can become
more autonomy supportive. Educational Psychologist, 44(3), 159 - 175. doi:
10.1080/00461520903028990
Reeve, J., Deci, E. L., & Ryan, R. M. (2004). Self-determination theory: A dialectical framework for
understanding sociocultural influences on student motivation. In D. M. McInerney & S. Van Etten
(Eds.), Research on sociocultural influences on motivation and learning: Big theories revisited (Vol. 4,
pp. 31-60). Greenwich, CT: Information Age.
Reeve, J., Jang, H., Hardre, P., & Omura, M. (2002). Providing a rationale in an autonomy-supportive way as a
strategy to motivate others during an uninteresting activity. Motivation and Emotion, 26(3), 183-207.
doi: 10.1023/A:1021711629417
Reeve, J., Nix, G., & Hamm, D. (2003). Testing models of the experience of self-determination in intrinsic
motivation and the conundrum of choice. Journal of Educational Psychology, 95(2), 375-392. doi:
10.1037/0022-0663.95.2.375
Reeve, J., Ryan, R. M., Deci, E. L., & Jang, H. (2008). Understanding and promoting autonomous self-
regulation: A self-determination theory perspective. In D. H. Schunk & B. J. Zimmerman (Eds.),
Motivation and self-regulated learning: Theory, research, and applications (pp. 223-244). New York:
Lawrence Erlbaum.
Renninger, K. A., Cai, M., Lewis, M., Adams, M., & Ernst, K. (2011). Motivation and learning in an online,
unmoderated, mathematics workshop for teachers. Educational Technology Research & Development,
59(2), 229-247. doi: 10.1007/s11423-011-9195-4
Rentroia-Bonito, M. A., Jorge, J., & Ghaoui, C. (2006). Motivation to e-learn within organizational settings: An
exploratory factor structure. International Journal of Distance Education Technologies, 4(3), 24-35.
Rienties, B., Giesbers, B., Tempelaar, D., Lygo-Baker, S., Segers, M., & Gijselaers, W. (2012). The role of
scaffolding and motivation in CSCL. Computers & Education, 59(3), 893-906. doi:
10.1016/j.compedu.2012.04.010
Rienties, B., Tempelaar, D., Van den Bossche, P., Gijselaers, W., & Segers, M. (2009). The role of academic
motivation in computer-supported collaborative learning. Computers in Human Behavior, 25(6), 1195-
1206. doi: 10.1016/j.chb.2009.05.012
Rovai, A. P. (2000). Building and sustaining community in asynchronous learning networks. The Internet and
Higher Education, 3(4), 285-297. doi: 10.1016/S1096-7516(01)00037-9
Rovai, A. P. (2002). Sense of community, perceived cognitive learning, and persistence in asynchronous
learning networks. The Internet and Higher Education, 5(4), 319-332. doi: 10.1016/S1096-
7516(02)00130-6
Rovai, A. P. (2007). Facilitating online discussions effectively. The Internet and Higher Education, 10(1), 77-88.
doi: 10.1016/j.iheduc.2006.10.001
Rovai, A. P., & Lucking, R. (2003). Sense of community in a higher education television-based distance
education program. Educational Technology Research and Development, 51(2), 5-16. doi:
10.1007/BF02504523
Rovai, A. P., Ponton, M., Wighting, M. J., & Baker, J. (2007). A comparative analysis of student motivation in
traditional classroom and e-learning courses. International Journal on E-Learning, 6(3), 413-432.
Ryan, R. M., & Deci, E. L. (2000a). Intrinsic and extrinsic motivations: Classic definitions and new directions.
Contemporary Educational Psychology, 25(1), 54-67. doi: 10.1006/ceps.1999.1020
Ryan, R. M., & Deci, E. L. (2000b). Self-determination theory and the facilitation of intrinsic motivation, social
development, and well-being. American Psychologist, 55(1), 68-78. doi: 10.1037/0003-066X.55.1.68
Ryan, R. M., & Deci, E. L. (2002). Overview of self-determination theory: An organismic perspective. In E. L.
Deci & R. M. Ryan (Eds.), Handbook of Self-Determination research (pp. 3-33). Rochester, NY: The
University of Rochester Press.
Ryan, R. M., & Deci, E. L. (2006). Self-regulation and the problem of human autonomy: Does psychology need
choice, self-determination, and will? Journal of Personality, 74(6), 1557-1585. doi: 10.1111/j.1467-
6494.2006.00420.x
17
Ryan, R. M., La Guardia, J. G., Solky-Butzel, J., Chirkov, V., & Kim, Y. (2005). On the interpersonal regulation
of emotions: Emotional reliance across gender, relationships, and cultures. Personal Relationships,
12(1), 145-163. doi: 10.1111/j.1350-4126.2005.00106.x
Sansone, C., Fraughton, T., Zachary, J., Butner, J., & Heiner, C. (2011). Self-regulation of motivation when
learning online: The importance of who, why and how. Educational Technology Research &
Development, 59(2), 199-212. doi: 10.1007/s11423-011-9193-6
Schallert, D. L., & Reed, J. H. (2003). Intellectual, motivational, textual, and cultural considerations in teaching
and learning with computer-mediated discussion. Journal of Research on Technology in Education,
36(2), 103-118.
Schunk, D. H., Meece, J. L., & Pintrich, P. R. (2014). Motivation in education: Theory, research, and
applications (4th ed.). Boston, MA: Pearson.
Schunk, D. H., & Usher, E. L. (2012). Social cognitive theory and motivation. In R. M. Ryan (Ed.), The Oxford
handbook of human motivation (pp. 13-27). Oxford, UK: Oxford University Press.
Schunk, D. H., & Zimmerman, B. J. (2006). Competence and control beliefs: Distinguishing the means and
ends. In P. A. Alexander & P. H. Winne (Eds.), Handbook of educational psychology (2nd ed., pp. 349-
367). Mahwah, NJ: Lawrence Erlbaum.
Shea, P., Swan, K., & Pickett, A. (2005). Developing learning community in online asynchronous college
courses: The role of teaching presence. Journal of Asynchronous Learning Networks, 19(4), 59-82.
Sheldon, K., M. , & Krieger, L., S. (2007). Understanding the negative effects of legal education on law
students: A longitudinal test of self-determination theory. Personality and Social Psychology Bulletin,
33(6), 883-897. doi: 10.1177/0146167207301014
Shen, D., Cho, M.-H., Tsai, C.-L., & Marra, R. (2013). Unpacking online learning experiences: Online learning
self-efficacy and learning satisfaction. The Internet and Higher Education, 19, 10-17. doi:
10.1016/j.iheduc.2013.04.001
Shroff, R. H., Vogel, D., Coombes, J., & Lee, F. (2007). Student e-learning intrinsic motivation: A qualitative
analysis. Communications of the Association for Information Systems, 2007(19), 241-260.
Shroff, R. H., & Vogel, D. R. (2009). Assessing the factors deemed to support individual student intrinsic
motivation in technology supported online and face-to-face discussions. Journal of Information
Technology Education, 8, 59-85.
Siemens, G. (2005). Connectivism: A learning theory for the digital age. Instructional Technology and Distance
Education, 2(1), 3-10. Retrieved from http://www.elearnspace.org/Articles/connectivism.htm
Thach, E. C., & Murphy, K. L. (1995). Competencies for distance education professionals. Educational
Technology Research and Development, 43(1), 57-79. doi: 10.1007/BF02300482
Turner, J. C., & Patrick, H. (2008). How does motivation develop and why does it change? Reframing
motivation research. Educational Psychologist, 43(3), 119-131. doi: 10.1080/00461520802178441
Vallerand, R. J., Pelletier, L. G., & Koestner, R. (2008). Reflections on self-determination theory. Canadian
Psychology, 49(3), 257-262. doi: 10.1037/a0012804
Van Etten, S., Pressley, M., McInerney, D. M., & Liem, A. D. (2008). College seniors' theory of their academic
motivation. Journal of Educational Psychology, 100(4), 812-828. doi: 10.1037/0022-0663.100.4.812
Vrasidas, C., & McIsaac, M. S. (1999). Factors influencing interaction in an online course. The American
Journal of Distance Education, 13(3), 22-35. doi: 10.1080/08923649909527033
Vygotsky, L. (1978). Mind and Society: The development of higher psychological processes. Cambridge, MA:
Harvard University Press.
Wang, S.-L., & Lin, S. S. J. (2007a). The application of social cognitive theory to web-based learning through
NetPorts. British Journal of Educational Technology, 38(4), 600-612. doi: 10.1111/j.1467-
8535.2006.00645.x
Wang, S.-L., & Lin, S. S. J. (2007b). The effects of group composition of self-efficacy and collective efficacy on
computer-supported collaborative learning. Computers in Human Behavior, 23(5), 2256-2268. doi:
10.1016/j.chb.2006.03.005
Wang, S.-L., & Wu, P.-Y. (2008). The role of feedback and self-efficacy on web-based learning: The social
cognitive perspective. Computers & Education, 51(4), 1589-1598. doi:
10.1016/j.compedu.2008.03.004
Wenger, E. (1998). Communities of practice: Learning, meaning, and identity. Cambridge, U.K: Cambridge
University Press.
Whipp, J. L., & Chiarelli, S. (2004). Self-regulation in a web-based course: A case study. Educational
Technology Research & Development, 52(4), 5-22. doi: 10.1007/BF02504714
Wighting, M. J., Liu, J., & Rovai, A. P. (2008). Distinguishing sense of community and motivation
characteristics between online and traditional college students. Quarterly Review of Distance
Education, 9(3), 285-295.
Xie, K., DeBacker, T. K., & Ferguson, C. (2006). Extending the traditional classroom through online discussion:
18
The role of student motivation. Journal of Educational Computing Research, 34(1), 67-89. doi:
10.2190/7BAK-EGAH-3MH1-K7C6
Yukselturk, E., & Bulut, S. (2007). Predictors for student success in an online course. Educational Technology
& Society, 10(2), 71-83. Retrieved from http://www.ifets.info/
Zaharias, P., & Poylymenakou, A. (2009). Developing a usability evaluation method for e-learning applications:
Beyond functional usability. International Journal of Human-Computer Interaction, 25(1), 75-98. doi:
10.1080/10447310802546716
19
... However, motivation remains one of the main issues in online learning which should maintain the students' feelings of behavior, emotional and cognitive engagement, and success [1,6,15,21,24]. Several authors found that online studies conducted in the context of the COVID-19 pandemic had a strong impact on students' learning motivation [18,23,33]. ...
... There is no doubt that in various circumstances (in this case, due to pandemic conditions) motivation remains an essential factor. According to researchers, motivation is an indispensable reason for fostering student academic achievement [15,37]. Categorically, motivation would be a crucial path towards personal/social success or to academic/professional performance. ...
... On the other hand, there is a tendency to view intrinsic and extrinsic motivation as opposites, several studies being focused exclusively on intrinsic motivation. It is important to consider and explore motivation in "real-life" online environments, taking into account a varied range of social and contextual influences [15]. ...
Conference Paper
Full-text available
The pandemic generated by the Coronavirus COVID-19 challenged universities to go exclusively online thus affecting both the education and personal life of teachers and students. This work analyzes the perceptions of Lithuanian and Romanian university students as regards the extrinsic and intrinsic motivation of online learning during the pandemic. Following the technology acceptance theory, extrinsic motivation has been operationalized as the perceived usefulness and intrinsic motivation as the perceived enjoyment. Although the results show a low to moderate motivation for online/distance learning, interesting differences between the two motivating factors have been found, as well as between the perceptions of students from the two countries.
... Relative to this, motivation can impact how we are learning, what we are learning, and when we decide to learn (Schunk & Usher, 2012, as cited in Hartnett, 2016). Hence, the amount of motivation that students possess can affect the process of their learning. ...
... Moreover, the absence of intrinsic and extrinsic motivation is associated with amotivation, wherein the students are unwilling to learn or have less motivation in learning (Gustiani, 2020). Additionally, amotivation also leads to having low self-efficacy, the feeling of being incapable, believing that one's action will have no good outcome, and placing a low value on the tasks that one needs to undertake (Hartnett, 2016). Supportive of this Chambers (1993, as cited in Yadav & BaniAta, 2013) posited that various elements can cause students to be unmotivated and it functions differently with students under different circumstances. ...
... Also, the study conducted by Ushida (2005), investigated students' attitudes and motivation in second language learning in online language courses. As a matter of fact, it is believed that motivation and learning have a significant correlative relationship (Brophy, 2010, as cited in Hartnett, 2016). Thus, motivation has been evidently investigated across various traditional academic realms (Schunk, Meece, & Pintrich, 2014, as cited in Hartnett, 2016). ...
Article
Full-text available
The COVID-19 outbreak brought unprecedented challenges in the academe. Educational institutions transitioned to distance learning primarily through online and modular learning. Moreover, with the rapid development of online learning, one of the concerns in the field of online education is regarding students' motivation to learn and to stay engaged in an online environment. Thus, this present study aimed to determine the motivations and amotivations of language learners in online language learning. The participants of the study include seven language learners, who were selected through a purposive sampling method. A descriptive qualitative research design was employed, and individual in-depth interviews were conducted to gather the data. The responses were transcribed and analyzed through thematic analysis. Furthermore, it was found out in the interviews that the motivations of the respondents in learning English online include learning in a convenient setup , creating a room for independent learning, utilizing online learning tools, apps, and resources, developing technological competence, watching multimedia videos for learning, and exploring the features of the educational applications. On the contrary, the limited interaction, less interactive activities, poor and unstable internet connectivity, less student participation, lack of comprehensive discussion of the lesson, rare opportunity to enhance speaking skills, absence or delayed feedback from the teacher, inconsiderate teachers towards students' situation, and the fact that online language learning can trigger procrastination and unproductivity are found to be the amotivations of the respondents in learning English online.
... The problem of motivation in online learning was recognized by academics before COVID-19 caused the sudden increase in its use [35,36]. As participation in online courses is more anonymous, it is easier to zone out and/or do something unrelated to the lecture than in physical classrooms. ...
Article
Full-text available
COVID-19 has forced students to readjust to online learning. The current study aimed to investigate attitudes of Polish students towards online education, relationships between learning preferences and temper traits, and differences in learning preferences among extramural and full-time students. The study recruited 185 college students between May and June 2021. The findings indicated between group differences in learning preferences, with extramural students preferring online education slightly more than full-time students. Two temper traits, briskness and activity, appeared to be significant predictors of positive attitude towards online learning. However, as this was a pilot study, further investigations are recommended.
... Some studies cited the lack of motivation in online classes was due to the problems with the internet connection, and delayed feedback from the teachers, and these would not be likely to happen in physical classes (Allam et al., 2020;Chung et al., 2020;Wulanjani & Indriani 2021). On the contrary, students would have to adopt the self-learning concept, need to be highly motivated, and engaged in online tasks to succeed in this situation (Hartnett, 2016;Sansone, 2011). These contexts would be related to learner readiness for online learning, but as reported by Chung et al. (2020, p. 55), "more than half of the respondents indicated that if given a choice, they did not want to continue with online learning next semester" since they were not familiar with selfdirected learning and having difficulties in engaging in online lessons. ...
Article
Full-text available
This study intended to fill the gap of undergraduates’ academic motivation in Malaysia and Indonesia where, to date, little study has been done. It investigated and compared undergraduates’ academic motivation levels in English online classes in two universities in both countries. Online questionnaires on students’ self-regulated learning (SRL) and self-efficacy towards online learning adapted from Motivated Strategies for Learning Questionnaire (MSLQ) were distributed to 206 undergraduates from University A in Malaysia and 174 undergraduates from University B in Indonesia. Switching from physical traditional to online classes is the new norm that could be challenging and demotivating, but the results showed that the students from both universities achieved mostly high mean scores of the SRL and self-efficacy items. This indicated that their academic motivation levels were high, they were in control over their learning process, and have positive perceptions towards online classes. This uniformity also implied that although English is a second language in Malaysia, and a foreign language in Indonesia, the undergraduates were not affected by their linguistics, and institutional contexts. This study has contributed towards the extension of the current knowledge involving undergraduates’ academic motivation towards learning English online and suggested that teachers could help to strategise students’ SRL and self-efficacy to increase their English language performances, particularly in the pandemic era. Further research could explore the effects of academic motivation on learning outcomes or language performance as this could assist teachers to improve learners’ English proficiency in online classes.
... Accordingly, our study revealed that intrinsic motivation is more important than extrinsic motivation for online learners to succeed in both modes. This is consistent with the tendency to mainly focus more on intrinsic than extrinsic motivation in online learning settings [49][50][51]. ...
Article
Full-text available
Due to the COVID-19 pandemic, schools and universities across the world have had to switch to online learning, which is offered either synchronously or asynchronously. This study examined the role of self-regulation on students’ performance in each of these modes by comparing the use of self-regulation skills between high and low achievers in each mode and assessing the relationships of using these skills with students’ performance. The data were collected from students who enrolled in a data structures course in fall 2020 in either synchronous or asynchronous mode. The results show that self-regulation is an essential factor for learners’ success in both modes of online learning. However, there was a variance of using self-regulating learning strategies between students in synchronous and asynchronous modes.
... Motivation may be defined as the internal condition that secures and maintains an individual's engagement in the learning process, and although cognitive theory has described the importance of motivation in connection with multi-media material, this phenomenon has not been sufficiently researched (Mayer, 2005a, p.171). Despite the lack of an agreed upon theoretical framework for the incorporation of motivational elements within instructional design, designers are able to incorporate a variety of characteristics into instructional material to increase user motivation and interest (Hartnett, 2016). Another aim of the present study was to examine the effects that the inclusion of attractive elements such as interactivity and animation in distance-learning material has on the level of motivation among participants. ...
Article
Full-text available
One of the objectives of this research is to develop and validate the Instructional Material Motivation Scale for Single-Use (IMMS-SU) instrument in the Turkish context. The IMMS-SU was developed and validated in a two-phased process on a sample of 1654 students. The Exploratory Factor Analysis revealed that IMMS-SU included 14 items (χ2 = 332.59; sd = 74; p < 0.001), the fitness indices were found to be RMSEA = .077; SRMR = .040; AGFI = .88; NFI = .95; CFI = .96; and GFI = .92. The Cronbach’s Alpha coefficients regarding the whole scale was calculated as α = 0.95. Thereafter, in the second study, the animated and interactive video materials used in distance education were scrutinized in the context of openness to different materials, time spent viewing, motivation, and cognitive load. A total of 933 students participated who had a distance education experience. In order to collect data, the extraneous cognitive load instrument (Kalyuga et al., Human Factors, 40(1), 1-17, Kalyuga, S., Chandler, P., & Sweller, J. (1998). Levels of expertise and instructional design. Human Factors, 40(1), 1–17. 10.1518/001872098779480587), IMMS-SU, and questionnaire items were used. According to the findings, it was determined that animation and interactive video materials did not cause a higher level of cognitive load on the participants, and both groups had higher material motivation. In addition, it was revealed that interactive video materials caused a higher extraneous cognitive load in participants than animation group. It was figured out that as the openness levels of the participants watching the animation and interactive materials decreased, their cognitive load levels increased. In the light of the results, some suggestions have been recommended for further research.
... The respondents almost confirmed this issue as they see a downside of e-learning under the COVID-19 crisis is lack of confidence in the test results with a mean value of 1.7 displayed for item 19. Also, motivation was identified as a critical factor in achieving a successful online learning environment (Hartnett, 2016). Question 21 reflected respondents' opinions that instructors and students do not feel confident that their institutions encourage or motivate the use of e-learning with a mean value of 1.87. ...
Article
Full-text available
Educational systems worldwide have been forced into shifting to online learning during COVID-19 pandemic. This decision faced diverse challenges, especially in underdeveloped countries that still use traditional teaching methods, with minimal or no integrated technology, and no guidance in the literature. This study explores factors, challenges, and adaptation initiatives that might underlie the success and failure of abrupt shifting and accepting online learning systems. To explore the acceptance of online learning under these extreme circumstances, the reactions of Kuwait educational institutions to COVID-19 were collected and analyzed. A framework was utilized, and a questionnaire developed to enable quantitative analysis of these data. In total, 4,024 responses were gathered from instructors and students with acceptable reliability. Findings from the statistical analysis unveiled specific acceptance facts relevant to the crisis within its environment. This study establishes the utility of this framework for researchers to synthesize users' acceptance of online learning systems.
Article
Full-text available
E-learning systems are widely deployed in higher education institutions but sustaining students’ continued use of e-learning systems remains challenging. This study investigated the relationship between e-learning engagement, flow experience and learning management system continuance via a mediated moderation interaction model. The context of the study is a Moodle LMS supporting a blended learning environment. After controlling age and gender, a PLS analysis of 92 students’ samples with a reflective flow construct explained 49% of the variance in the research model. The analysis shows that flow mediates e-engagement and perceived ease of use with a direct positive impact on e-learning system continuance. Flow has an indirect impact through perceived usefulness on e-learning system continuance. However, the direct impact of flow on system continuance weakens as e-learning engagement increases. This finding may help to explain the mixed and inconsistent impact of flow in the e-learning system continuance literature. The dual effect of flow suggests that instructors must carefully balance pedagogical decisions intended to heighten flow experience to generate positive learning outcomes through e-engagement and its consequence of reduced impact on continued system use.
Book
I: Background.- 1. An Introduction.- 2. Conceptualizations of Intrinsic Motivation and Self-Determination.- II: Self-Determination Theory.- 3. Cognitive Evaluation Theory: Perceived Causality and Perceived Competence.- 4. Cognitive Evaluation Theory: Interpersonal Communication and Intrapersonal Regulation.- 5. Toward an Organismic Integration Theory: Motivation and Development.- 6. Causality Orientations Theory: Personality Influences on Motivation.- III: Alternative Approaches.- 7. Operant and Attributional Theories.- 8. Information-Processing Theories.- IV: Applications and Implications.- 9. Education.- 10. Psychotherapy.- 11. Work.- 12. Sports.- References.- Author Index.
Chapter
This chapter explores approaches to learning that we argue best reflect a constantly changing, dynamic environment as reflected in current thinking (Giddens, 2000; Beck, 1992; Castells, 1996). We acknowledge that there are many different schools of thought in terms of learning theories, but we would like to focus here on those we believe are most relevant and applicable to e-learning. This will include a discussion of the following: a critique of behaviourist approaches and their impact, advocacy of the application of experiential/reflective, social constructivism and socio-cultural approaches, and the argument that effective e-learning usually requires, or involves, high quality educational discourse (Ravenscroft, 2004a) combined with an experiential and reflective approach (Conole et al., 2004; Mayes and de Freitas, 2004).