The importance of students’ motivational dispositions for designing learning analytics

Abstract

Depending on their motivational dispositions, students choose different learning strategies and vary in their persistence in reaching learning outcomes. As learning is more and more facilitated through technology, analytics approaches allow learning processes and environments to be analyzed and optimized. However, research on motivation and learning analytics is at an early stage. Thus, the purpose of this quantitative survey study is to investigate the relation between students’ motivational dispositions and the support they perceive through learning analytics. Findings indicate that facets of students’ goal orientations and academic self-concept impact students’ expectations of the support from learning analytics. The findings emphasize the need to design highly personalized and adaptable learning analytics systems that consider students’ dispositions and needs. The present study is a first attempt at linking empirical evidence, motivational theory, and learning analytics.

Full text

The importance of students’ motivational dispositions
for designing learning analytics
Clara Schumacher
1
•Dirk Ifenthaler
1,2
Published online: 10 September 2018
The Author(s) 2018
Abstract
Depending on their motivational dispositions, students choose different learning
strategies and vary in their persistence in reaching learning outcomes. As learning is
more and more facilitated through technology, analytics approaches allow learning
processes and environments to be analyzed and optimized. However, research on
motivation and learning analytics is at an early stage. Thus, the purpose of this
quantitative survey study is to investigate the relation between students’ motiva-
tional dispositions and the support they perceive through learning analytics. Find-
ings indicate that facets of students’ goal orientations and academic self-concept
impact students’ expectations of the support from learning analytics. The findings
emphasize the need to design highly personalized and adaptable learning analytics
systems that consider students’ dispositions and needs. The present study is a first
attempt at linking empirical evidence, motivational theory, and learning analytics.
Keywords Learning analytics Learning motivation Achievement
motivation Self-concept
Introduction
Learning theories such as self-regulated learning highlight the importance of
motivation for learning (Boekaerts 1999; Pintrich 2000c; Schunk et al. 2008;
Zimmerman 2002). Motivation is a multifaceted concept several disciplines pay
attention to as it is considered to be the driver for a person’s actions and not obvious
&Clara Schumacher
clara.schumacher@bwl.uni-mannheim.de
Dirk Ifenthaler
dirk@ifenthaler.info
1
Economic and Business Education – Learning, Design and Technology, University of
Mannheim, L4, 1, 68161 Mannheim, Germany
2
UNESCO, Deputy Chair of Data Science in Higher Education Learning and Teaching, Curtin
University, Bentley, Perth, Australia
123
Journal of Computing in Higher Education (2018) 30:599–619
https://doi.org/10.1007/s12528-018-9188-y(0123456789().,-volV)(0123456789().,-volV)
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from external. Focusing on a cognitive approach, motivation can be defined as ‘‘the
process whereby goal-directed activity is instigated and sustained’’ (Schunk et al.
2008, p. 4). This definition implies that motivation is a process as well as goal-
oriented and that both initiating activities and persisting in activities are crucial to
achieving the designated goals.
Motivational factors, such as interest, autonomy, competence, relatedness, and
self-efficacy, determine students’ regulation effort towards a learning goal (Eseryel
et al. 2014; Vansteenkiste et al. 2004; Zimmerman and Schunk 2008). Hence,
differences in learning outcomes are related to students’ capability to self-regulate
their learning, individual characteristics, and motivational dispositions (Schunk and
Zimmerman 2008; Zimmerman 2002). Especially in highly self-regulated learning
environments, such as higher education or online learning, motivation is crucial for
successful learning (Chen and Jang 2010; Joo et al. 2015; Keller 2008a; Keller and
Suzuki 2004; Moos and Bonde 2016). Self-regulated learning processes are
considered to be interdependently connected to motivational processes, as
motivation affects learning strategy selection, learning processes, and outcomes.
Likewise, self-regulation can influence learners’ motivation (Lehmann et al. 2014;
Zimmerman 1990,2011; Zimmerman and Schunk 2008).
In the past few years, higher education has seen various changes, due to larger
study cohorts but also higher withdrawals (Mah 2016), as well as the advancement
of applying technologies for learning. One important driver for changing learning
and learning environments is the availability of vast amounts of educational data
and unforeseen possibilities to make use of them (Long and Siemens 2011).
Learning analytics are a key concept related to this increase in educational data.
They use static and dynamic information about learners and learning environments,
assessing, eliciting, and analyzing it, for real-time modeling prediction, and
optimization of learning processes, learning environments, and educational
decision-making (Ifenthaler 2015). Current research on learning analytics focusses
on technical issues and data processing (Berland et al. 2014; Costa et al. 2017), on
data privacy (Drachsler and Greller 2016; Ifenthaler and Schumacher 2016; Rubel
and Jones 2016; West et al. 2016), on developing user systems (d’Aquin et al.
2014), on relationships between learner characteristics and learning outcome (Ellis
et al. 2017; Gas
ˇevic
´et al. 2017; Liu et al. 2017), or on specific applications for
dashboards (Park and Jo 2015; Schumacher and Ifenthaler 2018; Verbert et al.
2013). However, linking learning analytics with learning theories is still at an early
stage (Marzouk et al. 2016). Additionally, student motivation is not yet sufficiently
considered for analyses of learning analytics (Lonn et al. 2015). In a qualitative
study, Corrin and da Barba (2014) investigated how feedback through learning
analytics dashboards impacts students’ motivation. Their findings indicate that
students mostly perceived a positive effect on their motivation in terms of effort
regulation or awareness of their progress. However, some participants also indicated
that it did not influence their motivation at all. Accordingly, further empirical
studies are required to identify the capabilities of learning analytics for facilitating
learning processes and especially for supporting and not impairing learning
motivation.
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To add evidence to this gap in research, the focus of this study was to investigate
students’ motivational dispositions and its relationship to perceived support from
learning analytics systems.
Theoretical framework
Motivation in (self-regulated) learning processes
Motivation is considered to be a result of an interaction between environmental and
individual factors (Cook and Artino Jr 2016; Hartnett et al. 2011; Keller 2008b;
Svinicki and Vogler 2012). Thus, internal as well as external factors can influence a
person’s motivation, such as self-efficacy beliefs (Bandura 1977; Zimmerman et al.
2017), perceived autonomy (Deci and Ryan 2008; Deci et al. 1996), attributions
(Schunk 2008; Weiner 1985), value of the task, and expected difficulty in reaching
the goal (Eccles and Wigfield 2002; Engelschalk et al. 2016; Wigfield et al. 2009),
goal orientations (Elliot 2005; Elliot and Hulleman 2017), academic self-concept, or
the design of the learning environment (Keller and Suzuki 2004).
Self-regulating their learning demands a great effort of students; thus, they need
to be motivated to initiate and sustain within these processes (Pintrich 1999). Self-
regulation requires metacognitive monitoring, control of learning activities, and
motivational states to reach the designated learning outcomes. Learners need to
adjust their behavior, cognition, or motivation accordingly (Lehmann et al. 2014;
Winne and Hadwin 2008). Learning motivation and goal setting in self-regulated
learning are influenced by task conditions and requirements, students’ beliefs about
self-efficacy, outcome expectancies, and individual characteristics (e.g., disposi-
tions, prior experiences, and knowledge) (Winne and Hadwin 2008; Zimmerman
et al. 2017). While engaging in the performance phase, motivation is crucial to
maintaining learning activities. Additionally, self-regulated learners use several
strategies to control and regulate their motivation, such as (a) extrinsic regulation
(self-rewarding, reminding of performance goals), (b) intrinsic regulation (increase
task value, interest, or self-efficacy beliefs), (c) volition (change the environment,
attention), and (d) information processing (help-seeking, cognitive strategies)
(Corno 1993; Winne and Hadwin 2008; Wolters 1998). Thus, motivational
constructs are considered to have an impact on self-regulated learning processes
(Duffy and Azevedo 2015; Zimmerman 2011; Zimmerman and Schunk 2008).
This study focusses on students’ goal orientations and their academic self-
concept as crucial motivational components of self-regulated learning (Eccles and
Wigfield 2002; Pintrich 1999,2000c). Key aspects of motivational components are
described below and will be linked to learning analytics.
Goal orientation
Achievement goals aim to explain ‘‘the purpose or reason students are pursuing an
achievement task as well as the standards or criteria they construct to evaluate their
competence or success on the task’’ (Pintrich 2000a, p. 94). They are described as
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patterns of beliefs and feelings about success, effort, ability, errors, feedback, and
standards of evaluation (Elliot 2005). Thus, achievement goal theories assume that
students have different learning behaviors because they have different goal
orientations when engaging in learning processes (Cook and Artino Jr 2016; Dweck
and Leggett 1988; Elliot 2005; Elliot and Hulleman 2017; Schunk et al. 2008).
The assumption is that there are two different types of achievement goal
orientations (see Table 1). (1) Learning goal orientation (also labeled as mastery
goal orientation): these learners focus on the intrinsic value of learning, such as
gaining new knowledge and skills. Learners who have a learning goal orientation
assume that intelligence and skills are controllable via learning activities as success
is related to effort whereas failure is considered to be an opportunity to learn
(Dweck and Leggett 1988). These learning goals are divided into (1a) learning-
approach goals, where learners focus on gaining competence by seeking challenging
tasks and persisting in goal-achievement behavior even when facing obstacles and
(1b) learning-avoidance goals when learners try to avoid losing skills or abilities and
being wrong, not relative to others but only in reference to themselves or the task
(Elliot 2005; Pintrich 2000a; Senko et al. 2011).
(2) Performance goal orientation: these learners focus on achieving better
learning outcomes than others and avoid appearing as unintelligent. This goal
orientation is associated with perceiving intelligence as being static, avoiding
challenges and giving up quickly, as failure is seen as a lack of ability; only if
learners are self-confident in their intelligence or competence they seek challenges
(Dweck and Leggett 1988; Elliot 2005). Performance goals are further divided into
(2a) performance-approach goals, as those of students who are willing to show their
competences to others or to outperform their peers; and (2b) performance-avoidance
goals, related to students who try to hide their incompetency by avoiding challenges
or uncertainty. Additionally, work-avoidance goals refer to students’ tendency to
reach goals by avoiding work or effort at all (Harackiewicz et al. 1997; Spinath et al.
Table 1 Exemplary overview about the characteristics of achievement goals
Approach Avoidance
Learning goal
orientation
Learning-approach goals:
Develop skills and abilities
Understand a task
Seek for challenging tasks
Develop competence
Learning-avoidance goals:
Avoid losing skills and
abilities
Avoid being wrong
Avoid not understanding a
task or material
Avoid intrapersonal
incompetence
Performance goal
orientation
Performance-approach goals:
Show competence to others by seeking appropriate
tasks to appear talented
Outperforming peers
Performance-avoidance
goals:
Avoid showing
incompetence to others
Avoid challenges
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2012). This goal orientation is assumed to be distinct from the above achievement
goals as it is specifically characterized by the absence of achievement goal adoption
(Elliot 1999).
In general, approach goals (learning and performance goal orientation) are
positively related to performance or achievement while avoidance goals (learning
and performance goal orientation) are negatively related (Van Yperen et al. 2014).
Linnenbrink-Garcia et al. (2008) reported in a meta-analysis that more studies found
significant relations of learning-approach goals with achievement than with
performance-approach goals, in addition some found negative relations of
performance-approach goals with achievement. Further, performance goal orienta-
tion is associated with higher academic outcomes in competitive educational
contexts whereas learning goal orientation is related to interest and deeper learning
strategies (Harackiewicz et al. 1998). Performance-avoidance goals are associated
to a negative learning outcome (Elliot and Hulleman 2017). Learners with
performance orientation are likely to attribute failure and effort to personal
incompetence or low ability and thus as non-controllable (Dweck and Leggett
1988). Help seeking, which is considered to be a self-regulatory strategy
(Zimmerman and Schunk 2008), is related to the goal orientations of learners, as
learning-approach goal oriented learners think of this as a possibility to enhance
their competence whereas avoidance-oriented learners might fear showing low
ability (Zimmerman and Schunk 2008). Depending on the context and situation, one
goal orientation might be predominant. However, some learners might generally
tend to adopt a learning oriented goal approach whereas others are more likely to
behave more performance goal oriented (Pintrich 2000a).
Goal orientations are related to perceived competence as learners who feel highly
competent are more likely to adopt approach goals (e.g., 1a or 2a) whereas low
perceived competence leads to higher expectancies of failure and adoption of
avoidance goals (e.g., 1b or 2b) (Elliot 2005). A person’s competence can be
evaluated against (a) an absolute standard, based on the requirements of a task,
(b) an intrapersonal standard with reference to past performance or maximum
potential performance of the self, and (c) a normative standard which is related to
the performance of others (Elliot 2005; Elliot et al. 2011).
Academic self-concept
The perceived abilities of learners influence their interest, persistence, motivation to
learn, and choice of learning strategies (Cook and Artino Jr 2016; Schunk et al.
2008). The academic self-concept describes a cognitive representation of a person’s
perceived abilities in an academic achievement situation (Bandura 1994; Skaalvik
and Skaalvik 2005). Relevant for learning outcomes is that intrinsic motivation is
associated with perceived competence of learners and can be supported by skill-
matching but also by challenging tasks and feedback (Hau and Marsh 2015). Deci
et al. (1996) postulate a causal effect of the academic self-concept on intrinsic
motivation.
When estimating the academic self-concept, a person refers to three reference
norms: (a) social reference, comparing own performance with that of relevant
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others; this reference is crucial for building the academic self-concept since learners
rely on external feedback about their performance, such as test results, attributions
and, feedback from relevant persons (e.g., teachers, peers, parents) (Dickha
¨user
et al. 2002); (b) individual reference, comparing own performance over domains
and time; and (c) criterion-based reference, comparing own performance to
objective criteria such as learning objectives. Furthermore, the academic self-
concept includes (d) performance perceptions of the learner without a reference
category (Dickha
¨user et al. 2002; Eccles and Wigfield 2002; Weidinger et al. 2016).
The academic self-concept seems to have conceptual analogies with academic
self-efficacy beliefs of learners, postulated as vital for motivation in Bandura’s
(1993) social-cognitive view on learning and motivation (Weidinger et al. 2016).
There are, however, differences. While the academic self-concept represents a
person’s perceived competence within an academic domain, characterized as more
past-oriented and relatively stable, academic self-efficacy is a learner’s perceived
confidence to successfully perform a certain academic task, considered to be more
context-specific, future-oriented, and malleable (Bandura 1977,1994). Bong and
Skaalvik (2003) state that the academic self-concept influences self-efficacy beliefs
but not vice versa. Self-efficacy beliefs are built upon prior experiences and
outcomes (Zimmerman and Schunk 2008). However, both concepts are considered
to have impacts on intrinsic motivation, strategy use, engagement, persistence, task
choice, goal-setting, performance, and achievement (Ferla and Valcke 2009).
Constructs of personal expectancy comparable to the academic self-concept are
included in motivational theories. Such constructs include the expectancy of success
in the expectancy value theory of motivation (Bong and Skaalvik 2003), and also
goal orientations, especially the performance-oriented goals are influenced by a
person’s belief in being able to reach a certain goal. Hence, self-efficacy beliefs or
more generally academic self-concept might influence all phases of self-regulated
learning as students select tasks or set goals depending on their perceived abilities
and differ in persistence as well as in dealing with challenges (Zimmerman and
Schunk 2008). Outcome expectancies are related to self-efficacy beliefs and are a
source of motivation as learners will not pursue goals they do not feel capable of
reaching (Bandura 1993; Schunk 1991).
Motivational design of (online) learning environments
The ARCS (attention, relevance, confidence, satisfaction) model aims to integrate
and thus illustrate the relations between the theoretical concepts of volition,
motivation, learning, and performance in order to facilitate research and instruc-
tional design to generate motivating (online) learning environments (Keller 2008b).
The original model consists of four components (Keller 2008b; Keller and Suzuki
2004; Li and Keller 2018): (1) attention, referring to the level of curiosity aroused;
(2) relevance of a given learning objective to a learner, including its perceived
value; (3) confidence in the individual belief of being successful in the learning
activity, including the attributions assigned to the learning outcome, and (4)
satisfaction about the evaluated overall quality of the learning outcome and process.
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These four components are complemented by volition: self-regulatory strategies to
persist in goal-oriented behavior (Keller and Suzuki 2004).
Thus, the expanded theory of motivation, volition, and performance provides a
supplement because it explains how external and internal self-regulatory processes
can support learners not only in selecting goals but in acting and persisting to reach
their goals (Keller 2008a). Its motivational foundation is based on the expectancy
value theory, self-efficacy beliefs, goal orientation, attribution theory, and
assumptions of self-determination theory. In going beyond goal setting and towards
action, the model refers to action control theory and volitional strategies.
Furthermore, it is assumed that external learning stimuli are processed with
reference to cognitive load and information processing theory but are influenced by
motivational components. Finally, this process should lead to learners who initiate
and sustain learning processes and perform successfully, thus achieving satisfying
learning outcomes.
The motivational design process originally consisted of ten steps, including
analysis of learners and learning environment, defining motivational goals, design
steps in identifying and selecting motivational tactics to reach these goals,
implementation, and post-instructional steps to evaluate the design (Keller
1987,2008a). Whereas other models [e.g., FEASP-approach (fear, envy, anger,
sympathy, and pleasure) (Astleitner 2000)] more broadly consider emotions in
learning in general, this theory aims to support research, diagnosing motivational
issues, and designing motivational learning environments.
Motivation in learning analytics
As demonstrated, motivation is a crucial factor in engaging in learning activities and
pursuing learning goals. However, combining motivational theory, learning theory,
and learning analytics is still at an early stage (Marzouk et al. 2016). Learning
analytics provide several benefits to all stakeholders including three perspectives:
summative, real-time, and predictive (Ifenthaler and Widanapathirana 2014). In
relation to learners’ benefits and motivational dispositions, learning analytics may
support for example: (a) evaluating learning outcomes against efforts, (b) monitoring
the current progress towards goals, (c) integrating just-in-time feedback from
assessments into learning processes, (d) adapting learning activities according to
learning recommendations and thus increasing learning success.
To react accordingly and provide motivational interventions, learning analytics
require information about the learners, their characteristics, and especially about
their current motivational state, as well as the perceived relevance of the learning
tasks (Keller 2008b; Liu et al. 2017). Learning analytics may provide motivational
interventions using data about learners, their behavior in the learning environment,
and their interaction with the learning material. Because of the high adaptability of a
learning analytics system (Ifenthaler and Widanapathirana 2014), it can react to
motivational changes during the learning process.
Learning analytics systems should offer guidance by giving appropriate and
personalized feedback on successful and amendable results as learners’ self-efficacy
beliefs are based on prior success as well as on feedback on their previous
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performance (Bandura 1993,1994; Schunk 1991; Zimmerman et al. 2017).
However, the feedback provided through learning analytics should not be perceived
as too intrusive or controlling (Roberts et al. 2017) as the perceived autonomy of
learners is central for learning motivation (Deci et al. 1996). To take into account
students’ motivation and the need for autonomy, learning analytics should allow the
beneficiaries to set their own learning goals and provide several voluntary learning
recommendations to increase students’ choice and relevance of learning content.
Learning analytics systems should serve appropriate learning recommendations and
self-assessments, in line with individual capabilities and ones that do not cause
overextension but lead to a challenge and thus to increased curiosity, intrinsic
motivation, increased perceived competence, and higher self-efficacy beliefs (Hau
and Marsh 2015; Keller 2008a). Real-time feedback on current performance and
progress towards goals can increase students’ perceived confidence in successfully
fulfilling the learning requirements and thus lead to strategy adjustments, and
ideally to better learning outcomes. However, if students are struggling, the system
may provide appropriate guidance on how to reach the designated learning
objectives. The feedback could also influence students’ dispositions on their
learning outcome, leading to changes in upcoming pre-actional phases of
motivation (Ifenthaler and Lehmann 2012). To increase learners’ curiosity, various
types of learning material such as videos, texts, podcasts, or external links are
provided to meet all learners’ preferences. Additionally, to increase the relevance of
the learning content, learning analytics systems illustrate the connections between
different learning content and previous learning artifacts. Furthermore, prompts can
be used to investigate and to expand learners’ motivation (Bannert 2009; Ifenthaler
2012).
Competitive environments might be perceived as reducing autonomy and so are
related to a decrease of intrinsic motivation (Deci et al. 1996). A qualitative study
investigating students’ expectations on learning analytics features revealed differ-
ences in students’ attitudes towards receiving analyses comparing their performance
as it might reduce their motivation (Schumacher and Ifenthaler 2018). Especially
for students who are not performing well in comparison with others, this
information might impair their academic self-concept (social reference) and thus
their self-efficacy beliefs and motivation. However, a feature comparing one’s
performance with those of others might be of interest to performance-approach
oriented learners.
Considering the assumptions on motivation and (self-regulated) learning of
Keller (2008b), Pintrich (2000c), and Zimmerman (2005,2011), learning analytics
can be supportive in initiating and sustaining learning motivation, as summarized in
Table 2.
Research questions and hypotheses
The purpose of this study is to investigate the relationship between students’
motivational dispositions and their perceived support of learning analytics systems.
Depending on their goal orientations, students have different reasons for pursuing
an achievement task (Pintrich 2000a), which leads to the use of varying learning
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Table 2 Learning analytics potential support on motivation in the cyclical phases of self-
regulated learning
Forethought phase
Providing clear learning objectives and relating them to tasks
•Goals, goal setting; task value, interest
Connecting learning material to prior knowledge, previous course content, learning objectives, or
external data (news, videos)
•Task value, interest, relevance
Offering skill matching but challenging tasks based on available data
•Curiosity, interest; outcome expectancies; self-efficacy
Motivational prompts if learners are not beginning to learn or not learning appropriately to reach goals
•Effort initiation
Comparison with peers and their learning activities
•Self-efficacy; performance goal orientation
Feedback on predicted learning outcomes
•Expectancies, self-efficacy beliefs
Feedback on previous learning outcomes and activities
•Self-efficacy; outcome expectations
Performance phase
Analyzing learner’s motivational state based on behavior and by using prompts
•Early interventions to increase motivation
Offering different learning material (videos, slides, texts, external links, news)
•Arouses curiosity; autonomy/choice, interest
(Prompts) for self-assessments and inform learners about their current state of knowledge not grading
•Autonomy/control; effort regulation; help-seeking; effort initiation
Just-in-time feedback
•Outcome expectancies, learning actions, rewards, persistence
Feedback on progress towards learning objectives
•Self-rewards; positive/negative outcomes; reminds of goals
Providing appropriate learning recommendations on how to reach learning objectives
•Adapt strategies, effort persistence
Motivational prompts
•Motivation regulation, effort regulation, attention control
Advising to change learning environment (noise, light etc.)
•Attention control
Recommendation of learning partners dealing with the same problem
•Help seeking, social reference and embeddedness
Expected time for completing tasks
•Reward, pausing, monitoring
Self-reflection phase
Feedback about learning outcomes
•Attributions, self-efficacy beliefs
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strategies and motivational sources. Thus, students’ goal orientations also have an
impact on their expectations towards support of their learning processes and
motivational states. Therefore, it is hypothesized that students’ learning goal
orientation (Hypothesis 1a), performance-approach goal orientation (Hypothesis
1b), performance-avoidance goal orientation (Hypothesis 1c), and their work-
avoidance goal orientation (Hypothesis 1d) are related to their rating of perceived
support from learning analytics.
Likewise, learners’ anticipated abilities impact their interest, persistence,
motivation to learn, and the learning strategies selected (Cook and Artino Jr
2016; Schunk et al. 2008). Depending on the predominant reference norm on which
learners build their academic self-concept, they might demand different support in
terms of motivation and learning. Thus, it is assumed that students’ academic self-
concept based on individual reference (Hypothesis 2a), criterion-based reference
(Hypothesis 2b), social reference (Hypothesis 2c), and without reference (Hypoth-
esis 2d) significantly predict their rating of perceived support from learning
analytics. Additionally, students’ background (i.e., age, gender, final school grade),
and study related characteristics (i.e., semester load, current study grade, study
program) were reviewed for their influence on how they predicted the anticipated
support from learning analytics (Hypothesis 3).
Method
Participants and design
We recruited a purposive sample of 802 students (472 female, 330 male) from a
European university. Most students were enrolled in a Bachelor program
(n
BA
= 588), followed by Master students (n
MA
= 137), and students in other study
programs (e.g., diploma; n
OTHER
= 77). The participants were enrolled in economics
and law (56.6%), STEM (16.1%), languages, culture and arts (13.5%), social
sciences (9.7%), medicine (2.6%), and other fields of study (.9%) [4 missing
responses]. Students were asked to participate in an online study that was
implemented on the university’s server.
Table 2 continued
Facilitating learner’s evaluation of learning outcomes against goals/standards
•Satisfaction; leading to adaptive/defensive reactions
Recommendations about improvements for upcoming tasks
•Attributions, increase perceived control of outcomes, adapt strategies, prepare upcoming strategic
planning
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Instruments
Learning and achievement motivation scales
The scales for the assessment of learning and achievement motivation (Spinath et al.
2012) measured four factors: learning goal orientation, performance-approach goal
orientation, performance-avoidance goal orientation, and work-avoidance goal
orientation (31 items; split-half reliability ranging from .73 to .78).
Academic self-concept scale
The academic self-concept scale (Dickha
¨user et al. 2002) measures academic self-
concept based on four factors: social, individual, criterion-oriented, and no
reference norms (22 items; Cronbach’s aranging from .74 to .92).
Expected learning analytics support
The instrument consists of 20 items investigating how learning analytics may
support learning (LAS; Cronbach’s a= .936). Sample items of LAS are ‘‘Learning
analytics would help me to track my progress towards my learning goals’’,
‘‘Learning analytics would help me to facilitate my learning activities’’, ‘‘Learning
analytics would help me to better analyze my learning outcomes’’. All items were
answered on a 5-point Likert scale (1 = strongly disagree; 2 = disagree; 3 = neither
agree nor disagree; 4 = agree; 5 = strongly agree).
Demographic information
Demographic information included gender, age, course load, study program (15
items in total).
Procedure
Students of various disciplines were invited to participate in the online study, which
consisted of four parts. First, students answered questions about their learning and
achievement motivation (3.2.2.1; 8 min). Second, they were asked to disclose
information about their academic self-concept (3.2.2.2; 7 min). Then, they rated
benefits they thought learning analytics systems could offer in order to support
learning (3.2.2.3; 6 min). Finally, students revealed their demographic information
(3.2.2.4; 10 min).
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Table 3 Descriptives and zero-order correlations for students’ background, study related characteristics and goal orientations (N= 469)
Variable 1 2 3 4 5 6 7 8 9 10 11 12
1. Final school grade –
2. Semester load -.062 –
3. Current study grade .494*** .042 –
4. Learning goal orientation -.089* .144** -.132** –
5. Performance-approach goal
orientation
-.132** .084* -.132** .208*** –
6. Performance-avoidance goal
orientation
-.070 .034 -.007 -.143*** .475*** –
7. Work-avoidance goal orientation .073 -.058 .112** -.337*** .183*** .435*** –
8. Individual reference -.040 -.014 -.227*** .240*** .178*** -.051 -.195*** –
9. Criterion-based reference -.245*** .053 -.422*** .180*** .275*** -.008 -.032 .546*** –
10. Social reference -.259*** .005 -.354*** .063 .252*** .075 .082* .370*** .729*** –
11. No reference -.258*** .071 -.390*** .152*** .244*** .001 -.043 .471*** .828*** .743*** –
12. Perceived learning analytics
support
.113** .044 .155*** .208*** .260*** .185*** .105** .116** .054 .051 -.003 –
M2.25 30.64 2.25 4.27 3.01 2.31 2.23 4.02 3.63 3.32 3.60 3.44
SD .59 25.57 .57 .56 .73 .86 .79 .61 .63 .62 .59 .70
*p\.05; **p\.01; ***p\.001
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Results
Table 3shows the descriptive statistics and zero-order correlations of predictors
used in the regression analysis indicating significant correlations between students’
background, study characteristics, goal orientations, academic self-concept, and
perceived learning analytics support.
A hierarchical regression analysis was used to determine whether students’
background (age, gender, final school grade), study-related characteristics (semester
load, current study grade, study program), goal orientations (learning goal
orientation, performance-approach goal orientation, performance-avoidance goal
orientation, work-avoidance goal orientation), and academic self-concept (individ-
ual reference, criterion-based reference, social reference, no reference) were
significant predictors of perceived learning analytics support. The results of the
regression analyses for perceived learning analytics support are presented in
Table 4yielding a DR
2
of .183, F(14, 454) = 8.49, p\.001. With regard to
hypothesis 1a, students’ learning goal orientation positively predicted the perceived
learning analytics support, indicating that the higher the students’ learning goal
orientation, the higher the perceived support from learning analytics. Further,
students’ performance-approach goal orientation (Hypothesis 1b) positively
Table 4 Regression analysis for students’ background, study related characteristics, goal orientations,
and academic self-concept predicting perceived support from learning analytics (N= 469)
BSEBb
Perceived learning analytics support
Students’ background
Age .025 .016 .075
Gender (0 = male) -.101 .062 -.071
Final school grade .026 .061 .022
Study related characteristics
Semester load .000 .001 .001
Current study grade .217 .065 .178**
Study program (1 = Bachelor) -.308 .086 -.178***
Goal orientations
Learning goal orientation .257 .060 .206***
Performance-approach goal orientation .156 .050 .164**
Performance-avoidance goal orientation .082 .043 .102
Work-avoidance goal orientation .082 .045 .093
Academic self-concept
Individual reference .132 .061 .115*
Criterion-based reference .101 .095 .091
Social reference .089 .076 .079
No reference -.200 .095 -.170*
*p\.05; **p\.01; ***p\.001
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predicted the perceived learning analytics support, indicating that the higher the
students’ performance-approach goal orientation, the higher the perceived support
from learning analytics. With regard to hypothesis 2a, students’ individual reference
orientation positively predicted the perceived learning analytics support, indicating
that the higher the students’ individual reference norm, the higher the perceived
support from learning analytics. Finally, students’ no reference (H2d) orientation
negatively predicted the perceived learning analytics support, indicating that the
lower students’ no reference norm, the higher the perceived support from learning
analytics. With regard to hypothesis 3, no significant predictors related to students’
background could be identified. However, current study grade positively predicted
the perceived learning analytics support, indicating that the weaker the students’
study performance, the higher the support from learning analytics is perceived. In
addition, study program negatively predicted the perceived learning analytics
support, indicating that students in lower semester levels expect higher support from
learning analytics systems.
To sum up, Hypothesis 1 is accepted for students’ goal orientations (learning goal
orientation (H1a), performance-approach goal orientation (H1b)), Hypothesis 2 is
accepted for students’ academic self-concept (individual reference orientation
(H2a), no reference orientation (H2d)), and Hypothesis 3 is accepted for current
study grade and study program.
Discussion
Research on motivation particularly emphasizes the influence of self-efficacy, self-
determination, and goal orientation on the quality and outcome of learning
(Dickha
¨user et al. 2002; Ryan and Deci 2000; Zimmerman and Campillo 2003;
Zimmerman et al. 2017). Further, research on motivation draws on several well-
established theoretical perspectives, such as expectancy value theory (Wigfield and
Eccles 2000), attribution theory (Weiner 1985), social-cognitive theory (Bandura
1977), goal-orientation theory (Dweck and Leggett 1988; Elliot 2005), or self-
determination theory (Deci and Ryan 1991). Contemporary motivational theories
influencing research in learning sciences recognize that aspects that motivate one
learner might not motivate another (Svinicki and Vogler 2012). Furthermore,
modern theories of motivation presume the intentionality of human behavior: people
are motivated when they are willing to achieve a certain future state (Deci and Ryan
1991). Especially in online learning environments and higher education motivation
of students needs to be considered already when designing the learning environ-
ment. Therefore, Keller’s theory (2008b) which includes relevant theoretical
concepts such as volition, motivation, learning and performance can be used as a
guiding framework. As recent empirical findings in the field of learning analytics
document a successful implementation relies on a broad variety of information
about individual learners, such as their motivational dispositions as well as
individual characteristics (Ifenthaler and Widanapathirana 2014). Hence, to adapt
the learning environment to students’ (motivational) needs the design of learning
123
612 C. Schumacher, D. Ifenthaler
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environments can be iteratively informed by learning analytics (Ifenthaler 2017;
Ifenthaler et al. 2018).
In this study, learning goal orientation and performance-approach goal orienta-
tion significantly predicted the perceived support from learning analytics. As these
two goal orientations are related to either deeper interest and learning strategies or
higher academic achievement (Harackiewicz et al. 1998), students assumed more
benefits in terms of supporting learning and motivation. However, students with
learning goal orientation and performance-approach goal orientation might demand
different support (Duffy and Azevedo 2015). The former might ask for challenging
tasks and additional resources, as they are interested in increasing their knowledge
and skills (Zimmerman and Schunk 2008). Whereas the latter might prefer social
comparisons related to performance, progress, used material, etc. (Seifert and
O’Keefe 2001) to achieve the designated outcome and outperform others.
The students with performance-avoidance and work-avoidance goal orientation
seem not to anticipate support from learning analytics. Nevertheless, it could be
especially necessary to point out learning analytics benefit to theses learners as they
might particularly risk a lack of motivation to learn or be less able to apply
suitable learning strategies and achieve favorable results (Meece et al. 1988;
Pintrich 2000b; Wolters 2003). Thus, further research should investigate differences
in terms of motivational dispositions and preferred learning analytics features.
Table 2presents potential learning analytics features related to the three phases of
self-regulated learning which may support learners’ motivation. The identified
features can serve as basis for designing learning analytics systems and for further
(experimental) studies on potential differences of students’ engagement with and
perceptions of these features related to their motivational dispositions. Additionally,
research should also be complemented by considering other motivational constructs.
Support provided in online learning environments which is not aligned with
students’ needs might even lead to negative learning outcomes (Chen and Jang
2010). Support such as scaffolding had a positive impact on learning and
achievement of students with performance-approach goal orientation but not or
rather a negative impact on students’ learning outcomes when adopting learning-
approach goals (Duffy and Azevedo 2015). This emphasizes further the necessity to
investigate the relation of motivational dispositions and (perceived) support from
learning analytics in terms of learning processes and outcomes.
Regarding the academic self-concept beliefs, students with an individual
reference norm assume that they could benefit from learning analytics. Students
who build their academic self-concept upon comparisons with their own work might
be interested in learning analytics for contrasting previous performance with current
performance. Surprisingly students with criterion-based and social reference seem
not to assume benefits from learning analytics. And furthermore, students whose
academic self-concept is based on a more general reference (no reference norm),
which is considered to include the other reference norms (Dickha
¨user et al. 2002),
perceive reverse benefits. Thus, a deeper analysis differentiating the various benefits
of learning analytics or relating them to offered learning analytics features might
lead to a more profound understanding of learners’ perceived support from learning
analytics.
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The importance of students’ motivational dispositions for…613
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Furthermore, the results indicated that students with lower academic performance
perceive more support from learning analytics. Thus, learning analytics seem to
these students a meaningful support to their performance and how they could
improve their learning approach, which both impact motivation as well. The guiding
character of learning analytics is also indicated by the result that undergraduate
students anticipate more support from learning analytics than more experienced
Master students.
The present study has obvious limitations as self-reported measurements are used
to assess students’ motivational dispositions and as the focus is on only two
motivational concepts. Furthermore, the students were not able to use a learning
analytics system, thus, the perceived support was based on a hypothetical system,
which might lead to biases. Additionally, even though the sample size was
appropriate using a purposive sample by actively approaching students to participate
in this study without ensuring representativity of age, gender, study subject, etc.
might lead to biases due to self-selection and hence to difficulties in generalizability.
Learning analytics need to combine trace data and psychological inventories and
thus allow further investigation of the reciprocal relation of motivation and self-
regulated learning activities of students when engaging in online learning
environments (Ellis et al. 2017; Lonn et al. 2015; Winne and Baker 2013;
Zimmerman 2008). Such a holistic application of learning analytics may lead to a
better understanding of motivation and learning processes and thus enables the
creation of adaptable and personalized learning environments that meet learners’
individual needs (Ifenthaler and Widanapathirana 2014). However, establishing
valid and economic indicators on student motivation for learning analytics requires
further research to ascertain when and how to measure motivational states taking
account of its’ processual character related to the other components of self-regulated
learning (Moos and Stewart 2013). As learning analytics currently already provide
feedback to students such as results of comparisons with peers or forecasts about
their final course performance (Gas
ˇevic
´et al. 2015), the impact of timing and
content of feedback on learning motivation needs to be examined in future research.
For further insights into students’ responses to feedback, analyzing trace data seems
to be a promising approach (Zimmerman 2008). For example, investigating
students’ behavioral patterns when dealing with learning materials, prompts or
analyses of the learning analytics system related to their motivational dispositions
might allow a higher adaptivity of learning analytics (Liu et al. 2017).
Conclusion
Learners differ in their reasons for engaging in achievement tasks and thus seem to
expect different support while learning (Schunk and Zimmerman 2008). The
findings of this study indicate that motivational dispositions such as goal orientation
and academic self-concept as well as study-related characteristics impact students’
perceived support from learning analytics. As the focus of learning analytics is on
supporting learning where motivation is a crucial factor, students’ motivation needs
to be taken into account when designing learning analytics systems. This need is
123
614 C. Schumacher, D. Ifenthaler
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further supported by the assumption of motivation being a result of individual as
well as environmental factors (Svinicki and Vogler 2012). A study conducted by
Lonn et al. (2015) found that confronting students at risk in a summer bridge course
with feedback from an early warning system led to a decrease of their learning goal
orientation. As learning goal orientation is positively associated with intrinsic
motivation and learning outcomes, this emphasizes the need to consider motiva-
tional dispositions of students when designing learning analytics. Hence, improving
alignment with the needs of learners and their individual characteristics, person-
alization, and adaptivity are considered to be important, and for that, a broad data
source is required (Ifenthaler and Widanapathirana 2014; Schumacher and
Ifenthaler 2018). The appropriateness of learning analytics interventions and
feedback is vital as a balance between guidance and autonomy is to be achieved
that is not overcharging students’ capabilities to self-regulate or impairing their
motivation. However, to allow personalized learning analytics features considering
students’ motivational dispositions, appropriate indicators and data sources (e.g.,
inventories, physiological measures) need to identified to make this information
available for learning analytics algorithms.
Acknowledgements The authors acknowledge the financial support by the Federal Ministry of Education
and Research of Germany (BMBF, project number 16DHL1038).
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of interest.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, dis-
tribution, and reproduction in any medium, provided you give appropriate credit to the original
author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were
made.
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Clara Schumacher is research assistant at the chair of Economic and Business Education – Learning,
Design and Technology at the University of Mannheim. Her research interests focus on educational
technology, self-regulated learning, learning analytics and informal learning.
Dirk Ifenthaler is Chair and Professor of Learning, Design and Technology at University of Mannheim,
Germany and UNESCO Deputy Chair of Data Science in Higher Education Learning and Teaching,
Curtin University, Australia. His research focuses on the intersection of cognitive psychology,
educational technology, data analytics, and organizational learning.
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