=Paper=
{{Paper
|id=Vol-3076/paper01
|storemode=property
|title=Learning analytics supported goal setting in online learning environments
|pdfUrl=https://ceur-ws.org/Vol-3076/ECTEL2021_DC_paper01.pdf
|volume=Vol-3076
|authors=Gabrielle Martins Van Jaarsveld,Jacqueline Wong,Martine Baars,Fred Paas,Marcus Specht
|dblpUrl=https://dblp.org/rec/conf/ectel/JaarsveldWBPS21
}}
==Learning analytics supported goal setting in online learning environments==
https://ceur-ws.org/Vol-3076/ECTEL2021_DC_paper01.pdf
Learning analytics supported goal setting in online learning
environments
Gabrielle Martins Van Jaarsvelda, Jacqueline Wongb, Martine Baarsa, Fred Paasa, Marcus
Spechtb
a
Erasmus University Rotterdam, Burgemeester Oudlaan 50 3062PA, Rotterdam, The Netherlands
b
Delft University of Technology, Mekelweg 5 2628CD, Delft, The Netherlands
Abstract
The rapidly increasing role of technology in education has resulted in large amounts of data
being collected about student learning and behavior, and as a result, has given rise to the field
of Learning Analytics. Although much research in this field has focused on offering insights to
educators, researchers have suggested learning analytics may be most effectively employed
when they focus on insights which can be offered directly to students. Furthermore, researchers
have called for more focus on research driven by educational theory and given the highly self-
directed nature of higher education in general, and online learning environments specifically,
self-regulated learning can be highlighted as an important theoretical framework to consider in
future studies. Self-regulated learning (SRL) can be viewed as a cyclical process in which goal
setting and monitoring play an integral role in driving behavior, and prior research has shown
that SRL skills are positively related to academic performance. However, prior research on how
learning analytics can support goal setting to enhance SRL is extremely scarce. The aim of this
project is to explore the question of how learning analytics can support the goal setting process
in online learning environments to improve SRL and performance? In this project several
studies have been designed to (a) examine the effectiveness of a learning analytics supported
goal setting and monitoring tool to improve academic performance, (b) consider the influence
of individual student characteristics on the effectiveness of this learning analytics tool (c)
consider whether personalizing learning analytics tools to support goal setting can increase the
efficacy of the tools. Overall, the aim is to be able to offer guidelines for how learning analytics
tools can be designed and personalized to increase the effectiveness of goal setting interventions
to optimize SRL and performance in online learning environments.
Keywords 1
Goal setting, self-regulated learning, learning analytics, technology enhanced learning,
personalized interventions
enhanced learning (TEL) has become
1. Introduction increasingly commonplace in traditional face-
to-face education, and Information
Communication Technology (ICT) is now a
The past few decades have seen some major standard addition to the day-to-day learning
changes within the field of higher education,
activities of the average higher education
and a fast-paced move towards digitalization student [1]. Secondly, there has been a rise in
has changed the way a lot of education is new forms of education, which are either
carried out. This shift has brought about partially online, called blended learning, or
changes on two fronts; firstly, technology
Proceedings of the Doctoral Consortium of Sixteenth European
Conference on Technology Enhanced Learning, September 20–21,
2021, Bolzano, Italy (online).
EMAIL: martinsvanjaarsveld@essb.eur.nl
ORCID: 0000-0002-1864-8978
© 2021 Copyright for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
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fully online, like distance learning or massive engagement with SRL support tools is often
open online courses (MOOCs). While these low [15], [16], and those students who are most
kinds of education have been on the rise for in need of support are often the ones least likely
several decades, the past few years have seen to seek it out and make use of it [17], [18].
them become more widely available and Furthermore, tools which are developed to
accessible to a larger audience. This shift has support SRL differ widely in their approach and
offered the opportunity to expand and grow content, and as such, they are not all equally
both research and educational practice in many effective. Some SRL support tools are
novel directions. However, this shift to partially significantly more likely to result in behavioral
or fully digital learning environments has also change and have positive effects on academic
brought about some unique difficulties. It has outcomes than others [19]. Moreover, not all
become clear that the skills needed to thrive in students interact with SRL support tools in the
these digital learning environments are not same manner, and what is effective for one
always the same as those needed in traditional group of students might not be as effective for
face-to-face classrooms [2], [3]. This has been other groups [20], [21]. Thus, it is important to
highlighted during the COVID-19 pandemic, fully explore how to effectively design and
where the sudden and widespread shift to implement SRL support tools within TEL
digital education saw a lot of students environments, as well as how to tailor them to
struggling to effectively manage their own the needs of individual students and increase
learning [4]. This struggle has highlighted the the likelihood of students engaging with them.
fact that some of the most important skills
needed to thrive in TEL environments are self- 1.1. Self-regulated learning and
regulated learning (SRL) skills. According to
researchers, throughout their years in higher goal setting
education “students are on a journey to become
self-managing and self-directed learners.” [5, p. SRL is a broad framework which describes
130]. While they may be important in any several motivational, cognitive, and behavioral
higher education program, SRL skills are even processes which contribute to an autonomous
more important in TEL environments, which learning process [7]. These processes have been
often involve high learner autonomy, less extensively studied, and as a result, there are
teacher oversight, and a non-linear program many different models which have been
structure [6]. SRL is described as a process in proposed to describe them (for a review see
which students are metacognitively and [22]). The most commonly used model of SRL
behaviorally active in their own learning is that by Zimmerman [23]. Zimmerman
process, and implement self-monitoring, described SRL as the process of transforming
learning, and reflection strategies to strive mental and physical abilities into task-related
towards goal attainment [7]. As higher skills [7]. Zimmerman’s model describes the
education continues its current trend towards process as cyclical, with three separate stages:
digitalization, supporting students in their 1) the forethought stage, 2) the performance
development of SRL skills is likely to become stage, 3) and the self-reflection stage. Students
even more critical to ensure their success. start in the forethought stage by setting goals
Understanding how to support learners SRL and creating plans to achieve them. In the
is a topic which has garnered much attention performance stage they use regulatory
from researchers over the years [8]–[10]. strategies to guide their study activities and
Previous research has shown that high SRL monitor their progress towards their goals. And
skills are a predictor of effective learning finally in the self-reflection stage they reflect on
processes, and better academic performance their performance, and how well they have
[11]. Furthermore, research has shown that achieved their goals and adjust their plans for
many students lack effective SRL skills, and future learning accordingly. While it is
struggle to implement SRL strategies within important to support students throughout the
their daily learning processes [12]. However, whole SRL process, the first stage, goal setting,
effectively supporting SRL, especially within is especially critical as it drives the rest of the
online learning environments, has been shown cycle and forms the basis for motivated
to be a complex task [6], [13], [14]. Previous behavioral change [24]. A goal is defined as
studies have demonstrated that student “something an individual is trying to
accomplish” [25, p. 126] and goal setting is the environments, there has been very little
act of consciously deciding upon goals to strive research on the potential to enhance and support
for. Without effective goal setting, students are these tools when they are delivered digitally. To
not able to effectively carry out the second and support the process of SRL in TEL
third phases of the SRL cycle. This highlights environments, tools can focus on helping
the importance of understanding the underlying students set effective and meaningful goals, and
processes of the SRL cycle in order to support then offer additional support to guide them
it. Self-determination theory (SDT) describes through the remainder of the SRL cycle.
the elements which drive motivated behavior However, SRL interventions can be resource
[26]. According to SDT the three crucial heavy, especially given the fact that they are
elements for motivation are autonomy, often most effective when they can be adjusted
competence, and relatedness [26]. The to the needs of individual students. TEL
importance of allowing students autonomy environments can offer personalized and
within education has been demonstrated [27], adaptive interventions by making use of data
and the importance of autonomy within SRL collected about student performance and
has also been established [28]. Prior studies behavior, which is known as learning analytics.
show that while TEL tools may try offer Therefore, offering support tools in TEL
students autonomy in how they use them, the environments have a unique advantage in using
decisions students make may not always be the learning analytics over traditional face-to-face
most effective for learning or performance [14]. classrooms.
It therefore becomes clear that in order to
design an effective goal setting intervention, the 1.2. Learning analytics
goal setting process should be guided
sufficiently for students to set effective goals,
while still allowing students to feel autonomous Learning analytics is still a new area of
study, which arose as TEL became more
and motivated in the process.
common in day-to-day educational settings.
Goal setting as a means of improving
performance has been studied for many The definition of learning analytics still differs
decades, starting with Edwin Locke who across the literature, but The Society for
developed the Goal Setting Theory [29]. Learning Analytics Research defines it as “the
Locke’s original theory focused on how goal measurement, collection, analysis and reporting
of data about learners and their contexts, for
specificity and goal difficulty moderated the
relationship between goal setting and task purposes of understanding and optimizing
performance [29]. Goal setting has remained a learning and the environments” [33]. This
definition covers a broad range of data and
popular research topic, and research over the
years has suggested many other goal analysis opportunities which have arisen within
education. Learning analytics relies on data
characteristics which may affect effective goal
setting. However, despite a broad base of which is generated when students interact with
literature on the topic, there is very little digital learning environments, and this is called
trace data [34]. Trace data are interpreted as
consensus on what the characteristics of an
effective goal setting tool are. Prior research observable indicators of students’ underlying
does show that there is a delicate balance that learning processes [35]. Thus, the aim of
needs to be struck between guiding students to learning analytics studies is often to draw
set effective goals and giving them autonomy to conclusions about learning processes based on
create their own goals. Studies show that how students behave in online learning
students are generally ineffective goal setters environments. While researchers have
previously theorized that learning analytics
when allowed to set their own goals [30], [31].
However, merely having a goal in mind is not offer a powerful and efficient means of
enough, the kinds of goals which are set as well supporting SRL [36]–[38], few studies have
as the act of creating plans to achieve them are implemented learning analytics as a means of
also important [32], and therefore providing enhancing and personalizing goal setting tools
guidance is crucial. [9]. Furthermore, while prior research has
Furthermore, although some studies in shown that student engagement in online
recent years have started to carry out goal learning environments can be a challenge,
learning analytics and technology in general
setting activities in online learning
offer means of combating this problem. SRL interact with these tools, and it is therefore
tools in online environments can combat low important to take this into consideration and
engagement by offering personalized create adaptive tools which can adjust to the
experiences using learning analytics data. needs of individuals [9], [46].
Personalization in education, and within the Therefore, during this project we aim to
field of TEL tools is a popular topic, but it’s address the importance of SRL in TEL
important to understand in what ways environments, by investigating how to best
personalizing tools using learning analytics can design and implement goal setting support
be beneficial. There are many different tools, enhanced by learning analytics, to
characteristics which affect the way in which improve student SRL skills and academic
students interact with TEL environments, such performance. We aim to use learning analytics
as personality traits [39], [40]. In the context of to not only offer personalized goal setting,
learning analytics, personalization can include monitoring and reflection tools, but also to
identifying groups of students on the basis of create a tool which adapts based on a student’s
their individual characteristics, examining what prior performance, and personal characteristics.
their patterns of use reveal about their
interaction with the tool, and their individual 2. Proposed approach
needs, and creating a tool which is adaptive in
nature can be personalized in response. While
this kind of personalization can take many With this project, we aim to apply a
forms, the aim is to create a tool which moves multidisciplinary approach by combining
away from the one-size-fits-all approach of insights from the fields of psychology,
educational tools, and to take advantage of the educational sciences, learning analytics, and
affordances offered by TEL tools. educational data mining. Figure 1 below shows
Another powerful means of leveraging an overview of the studies planned for this
project. Overall, with this project we aim to
technology and data to support goal setting is
understand how best to implement goal setting
using conversational agents. Prior studies have
shown that goal setting guidance is and monitoring tools in online learning
significantly more effective when delivered by environments, and to explore how learning
an experimenter, as opposed to via a worksheet analytics can be used to enhance and
[41]. Furthermore, it has been suggested that personalize them, to offer students support that
is tailored to their individual needs. The main
conversational agents could significantly
improve the effectiveness, and scalability, of research question of this project is “How can
goal setting based interventions [42]. Existing learning analytics support goal setting in online
learning environments to improve learning and
studies have shown that conversational agents
can have a positive effect on student performance?” We will attempt to address this
question using a design-based research
engagement with the tools, as well as increasing
their effectiveness [43]. However, there is little approach, in which we develop a learning
experimental work on the effect of delivering analytics supported goal setting tool, which is
then implemented, tested, and refined in an
goal setting interventions via conversational
agents. This demonstrates the power of iterative process. During each study carried out
leveraging learning analytics and TEL in this project, the developed tool will be tested
environments to enhance SRL tools to increase in real-life educational settings and refined and
their effectiveness, but also the gap in the improved based on the findings during that
literature about effective means of doing so. study. Each study will build upon the findings
These methods of creating adaptive and of the previous study in an iterative process
aimed at improving the effectiveness of the tool
personalized interventions are especially
important given that current literature suggests and expanding its functionality with each study.
that not all students interact with learning During studies 2-4 the learning analytics
analytics tools in the same manner, and it is supported goal setting tool will be embedded in
therefore important to offer individuals a learning management system (LMS), used by
personalized experiences to maximize their students carrying out their bachelor’s degree
benefits [44], [45]. Given the literature which within a large Dutch higher education
suggests that that individual student institution. Students will be able to interact with
the directly from their browser while using their
characteristics affect the way in which students
LMS. Student performance will be measured Study 2 focuses on developing and
using course grades, and trace data about implementing the goal setting tool, alongside
student performance and behavior will be learning analytics support in the form of goal
drawn from the LMS, as well as the learning monitoring and reflection elements and testing
analytics tool directly. what effect the tool has on SRL skills and
academic performance. The research questions
for this study are as follows:
1. What is the effect of goal setting
interventions on self-efficacy, self-
regulated learning, and student
performance in an online learning
environment?
2. How can real time goal monitoring
supported by learning analytics enhance the
effect of goal setting interventions on
student performance and engagement in an
Figure 1. Overview of planned studies in online learning environment?
project
This tool will be designed based on
2.1. Study 1: literature review findings from the literature review carried out
in study 1, as well as on theory from the
relevant fields. Study 2 will be a randomized
The first study will be a literature review,
which will give an overview of the field and controlled trial (RCT) with two types of goal
existing relevant literature. This will culminate setting interventions and a control group.
in the development of a goal setting tool, which Analyses of Variance (ANOVAs) will be used
will be used in later studies. The research to test whether the experimental groups differ
questions for this study are as follows: in performance after the intervention tool has
been used for a semester, and repeated
1. How have guided goal setting interventions measured ANOVA will test whether there is a
been carried out in previous studies in difference in pre- and post-intervention self-
higher educational institutions? efficacy, engagement, and SRL. Throughout
this project Zimmerman and Pintrich’s models
1.1. What kinds of goals are students
guided to set? of SRL will be used to evaluate the
1.2. How are the interventions designed interventions and SRL skills [22]. Trace data
and implemented? will be examined to identify patterns of
2. What is the effect of the guided goal setting behavior in the learning environment and when
intervention on academic performance and using the tool, to inform the design of future
SRL skills? iterations of the tool. This step is more
3. How has technology, and learning analytics exploratory in nature and will be used to inform
been used to support goal setting in prior decisions made during Study 3.
studies?
2.3. Study 3: personalizing SRL
This study followed the Preferred tools
Reporting Items for Systematic reviews and
Meta-Analyses (PRISMA) statement to carry
Study 3 focuses on individual student
out a systematic search of the relevant literature
characteristics, and how the goal setting tool
[47].
can be personalized using learning analytics, to
increase its effectiveness. The research
2.2. Study 2: goal setting and questions for this study are as follows:
monitoring
1. To what extent are the effects of goal
setting and monitoring interventions
moderated by individual student interventions in higher education settings. In
characteristics? this study, a systematic literature review was
2. How can personalizing learning analytics carried out following the PRISMA guidelines,
tools based on student characteristics and we aimed to examine all papers published
improve their effectiveness? after 2010, which had an active academic goal
setting tool that was implemented amongst
This study takes place in two parts. The first higher education students. The final sample
part will follow a similar design to study 2, but included 37 papers. The final sample of papers
with a focus on testing the effectiveness of the were then examined, and the goal setting tools
tool, and students’ interaction with the tool presented in them were broken down into
based on their individual characteristics. The various characteristics covering two main
second part aims to personalize elements of the areas: 1) the intervention implementation and
intervention and examine whether this design, 2) the characteristics of the goal setting
personalization improves the tools activity.
effectiveness. This personalization will be Regarding the intervention implementation
based on the exploration of groups of students and design, the results showed that less than
and their patterns of behavior from Study 2, as half of the papers (n = 16; 43%), were
well as existing theory and literature, and will experimental designs which tested the
focus on characteristics like personality traits, effectiveness of the intervention. This means
maladaptive study behaviors (like most of the papers were implementing goal
perfectionism or procrastination) and prior setting activities without testing whether they
performance. The effectiveness of the tool will were having the intended effect on student
be tested in an RCT using an ANOVA to behavior or academic performance. This result
compare experimental groups. may seem surprising given previous studies
showing that not all goal setting activities are
2.4. Study 4: SRL supporting effective at bringing about behavioral change
[48], [49], however prior work has noted the
conversational agent gap between educational theory and what
researchers want to measure, and the
Finally, study 4 focuses on how to increase implementation of TEL tools [50].
student engagement with the tool, by testing its Furthermore, the results showed that while
implementation in the form of a conversational the interventions were delivered digitally in
agent. The research questions for this study are almost half of the papers (n = 17; 46%) of, for
as follows: the most part, these interventions had no form
of technology support or enhancement and
1. How does delivering the learning analytics were neither personalized nor adaptive. Instead,
supported goal setting tool via most digitally delivered goal setting
conversational agent affect engagement, interventions were merely computer-based
self-efficacy, and student performance? versions of a static pen and paper type
intervention. This made it clear that while there
This study will follow a similar layout to is a definite shift in SRL interventions towards
Study 2 and 3 and will test the effectiveness of digitalization, at the current time most tools do
the tool when it is integrated with and delivered not make use of the full potential of technology
by a conversational agent. We will then to improve or support their interventions.
examine whether this improves the Regarding the characteristics of the goal
effectiveness of the tool by examining setting activities, several elements were
differences student performance in a RCT. examined including goal type, goal context,
Patterns of student engagement with the tool goal depth, and goal distance. Overall, what
will also be examined. could be seen from this examination was that in
general, goal setting interventions offered very
3. Current results little guidance as to the kinds of goals students
should be setting. It was observed that students
were asked to set goals, but not given any
Currently, study 1 has been carried out. This
specific characteristics or content that their
is a systematic literature review of goal setting
goals should contain in most studies. While this
allows for a lot of student autonomy, it is how learning analytics and conversational
troubling in the face of prior research which agents can be used to enhance goal setting
shows that when unguided, students generally interventions in TEL environments in order to
don’t set very effective or meaningful goals, make them more engaging and better tailored to
and that some types of goals are more effective the individual needs of students. With the
at bringing about behavioral change than others results from this project, we aim to advance the
[51]. understanding of how to best implement goal
The focus on unguided forms of goal setting, setting support tools within online
and non-experimental designs in the studies environments, to help enhance students’ SRL
reviewed makes it hard to draw conclusions skills that are needed to succeed in an
regarding the most effective way of scaffolding increasingly digital educational landscape.
goal setting. However, the results did suggest While this project has wide-reaching
that delivering interventions digitally, scientific significance, it also has important
combining goal setting with support for other practical significance. It will focus on using
stages of the SRL cycle, and requiring that education sciences theories to shape learning
students set more detailed, specific goals were analytics tools and offer insight into the role of
all associated with goal setting having a individual student characteristics in shaping the
positive effect. From these results, it is clear way students interact with learning analytics
that more studies are needed to actively tools. These insights can be used to form the
examine the characteristics of effective goal basis of future research into, and development
setting interventions. of, learning analytics tools. The rise of
Taken together this suggests several things technology enhanced learning has highlighted
for the future of this project; 1) there is a the need to create tools which can support
disconnect between the existing literature on students learning in online environments in a
how to set effective academic goals, and the personalized manner. The studies in this project
development of many of the goal setting tools aim to understand how learning analytics tools
implemented in previous literature. And 2) can best offer this support, and to create
while these kinds of interventions tend to be guidelines for the development of these tools in
delivered digitally, there is a lot of room for the future.
improvement in how technology and learning While several studies have examined the use
analytics can be used to support and enhance of learning analytics to support performance,
these tools. very few have focused on the use of learning
analytics tools to support goal setting and goal
4. Contribution to TEL domain monitoring. Furthermore, there is currently
very limited research on how individual student
characteristics like perfectionism or self-
While the TEL domain has been around for
efficacy affect the way students interact with
several decades, the last decade has seen a learning analytics tools, and to what extent
massive increase in its popularity in the average these tools are effective for students who differ
higher education classroom. As such, it is more
on these characteristics. This project aims to
important than ever to address how to best develop tools which can be used to offer
support students while learning in TEL personalized learning analytics supported SRL
environments. This project contributes to the tools.
understanding of how learning analytics can be
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