Translating the Concept of Goal Setting into Practice: What ‘else’
Does It Require than a Goal Setting Tool?
Gábor Kismihók
1a
, Catherine Zhao
2b
, Michaéla C. Schippers
3c
, Stefan T. Mol
4d
,
Scott Harrison
5e
and Shady Shehata
6f
1
Leibniz Information Centre for Science and Technology, Hannover, Germany
2
The University of Sydney, Sydney, Australia
3
Erasmus University of Rotterdam, Rotterdam, The Netherlands
4
University of Amsterdam, Amsterdam, The Netherlands
5
Leibniz Institute for Research and Information in Education, Frankfurt, Germany
6
YOURIKA, Waterloo, Canada
Harrison@dipf.de, sshehata@yourika.ai
Keywords: Goal Setting, Self-regulated Learning, Learning Intervention, Curriculum, MOOC, Higher Education.
Abstract: This conceptual paper reviews the current status of goal setting in the area of technology enhanced learning
and education. Besides a brief literature review, three current projects on goal setting are discussed. The paper
shows that the main barriers for goal setting applications in education are not related to the technology, the
available data or analytical methods, but rather the human factor. The most important bottlenecks are the lack
of students’ goal setting skills and abilities, and the current curriculum design, which, especially in the
observed higher education institutions, provides little support for goal setting interventions.
1 INTRODUCTION
Educational technology and ‘big’ data are having a
major impact on learning these days: disruptive forces
are modifying the modalities and strategies we choose
to learn. Subsequently, the mastery of skills and
competences enabling lifelong learning in the vast
majority of aspects and fields of education are
critically important in the 21st century (Ramsden,
2003, EUR-Lex, 2017). This movement is also
visible in the area of Self-Regulated Learning (SRL),
which has never been so actual and timely as it is
these days (Archer, 1988; Schunk and Zimmerman,
2012). As a result, the needs of individual learners,
and the integration of these needs into particular
social and technical contexts play a more and more
a
https://orcid.org/0000-0003-3758-5455
b
https://orcid.org/0000-0002-2791-4019
c
https://orcid.org/0000-0002-0795-5454
d
https://orcid.org/0000-0002-9375-3516
e
https://orcid.org/0000-0001-6712-7784
f
https://orcid.org/0000-0002-3258-6734
important role in contemporary education (Ferguson,
2012; Buckingham Shum and Ferguson, 2012).
Goal Setting (GS), as a critical and instrumental
component of SRL (Pintrich, 2000), is suggested to
be an important activity in learning intervention
designs (Wise et al, 2014). Nevertheless, GS is still
rarely used, especially in higher education despite its
demonstrated positive effects on study success (Mol
et al, 2016). Research also has shown already that
through dashboards learners can visualise and inter-
nalize learning objectives (Scheffel et al, 2014;
Verbert et al, 2014).
This paper sets out to rekindle discussions around
GS to ensure that this important aspect of SRL gets
attention and lands on the agenda of Technology
Enhanced Learning (TEL) research and practice
communities. To facilitate this conversation, we aim
388
Kismihók, G., Zhao, C., Schippers, M., Mol, S., Harrison, S. and Shehata, S.
Translating the Concept of Goal Setting into Practice: What ‘else’ Does It Require than a Goal Setting Tool?.
DOI: 10.5220/0009389703880395
In Proceedings of the 12th International Conference on Computer Supported Education (CSEDU 2020) - Volume 1, pages 388-395
ISBN: 978-989-758-417-6
Copyright
c
2020 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
to summarize lessons learned from three recent
European investigations in order to illustrate not only
the potential, but also the pitfalls of GS. We also
consider what should be the next steps for TEL
researchers and practitioners to realize the power of
GS. We hope that this paper will ignite dialogues
within the TEL community about this important SRL
concept, and that this will yield more studies and
experiments in the near future.
To achieve this, this paper starts with a brief lit-
erature review on the current state of the art of GS.
Then we discuss three GS investigations and their
outputs, followed by a discussion on the bottlenecks
and barriers facing GS research. We close our paper
with suggesting future directions for GS stakeholders.
2 GOAL SETTING IN
EDUCATION
2.1 State of the Art
The principles of GS, which were developed in the
mid-1960s by Edwin Locke, provide practical ac-
counts of motivation in both managerial and aca-
demic contexts (Locke and Latham, 2006). Locke and
his colleagues also showed that specific objectives
lead to greater performance improvement than
general ones. Furthermore, a linear relationship
between goal difficulty and task performance has
been established (Locke and Latham, 2006). Thus,
GS is economical in financial terms, and has the
potential to optimize task and academic performance
(Schippers, 2017; Schmidt, 2013).
Recent studies also confirmed the importance of
GS. Learning goals contribute to high interest, (Valle
et al, 2017) and predict improvements in academic
performance both in high school and higher education
environments (Neroni et al, 2018, Burns et al, 2018;
Schippers et al., 2020). In the area of educational
computer games, GS increases comprehension (Erhel
et al, 2019), especially when negotiation between
learners is also facilitated (besides individual GS)
(Chen et al, 2019), and affects the success of learners
on the leaderboard (Landers et al, 2017).
In a more specific frame, GS can be an integral
part of the feedback process that supports individual
learning. For students in higher education, providing
well defined feedback processes can enhance the
learning process, especially when a formal GS pro-
tocol is included in the feedback cycle (Evans, 2013,
Duffy and Azevedo, 2015). This also needs to be done
tactfully, without affecting the student's ego: “Self-
efficacy influences motivation and cognition because
it affects students’ task interest, task persistence, the
goals they set, the choices they make and their use of
cognitive, meta-cognitive and self-regulatory
strategies” (Van Dinther et al, 2010, p. 97).
This reinforces the importance of understanding
the student's state of mind and willingness to under-
take GS as a learning strategy (Lazowski and
Hulleman, 2016). “When students believed that they
could get smarter over time, they were more likely to
believe that working hard could help them succeed in
school and they endorsed the goal of learning from
coursework. These beliefs and goals motivated
greater use of effective learning strategies” (Yeager
and Walton, 2011, p. 286). Since GS can hence be
seen as an effective strategy for improving learning
trajectories, the question arises: what are the major
obstacles to the more widespread adoption of GS in
higher education?
We have seen that scepticism of psychological
intervention studies is prudent where potential bias
can be introduced, either through limited sample sizes
or where incentives artificially inflate engagement.
For example, Chase et. al (2013) constructed an
experiment testing the effects of GS under the
condition of values training. Students recruited for the
experiment (N=132) had the opportunity to “win”
goods with tangible value. Importantly, this study
found that GS alone had no effect on learning
trajectories. Only when values training was included
did students perform better, thus putting scepticism in
the centre of the issues of interventions’ scalability
and innovations, which facilitate GS.
In sum, there is evidence to show that GS can
improve students’ learning trajectories and outcomes.
However this evidence needs to be critically
challenged to best understand, what dimensions of the
GS process can be scaled, to provide support above
and beyond small scale interventions (Schippers &
Ziegler, 2019).
2.2 Three Cases of Goal Setting
Experimentations and Deployment
With the support of educational technology, design-
ing and running GS interventions – also on large
(institutional) scale – is possible. To demonstrate this,
in this paper we examine three recent attempts, which
use GS in two different educational settings (higher
education and MOOC) to investigate the relationship
between GS and learning outcomes. As it was
elaborated in these studies, researchers face a range
of problems, when it comes to motivating learners
to
set and to monitor their goals throughout their
Translating the Concept of Goal Setting into Practice: What ‘else’ Does It Require than a Goal Setting Tool?
389
Table 1: Comparison of three goal-setting studies.
Element Dimension Schippers et. al ProSOLO Mol et. Al
Intervention
setup
Educational context
University program
based
MOOCs
University program
based
Learner participation
Opt-in informed
consent
Optional
Opt-in informed
consent
Dashboard No Yes Yes
Learners’
prior
experiences
Background of
targeted learners
University students
Corporate
professionals
University students
Assumed learner skills Metacognitive skills Not specified Not specified
Goal related
activities
Engagement time
Stage 1&2, 10 min,
photography in stage 3
Not specified Not specified
Means to set goals Write own goals
Write/Adopt external
pre-specified goals
Write/Adopt peer’s goal
Criteria for setting
goals
Practical & attainable No SMART
Feedback
Instructor feedback to
students
No No
Ratings of goals against
criteria
Support
Peer-student support No Yes
Depending on student’s
choice
Other support
Scaffolding through
‘steps’ in the system
Ad-hoc inquiry &
technical support
Ad-hoc inquiry &
technical support
Outcome
Anticipated outcome A package intervention:
“life crafting”
Foster effective self-
regulated learning
Improved academic
performance
Actual outcomes Enhanced student well-
being and performance
Learners’ uncertainty Low participation
learning journey. Therefore, we aim to shed light on
the criticality of the educational context, with a focus
on how decisions have led to different outcomes in
these studies (see table 1).
Schippers and her team (Schippers et al, 2015;
2020) designed a three staged GS intervention (with
a GS application) that scaffolds the GS process for
university first year students (n=2928) and encour-
ages them to achieve their goals. The intervention
requires students to start explicitly to conceptualize
by writing their desired future (in stage 1), and to
articulate a step-by-step plan for achieving their goals
(in stage 2). Alongside these procedures, students are
encouraged to assess practicality and attainability of
their goals, in order to stay on the ‘right’ track. At the
operational level the intervention is an integral part of
the curriculum across campus, despite the fact that it
is technically a stand-alone system. The studies by
Schippers et al. (2020) show that participation in the
intervention closed the gender and ethnicity
achievement gap (Schippers et al, 2015). Further, the
results indicate that formal participation (e.g. an
element of the assessment task) in the intervention,
the amount of writing and the quality of the writing
are the three key factors that determine the
effectiveness of GS, whilst whether or not students set
academic goals does not seem to matter. In other
words, the process by which students engage
psychologically in setting goals makes the difference
– in that it enhances the student’s self-efficacy,
optimizes effort, and psychologically better prepares
them to achieve their goals (Schippers, 2017;
Schippers et al, 2017).
The GS intervention designed by Mol and col-
leagues (Kobayashi et al, 2017) investigates the
simultaneous effect of GS on university students’
approaches to learning, and their academic perfor-
mance. The study adopts the SMART (Specific,
Measurable, Attainable, Realistic and Time-bound)
characteristics as criteria (Conzemius and O’Neill,
2009; O’Neill, 2000) that guide students in setting
effective goals. They also developed a GS tool, in
which students could compose their goals, and ap-
pend these with deadlines. This GS tool was con-
nected to a Learning Record Store (LRS) which also
set up to record additional data from the Learning
Management Systems (LMS) about students’ actual
performance during the course. The study involved
one university course at the University of Amsterdam
and courses at three Australian-based universities. In
the first lecture, students were introduced to GS
theory with an emphasis on its benefits, and the
custom developed tool and its key features. Specifi-
cally, the tool 1) allows students to set multiple main
CSEDU 2020 - 12th International Conference on Computer Supported Education
390
goals and associated sub-goals, (based on the fact that
one oftentimes pursues multiple goals simultaneously
(Austin, and Vancouver, 1996)); 2) students can view
and adopt each other’s goals, but only those that are
made ‘public’ by the students who set them; 3)
students can edit or delete goals after setting them; 4)
students can view properties of goals e.g. structure,
deadlines through a dashboard, against the timeline of
their university course; 5) instructors can rate the
quality of goals against the SMART criteria, students
can view the ratings should the goal be made public.
The intervention however has attracted low student
participation in the pilot stage. The authors speculate
that reasons of, 1) GS being optional, and as such
independent of the course curriculum and not
rewarded with course credit, 2) variability in
instructors and tutors understanding of GS, 3) lack of
student support (e.g. feedback) and GS learning
resources, may have contributed to this outcome.
Furthermore, the informed consent procedure that
was employed, may have unintentionally scared some
students off, as it also requested access to their LMS
data and assessment outcomes. This latter issue also
ties into the larger question of whether GS
interventions should be positioned as a teaching tool,
a research project, or both. Framing GS in terms of a
teaching resource, may enhance face validity in the
eyes of students, although evidencing such
interventions is clearly more of a research question.
Gasevic and colleagues (Rosé et al, 2015; Jo et al,
2016; Jo et al, 2016) implemented the ProSOLO
system to encourage learners to set goals and to foster
social learning in a Massive Open Online Learning
Course (MOOC) called Data, Analytics, and
Learning. It targets corporate working professionals,
who are assumed to have a reasonably high level of
digital literacy. Compared to university campus-
based courses, MOOCs generally target educated
adult learners from much more diverse demographic
backgrounds and with a wider range of motivations.
Furthermore, there is evidence that GS predicts the
attainment of course objectives (Kizilcec et al, 2018).
Thus in the highly autonomous learning space,
ProSOLO is designed to personalize the development
of competencies, which are mapped to learning
activities throughout the MOOC. Learners are
encouraged to set up their own space that is
comprised of predefined competencies (by course
instructors) they want to develop, or their own
learning goals (if the competencies do not match), a
social network they can build by being able to follow
one another through social media, and a learning
progress feature. Learners are expected to link this
personalized hub to the assignment submission
process on the MOOC platform, toward course
completion. This approach provides opportunities for
learners who intend to purchase a certificate to
demonstrate competencies with ‘evidence’. However,
the patterns of MOOC learners’ engagement with the
system, and their discussions in the MOOC forum
point out a number of problems: 1) some learners
seem to be confused with regard to having to engage
with both the MOOC platform and ProSOLO; 2)
some learners were not familiar with the technology;
and more importantly, 3) despite high autonomy in
MOOCs, when learners are not able to make informed
choices of how to effectively learn, they fall back to
what they are familiar with, which is oftentimes, a
linear learning progression and a structured
instructional norm, rather than the social construction
of knowledge.
3 DISCUSSION
3.1 Lessons Learned from Goal Setting
Experiments
This paper unpacked the TEL related GS literature
and reviewed three technology-enhanced interven-
tions to bring forward the dimensions that are be-
lieved to be important to the success of GS interven-
tions in education. From this analysis, it emerged that
the course instructor, peer learners, and the goal-
setting interface designer play key roles in shaping
the learner’s perception of GS from the outset. In the
studies where researchers were course instructors, the
GS concept was ‘translated’ more effectively into
meaningful actions and thinking processes that were
relevant to students. However, it is worth considering
the relationship between GS and assessment. In both
Schipper’s intervention and the ProSOLO
experiment, GS is an element of assessment (despite
having a minimal or no grade attached). The possible
explanation of the difference is that assessment
matters in university learning but not in a MOOC.
Furthermore, the two interventions are very different:
The one used by Schippers and colleagues is based on
expressive writing and personal GS, (also referred to
as “life crafting”, Schippers & Ziegler, 2019), while
ProSolo is aimed at competency development. Future
research should investigate this relationship carefully.
Translating the Concept of Goal Setting into Practice: What ‘else’ Does It Require than a Goal Setting Tool?
391
Figure 1: The multi-layer framework for an effective technology-enhanced goal-setting intervention.
Less apparent is the broader context in which
more distal stakeholders come into the play. These
include the nature of the learning episode (e.g. a
course, a program, or a MOOC), the technological
readiness of the offering university, and the institu-
tional culture. Meanwhile, the forms of education are
becoming more diverse, which attract learners with
diverse motivations to learn. Especially in MOOCs,
learning is often not tied to assessment (Jordan, 2015;
Vigentini and Zhao, 2016). While what drives
learning in MOOCs is debatable, future GS research
should respond to the challenge of how to integrate
GS into a personalized learning journey with the
support of analytics.
How to implement a technology-enhanced GS
intervention for students to make learning more
effective does not have a straight answer. While the
design and delivery process is complex, the debate
remains an educational one - how does the student
benefit from setting goals in a university course, or a
degree program? Furthermore, how may researchers
effectively demonstrate the value of GS to university
stakeholders, to initiate system development and (re-
)configuration that lays the technical foundation for
TEL to empower GS interventions?
On the other hand, the program conveyor’s per-
ception of what matters most to developing graduate
capabilities may determine the scale at which a GS
intervention is implemented (e.g. in an individual
course vs. core courses) throughout the program.
Secondly, institutional technology readiness directly
impacts on how a GS tool can be integrated into other
university supported systems. Thus university culture
to an extent shapes the way the student learns and
what the teacher teaches. To this end, researchers
should consider, where GS fits in an educational
experience that is unique to the institution. Figure 1
presents these influences at different levels on GS
interventions.
3.2 Bottlenecks for Goal Setting in
Higher Education
GS in general is “a short and seemingly simple in-
tervention (that) can have profound effects” (Wilson,
2011), and it has been supported a number of times in
the past (Morisano et al, 2010, Travers et al, 2015,
Schippers et al, 2020). However, there are several
reasons why GS implementations in higher education
can fail. Here we will focus on discussing the three
most important potential bottlenecks.
The first bottleneck is the lack of ability from the
student side to self-regulate and set goals. This has
been confirmed by a previous study (McCardle et al,
2017), and it has been especially apparent in the Mol
et al. pilot, where students failed to come up with
goals altogether. Here students’ looked at GS as an
extra assignment on top of their curricular work,
which does not help their progress, but only limits the
time to spend on reaching the course objectives.
Researchers think that this is a critical point. It is very
difficult to direct students towards setting their own
goals in relation to a course or a learning programme,
if those goals are already set by the organization or
the teacher. What happens in this case is, that students
simply copy those course objectives and spend very
little time about thinking and operationalizing their
CSEDU 2020 - 12th International Conference on Computer Supported Education
392
own self developed objectives – which would be the
real benefit of GS. When this happens, goals are set
to be unrealistic and they fail to consider resources or
capabilities.
Furthermore, when students actually set goals,
oftentimes they lack the ability to evaluate crucial
information about the obstacles and challenges that
they face, in achieving their goals. Despite the evi-
dence that SRL supports cognitive and meta-cogni-
tive abilities of students (Thomas, et al, 2016), in a
learning environment, where students are pushed into
a reactive rather than a proactive role when it comes
to designing and controlling their own learning, GS
can play only a marginal role. To overcome this
problem, in the intervention used by Schippers,
students had to come up with a detailed plan to
overcome obstacles and challenges.
The second bottleneck is a more methodological
one. From the available literature and experiments it
is not obvious, what the best methods are to incorpo-
rate GS in course design (in various contexts). As it
was mentioned earlier, it is very difficult to imple-
ment effective GS mechanisms in the curricula, if
learning goals are already pre-developed and made
available for the course participants beforehand.
Methods need to be in place to co-develop these
course objectives with the students, which require
more flexible curricula. Nevertheless, the design of
GS interventions may share some similarities with
other educational approaches such as the use of e-
Portfolios (Berg et al, 2018) to develop a ‘learning
journey’.
The third methodological issue is about rewarding
students, who actually set goals. According to the
pilots, oftentimes students do not believe that the
rewards they will receive for goal accomplishment
are worth the effort that they need to invest to achieve
them. For instance, when there are too many goals to
achieve, a mechanism should be in place to prioritize
certain goals over others. In the case of the successful
Schippers’ intervention, it was shown that setting
personal goals has a rewarding effect on students.
However, the skill of setting (personal) goals
effectively is not an easy one to master, and training
this skill should not only happen in higher education,
but also much earlier in primary and secondary
education.
Thus the authors suggest further opportunities for
teaching academics to gain a more thorough under-
standing of the concept and practice of GS through
professional development programs. This skill is not
only important for students, but also for their teachers
and indeed researchers.
3.3 Integrating Goal Setting in the
Academic Program
Given that GS enhances study success, the next
question is how to make sure that as many students
profit from this intervention as possible (Schippers,
2017). However, if the GS intervention is made
optional, students may not engage with it, especially
the poor performers who may stand to benefit most.
It was learned from one of the abovementioned pilots
that when the third part of the intervention was made
optional, from 1,200 students, only 45 students
participated in that third part! Therefore, it may be
important to make the GS intervention part of the
curriculum, so both students and educational institu-
tions benefit from the positive outcomes (Schippers,
2020; Clonan et al, 2004). GS may be notably useful
when learners are in a transitional period of their
lives, as for instance progressing from school to
higher education (Schippers & Ziegler 2019; Wilson,
2011), or from higher education to the labour market
(Schippers, 2020; Berg et al, 2018). However the
effects of making GS mandatory should be further
investigated, as ownership is critical to the success of
GS.
A positive outcome from the pilots is that tech-
nical infrastructure, for collecting and analysing
learning related data in relation to goals is, in general,
not perceived as a bottleneck in GS experimentations.
4 CONCLUSIONS
GS has a number of advantages, when it comes to
applications in a number of educational contexts. The
method is easy to implement from a technical point of
view, and it works well together with existing
educational and analytical technologies. Evidence
also shows that GS can significantly improve both the
self-regulation, and the academic performance of
learners. However there are a number of barriers on
the human side, which still need substantial efforts to
overcome. The most important barriers are the low
levels of student abilities to set goals, and the current
– especially in traditional classroom settings –
methods for pre-defining learning outcomes for
learners and classes. It comes without saying that
these issues need further investigation.
The authors think that teaching and research
communities should engage in more in depth con-
versations about GS in order to understand and use
this concept better in the future. Therefore, the most
important aim of this paper was to provide ammuni-
tion for these discussions by highlighting the above
mentioned critical observations. On a positive note,
Translating the Concept of Goal Setting into Practice: What ‘else’ Does It Require than a Goal Setting Tool?
393
the authors of this paper strongly believe that, espe-
cially in the light of the ongoing GS experiments and
implementations, there is a bright future for GS in
education.
REFERENCES
Ames, C., Archer, J. 1988. Achievement goals in the
classroom: Students’ learning strategies and motivation
processes. Journal of Educational Psychology 80, 3
(1988), 260–267.
Austin, J. T., & Vancouver, J. B. 1996. Goal constructs in
psychology: Structure, process, and content.
Psychological bulletin, 120(3), 338.
Berg, A.M., Branka, J., Kismihók, G. 2018 Combining
Learning Analytics with Job Market Intelligence to
Support Learning at the Workplace. In: Digital
Workplace Learning. pp. 129–148. Springer, Cham.
https://doi.org/10.1007/978-3-319-46215-8_8.
Burns, E.C., Martin, A.J., Collie, R.J. 2018. Adaptability,
personal best (PB) goals setting, and gains in students’
academic outcomes: A longitudinal examination from a
social cognitive perspective. Contemporary
Educational Psychology. 53, 57–72
Chase, J. A., Houmanfar, R., Hayes, S. C., Ward, T. A.,
Vilardaga, J. P., Follette, V., 2013. Values are not just
goals: Online ACT-based values training adds to goal
setting in improving undergraduate college student
performance, In Journal of Contextual Behavioral
Science, Vol 2, Iss 3–4, pg. 79-84
Chen, Z.-H., Lu, H.-D., Chou, C.-Y. 2019. Using game-
based negotiation mechanism to enhance students’ goal
setting and regulation. Computers & Education. 129,
71–81
Clonan, S. M., Chafouleas, S. M, McDougal, J. L., Riley-
Tillman T. C. 2004. Positive psychology goes to school:
Are we there yet? Psychology in the Schools 41, 1, 101–
110.
Conzemius A., O’Neill, J., 2009. The Power of SMART
Goals: Using Goals to Improve Student Learning.
Solution Tree Press.
Duffy, M.C., Azevedo, R. 2015. Motivation matters:
Interactions between achievement goals and agent
scaffolding for self-regulated learning within an
intelligent tutoring system. Computers in Human
Behavior. 52, 338–348.
Erhel, S., Jamet, E., 2019. Improving instructions in
educational computer games: Exploring the relations
between goal specificity, flow experience and learning
outcomes. Computers in Human Behavior. 91, 106–
114.
EUR-Lex - c11090 - EN - EUR-Lex. 2017. http://eur-
lex.europa.eu/legal-
content/EN/TXT/?uri=LEGISSUM:c11090
Evans, C., 2013 Making Sense of Assessment Feedback in
Higher Education, In Review of Educational Research,
Vol. 83, No. 1, pp. 70–120,
Ferguson, R.,2012. Learning analytics: drivers,
developments and challenges. International Journal of
Technology Enhanced Learning 4, 5–6, 304–317.
Jo, Y., Tomar, G., Ferschke, O., Rosé, C. P., Gasevic, D.
2016. Expediting support for social learning with
behavior modeling. arXiv preprint arXiv:1605.02836.
Jo, Y., Tomar, G., Ferschke, O., Rosé, C. P., & Gašević, D.,
2016. Pipeline for expediting learning analytics and
student support from data in social learning. In
Proceedings of the Sixth International Conference on
Learning Analytics & Knowledge, 542-543. ACM.
Jordan. K., 2015. Massive open online course completion
rates revisited: Assessment, length and attrition. The
International Review of Research in Open and
Distributed Learning 16, 3 (2015).
Kizilcec, R.F., Pérez-Sanagustín, M., Maldonado, J.J., 2017
Self-regulated learning strategies predict learner
behavior and goal attainment in Massive Open Online
Courses. Computers & Education. 104, 18–33.
Kobayashi, V., Sanagavarapu, P., Zhao, C., Mol, S.T., &
Kismihok, G., 2017. Investigating the relationships
among self-regulated learning, approach to learning,
goal orientation, LMS activity and academic
performance. In Proceedings of the 1st Learning &
Student Analytics Conference: Implementation,
Institutional Barriers and New Development,
Amsterdam, the Netherlands.
Landers, R.N., Bauer, K.N., Callan, R.C., 2017
Gamification of task performance with leader-boards:
A goal setting experiment. Computers in Human
Behavior. 71, 508–515.
Lazowski, R. A., Hulleman, C. S. 2016 Motivation
Interventions in Education: A Meta-Analytic Review,
In Review of Educational Research, Vol. 86, No. 2, pp.
602– 640
Locke E. A., Latham, G. P., 2006. New directions in goal-
setting theory. Current directions in psychological
science 15, 5, 265–268.
McCardle, L., Webster, E.A., Haffey, A., Hadwin, A.F
2017. Examining students’ self-set goals for self-
regulated learning: Goal properties and patterns.
Studies in Higher Education. 42, 2153–2169.
Mol, S.T., Kobayashi, V.B., Kismihók, G. and Zhao, C.
2016. Learning through goal setting. In Proceedings of
the Sixth International Conference on Learning
Analytics & Knowledge, 512–513
Morisano, D., Hirsh, J. B., Peterson, J. B., Pihl, R. O., &
Shore, B. M. 2010. Setting, elaborating, and reflecting
on personal goals improves academic performance.
Journal of Applied Psychology, 95(2), 255–264.
Neroni, J., Meijs, C., Leontjevas, R., Kirschner, P.A., De
Groot, R.H.M. 2018. Goal Orientation and Academic
Performance in Adult Distance Education. irrodl. 19.
O’Neill, J., 2000. SMART Goals, SMART Schools.
Educational Leadership 57, 5 (2000), 46–50.
Pintrich, P.. 2000. The role of goal orientation in self-
regulated learning. Handbook of self-regulation 451,
(2000), 451–502
Ramsden,P., 2003. Learning to Teach in Higher Education.
Routledge
CSEDU 2020 - 12th International Conference on Computer Supported Education
394
Rosé, C. P., Ferschke, O., Tomar, G., Yang, D., Howley, I.,
Aleven, V. & Baker, R. 2015. Challenges and
opportunities of dual-layer MOOCs: Reflections from
an edX deployment study. In Proceedings of the 11th
International Conference on Computer Sup-ported
Collaborative Learning (CSCL 2015) (Vol. 2).
Scheffel, M., Drachsler, H., Stoyanov, S., Specht, M. 2014.
Quality in-dicators for learning analytics. Journal of
Educational Technology & Society 17, 4, 117.
Schippers, M. C. 2017. IKIGAI: Reflection on life goals
optimizes performance and happiness (EIA-2017-070-
LIS ed.). Rotterdam: Erasmus Research Institute of
Management
Schippers, M. C., Morisano, D., Locke, E. A., Scheepers,
A. W. A., Latham, G. P., & de Jong, E. M. 2020.
Writing about personal goals and plans regardless of
goal type boosts academic performance. Contemporary
Educational Psychology, 60, 101823.
Schippers, M. C., Scheepers, A., Morisano, D., Locke, E.
A., & Peterson, J. B. 2017. Conscious goal reflection
boosts academic performance regardless of goal
domain. Manu-script submitted for publication.
Schippers, M. C., & Scheepers, A. W. & Peterson, J. B.
2015. A scalable goal-setting intervention closes both
the gender and minority achievement gap. Palgrave
Communications 1:15014
Schippers, M.C. & Ziegler, N. 2019. Life crafting as a way
to find purpose and meaning in life. Frontiers in
Psychology, 10(2778)
Schmidt. F. I. 2013. The economic value of goal setting to
employers. New develop-ments in goal setting and task
performance. 16–20.
Schunk, D. H. Zimmerman. B. J., 2012. Motivation and
Self-Regulated Learning: Theory, Research, and
Applications. Routledge.
Buckingham Shum, S., Ferguson, R. 2012. Social learning
analytics. Journal of educational technology & society
15, 3, 3.
Thomas, L., Bennett, S., Lockyer, L. 2016. Using concept
maps and goal-setting to support the development of
self-regulated learning in a problem-based learning
curriculum. Medical Teacher. 38, 930–935.
Travers, C. J., Morisano, D., & Locke, E. A. 2015. Self-
reflection, growth goals, and academic outcomes: A
qualitative study. British Journal of Educational
Psychology, 85(2), 224–241.
Valle, A., Núñez, J.C., Cabanach, R.G., Rodríguez, S.,
Rosário, P., Inglés, C.J. 2015. Motiva-tional profiles as
a combination of academic goals in higher education.
Educational Psychology. 35, 634–650.
Van Dinther, M., Dochy, F., & Segers, M., 2010, Factors
affecting students’ self-efficacy in higher education, In
Educational Research Review, Vol 6, Iss 2, Pages 95-
108
Verbert, K., Govaerts, S., Duval E.,, Santos J. l.,, Van
Assche, F., Parra, G. and Klerkx, J.. 2014. Learning
dashboards: an overview and future research
opportunities. Personal and Ubiquitous Computing 18,
6 (2014), 1499–1514.
Vigentini, L., Zhao, C., 2016. Evaluating the ’Student’
Experience in MOOCs. In Proceedings of the Third
(2016) ACM Conference on Learning@ Scale, 161–
164.
Wilson, T. 2011. Redirect: The surprising new science of
psychological change. Penguin UK.
Wise, A., Zhao, Y., Hausknech, S.T. 2014. Learning
analytics for online discussions: Embedded and
extracted approaches. Journal of Learning Analytics 1,
2, 48–71.
Yeager D. S., & Walton, G. M. 2011. Social-Psychological
Interventions in Education: They’re Not Magic. In
Review of Educational Research, Vol. 81, No. 2, pp.
267-301.
Translating the Concept of Goal Setting into Practice: What ‘else’ Does It Require than a Goal Setting Tool?
395