=Paper=
{{Paper
|id=Vol-1278/paper11
|storemode=property
|title=Self-Regulated Learning Nudges
|pdfUrl=https://ceur-ws.org/Vol-1278/paper11.pdf
|volume=Vol-1278
|dblpUrl=https://dblp.org/rec/conf/dmrs/KravcikK14
}}
==Self-Regulated Learning Nudges==
https://ceur-ws.org/Vol-1278/paper11.pdf
Self-Regulated Learning Nudges
Miloš Kravčík, Ralf Klamma
Advanced Community Information Systems (ACIS), Informatik 5,
RWTH Aachen University, Germany
{kravcik, klamma}@dbis.rwth-aachen.de
Abstract Self-Regulated Learning increases the effectiveness of education and
self-control has a high impact on the successful life generally. Cognitive biases
heavily influence the decision making process, often against interests of those
who make them. Therefore technological solutions that would support meta-
cognitive scaffolding of learners may be very helpful. Our approach is based on
Personal Learning Environments that provide both reflection and
recommendation facilities. Preliminary results suggest that it can be a
promising solution. Nevertheless, there are still challenges to be addressed,
especially regarding the evaluation of this type of learning and supporting tools.
Keywords: Self-Regulated Learning, Personal Learning Environments.
1 Motivation and Problem
Self-regulation has a high impact on successful learning and life [1]. Evidence has
shown that Self-Regulated Learning (SRL) enhances student performance in courses,
the amount and depth of student thinking, students’ conscious focus on their learning,
as well as the development of reflective and responsible professionalism [2]. SRL
includes the control over meta-cognitive processes. The freedom of choice can raise
the motivation of learners. On the other side, suitable guidance can increase the
effectiveness of the learning process. Therefore to support efficiency of learning, it is
crucial to find the right balance between guidance and freedom of learners. As the
ultimate goal of SRL is learning without instructors (for lifelong learning purposes
they are often not available), meta-cognitive scaffolding becomes highly important.
This was one of the main challenges in the ROLE project [3], aiming to allow flexible
configuration and design of learning environments, including more traditional
learning management systems and newer Personal Learning Environments (PLE),
which enable customization and personalization of the whole learning environment.
The main problem remains the same – it is the degree of the learner control, which
can be considered at various levels (e.g. design of learning environment, selection of
learning processes and resources). Generally, a self-regulated learner should have a
full control over his or her learning. Nevertheless, as we know it from other areas, due
to information overload and various missing competences, people often delegate a
part of their control to other subjects – either human experts or technological
solutions. Actually, their recommendations do not have to be blindly followed – these
can just inform learners in order to make up their own mind.
2 Proposed Solution and Implications
Psychological research has shown that humans are not well described by the rational-
agent model and often need help to make good decisions [4]. Their long-term aims are
often in conflict with immediate emotional incentives. In addtion, various cognitive
biases shape their decision making process. The framing effect emphasizes the
importance of the context, as the way how the same information is presented can
influence the decision. Moreover, user preferences can change very quickly and may
be difficult to recognize. People often do not consciously know their preferences [5]
and their actions alter them – when they select something, they will value it more [6].
A lot of information with various level of relevancy may need to be considered and
therefore competent recommendations from an expert or an algorithm can be very
helpful. Also choice architecture, which describes the way alternative items are
presented to the chooser, can have massive effects on people’s behaviour. Options
and their implementations provide the mechanism to facilitate various degrees of
guidance and freedom. Libertarian paternalism approach has been proposed [7] to
preserve liberty and to influence choices in a way that will make choosers better off,
as judged by them. This can be realized via suitable nudges, which should alert
people’s behaviour in a predictable way and at the same time should be easy and
cheap to avoid. The golden rule of libertarian paternalism states: offer nudges that are
most likely to help and least likely to inflict harm. These principles suggest that a
suitable way to support SRL is by means of flexible and adaptable learning
environments that provide enough freedom as well as context-dependent
recommendations. Their right balance depends on the context, including the learner,
the subject domain, and the current constraints. From our perspective this means the
freedom to organize and control one’s own learning process, design the PLE, and
choose learning resources, as well as an opportunity to receive suitable, context-
dependent guidance in the form of recommendations, together with the opportunity to
select or avoid the provided offers.
A cyclic model of SRL as a process of meta-cognitive activities was proposed by
Zimmerman [8]. It has been adjusted for the ROLE purposes [9], resulting in a model
with 4 phases: planning, preparing, learning, and reflecting. Moreover, ontology of
learning activities was created in ROLE, which can be used for contextual
recommendations. From the technological perspective our solution was based on PLE
[10]. It is assumed that learners define their own learning goals and manage their
learning environment, contents or processes with a high degree of autonomy [11]. The
ROLE Software Developer Kit [12] enables creation of PLEs that should facilitate
SRL. They consist of widgets, which can support both cognitive and meta-cognitive
learning processes. In the ROLE Widget Store one can find tools for planning
(including goal setting), learning (nudges in form of contextual recommendations)
and reflection (learning analytics). A good SRL solution should be customizable,
providing a right balance between the learner’s freedom and guidance, and motivating
the learner. Evaluation of the effectiveness and usefulness of our approach has been
undertaken in different settings, involving both students and teachers. The overall
findings suggest that this is a promising direction, but behavioural changes in this
field have also limits and require long term research. We further build on the ROLE
results in newer projects: Learning Layers [13] and BOOST [14].
References
[1] Shoda, Y., Mischel, W. and Peake, P. K. (1990). Predicting adolescent cognitive and self-
regulatory competencies from preschool delay of gratification: Identifying diagnostic
conditions. Developmental Psychology 26(6), 978-986. American Psychological
Association.
[2] Nilson, L. (2013). Creating Self-Regulated Learners: Strategies to Strengthen Students’
Self-Awareness and Learning Skills. Stylus Publishing, LLC.
[3] http://www.role-project.eu/
[4] Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.
[5] Dijksterhuis, A., Bos, M. W., Nordgren, L. F., & Van Baaren, R. B. (2006). On making
the right choice: The deliberation-without-attention effect. Science, 311(5763), 1005-1007.
[6] Sharot, T., Velasquez, C. M. and Dolan, R. J. (2010). Do decisions shape preference?
Evidence from blind choice. Psychological Science 21(9), 1231-1235. SAGE Publications.
[7] Thaler, R. H. and Sunstein, C. R. (2008). Nudge: Improving Decisions About Health,
Wealth, and Happiness. New Haven: Yale University Press.
[8] Zimmerman, B. J. (2002). Becoming a Self-Regulated Learner: An Overview. Theory Into
Practice, 41(2), 64-70.
[9] Fruhmann, K., Nussbaumer, A. and Albert, D. (2010). A Psycho-Pedagogical Framework
for Self-Regulated Learning in a Responsive Open Learning Environment. In S. Hambach,
A. Martens, D. Tavangarian and B. Urban (eds.): eLearning Baltics 2010: Proceedings of
the 3rd International eLBa Science Conference. Fraunhofer, pp. 1-2.
[10] Olivier, B. and Liber, O. (2001). Lifelong Learning: The Need for Portable Personal
Learning Environments and Supporting Interoperability Standards. Tech. Rep. The JISC
Centre for Educational Technology Interoperability Standards, Bolton Institute.
[11] Downes, S. (2007). Learning networks in practice. Emerging Technologies for Learning,
2, 19-27.
[12] ROLE Software Developer Kit.
[13] http://learning-layers.eu
[14] http://boost-project.eu