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Adult Self-regulated Learning through Linking Experience in Simulated and Real World: A Holistic Approach


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This research considers the application of simulated environments for adult training, and adopts the view that effective adaptive solutions for adults should be underpinned by appropriate adult learning theories. Such environments should offer learning experiences tailored to the way adults learn: self-directed, experienced-based, goal- and relevancy oriented. This puts andragogy and self-regulated learning at the heart of the pedagogical underpinnings of the intelligent augmentation of simulated environments for experiential learning. The paper presents a holistic approach for augmented simulated experiential learning. Based on andragogic principles, we draw generic requirements for augmented simulated environments for adult learning. An extended self-regulated learning model that links experiences in simulated and real world is then presented. A holistic framework for augmenting simulators - SRL-A-LRS - is presented and illustrated in the context of the ImREAL EU project. This points at a radically new approach for augmenting simulated systems for adult experiential learning. KeywordsSimulated Environments for Learning–Self-regulated Learning–Andragogy–TEL Requirements
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C. Delgado Kloos et al. (Eds.): EC-TEL 2011, LNCS 6964, pp. 166–180, 2011.
© Springer-Verlag Berlin Heidelberg 2011
Adult Self-regulated Learning through Linking
Experience in Simulated and Real World:
A Holistic Approach
Sonia Hetzner1, Christina M. Steiner2, Vania Dimitrova3,
Paul Brna3, and Owen Conlan4
1 University of Erlangen-Nuremberg, Germany
2 Graz University of Technology, Austria
3 University of Leeds, UK
4 Trinity College Dublin, Ireland
Abstract. This research considers the application of simulated environments for
adult training, and adopts the view that effective adaptive solutions for adults
should be underpinned by appropriate adult learning theories. Such
environments should offer learning experiences tailored to the way adults learn:
self-directed, experienced-based, goal- and relevancy oriented. This puts
andragogy and self-regulated learning at the heart of the pedagogical
underpinnings of the intelligent augmentation of simulated environments for
experiential learning. The paper presents a holistic approach for augmented
simulated experiential learning. Based on andragogic principles, we draw
generic requirements for augmented simulated environments for adult learning.
An extended self-regulated learning model that links experiences in simulated
and real world is then presented. A holistic framework for augmenting
simulators - SRL-A-LRS - is presented and illustrated in the context of the
ImREAL EU project. This points at a radically new approach for augmenting
simulated systems for adult experiential learning.
Keywords: Simulated Environments for Learning, Self-regulated Learning,
Andragogy, TEL Requirements.
1 Introduction
Experiential learning environments (e.g. simulations, serious games, immersive
activities) create a practical, and often social, context in which novel skills can be
learned, applied and mastered. These environments are increasingly popular as a
means to turn experience into knowledge, and are being applied in a variety of
domains and learning contexts. A class of simulated learning environments –
simulated situations for learning – provide the means for learners to immerse
themselves in simulated situations by performing activities that resemble actual job
activities. In this paper such environments will be referred to as simulated
environments or simulators. The popularity of these environments for experiential
learning is growing; and there is a strong expectation that they will become one of
Adult Self-regulated Learning through Linking Experience 167
tomorrow’s key learning technologies. Furthermore, we believe that successful
simulated environments will need to be adaptive. The research described here
considers the application of simulated environments for adult training, and adopts the
view that effective adaptive solutions for adults should be underpinned by appropriate
adult learning theories. Such environments should offer learning experiences tailored
to the way adults learn: self-directed, experienced-based, goal- and relevancy
oriented. This puts self-regulated learning (SRL) and andragogy at the heart of the
pedagogical underpinnings of the intelligent augmentation of simulated environments
for experiential learning. Offering experiences that are personalised to each learner’s
abilities involves balancing their need for support with their capabilities to self-
regulate. Personalisation can span from fully automated adaptation to user-defined
adaptability. As a user gains experience they will require less adaptive scaffolding.
A key factor for experiential learning in simulators is how well the learner
connects learning to the real world. However, current intelligent simulators for
learning suffer from a major deficiency because they incorporate a limited
understanding of the learner usually based on the skills and knowledge only required
within the simulated world. Consequently, such environments may be designed to
satisfy the perceived needs of learners but they are often totally disconnected from the
learners’ real job experiences. For example, attempting to teach trainee doctors to
communicate a life threatening diagnosis when they have no clinical experience at all
highlights such a disconnect. This often hinders learners’ engagement and motivation
to undertake training since: (a) assessment in the learning environment is distinctly
different from the consequences of performing the real job; (b) skills developed in the
simulated learning environment are not effectively connected to the skills used in real
job practice; (c) tacit and peer knowledge is buried in practical experience and is not
capable of being utilised for skills development and effective assessment with the
simulation; (d) because of the disconnect, it is not possible to relate feedback and
guidance to real life experiences with the result that the feedback and guidance
received can often be ignored by learners as irrelevant or even wrong.
Therefore, the key challenge is:
How to effectively align the learning experience in the simulated
environment with the real world context and the day-to-day job practice
where the skills are deployed.
This challenge is addressed in the ImREAL1 (Immersive Reflective Experience-
based Adaptive Learning) project. A novel approach is proposed that broadens the
scope of the work to include activities that take place both within and around the
simulation. We call this approach augmented simulated experiential learning. The
approach also seeks to connect the learner's experiences both in the real and simulated
worlds to others who have related experiences. This approach goes beyond normal
work with simulations in training and requires technological innovations together
with the development of theories tailored and adapted from those that currently exist.
The paper presents a psycho-pedagogical framework that underpins the design of
innovative intelligent services to augment simulated environments for learning.
Starting with generic andragogic principles, Section 2 focuses on self-regulated
168 S. Hetzner et al.
learning (SRL), which is encompassed in andragogic principles, and presents an
extended SRL model for augmented simulated experiential learning. This extended
model underpins the ImREAL framework for adult self-regulated learning through
linking experiences in the real and simulated world (SRL-A-LRS), which is presented
in Section 4. A comparison with relevant TEL work is given in Section 5, while
Section 6 concludes and points to future work.
2 Simulator Augmentation
ImREAL adopts a holistic approach to improving a simulator and facilitating its
integration in a training environment. This requires improvement of the main
components of a simulator.
2.1 Simulator Components
Simulators provide opportunities for a learner to engage in simulated situations that
resemble real world job situations. The content in the simulator is created around a
simulated scenario. This includes selected aspects of real world job situations driven
by learning objectives and target competences. The simulator is based on an internal
simulator structure, called hereafter the simulation model, which includes a
simulation graph (a directed graph with the key steps the learner goes through and the
choices given at each step) and assessment decisions associated with the learner's
choices. As the learner progresses through the simulation graph, certain
characteristics about the learner (e.g. knowledge, skills, competences) are observed,
and represented in a learner model. This model can be used to select individualized
paths through the simulation graph or to provide learner-tailored feedback. The
simulators can vary based on the media used to represent simulated situations (e.g.
graphics, audio, video, virtual reality) or the interaction means provided to the learner
(e.g. menu choices, free text input, speech input). To abstract from the specific
simulator implementation, we will focus on the key models common for a broad
range of simulators - the simulation model and the learner model.
During their deployment and repurposing simulators usually undergo a series of
improvements. Often, a simulator is re-used in different training contexts, which can
require tuning the simulation model, e.g. by changing the simulation graph by
adding more simulated situations (graph nodes) or suppressing some situations.
Furthermore, intelligent functionality is added to improve the effectiveness of the
simulator. This usually includes improving the adaptation and improving the
feedback features.
The simulator is usually integrated in a training environment, which can include
traditional courses, online training, or practical job activities. Notably, the learners
using the simulators usually have pre-simulator and post-simulator practical
experience, where they may be engaged in real world situations similar to the
situations practiced within the simulator. Intelligent functionality can be added in the
Adult Self-regulated Learning through Linking Experience 169
simulator to help learners link their experience in the simulator with ‘real-world’ job
We argue that simulators should be augmented in a holistic and cost-effective way
by developing appropriate augmentation services. Importantly, the augmentation is
considered outside the simulator in the form of intelligent services that can be:
plugged into a simulator to augment simulator components; or
used by simulator developers or tutors to facilitate the simulator integration
in a training environment.
ImREAL aims at developing such intelligent services for simulator augmentation,
and will validate them via example simulators.
2.2 Simulators Used in ImREAL
Intelligent services for simulator augmentation developed in ImREAL will be
illustrated in two simulators developed by consortium partners:
the ASPIRE simulator developed by EmpowerTheUser (ETU)2 Ltd; and
a simulator developed by Imaginary3 Srl.
ImREAL focuses on extending simulated environments for dialogic
interactions. These simulators offer branching scenarios consisting of a series of
steps (presented using text, graphics, videos) together with some interactive method
for the learner to respond via menu selection. Such simulators provide affordable
technological solutions suited to the trends in the growing adult learning market
where demand is increasing, while budget is decreasing. ImREAL will aim at a
generic approach for augmenting simulators developed by project partners, which will
also be applicable to simulators developed outside the project.
This generic approach will be applied to illustrative use cases within a common
training domain: interpersonal communication. We mainly focus on dyadic
conversations (e.g. doctor-patient interview; job interview between an applicant and
interviewer; interaction between two buddies from different cultures). Interpersonal
communication is a highly complex, ill-defined domain. In such domains, training
solutions are extremely challenging, and are mostly based on practical role-based
experience (which makes the domain particularly suited to simulated environments
for learning). We focus on selected key competences related to recognition,
awareness, and use of verbal and non-verbal signals.
The design of intelligent services for simulator augmentation is underpinned by the
ImREAL pedagogical framework, presented next.
170 S. Hetzner et al.
3 Andragogic Principles and the Extended SRL Model
After developing the andragogic principles needed and outlining the relevance of self
regulated learning, the need for an extended SRL model is introduced.
3.1 Andragogic Principles
Andragogy has been defined as the art and science of helping adults learn and the
study of adult education theory, processes, and technology to that end [1, p. 19].
Andragogy, as counterpart to pedagogy (children’s learning), has gained popularity
through the work of Knowles (1970, 1984, 2005) [2,3,4] who introduced his theory of
adult learning (i.e. andragogy). Knowles’ theory is focused on the development of a
set of assumptions and principles that reflect specific aspects of adult learning in the
work context (primarily). Knowles defines adult learners as independent and self-
directing, i.e. learners that have accumulated a great amount of experience, which is a
rich resource for learning. Furthermore, Knowles describes adults as persons who
value learning that integrates well with the demands of their everyday life and are
more interested in immediate, problem-centred learning approaches.
Andragogy provides a set of principles to encourage adult learning. These are
summarized, as follows:
Learning situations are seen as directly relevant to the real world job context
Learners need to know what they need to learn and why
Training experiences have to be aligned with the learner's own goals
Learning supports the learner's own sense of self, respecting individual
Learning situations provide intrinsic motivation
The learners are self-directing: they set their own agenda and learning path;
assess their learning experience
The foundation of Knowles’ principles is the perception of a learner that
recognizes the relevance of learning, and takes the initiative as well as the
responsibility for their own decisions in the learning process. The learner is able to
diagnose his/her learning needs, formulate learning goals, identify resources for
learning, select and implement learning strategies, and evaluate learning outcomes
[5]. Andragogy underlines the importance of supporting the learner to become a
competent and self-directed learner, which implies the development of learning
environments that guide and support the learner in his/her learning process without
taking away learner control. This brings forth two key issues for the design of
learning environments for adult learners: (a) learner context linking learners’
experiences and contexts of action (e.g. work, study) with the learning process; and
(b) support in terms of scaffolding which assists learners in reaching levels beyond
their current abilities [6].
Following the andragogic principles discussed above, two generic requirements
for augmented simulated experiential learning can be drawn:
Adult Self-regulated Learning through Linking Experience 171
Firstly, the need to provide a simulated environment for learning that aligns
the virtual, simulated world with the ‘real-world’ job experiences. This
supports the leaner to recognise the relevance of the learning activity and to
transfer created knowledge to the real world as well as to integrate real
world experiences in his/her learning process. Experiences are personal,
from the learner him/herself as well as from others (peers, tutors, colleagues,
Secondly, the necessity to develop and integrate concepts and services for
supporting the learner to develop and enhance self-directness and self-
regulation in the learning process. This means supporting the learner to have
control over his/her learning process, to be aware of his/her goals and
strategies, and to be capable of monitoring, re-thinking and reflecting on
his/her learning strategies.
3.2 Self Regulated Learning
As emphasised by andragogy, a key characteristic of adult learning is the need to
learn to be self-directed and self-regulated. Self-regulated learning is a current focus
of educational research as well as educational practice. The tradition of SRL considers
learning as an active and constructive cognitive process, in which learners take
responsibility and control over their own learning, e.g. [7,8]. SRL is carried out in a
proactive manner with individuals regulating their own cognitive, meta-cognitive, and
motivational processes within educational settings. SRL involves aspects of cognition,
metacognition, motivation, affect and volition [9]. Self-regulated adult learners use:
Cognitive strategies, such as elaboration, rehearsal, and organizational
strategies [10].
Metacognitive strategies, which are essential components of self-regulated
learning, for enabling the formation of knowledge or beliefs about what factors
or variables affect the course and outcome of cognitive enterprises [11].
Motivational strategies, which have a strong positive impact on learning, on
enhancing learners’ attention to their learning, on progressing the task itself
and on their satisfaction and affect [11, 12]. Aspects of motivation, such as
“expectancy of success, interest, utility, and task value”, influence learners’
task engagement [11].
Furthermore, learning includes feelings, emotions, and attitudes: metacognition
has an effect on cognition both through the cognitive regulatory loop and also through
affect [9]. Volition strategies relate to emotions, which influence the learning process,
such as activity-related emotions (interest and boredom) or outcome-related emotions
(anger, pride, etc.). There is another category – “metacognitive feelings“ – which are
focused on cognition and result from monitoring (e.g. the feeling of knowing).
A range of different theories and models have been devised to distinguish and
model individual phases of SRL, most of them representing SRL as a cyclic process
172 S. Hetzner et al.
(for an overview see e.g. [7]). One of the most influential models has been devised by
Zimmerman [8] and postulates that the cyclical phases of self-regulation are
comprised of:
Forethought, which involves activities that precede and prepare for
learning actions, like goal setting and strategic planning. It also involves
processes of self-motivation based on self-efficacy beliefs, outcome
expectations, intrinsic interest and values.
Performance, which refers to the actual process of learning and involves
strategies aimed at fostering the quality and quantity of learning
performance through self-instruction, self-control, and self-observation.
Self-reflection, which involves processes of self-evaluation, causal
attribution (i.e. beliefs about cause of error and success), and self-reaction.
This influences, in turn, the forethought phase of subsequent learning
3.3 Augmented Simulated Experiential Learning Needs an Extended SRL
The benefits of self-regulated learning need to be transferred to the context of
experiential learning environments for work-related contexts. In simulated worlds,
learners need to be put into situations that engage their self-regulated learning skills
and provide high relevance for real-world experiences. To achieve this, adaptive
scaffolding of SRL is very promising [13,14]. However, ImREAL goes beyond
current adaptive scaffolding of SRL focused on learning and performance within
primary and secondary educational systems. To minimise the gap between the virtual
and real world requires a holistic approach explicitly considering, harmonizing, and
combining simulated and real-world learning. As a result, the SRL model [8] needs to
be extended to account for the integrated consideration of learning processes in the
virtual world and in the real world (this makes ImREAL unique). Taking the two
worlds and the experiences therein, learning can be regarded as consisting of two SRL
cycles, one for the simulated world and one for the real world, see Figure 1. Activities
of forethought, performance, and reflection occur in both the virtual environment and
the real world. Each of these two cycles is understood in a dynamic manner, i.e. with
the individual phases being recursive and triggering and influencing each other. While
current simulated environments commonly suffer from the deficiency that these two
cycles are rather detached and separated from each other, ImREAL aims at
connecting and integrating the two SRL cycles. The two SRL cycles influence each
other, i.e. the learning process in the context of the simulation may trigger a certain
SRL phase in the real world. The most straightforward case is that reflection in the
virtual world triggers forethought in the real world. However, the opposite link may
also happen, e.g. during forethought in the real-world a learner may recognize skill
gaps, which can trigger a SRL cycle in the virtual world first, before going over to
performance in the real world.
Adult Self-regulated Learning through Linking Experience 173
Fig. 1. Extended SRL model for learning in simulated and real worlds
For an even stronger integration between simulated and real world learning, the
forethought and the reflection phase of the real world SRL cycle can be shifted
to the simulation (see the next section for the approach taken in ImREAL). The real
world performance phase naturally can only happen within the real world, but
planning and reflecting on the real world activity can realistically be done also in the
context of the virtual environment – at least to some extent (see Figure 2). This can be
realized by prompting learners to reflect on their real world experiences, the
knowledge and competence needed and acquired and to record this kind of
information in the simulation. These real world records can be exploited for user
modelling in the simulated environment in order to refine the picture of a learner’s
characteristics and prior knowledge, which in turn may serve the improvement of the
simulated learning by being better able to select suitable learning situations and paths.
The learner may use his/her records of prior experiences as a basis for future
forethought phases i.e. for planning further learning activities.
The view from individual learning is further broadened to peer experiences (see
Figure 2). The real world experiences that a learner has recorded in the context of the
simulation may not only serve his/her own reflection and further planning, but the
respective information can also be shared with peers and serve as a valuable source of
information for other learners. Conversely, the learner may benefit from getting to
know about the experiences of others, by contrasting them with his/her own
experience or by using them as a source of information for planning upcoming
learning tasks in the simulation. A learner may even look up peer experiences made in
the real world and recorded in the simulated environment without actually doing the
simulation itself, a form of vicarious learning. In this way, the link between virtual
and real world learning can be considerably strengthened by integrating real world
data and experiences into the simulation. Through an open learner model approach
[15] practical and peer experiences can be made more tangible among learners and
can be utilised for skill development and assessment in the simulation. This allows
increased individualization and better tailoring of the virtual experience to the
individual learner.
174 S. Hetzner et al.
Fig. 2. An extended SRL model integrating simulated and real world learning and incorporating
peer experiences
4 The ImREAL Approach
The ImREAL model integrates the extended andragogic principles for simulation
design with the extended self-regulated learning model. The principles of andragogy
are key requirements for the ImREAL services and for the way ImREAL services are
integrated into the (sequential) self-regulated learning model.
4.1 ImREAL Framework: SRL-A-LRS
Following the andragogic principles and the extended SRL model presented in the
previous section, we derive a framework for Adult Self-Regulated Learning through
Linking Real and Simulated world experiences (SRL-A-LRS). The framework
underpins the design of intelligent services for simulator augmentation by enabling
the connection between the performances in the virtual and the real world in a self-
regulated learning and experiencing activity.
The SRL-A-LRS framework includes three main aspects:
The adapted and extended andragogic principles;
The self-regulated learning model linking real and virtual performance
phases; and
The integration of real world experiences from the learner and from others
(peers, colleagues, experts).
The framework requires/embeds services that support the realisation of the
andragogical model for manifold learning experiences with simulation environments
as key elements of the learning activity. Figure 3 illustrates the relation between the
ImREAL model and the ImREAL services.
Adult Self-regulated Learning through Linking Experience 175
Fig. 3. The ImREAL SRL-A-LRS framework and the ImREAL services
4.2 Design Requirements
Based on the SRL-A-LRS model, we derive design requirements for intelligent
services for simulator augmentation:
[R1] Appropriate services should be provided to support trainers and simulator
developers to identify key aspects of real-world job situations that can be included in
the simulator.
[R2] Appropriate services should be provided to facilitate trainers and simulator
developers to find authentic, relevant examples with real-world job experience related
to specific simulated situations.
[R3] Appropriate services should be provided to facilitate trainers and simulator
developers to find relevant user characteristics in order to specify simulator usage
scenarios and decide on feedback.
[R4] Learners should be helped to see relationships between the experience in the
simulator and real world job situations they can engage in.
[R5] Feedback and prompts should be related to relevant real world experiences of
the learner or of peers who have similar characteristics.
[R6] Intelligent services should be developed to identify what a learner needs to
learn based on his/her experiences both in the simulated and real world.
[R7] The learner should be offered help to see what he/she needs to learn based on
his/her experiences both in the simulated and real world.
176 S. Hetzner et al.
[R8] Appropriate support should be provided to help the learner set learning goals
and agenda (before, during, and after engaging in a simulator).
[R9] Intelligent support should be provided to help the learner assess their
learning experiences (before, during, and after engaging in a simulator).
[R10] Learners and tutors should be facilitated to become aware of the relationship
between the simulated situation and real-world job situations.
[R11] Learners and tutors should be made aware of the learner’s experience in the
simulator, e.g. by providing ways to review the learner’s experience and the skills the
learner has practiced.
4.3 ImREAL Services
To address the above requirements, ImREAL will develop three key services.
Real World Modelling and Semantic Content Annotation. Applying social science
methods to model real world activities based on Activity Theory [16], ImREAL will
develop an activity model, represented as an ontology, which captures real world
experiences derived from many different sources. We aim to find innovative ways of
providing access to these experiences to tutors and simulator developers (i.e. to address
[R1], [R2], and [R10]), as well as to the learner both prior to using the simulator, within
the simulator and after using the simulator (i.e. to address [R4] and [R5]). This service
represents a vital andragogical link between the learner’s learning and how they may
wish to apply it in real world settings. From an SRL perspective, the reflection in the
simulation should lead to improved performance in the real world.
Augmented User Model. Following recent trends in user modelling4, we will address
the challenge of user model augmentation. ImREAL services will make an adaptive
learning system, such as a simulator, better by selecting relevant external data about
real-world experiences, and providing ways to integrate this data into the user model
such that the user model becomes richer (for the purposes of adaptation). Hence, the
augmented user modelling services developed in ImREAL will help address
requirements [R6], [R7], and [R9]. Furthermore, deriving social profiles of similar
users (user stereotypes), we aim to provide key user characteristics to simulator
developers, i.e. to address requirement [R3].
Meta-cognitive Scaffolding. ImREAL will take the approaches for providing
adaption in simulators to the next level by providing meta-cognitive scaffolding in an
adaptive manner [17]. Specifically, we focus on how to provide salient, timely
services to support the metacognitive processes of self-regulated learning within the
framework of experiential training in a cognitively sensitive (non-invasive) manner
related to, but not embedded within, e-Learning simulation execution environments.
Through appropriate meta-cognitive scaffolding the learner will enhance key traits,
such as planning, comprehension and evaluation, to allow them to discern, regulate
and manage their learning. The meta-cognitive scaffolding services will utilise the
real world model, semantic content annotation, and augmented user model, to
augment the simulator addressing requirements [R4], [R5], [R6], [R7], [R8], [R9],
[R10], [R11].
4 E.g. see workshop on Augmented User Modelling:
Adult Self-regulated Learning through Linking Experience 177
5 Related Work
Immersive learning experiences such as successful simulator-based training and
digital educational games (DEGs) are designed to be inherently motivational with
narratives that engage the learner. A key challenge lies in maintaining this motivation
whilst making the experience more meaningful for each individual learner. In DEGs,
such as ELEKTRA5 and 80Days6, the challenge of realizing real-time, non-invasive
adaptations were addressed through leveraging the Adaptive Learning In Games
through Non-invasion (ALIGN) [18] system. This proved successful, but DEGs are
typically employed within a controlled curriculum where the desired learning
objectives are usually known a priori and adaptive hinting is employed to assist the
learner in achieving these objectives. The majority of adaptive systems developed
work within such a closed and prescriptive corpus and domain.
Applying the principles of andragogy implies that the adult learner will have a
much higher degree of freedom, compared to that offered in typical DEGs, and that
they will expect the learning experience to be closely correlated to their needs. Whilst
the idea of SRL is increasingly taken up in the context of TEL the challenge of
maintaining the flow of experience and supporting the learner in a non-intrusive
manner remains [19]. Many learning systems are designed and built in order to
support explicit (and somewhat intrusive) self-regulation by providing tools and
supporting different self-regulatory processes [20, 21, 22, 23]. In recent years, the
idea of utilising adaptive technologies for supporting SRL has emerged and
approaches and computational frameworks have been devised in order to exploit
intelligent tutoring and adaptation for measuring self-regulated learning [24]
scaffolding metacognition [14, 17, 25]. However, the challenge of incorporating real-
world experiences to augment and ground the metacognitive gain has not been
SRL and the use of adaptive mechanisms might seem a contradiction at first sight,
in particular when thinking of adaption in terms of automated customization and strict
guidance. However, through striking a balance between the learner’s experience and
the degree of adaptive support provided, a continuum of personalization may be
achieved. By monitoring observable evidences of cognitive, metacognitive, and
motivational processes while using a learning environment, self-regulated learning
can be measured in an unobtrusive way [24, 26]. One potential way for realizing such
non-invasive assessment and adaptive scaffolding is the so-called micro-adaptivity
approach of Competence-based Knowledge Space Theory ([27]. This approach,
which has been developed in the context of DEGs and to date has been primarily
focusing on supporting domain skill measurement and acquisition, can be adopted for
realizing non-invasive assessment of SRL skills and intelligent adaptive support as
reaction. During learning, user actions in the learning system (e.g. the use of certain
tools or actions within a tool) can be interpreted in terms of available and lacking
skills based on competence assessment. Through continuous, unobtrusive monitoring
of a learner’s interactions with the system, an image of the SRL competence of a
learner can be built up. This model can be used to trigger prompts about forethought,
178 S. Hetzner et al.
performance and reflection specifically tailored to the learner and enhancing the
acquisition of lacking but necessary skills [27].
6 Conclusions and Future Work
This paper presents a novel approach to augment simulated environments for adult
experiential learning by linking the learner experience in the simulated world with
experience (of the learner or peers) in the real world. A holistic view of
augmentation is adopted, aiming at (a) supporting the current practice of developing
simulators for adult learners by facilitating tutors and simulator developers to become
aware of relevant real world experiences and identify relevant user profiles; (b)
extending the simulators with intelligent features to provide feedback and promote
reflection in a motivating manner; and (c) supporting the training environment in
which simulators are integrated by extending the simulator’s understanding of users
and providing support for the learner’s experience before, during, and after the
The paper makes the first step towards addressing the challenge of ‘how to link
experiences in simulators (and in virtual training environments in general) to
experience in real-world job practice’, which is paramount for developing effective
TEL solutions for adult training. The work presented here makes the following novel
contributions to TEL research:
Links andragogic principles and design requirements for simulated
environments for adult learning;
Presents an extended model of self-regulated learning that links experience
in the real and simulated world; and
Presents a framework (SRL-A-LRS) for developing intelligent services for
augmenting simulator based on adult self-regulated learning through linking
experience in real and simulated worlds;
Outlines three key services (real world modelling and semantic content
annotation, augmented learner modelling, and meta-cognitive scaffolding) to
implement the SRL-A-LRS framework within the ImREAL project.
We are currently implementing the first versions of the three key services, which
are independent from simulators but will be illustrated in two use cases with the
simulators used in ImREAL. The ASPIRE simulator for medical training (training
doctors to interview patients in distress, e.g. with depression) is being extended with
adaptive meta-cognitive scaffolding augmenting the performance in the simulator and
the self-reflection after the experience in the simulator [19]. The augmentation also
includes extending the user model based on user characteristics from social web
environments [28]. The Imaginary simulator is being developed for job interview
training (training interviewers to interview applicants) and will then be extended to
link verbal and non-verbal signals with multi-cultural awareness. For this, we use an
activity model ontology developed following Activity Theory and using intuitive
ontology engineering tools [29]. We have annotated user comments on job interview
examples (following a youtube style) to provide authentic content about activity
aspects [30].
Adult Self-regulated Learning through Linking Experience 179
Our next step is to test the services within realistic settings against the
requirements of the SRL-A-LRS framework. We are also designing intelligent
prompts in line with the extended SRL model, to be integrated in both the meta-
cognitive scaffolding services and semantic content assembly services.
Acknowledgements. The research leading to these results has received funding from
the European Union Seventh Framework Programme (FP7/2007-2013) under grant
agreement no ICT 257831 (ImREAL project).
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A correlational study examined relationships between motivational orientation, self-regulated learning, and classroom academic performance for 173 seventh graders from eight science and seven English classes. A self-report measure of student self-efficacy, intrinsic value, test anxiety, self-regulation, and use of learning strategies was administered, and performance data were obtained from work on classroom assignments. Self-efficacy and intrinsic value were positively related to cognitive engagement and performance. Regression analyses revealed that, depending on the outcome measure, self-regulation, self-efficacy, and test anxiety emerged as the best predictors of performance. Intrinsic value did not have a direct influence on performance but was strongly related to self-regulation and cognitive strategy use, regardless of prior achievement level. The implications of individual differences in motivational orientation for cognitive engagement and self-regulation in the classroom are discussed.
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In this contribution the four papers of this special issue on "Promoting Self-Regulated Learning Through Prompts" are discussed with the help of two crucial questions: What learning activities should be prompted and how should they be prompted. Overall, it is argued that future research has to conduct more in depth process analysis that incorporates multi-method assessment methods and to further account for individual learner characteristics. Prompting research, at present, needs more insights on how students actually deal with learning prompts.