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Supporting Self-regulated Learning with Tabletop Concept Mapping



This article presents an approach to foster self-regulated learning by technological means. Self-regulated learning aids the development of meta-cognitive skills but is regarded a cognitively demanding process, which should be supported by appropriate tools. Concept mapping is considered a promising approach in this context, especially aiding the reflection and evaluation of a learning task. Tool support here is provided in the form of a (physical) tabletop concept mapping environment, which enables seamless interaction among learners and / or with the learning environment and provides specific tools for manipulation and reflection of the generated concept maps. The CbKST framework would provide a theoretical basis for extending the tool by adaptive guidance. An automated subsystem based upon this framework would be able to track the mapping process and to automatically trigger interventions if didactically advisable. In this way, the self-regulated learning process can be supported implicitly by ambient modelling tools and explicitly by didactically motivated interventions. Need for further research is identified in the field of self-regulated cooperative learning scenarios.
Supporting Self-regulated Learning with Tabletop Concept
Stefan Oppl, Christina M. Steiner, Dietrich Albert
This article presents an approach to foster self-regulated learning by
technological means. Self-regulated learning aids the development of meta-
cognitive skills but is regarded a cognitively demanding process, which should
be supported by appropriate tools. Concept mapping is considered a promising
approach in this context, especially aiding the reflection and evaluation of a
learning task. Tool support here is provided in the form of a (physical) tabletop
concept mapping environment, which enables seamless interaction among
learners and / or with the learning environment and provides specific tools for
manipulation and reflection of the generated concept maps. The CbKST
framework would provide a theoretical basis for extending the tool by adaptive
guidance. An automated subsystem based upon this framework would be able
to track the mapping process and to automatically trigger interventions if
didactically advisable. In this way, the self-regulated learning process can be
supported implicitly by ambient modelling tools and explicitly by didactically
motivated interventions. Need for further research is identified in the field of
self-regulated cooperative learning scenarios.
1. Introduction
Self-regulated learning is a pedagogical approach that puts learners in charge
to control and direct their learning process (Puustinen & Pulkkinen, 2001). As
such, it not only aids the development of cognitive competencies but also
explicitly incorporates meta-cognitive skills through planning, monitoring and
evaluation of the learning process (Zimmerman, 2002). This approach has
especially been adopted in the field of technology-enhanced learning in the last
years (e.g. Kay, 2001). Self-regulated learning, however, puts high demands on
the learners and can easily overstrain them if not supported appropriately
(Bannert, Hildebrand, & Mengelkamp, 2009).
In technologically augmented learning settings, support for self-regulated
learning has to address several dimensions. In terms of tool support, (a) tools
have to be identified and provided that explicitly facilitate self-regulated
learning. Additionally, (b) the learning environment must not pose additional
(technical) burdens on the learners and should adapt to the individual learning
process. In terms of procedural support during self-regulated learning, (c)
Oppl, S., Steiner, C.M., & Albert, D. (in press). Supporting self-regulated learning with tabletop concept
mapping. In A. Kaminski, M. Mühlhäuser, W. Sesink, & J. Steimle (Eds.), Interdisciplinary Approaches
to Technology-Enhanced Learning – Interdisziplinäre Zugänge zu technologiegestütztem Lernen.
Münster: Waxmann.
Preliminary version. Final version may slightly differ in layout and content.
learners can be supported and trained by guidance through the learning process.
Guidance is normally provided by a teacher or a tutor but might also be
triggered and performed automatically in computer-supported learning settings.
In this article, we are addressing these three core requirements of
technologically enhanced self-regulated learning. We propose an environment
to meet these requirements based on concept mapping techniques and reflect
our findings in terms of seamlessness or disruptiveness in the learning
“Seamlessness“ in this context refers to the ability to keep focused on the
learning content and not having to explicitly control, adapt to or make
modifications in the concept mapping environment. By extensively adopting the
design paradigm of integrating the objects for data representation with those
used for data manipulation, users do not have to use dedicated tools to create or
manipulate the properties of modelling content but can use the objects
representing this content itself to manipulate it. If designed well, this in turn
keeps users focused on the content and does not force them to interrupt their
externalisation process by for example searching for some manipulation
functionality in a menu.
On the higher levels of interaction, that are more oriented towards the content
and methodology of concept mapping, we argue for a more “disruptive”
process. Disruptive interventions are used to provide guidance of the learners to
help them acquiring both, skills in concept mapping and knowledge in the
problem domain at hand. The Competence-based Knowledge Space Theory
(CbKST) framework allows adapting disruptive interventions to the learner’s
current needs. It can be applied on all interaction levels and thus can be used to
improve a learner’s skills in both, concept mapping and the problem domain.
For users inexperienced in using the proposed toolset, interventions could also
be triggered for learning the features and the handling of the mapping tool.
In the remainder of the article, we first revisit the conceptual foundations of
concept mapping, an approach proven to support the process of self-regulated
learning by fostering explication and reflection of the learning content. We
present an interactive tool that supports the mapping process by adding
technological support to a physical (i.e. not computer-based) modelling
environment. This tool can be augmented with an approach for automated
guidance in the self-regulated learning process, which is described conceptually
and put in relation to the concept mapping process. We conclude with a
summary of the seamless and disruptive aspects of the proposed system and
potential for future work.
2. Concept Mapping Techniques
Concept mapping is a technique for eliciting and representing knowledge in
network structures (Novak, 1998). The origin of concept mapping can be seen
in the network theories of cognitive psychology (e.g. Quillians, 1966;
Rumelhart, Lindsay & Norman, 1977) modelling human memory in terms of
network representations similar to concept maps. Concept maps were initially
applied to represent and analyse knowledge and changes of knowledge from
interviews with students (Novak & Musonda, 1991), but soon their potential as
powerful and effective learning and teaching instrument has been appreciated.
Concept maps contain mutually related concepts, i.e. mental constructs (for an
example see Figure 1). Concept mapping can be used for learning either in
structured domains, such as mathematics, allowing for individually arranging
domain content (Brinkmann, 2003), or for generating meaningful
representations from scratch according to individual mental models (Coffey and
Hoffman, 2003). While for the first setting, the focus of concept mapping lies
on the arrangement of previously known elements, the latter requires an open
space to identify, name, and arrange meaningful content.
In the course of concept mapping, constructs are arranged according to an
issue of interest, e.g., a certain learning task. The concepts are named and
mutually related, setting up and naming all relationships considered to be
relevant. In this way, a contextual representation is established.
Today, the concept mapping technique is often suggested to be realised
through the use of computer based tools for creating concept maps (Canas et al.,
2004). The capability to easily store and retrieve, revise, or expand the maps
whenever necessary is seen as the main reason to use computers for modelling.
Such an endeavour is difficult when not using software to create models. In
addition, software tools enable a selective focus on parts of the map (e.g. by
zooming or hiding other parts), which may reduce cognitive load on users.
Finally, the persistence of maps can be easily ensured when using file
repositories to save and retrieve concept maps.
As an alternative approach to externalize and structure conceptual domains,
structure-elaboration techniques can be adopted for concept mapping. Structure-
elaboration techniques are an effective means to create physical representations
of mental models (Dann, 1992) and thus allow to communicate and share them
in order to learn from each other. In a moderated process (the dialogue-
< Hier einfügen: Oppl-Supporting-01.tif. Bildunterschrift: Example of a concept map
(adapted from> (zur besseren Lesbarkeit zusätzlich als Grafik eingefügt)
hermeneutic method), the participants create a graphical representation of the
their mental models by placing labelled cards on a modelling surface.
Subsequently, they relate them using associations.
Dann (1992) stresses the importance of the immediacy of representation in the
structuring process. This immediacy is attained by the physical creation of the
model. Participants immediately refer to a physical representation rather than
abstract items. They create and alter the model in a dialogue-based way until
reaching consensus about what is represented. Mental models of individuals are
externalized, questioned and can be modified at the same time. The procedure
ends once all participants feel comfortable with the result.
2.1. Requirements on Tool Support
Researchers have proposed several approaches supporting the creation of
concept maps. These approaches mainly differ in form and focus, as they refer
to various features and application scenarios.
Concept mapping is already supported by a range of elaborated software tools
(e.g. CMapTools as presented in (Canas et al., 2004)). Some of them do not
only support the modelling process but also assessing the quality of the map
based on metrics derived from graph-theory (Ruiz-Primo and Shavelson, 1996).
Other approaches offer a tight coupling to the computer desktop environment
and consequently, enable direct links to digital resources (Canas et al., 2004).
Both, structure-elaboration techniques and computer-based concept mapping
provide effective features in different aspects of user support and might
complement each other. Structure-elaboration techniques are designed to
support cooperative settings, which are not explicitly targeted in concept
mapping. The immediateness of representation supports the creation of both,
individual and shared understanding, even for complex concept-mapping tasks.
The cooperative manipulation of models raises mutual awareness of key issues.
It also helps identifying potential areas of conflict when overseeing individual
mental models. Thus, a physical, tangible approach as used for structure
elaboration seems to be appropriate to aid cooperative concept mapping.
Concept mapping tends to lead to complex structures, in particular in
collaborative settings. In those cases the mental models of several learners have
to be integrated into a single representation. This process is not strictly linear. It
is rather an iterative procedure, characterized by continuous switching between
creation (identification, naming), reflection and modification of representations.
In such situations of high complexity it is hard for learners to keep an
overview of what has been modelled so far, and what is the current focus of
elaboration. It is thus inevitable to provide tool support addressing these issues.
Computer-based approaches can help to trace the focus and status of structure
elaboration by providing flexible and customizable visualizations. Support for
experimental model changes are provided by implementing an undo-function.
The result of a concept-mapping session should be a descriptive model of how
the learners structure and explain a perceived phenomenon of the world. Such
models, however, must not considered “stable” or “finished” at a certain point
in time, as they undergo constant review and revision (Herrmann et al., 2002).
Consequently, a computer-based system should allow archiving initial
contributions and revisions of representations, preserving information about the
modelling process besides the actual models. In this way, the reflection and
modification of models and their development can be facilitated, without
additional memorization effort, and regardless of editing times.
3. TCM a Tool for Tabletop Concept Mapping
The use of a tabletop interface is grounded in the traditional means used for
structure elaboration, namely the surface of a table. Figure 2 shows the tangible
combination of physical and software-based modelling, leading to intertwined
tabletop and desktop representations. The use of tangible items (instead of
multi-touch tables, which have also been adopted for concept mapping purposes
– e.g. Baraldi Del Bimbo & Valli (2006)) is motivated by the necessity of
physical model representations introduced by structure elaboration techniques.
The immediacy of physical representations is claimed to have positive effects
on the process of building a common understanding of the relevant mapping
content (cf. Dann, 1992).
The table surface is the main user interface. Users place physical tokens and
associate them accordingly. The tokens act as carriers for concepts. Nearly all
interactions occur on the surface to enable simultaneous manipulation of the
map. Only “digital” use cases like attaching digital resources to a token have to
be carried out using the keyboard or the mouse. The main hardware TCM
components are the table and the physical tokens. The table's surface is 80 x
100 cm in size and is made of semitransparent acrylic glass. It allows projecting
supplementary and supportive information onto the surface from underneath
during the modelling process.
< Hier einfügen: Oppl-Supporting-02.jpg. Bildunterschrift: The TCM system for concept
mapping on Tabletops > (zur besseren Lesbarkeit zusätzlich als Grafik eingefügt)
Based upon the specified requirements, the following functional modules
have been implemented:
(a) Labelling & Associating: Concept mapping relies on the ability to assign
names to concepts and to define associations between them. The user interface
for these features has been designed to avoid input devices like mouse or
keyboard in order to keep users focused on the mapping task. Several tools can
be used to manipulate the model directly on the surface: Tokens are associated
by putting them together briefly. Directed and undirected connections can be
created by placing differently shaped markers on them. A rubber token enables
users to delete connections.
Assigning designators to concepts is either performed by placing sticky notes
(post-its) on the top of the token (cf. Figure 3) or alternatively, by using the
keyboard to enter a label. In the first case, a camera captures a picture of the
sticky note. Image processing algorithms then extract a clearly readable image
from the written text.
< Hier einfügen: Oppl-Supporting-03.jpg. Bildunterschrift: Tangible Concept Mapping
Elements left: Tokens and Connections, right: Tokens acting as Containers > (zur
besseren Lesbarkeit zusätzlich als Grafik eingefügt)
(b) Abstraction Support: In order to overcome the limitation of restricted map
size due to the limited modelling space (a maximum of 10-15 tokens can be
used simultaneously) and to reduce complexity in models, the tokens also act as
containers (cf. Figure 3). Users can open a token and put additional information
into it. Additional information is bound to smaller tokens by registering them
with another camera built on top of the computer screen. They represent either
an arbitrary digital resource (file), or a previously captured model state. The
latter information type enables users to generate parts of a concept map
separately, and to connect these parts on a higher level of abstraction at a later
point in time. In this way, the common modelling concept of abstraction
through overview and detail representations is mapped to the physical world.
(c) History & Reconstruction: The traceability of the modelling process is
ensured in TCM through capturing the design history. This feature also
facilitates the understanding of a representation (Klemmer et al., 2002), in
particular for collaborative endeavours. In this case the design history enables
participants to recapitulate and reflect the modelling steps made so far, even
when they join a session later on, or have to continue working on a model
generated by different individuals.
The TCM tool captures the design history by automatically taking snapshots
of stable map states. In addition, a dedicated token enables users to take
snapshots on demand. It allows explicit capturing and storing a certain model
state using the backend system for later retrieval.
History navigation mode (i.e. recalling former model states) can be activated
using a round “clock” token. It can be rotated counter clockwise or clockwise to
go back and forth in time, respectively. The computer screen displays the
currently selected model state. Support for experimental changes of the concept
map is provided on top of the history mode and supports the reconstruction of a
previously selected model state. The TCM tool displays step-by-step on-surface
directions to show users which tokens to remove, move and/or add in order to
complete a reconstruction according to the differences between current and
requested model state.
3.1. Empirical Evaluation
Empirical evaluation of the prototype effects on concept mapping took place;
41 modelling sessions in two blocks involving 103 participants aged between
19 and 26 years (in groups of 2 to 3 people) have been conducted. All sessions
were situated in a university educational setting. Students were given the task to
cooperatively externalize their understanding of the relationships among
different process modelling languages as a concept map.
While detailed evaluation results are presented in Oppl (2010), the most
prominent results in the context of this work are: (a) Physical artefacts for
representation of abstract concepts (like work tasks, competencies or
responsibilities) seem to facilitate the dialogue among participants and allow
the in-depth alignment of their understanding of conceptual structures.
Immediate and long-term feedback of users shows that the physical interface is
perceived helpful and largely intuitive. However, some of the tokens used to
control the system are regularly misinterpreted, which leads to detours and
sometimes even breaks in the mapping process. While redesign of the affected
controls led to fewer misconceptions, especially complex features like
reconstruction support remained difficult to comprehend for users. (b) The
second block of evaluations was conduced as a comparative study using both
TCM and the computer-based CMapTools to cooperatively build concept maps.
The resulting CMap-models were much bigger (avg. 23 vs. 10 elements) and
appeared to be more elaborated. A similar study has been conducted by Do-
Lenh, Kaplan & Dillenbourg (2009), who report similar results and identify
better learning outcomes for screen-based concept mapping tools in comparison
to tabletop systems. While learning outcome was not evaluated in the study
presented here, models built with the TCM system, however, appeared to be
more focused while still containing all core concepts and associations of the
mapping case. The tabletop setting seemed to foster discussion about the
represented model. On average, 76 percent of total mapping time was spent
discussing the content around the tabletop, whereas only 57 percent of the
overall mapping time was spent on discussion in the computer-based setting.
Video analysis shows that the modelling process on average is slowed down
using the tabletop interface, leading to more discussion and reflection during
this phase. After an initial version is built, the model serves as an ambient
facilitator of communication. It is used for referencing the concepts under
review or for experimental changes that arise during conversation. Overall, the
proposed system seems to facilitate the meta-cognitive skills necessary for self-
regulated learning in cooperative educational settings.
4. Application in Guided Learning Settings
Constructing concept maps is an active process that requires to get intensively
engaged in a topic, and to elaborate and reflect information. Learners become
aware of the process of linking new information to already existing knowledge,
meaningful learning and deeper understanding are encouraged. Therefore,
concept mapping can be seen as a meta-cognitive tool fostering explication and
reflection and the development of auto-monitoring abilities and critical thinking
(Novak, 1998; Dabbagh, 2001). As learners are enabled and supported in
regulating their learning processes, this technique represents an instrument for
self-regulated learning. The openness and open space that concept mapping
provides to represent mental models and express ideas, on the other hand,
constitutes a concrete application of self-regulated learning. This openness,
however, can make concept mapping a challenging task that learners are
sometimes not able to complete without support. Self-regulative and meta-
cognitive skills are necessary in order to direct the concept mapping process.
Additionally, technical skills are needed in order to appropriately handle the
interactive concept mapping environment in cases where interaction is not as
seamless as necessary (e.g. due to misconceptions in tools usage, which have
been shown during the evaluations of the TCM system). Learners, however,
might not (yet) possess these necessary skills and therefore have difficulties in
coping with these demands. As a consequence, strategies to guide learners are
needed in order to support the self-regulated learning and concept mapping
process (Bannert, Hildebrand, & Mengelkamp, 2009; Narciss, Proske, &
Koerndle, 2007). This guidance can be provided by didactically meaningful
disruptive interventions that help learners to acquire all three types of skills, i.e.
skills on concept mapping, tool handling, and knowledge in the problem
domain at hand. These interventions will usually be provided by a teacher or
tutor. Alternatively, adaptive intervention technologies implemented in the
concept mapping tool may provide the required guidance. These two
possibilities of supporting self-regulated learning in a concept mapping context
shall be elaborated in the following sections.
4.1. Teacher-based Interventions during TCM-usage
The role of the teacher is a guiding one when learners use the TCM-system
for concept mapping. A learner is asked to model his or her view on a given
problem or scenario. The teacher passively accompanies this process and only
actively intervenes if he or she feels that the learning goal might not be reached.
Depending on the type of the task, these interventions might take place on two
levels. For the following discussion, we distinguish two types of learning tasks.
First, a mapping task can aim to lead the learner to create a commonly accepted
or “objectively correct” concept map of a given phenomenon. Second, a
mapping task can also aim at facilitating individual reflection, in which case
there is no such thing as a “correct” solution.
In the second case, intervention is restricted to methodical issues in concept
mapping, e.g. if concepts remain unconnected to others (i.e. the concept map is
syntactically incorrect), and cases, where certain aspects of the mapping topic
remain uncovered. In such cases, the teacher instructs the learner in the use and
construction of concept maps and points at open aspects. In the first case, the
teacher additionally may also intervene, if the map deviates too strong from the
intended solution in terms of represented content and relationships (i.e. the
concept map is semantically erroneous). Here, interventions have to aim at
adapting the respective mental model of the learner to fit the “correct model”.
This can be approached by temporarily applying the dialogue-consensus-
method proposed by (Groeben and Scheele, 2000). Intense interaction between
teacher and learner should however end again as soon as the learner has
recognized and corrected his or her “mistakes”.
4.2. Automated Interventions
Another way of realising guidance in concept mapping tasks are automated
intervention and feedback mechanisms provided by the software tool. Such
mechanisms shall mimic didactically motivated teacher guidance of intervening
if the learner has difficulties in accomplishing the task. This means, while in
computer-based concept mapping the interaction with the interface itself should
be seamless in order to be able to focus on the task, the provision of a certain
degree of disruptiveness in terms of didactical prompts and interventions
explicitly pointing to specific issues is desirable and useful (e.g. Bannert, 2009).
Automated intervention mechanisms need to be tailored to the learners’ and
situational needs. To this end, sound models and approaches of adaptation and
intelligent tutoring are needed.
Competence-based Knowledge Space Theory (CbKST; Albert & Lukas,
1999; Doignon & Falmagne, 1999) provides such a sound theoretical basis for
realising intelligent, adaptive e-learning and supporting self-regulated learning
(Heller, Steiner, Hockemeyer, & Albert, 2006; Steiner, Nussbaumer, & Albert,
2009). It constitutes a powerful set-theoretic framework for domain and learner
knowledge representation. Essentially, the skills of a knowledge domain are
modelled and structured in terms of prerequisite relationships. The evolving
skill structures give rise to meaningful learning paths and can be linked with
observable behaviour, more concretely, the learning and assessment tasks of the
knowledge domain. Learner knowledge is represented as a subset of skills of a
knowledge domain (i.e. skill state).
Recent developments of CbKST elaborated the approach of micro-adaptivity,
which is made up of non-invasive assessment procedures of a learner’s skills
through continuously interpreting his/her behaviour and interaction with the
system and the provision of adaptive interventions tailored to the learner’s
needs (Kickmeier-Rust, Hockemeyer, Albert, & Augustin, 2008). This
approach can be adopted for the realisation of automated interventions in
concept mapping tasks. Similar to teacher guidance, such interventions may
target methodological and technical issues in concept mapping and/or the
learning content itself.
Depending on the aim of the concept mapping task and according
interventions (reflection on a topic or building an objectively correct model),
the content and/or the concept mapping methodology including the involved
self-regulatory and meta-cognitive skills need to be modelled in the scope of
CbKST. This would mean to establish a formal model of the domain by
defining relevant skills and the prerequisite relations among them. For
illustration we use here example skills necessary for concept mapping itself and
reflection of a certain problem domain. Relevant skills in these cases might for
example be A) the ability to identify the most relevant concepts of a topic, B)
the ability to identify interconnections between concepts, or C) the ability to
explain the type of relationship between two concepts. The ability to concretely
explain or denote a relationship between two concepts will naturally also
require the ability of identifying a general interconnection between those two
concepts thus, skill B) is a prerequisite skill for C). In addition, a concept
mapping task needs to be modelled in terms of identifying possible actions that
a learner may perform. These actions are then associated with the underlying
available and lacking skills, which provide the cognitive basis for the
interpretation of the learner behaviour for non-invasive skill assessment. The
creation of a link between two concepts of a concept map, for instance, gives
evidence of the skill B) of being able to identify interconnections between
The assessment procedure is probabilistic; we apply a probability distribution
over all possible skill states and with each action the probabilities of those
states that include the relevant skills are increased and those states that include
the lacking skills are decreased. At the end of this procedure stands a more or
less well-founded assumption about the skills a learner has and does not have.
These continuously gathered and updated assumptions on a learner’s skills
serve the provision of adaptive interventions tailored to the learner’s current
needs. In a concept mapping environment such an intervention might be
presented by an intrusive or non-intrusive message on the modelling surface or
computer screen, respectively (thus tailoring the level of disruptiveness).
There are different types of interventions (for an overview see Steiner,
Kickmeier-Rust, Mattheiss, Albert, 2009), from the presentation of meta-
cognitive prompts (e.g. certainty question like ‘How sure are you about this?’)
to the provision of very concrete information (e.g. problem solving support like
‘Try to create interconnections between concepts by placing markers on them’).
The rules for triggering a concrete intervention are defined based on cognitive
psychological considerations in tight relation to the continuous assessment of
skills in terms of CbKST. Through the definition of threshold values on skill
probabilities an according intervention is prompted if the non-invasive
monitoring procedures of the learner’s actions provide substantial evidence for
lacking skills.
A learner might, for example, create in a sustaining manner a great many of
concepts without establishing relationships between them. By continuously
monitoring the digital model representation (e.g. by applying appropriate
metrics to the captured model states used for history display), the system can
identify such deficiency patterns. In the case at hand, the system will come to
the assumption that the learner lacks skills on identifying interconnections
between knowledge elements and consequently trigger a prompt to reflect on
whether the concepts represented are in some way interconnected. Later on, if
necessary, further interventions might be given, e.g. requesting to explicitly
establish and represent relationships among the concepts and/or providing
examples. Figure 4 provides an overview and example of the adaptation process
in a concept mapping task. As described above, users inexperienced in using the
proposed toolset could also be provided with interventions for learning the
features and the handling of the mapping tool itself.
< Hier einfügen: Oppl-Supporting-04.tif. Bildunterschrift: Overview and example of the
adaptation process.> (zur besseren Lesbarkeit zusätzlich als Grafik eingefügt)
Automated interventions as sketched above are currently not implemented in
the TCM tool. An according further development, however, is considered
desirable and would empower learners and broaden the application of the TCM
system. The adaptation mechanisms described require the system to have
significant understanding of the domain (the skill and skill structures), of the
concept mapping task (methodology and content), the learner (the learning
progress), and the rules that glue all the information together. From a technical
perspective, this data storage can be realised in form of ontologies (Kickmeier-
Rust & Albert, 2008). On this basis, the TCM system could be equipped with
reasoning services that query the ontologies in real time to interpret the
learner’s behaviour. The ontologies at the same time provide a storage medium
to link rules and available interventions.
5. Conclusions
In this article, we have presented an approach for self-regulated learning that
is supported by the use of concept maps. Concept mapping not only allows
structured externalisation and representation of an individual’s perception of a
problem domain, it also facilitates metacognitive tasks such as reflection on the
mapping content. In the course of self-regulated learning, learners are in control
of their individual progress and the according learning activities. Learners,
however, might – even unknowingly – lack skills that would be required to
perform successful self-regulated learning.
In traditional learning settings, a teacher’s job would include guiding the
learner to develop these skills by specific and timed interventions in the
learning process. In computer-supported learning scenarios, the role of the
teacher can be shifted to an automated system, which keeps track of the learners
progress, identifies potentially lacking skills and triggers according
interventions. Becoming independent of the availability of a guiding individual
empowers the learners and enables a more flexible learning process. We have
presented the CbKST framework as an approach to identify and trigger
appropriate interventions automatically and support the self-regulated learning
process where necessary (i.e. where lacking skills prevent it from being
completed successfully).
The process of creating a concept map can be challenging. Relevant concepts
and relationships among them have to be identified and graphically represented.
Tool support here can play a crucial role to let learners focus on the mapping
task at hand. In the following, we review tool support in the light of the
concepts introduced in the taxonomy presented in chapter XY (concepts printed
in italic letters). The tool presented here has been designed to provide a
seamless flow of interaction during the design of a concept map not only for
individuals but also for learning groups (e.g. seamless turn-taking or
simultaneous interaction on the concept map without the need explicit handover
of control). It, however, uses disruptive actions whenever learners switch
between the actual modelling activity and metacognitive actions such as
reflection. An example for such disruptive interactions can be found in the
process transformation that is triggered when switching the system to history
mode to revisit former states of the concept map.
The envisaged guiding interventions identified and triggered by the system
based on CbKST obviously have disruptive character and take the form of a
process break. Interventions can take place on three levels. They can aim at
improving the usage of the tool or the concept mapping methodology and help
to avoid shortcomings in the structuring of the problem domain.
Interventions that help the learners in handling the tool can be necessary for
inexperienced users. Evaluations of the tool (e.g. Oppl, 2009) have shown that
misinterpretation of control tokens lead to process detours and sometimes even
breaks in the process that are hard to resolve. Controlled, directed and specific
interventions to introduce the tool’s features here might help to reduce
frustration and improve the motivation of the learners to work with the tool.
In contrast to the interventions described above, interventions to improve
concept mapping skills create process breaks to let learner’s reflect on their
usage of the concept mapping approach (e.g. to prevent weak or missing
connectedness of concepts). In a similar fashion, interventions on the level of
the problem domain aim at reflection of the actual content of the map. This last
last category of interventions can only be realized if (a) there is a known desired
result (i.e. a “correct” concept map) and (b) the automated system has
information about the desired result. In some learning scenarios, condition (a)
might not be met for didactical or practical reasons and even if it is, condition
(b) requires a structured description of the problem domain to enable the system
to identify potentially lacking skills of the learner and appropriate interventions.
The approach presented here brings together seamless interaction on a user
interface level with didactically motivated disruptive interventions. Future
research will have to show the feasibility of the integration of the presented
concept mapping tool (TCM) and the CbKST framework to trigger automated
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... Using this structure, guidance on how to use and interact based upon content for knowledge acquisition is provided. Following a self-regulated learning paradigm (Oppl et al. 2010), cognitive meta-knowledge is not directly captured and represented explicitly in a platform; however, users are enabled to self-assess their existing knowledge and learning requirements. Elements specifically targeting at didactically reasonable interaction among learners and knowledge providers facilitate building transactive knowledge and allow making use of instance knowledge within and external to the platform. ...
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... Furthermore, a combined application of concept mapping and Knowledge Space Theory in the context of technology-enhanced learning appears promising. Taking combined advantage of these two methods, on the one hand personalised learning experiences may be realised based on the valid knowledge representation provided through Knowledge Space Theory, while simultaneously visualisations of validated concept map may serve the presentation of learning material or concept mapping as a learning strategy (Oppl, Steiner, & Albert, 2011). Another feasible approach to a combined use of the two methods would be the use of a concept map as navigation interface (e.g. ...
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... The approach of concept mapping for self-regulated learning is used in (Oppl, Steiner, & Albert, 2010): A tangible tabletop allows users to reflect and evaluate their learning tasks through externalising and representing their knowledge on concept maps represented through physical and digital elements. Users can, for instance, Using tangible user interfaces for technology-based assessment - Advantages and challenges Page 5 place physical tokens, assign names, make connections, and use tokens as containers. ...
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... This principle is followed by SystemBlocks and FlowBlocks, two physical, modular interactive systems, which children can use to model and simulate dynamic behaviour [22]. The approach of concept mapping for self-regulated learning is followed in [10]: A tangible tabletop allows users to reflect and evaluate their learning tasks through externalizing and representing their knowledge on concept maps. They can, for instance, place physical tokens, assign names, make connections, and use tokens as containers. ...
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... Using this structure, guidance on how to use and interact based upon content for knowledge acquisition is provided to potential learners. Following a selfregulated learning paradigm [11], cognitive meta-knowledge is not directly captured and represented in the platform, but users are enabled to self-assess their existing knowledge and learning requirements. Elements specifically targeting at di-dactically reasonable interaction among learners and with knowledge providers facilitate building transactive knowledge and allow making use of instance knowledge within and external to the platform. ...
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This article presents two current research trends in e-learning that at first sight appear to compete. Competence-Based Knowledge Space Theory (CBKST) provides a knowledge representation framework which, since its invention by Doignon & Falmagne, has been successfully applied in various e-learning systems (for example, Adaptive Learning with Knowledge Spaces [ALEKS] and Enhanced Learning Experience and Knowledge Transfer [ELEKTRA]), providing automated personalisation to learners' current knowledge and competence levels. Principles of self-regulated learning (SRL), pioneered by, for example, Zimmerman, however, argue for increased learner control, thus resulting in giving learners greater responsibility over their e-learning. The research presented in this article shows that skill-based visualisations in the tradition of CBKST and SRL-based autonomy are in no way conflicting but rather complement each other towards an integrated approach of self-regulated personalised learning. The research has been carried out and technologically translated into a set of visual tools for supporting the whole learning cycle within the scope of the iClass project.
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.
Audio-tutorial science lessons were provided to 191 first and second grade children (instructed), and interviews were conducted periodically to assess changes in science concept under standing from grades one through twelve. A similar sample (n = 48) not receiving audio-tutorial lessons in grades one and two (uninstructed) was also interviewed periodically from grades one through twelve. Instructed students showed substantially more valid concept understandings and fewer invalid concepts (misconceptions) than uninstructed students in grades two, seven, ten, and twelve. Concept maps prepared from interview transcripts showed wide variation in knowledge for both groups, and concept maps scored using a scoring algorithm also showed significant differences favoring instructed students. The data show the lasting impact of early instruction in science and the value of concept maps as a representational tool for cognitive developmental changes.
Questions connected with the regulation of one's own cognitive processes attract increasing numbers of researchers in psychology, as evidenced by the several different models of self-regulation that have been developed over the past two decades. The aim of this article was to present and compare the latest models of self-regulated learning (SRL), including those by Boekaerts, Borkowski, Pintrich, Winne and Zimmerman. The models were compared on four criteria (i.e. background theories, definitions of SRL, components included in the models and empirical work). The results show that theoretical background is an important differentiating feature. The two models that resembled each other more than any other two models (i.e. Pintrich and Zimmerman) were inspired by the same background theory (i.e. social cognitive theory). On the other hand, the models that differed most from the other models (i.e. Borkowski and Winne) were also theoretically the farthest removed ones.