What’s next? Different Strategies Considering Teachers’ Decisions
for Adapting Learning Paths in Serious Games
Javier Melero, Naïma El-Kechai and Jean-Marc Labat
Laboratoire d’Informatique de Paris 6, Université Pierre et Marie Curie, Paris, France
Keywords: Serious Games, Adaptation, Competence-based Knowledge Space Theory.
Abstract: Adapting Serious Games (SGs) plays an important role to offer personalized game experiences. A well-
fitting approach to create adaptive SGs is based on Competence-based Knowledge Space Theory (CbKST).
CbKST structures the SG activities with respect to knowledge and competences, and adaptation is based on
suggesting activities that improve learners’ competences. However, differences among learners and the
diversity of learning situations may drive teachers to use different adaptive approaches to address their own
needs. In addition to the current state of learners’ competences, we also propose to consider teachers’
decisions as a key parameter for adapting learning paths in SGs. As part of Play Serious project, several
teachers’ requirements have been identified. This paper presents three different recommendation strategies
based on the identified requirements, to build adaptive learning paths in SGs.
1 INTRODUCTION
Adaptation is considered a key issue in Technology-
Enhanced Learning (TEL) since learners are not
alike; they have different knowledge and skills, as
well as learning preferences, interests and attitudes.
The motivation for employing adaptive assessment
is that learners come to new learning tasks aligned
with their profiles (Shute and Zapata-Rivera, 2012).
Taking full advantage of such assessments requires
the use of adaptive techniques that yield information
about the student’s learning process and outcomes.
In Serious Games (SGs), adaptation is based on
decisions that suggest activities in such a way that
the learner is neither unchallenged nor overwhelmed
by the complexity of the contained tasks (Göbel et
al., 2010). As a consequence, learners become less
frustrated and their motivation is increased (Hocine
et al., 2011).
Competence-based Knowledge Space Theory
(CbKST) has been proven to be a well-fitting basis
for realizing adaptation in SGs (Augustin et al.,
2013). This methodology allows a non-invasive
assessment of the learner without interrupting the
game flow experience (Kopeinik et al. 2012).
CbKST allows modelling a knowledge domain as a
formal structure of admissible and meaningful
competence states on the basis of precedence
relations among the competences. In other words,
CbKST formally structures the activities of an SG
with respect to knowledge and competences (Heller
et al., 2006; Kopeinik et al., 2012). The SG activities
are related to the competences worked on. Learners
have to demonstrate that they master these
competences by performing the tasks contained in
the different SG activities. To this end, systems
compute confidence values, linked to learner’s
competences that represent learners’ proficiency
level. These confidence values are used as main
parameters in the adaptation rules.
In this work, we propose to also consider
teachers’ decisions as a key factor for adapting SGs
in order to address specific pedagogical needs.
Learners have different range of abilities, needs and
interests, and teachers may consider implementing
different approaches that fulfil their needs (Marne
and Labat, 2014; Santangelo and Tomlison, 2009;
Shute and Zapata-Rivera, 2012). In other words,
teachers’ decisions could be based on the variety of
teaching styles, learners’ knowledge and
performance, learning styles, and learning contexts
(Moreno-Ger et al., 2009; Shute and Zapata-Rivera,
2012).
Therefore, we propose to enhance adaptation in
SGs by considering not only the learner’s
competence states but also teachers’ decisions based
on their needs. More specifically, we have identified
different teachers’ needs concerning the possibility
101
Melero J., El-Kechaï N. and Labat J..
What’s next? Different Strategies Considering Teachers’ Decisions for Adapting Learning Paths in Serious Games.
DOI: 10.5220/0005448201010108
In Proceedings of the 7th International Conference on Computer Supported Education (CSEDU-2015), pages 101-108
ISBN: 978-989-758-108-3
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
of allowing their students’ to advance forward
learning paths of SGs, as well as to reinforce and
deepen specific subsets of competences. Therefore,
in this paper, we propose different recommendation
strategies and we describe how we implemented
these strategies by using CbKST.
The remainder of the paper is structured as
follows. In section 2 we introduce the context of this
work, describing the identified teachers’ needs for
adapting SGs. We also describe the basis of this
work that relies on Competence-based Knowledge
Space Theory. In section 3, we present the
recommendation strategies considering the identified
needs presented in the previous section. In Section 4,
we describe a preliminary evaluation on the
proposed strategies. Finally, in section 5, we
conclude with a discussion of the proposed
approaches, as well as future research directions.
2 CONTEXT
2.1 Teachers’ Needs in Adaptive SGs
This work is framed in the Play Serious Project
(Play Serious, 2013). The purpose of the project is to
develop tools that facilitate the design and
development of SGs in the field of adult vocational
training. The proposed tools are classified into three
different categories:
1. Authoring tools for supporting the development
of SGs (e.g. SG scenarios).
2. Monitoring tools for analyzing learning actions
and assessing learners’ competences.
3. Adaptive tools for modifying learning paths of
SGs.
This paper particularly focuses on advancing
forward the development of adaptive approaches for
serious games (3rd category of tools). In this
context, different strategies for adapting SGs have
been identified from the joint work with pedagogical
experts and teachers involved in the project.
Teachers and pedagogical experts from different
companies (e.g. sales market) express their needs to
deploy some pedagogical strategies. The identified
requirements and proposed strategies are described
as follows:
S1. The first requirement is related to allow learners
progressing autonomously and gradually to
achieve all competences of a knowledge domain.
The competences have to be worked on at the
end of the training session. To meet this
requirement, we define the “Advancing”
strategy. This strategy considers the learner’s
proficiency level and proposes activities that
work on the maximum number of competences.
At each step the proficiency level is updated
allowing a progression in the learning path until
all competences have been worked.
S2. The second requirement focuses on training
sessions that are divided into stages. Given a
stage, teachers aim to specify a subset of
competences to work on, as well as the degree of
achievement as prerequisites to let their learners
move forward in the following stages. For
instance, in the step “common ground” in sales
training, competences that have to be worked to
move forward in the following stage include
“identifying customer needs”, “collecting
information about the customer”. To meet this
requirement, we define the “Reinforcing”
strategy. This strategy allows the learner to
reinforce specific competences that have not met
a minimum threshold. This case arises when
these competences are needed/required in the
next stage of the training course.
S3. The third requirement is to offer teachers with
the possibility to choose specific competences to
let the learners to progress to a higher advanced
competence level. Teachers aim to identify
learners that are very good in specific
competences. The teachers’ intention is to lead
these learners achieve a very high level in those
competences to become quickly operational
within the company. For instance, in sales
enterprises, trainers could seek for employees
that are outstanding in “treating customer
objections” or “arguing different solutions to
meet the client’s needs” in order to become
managers of sales team. To meet this
requirement, we propose the “Deepening”
strategy. This strategy allows learners to become
expert in certain competences that they have
already mastered within a knowledge domain.
One competence has been mastered when the
proficiency level is above a threshold value
introduced by the teacher.
In order to implement the different strategies, the
partners of the project focus on SGs that are based
on activities that typically correspond to levels in
SGs. These SG activities contain the tasks that
learners can perform to train specific competences.
Besides, SGs activities have to be independent from
each other. The aim is to allow organising the SG
activities in different ways and hence create diverse
learning paths. Therefore, the SGs in the project can
be considered as curriculum sequencing environ-
ments in the sense that learning paths can be defined
CSEDU2015-7thInternationalConferenceonComputerSupportedEducation
102
as a set of independent entities that can be assembled
in different ways (Brusilovsky and Vassileva, 2003).
As representative works of curriculum sequen-
cing environments we can cite the adaptive
hypermedia (Brusilovsky and Vassileva, 2003) or
ALEKS (www.aleks.com), an environment of a
commercial spin off of the University of California
at Irvine. The concept of curriculum sequencing is
grounded on Knowledge Space Theory (KST)
(Falmagne et al., 2006). Thus, in order to provide
with a feasible implementation for the different
strategies, we based our work on KST, and more
precisely on its extension: Competence-based
Knowledge Space Theory (CbKST) (Heller et al.,
2006; Korossy, 1999), as a potential framework for
adapting learning paths in SGs.
2.2 Competence-based Knowledge
Space Theory (CbKST)
CbKST is an extension of KST (Falmagne et al.,
2006). KST was intended for the assessment of
learners’ knowledge. Advancements of KST
introduce a separation of observable performances
and the underlying abilities or knowledge, leading to
diverse competence-based approaches (Reimann et
al., 2013). CbKST relies on three main concepts:
precedence relations, competence states and the
competence structure. Basically CbKST assumes a
defined set of competences and precedence relations
between them. In other words, a precedence relation
a ≤ b indicates that competence ‘a’ is a prerequisite
to acquire another competence ‘b’. Considering
precedence relations, competence states are the
resulting meaningful combinations of single
competences. A competence structure is obtained by
deriving all the admissible competence states of a
certain domain. Figure 1 shows an example of
precedence relations between five competences and
the competence structure. In this example, the set {a,
c} cannot be a competence state since competence
‘b’ is also required to master competence ‘c’.
Given a competence structure, the lowest
competence state represents the naive state (i.e. the
learner has not mastered any competence yet) and
the highest competence state represents the state in
which the learner has mastered all the competences
for a given domain. Then, a learning path represents
a possible path in the competence structure that
moves from the lowest competence state to the
highest one.
There are diverse research works on adapting
SGs based on CbKST (Augustin et al., 2013;
Kickmeier-Rust et al., 2008; Kopeinik et al., 2012;
Figure 1: Example of precedence relations (left graph) and
competence structure (right graph).
Peirce et al., 2008). However, while the identified
literature focuses on the traditional approach based
on improving learners’ competences, as far as we
know there is a lack of research studies that consider
teachers’ needs as a factor when implementing
adaptive SGs. For this reason, we also introduce
teachers’ decisions as an input to enhance adaptation
in SGs.
In the next section, we describe the architecture
to implement the recommendation strategies to
suggest SG activities considering the requirements
expressed by teachers.
3 RECOMMENDATION
STRATEGIES
We propose the development of a decision module
based on an adaptation model proposed by Kopeinik
et al. (2012) in order to implement the different
recommendation strategies. Like Kopeiknik et al.,
we consider the learner’s current competences. In
addition, in our approach we consider the teachers’
decisions that mainly deal with selecting one of the
identified recommendation strategies. Also, we
consider recreational competences of SG activities.
The overall logic architecture of the decision module
is depicted in Figure 2.
In order to implement the recommendation
strategies and hence achieve adaptation, the decision
module considers the following elements to suggest
learning paths in SGs:
• The domain model of the SG. This means, the
pedagogical competences and the links between
competences. This information is used to build
the competence structure based on CbKST.
• The recreational competences. Together with the
domain model, these competences define the
game requirements to a particular SG. The
domain model and recreational competences do
not change during the game process.
• The list of activities (or levels). Each activity can
be linked to pedagogical competences, as well as
recreational competences.
What'snext?DifferentStrategiesConsideringTeachers'DecisionsforAdaptingLearningPathsinSeriousGames
103
Figure 2: Logic architecture of the Decision Module.
An activity corresponds to a way to perform a
task in an SG. In our work, we define an activity
as a basic unit and it corresponds to a level
within an SG.
• The learner model. This model keeps track of the
activities performed by the learner and it stores
the accumulated evidence about competences.
This means, each competence has a value
corresponding to the probability that a learner
master this competence. Initially, a learner
assessment is done before playing the game to
initialize the confidence or probabilistic values.
These probabilistic values are changing during
the game playing (after the learner has finished
each activity). As mentioned before in the
section 2.1, in the context of the project, we also
work on a monitoring tool that computes these
probabilistic values. This work, which is out of
the scope of this paper, extends a previous work
(
Thomas et al., 2012) by using Bayesian networks.
• The recommendation strategies that the teacher
can choose. These are: a) “Advancing”: suggests
activities that address the same competences as
those in the current learner’s competence state
and moves one step forward in the competence
structure; b) “Reinforcing”: suggests activities
that address a subset of competences specified by
the teacher. The percentage of accomplishment
of the selected competences must be below a
certain threshold (value that has to be reached by
the learner for improving the competences in
which he/she is weaker); and c) “Deepening”:
also suggests activities that address a subset of
competences specified by the teacher. Unlike
“Reinforcing” strategy, the percentage of
accomplishment of the selected competences
must be above a certain threshold value specified
by the teacher. This value indicates that the
learner is good in the set of competences and the
teacher aims that he/she becomes better.
Next section focuses on describing the behaviour
of the different recommendation strategies.
3.1 “Advancing” Strategy
The “Advancing” strategy addresses the first
requirement identified in the Play Serious project
that aims at working the maximum number of
competences in a certain domain (S1).
This strategy considers the current learner’s
competence state and moves to the next competence
states in order to propose an activity (see Figure 3,
1).
Figure 3: Graphical example of the behaviour of the
“Advancing” strategy.
The next activity to be played is suggested as
follows.
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• First, we get the possible next competence states.
Next competence states (i.e. successors) are
those which contain exactly the same the
competences of the current competence state plus
one more (see Figure 3, 2). In CbKST, the
additional competences in the successors are the
outer fringe of the current competence state.
• Then, we iterate the list of the next competence
states. For each competence state, we look at the
associated activities that have not been done by
the learner (see Figure 3, 3).
• If there are no activities (because there are no
activities designed for this competence state), we
move to the following competence state.
• If there are activities, we select one of them. The
next activity is selected considering the difficulty
level, if this option has been selected by the
teacher. Otherwise, a random function is applied.
Besides, if the pedagogical activity has
recreational competences, then if possible, we
suggest before an activity that only works the
recreational competences (if the learner has not
worked on these competences yet).
• If none of the next competence states contain
activities, we look at higher knowledge states.
This strategy finishes when the last competence
state (containing all the competences) is reached.
3.2 “Reinforcing” and “Deepening”
Strategies
The “Reinforcing” and “Deepening” strategies fit
the second and third requirements identified in the
Project, respectively. From an algorithmic point of
view, the behaviour of “Reinforcing” and
“Deepening” strategies is very similar, but they
address different pedagogical needs. These are:
providing the learner with activities to reinforce
certain competences (S2), and with activities to
become expert in certain competences (S3).
First, we consider the current learner’s
competence state and all previous competence states
from the competence structure (see Figure 4, 1). The
initial state of the algorithm considers the subset of
competences selected by a teacher, as well as the
specified threshold value. Then, from the subset, we
get those competences that are below (in
“Reinforcing” strategy) or above (in “Deepening”
strategy) a certain threshold specified by the teacher
(see Figure 4, 2).
From the selected subset of competences, the
algorithm follows an iterative process.
• First, we get one competence from the subset of
competences.
Figure 4: Graphical example of the behaviour of the
“Deepening” and “Reinforcing” strategies.
• Right afterwards, we look at the previous
competence states (from the initial to the current
learner’s state) that contain the selected
competence to be worked (see Figure 4, 2).
• Then, for each of these competence states we get
the activities that have not been done yet (see
Figure 4, 3).
• Similarly to the “Advancing” strategy, if there
are several activities linked to the competence
state, we select the next activity considering the
difficulty
level if specified by the teacher.
Otherwise, a random function is performed to
suggest the next activity. Besides, if the selected
pedagogical activity has recreational
What'snext?DifferentStrategiesConsideringTeachers'DecisionsforAdaptingLearningPathsinSeriousGames
105
requirements, then if possible, we suggest before
an activity that only works the recreational
requirements.
• However, if we reach the current learner’s
competence state and no activities has been
found for the selected competence, we choose
another competence from the considered subset
of competences, and we repeat the process.
• The strategy ends when the threshold is reached
(in “Reinforcing”) or when the maximum level
of proficiency has been reached (in
“Deepening”). Otherwise, both strategies can
also end when all activities for the subset of
competences have been done.
Next section presents a preliminary evaluation of
the strategies in Cristaux d’Éhère (Cristaux
d’Éhère, 2015), an SG for teaching physics.
4 PRELIMINARY EVALUATION
The different algorithms have been evaluated on the
SG called Cristaux d’Éhère, designed to teach
concepts related to physics consisting of 11
activities. The goal for each level is to solve
problems about competences related to water state
changes. Learners must move an avatar to interact
with certain objects to reach a solution concerning
physics-related topics.
A secondary education teacher, expert on
physics, designed the domain model for the SG (see
Figure 5). From this domain model (i.e. precedence
relations between competences), we generated the
competence structure.
The teacher also created the Q-Matrix (Tatsuoka,
1983); i.e. he linked the SG activities to the worked
competences considering the tasks that can be
performed in each activity (see Table 1). Besides,
the different SG activities were linked to competence
states (the set of competences worked on in each
activity forms the competence state).
Considering these information, we carried out an
evaluation of the proposed strategies. Table 2 shows
the obtained results. Expected results (used for
validating the obtained results) are explained as
follows:
• One possibility is to consider that the current
competence state of the learner is the initial one.
From the initial competence state, there is one
next competence state that can be reached. The
next competence state includes the competence
‘{h}’. Only the activity “Act1” is linked to this
competence state. Therefore, if we apply the
Advancing strategy and no previous activities
have been done by the learner, the next
suggested activity is “Act1”. However, if “Act1”
has been done, since there are not more activities
for this competence state, the Advancing strategy
has to look at a higher competence state
(containing more competences). That means, this
strategy will look at the competence state formed
by the competences ‘{h, i}’. Activities associated
to this competence state, and therefore, suggested
by the strategy are “Act8” and “Act9”.
• From the initial competence state we cannot
apply Deepening nor Reinforcing strategies since
no competences have been worked yet.
Therefore, no activities can be suggested for this
particular case.
Figure 5: Domain model for Cristaux d’Ehere.
Table 1: Extract of the matrix representing activities
indexation in Cristaux d’Ehère.
Activities
Competences
Competence
states
a
b c d e f h i
Act1
X
{h}
Act2
X X
X X
{b,d,h,i}
Act3
X
X X
{c,h,i}
Act4
X X
X
{b,e,h}
Act5
X X
X X
{b,e,h,i}
Act6
X X
X
{a,c,f}
Act7
X
X
X X
{a,c,h,i}
Act8
X X
{h,i}
Act9
X X
{h,i}
• Another possibility is to consider that the current
competence state of the learner is formed by
competences ‘{h, i}’. If we apply the Advancing
strategy, next competence state that can be reach
CSEDU2015-7thInternationalConferenceonComputerSupportedEducation
106
Table 2: Results obtained when applying the proposed strategies in Cristaux d’Ehère.
Current
competence state
Subset of competences
to train
(if applicable)
Activities done
System
confidence
Suggested activity
Advancing Deeping Reinforcing
Initial state =ø - None - Act1 None None
Initial state =ø - Act1 - Act8 None None
{h, i}
“h” (Reinforcing)
“i” (Deeening)
None
0.3 : “h”
0.7 : “i”
Act3 Act9 Act1
{h, i}
“h” (Reinforcing)
“i” (Deepening)
Act3
0.3 : “h”
0.7 : “i”
Act7 Act9 Act1
contains the competences ‘{c, h, i}’. There is
only one activity for this new competence state
which is “Act3”. Therefore, the Advancing
strategy should suggest this activity. However, if
this activity has been done by the learner, since
there are no more activities for this competence
state, the Advancing strategy has again to look at
higher competence states. Then, possible
activities to be suggested are: “Act7”, “Act5”, or
“Act2”.
• Furthermore, we can again consider that the
current competence state of the learner is formed
by competences ‘{h, i}’. For this competence
state we can suppose that the system confidence
for the first competence is 0.3, and for the second
competence is 0.7. Then, if a teacher wants to
apply the Deepening strategy to train the
competence ‘{i}’, expected activities to be
suggested are: “Act8” or “Act9”. These activities
are those from the same and previous
competence states.
• Similarly, if a teacher wants to apply the
Reinforcing strategy to train the competence
‘{h}’, expected activities to be suggested are:
“Act1”, “Act8” or “Act9”. These activities are
those from the same and previous competence
states.
From an algorithmic point of view, we validated
the results obtained by the strategies (see Table 2)
compared with the expected results. Indeed, a co-
designer involved in the implementation of the SG
also validated the obtained results. These promising
results lead us to consider a broad evaluation with
other experts and SGs.
5 DISCUSSION AND FUTURE
WORK
Current literature focuses on improving confidence
values computed by systems in regards to the
proficiency level of learners.
The innovative part of this work is: (a) to
combine specific needs of teachers with the
traditional approach (i.e. taking into account the
current competence state of the learner); and (b) to
implement this combination in adaptation strategies
by using CbKST.
The adaptation strategies result from the needs
expressed by teachers and companies involved in the
project. The implementation of these strategies is
based on different input parameters (mainly, subset
of competences and threshold). We believe that the
proposed approaches can be extended and applied to
other pedagogical needs, as long as these needs can
be translated into the concepts of CbKST (i.e.
competence state and competence structure).
We have developed a tool that allows teachers to
specify the different inputs required for adaptation
strategies: subset of competences, threshold, sorting
SG activities by level of difficulty. Currently, we are
testing the implementation of these strategies in
different SGs. Further work includes: (a) an
evaluation comparing the results provided by the
decision module to teachers’ expectations; and
(b) assessing learners by applying the proposed
strategies and evaluating the impact on learners’
performance.
Finally, other research line consists in using
CbKST as an analytical method to identify gaps in
the design of the SGs. Indeed, by using CbKST it is
possible to identify competence states for which
there are no associated activities.
ACKNOWLEDGEMENTS
This work was supported in part by the Region Ile
de France and by the French Ministry for the
Economy, Industry and Employment (FUI). We
would like to thank them for their support in the
“PlaySerious” project.
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