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Adaptive Gamification in Education: A Literature Review of Current Trends and Developments

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Gamification, the use of game elements in non-game settings, is more and more used in education to increase learner motivation, engagement, and performance. Recent research in the gamification field suggests that to be effective, the game elements should be tailored to learners. In this paper, we perform an in-depth literature review on adaptive gamification in education in order to provide a synthesis of current trends and developments in this field. Our literature review addresses 3 research questions: (1) What are the current kinds of contributions to the field? (2) What do the current contributions base their adaptation on, and what is the effect of this adaptation on the gamified system? (3) What is the impact of the adaptive gamification, and how is this impact measured? We also provide future research guidelines in the form of three needs that should be fulfilled for exploring this field.
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Adaptive gamication in education: A literature review
of current trends and developments
Hallifax Stuart, Audrey Serna, Jean-Charles Marty, Elise Lavoué
To cite this version:
Hallifax Stuart, Audrey Serna, Jean-Charles Marty, Elise Lavoué. Adaptive gamication in educa-
tion: A literature review of current trends and developments. European Conference on Technology
Enhanced Learning (EC-TEL), Sep 2019, Delft, Netherlands. �hal-02185634�
Adaptive gamification in education: A literature
review of current trends and developments
Stuart Hallifax1[0000000227439884] , Audrey Serna2[0000000314689761] ,
Jean-Charles Marty3[0000000318238415], and Elise
Lavou´e1[0000000226596231]
1University of Lyon, University Jean Moulin Lyon 3, iaelyon school of Management,
CNRS, LIRIS UMR5205 F-69621
2INSA de Lyon - CNRS, LIRIS UMR5205 F-69621
3Universit´e de Savoie Mont Blanc - CNRS, LIRIS UMR5205 F-69621
name.surname@liris.cnrs.fr
Abstract. Gamification, the use of game elements in non-game settings,
is more and more used in education to increase learner motivation, en-
gagement, and performance. Recent research in the gamification field
suggests that to be effective, the game elements should be tailored to
learners. In this paper, we perform an in-depth literature review on adap-
tive gamification in education in order to provide a synthesis of current
trends and developments in this field. Our literature review addresses 3
research questions: (1) What are the current kinds of contributions to
the field? (2) What do the current contributions base their adaptation
on, and what is the effect of this adaptation on the gamified system? (3)
What is the impact of the adaptive gamification, and how is this impact
measured? We also provide future research guidelines in the form of three
needs that should be fulfilled for exploring this field.
Keywords: Gamification Education Adaptation
1 Introduction
Gamification, defined as the use of game elements in non-game contexts [12],
has been used for close to ten years in educational settings to increase learner
performance, motivation, or engagement [1,24,27]. Recent studies conducted in
other contexts such as health [33] and sport [26] on the effects of gamification
show that to be effective, gamification should be tailored to users. In educa-
tion, research on adaptation has mainly concerned educational content and its
adaptation to learners and context. It is a well explored research topic [8] that
has been shown to be effective. Adaptive gamification in education attempts to
leverage both of these concepts in order to provide a better learner experience.
It is therefore important to take a step back and analyse how game elements
can be adapted to learners in educational contexts. In this paper, we review the
research on adaptive gamification in education and present the results of our
analysis. In order to examine the current state of research in this field, and to
2 S. Hallifax et al.
understand how adaptive gamification is applied in education, we performed a
literature review based on twenty papers. Through our review, we highlight the
advances in the field and limitations that need to be addressed. Our review aims
to answer the following questions:
What are the current kinds of contributions to the field of adaptive gamifica-
tion in education? We distinguish three kinds of contributions: 1) preliminary
research on recommendations for game elements adapted to learner profiles,
2) technical contributions on architectures that have not been tested yet and
3) studies that look at the impact of adaptive gamification that make use
of such architectures, and that provide valuable results into this research
approach. The analysis of these three contribution types show the maturity
of this field.
What do the current contributions base their adaptation on, and what is
the effect of this adaptation on the gamified system? We clearly distinguish
static (i.e. initial) adaptation and dynamic adaptation, that rely on different
kinds of information, such as player types or interaction traces.
What is the impact of the adaptive gamification and how is this impact
measured? We identify studies conducted on short and on long terms, as
results obtained may depend on the duration. We also distinguish studies
according to the adaptation mechanism used (static or dynamic).
In this paper we first present our literature review process in section 2. Then,
in section 3we present three parts, each part being dedicated to a research ques-
tion. We finally provide future directions for research in this field in section 4,
by pointing out three needs that future research in this field should aim to re-
solve: the need for richer learner models, the need to explore different adaptation
methods, and the need for more structured studies.
2 Literature review process
Our structured literature review process was based on the guidelines and pro-
cesses described in [38,39]. First, we defined our review scope, by specifying our
research questions and therefore explicating our search query (we explain this
more in detail in section 2.1). Then, we ran our search query and filtered the
papers that did not fit our review scope (see section 2.2).
2.1 Defining our review scope
We are interested in the current state of adaptive gamification in education re-
search. We studied what exactly is adapted, and what characteristics or variables
are used to tailor learner experience. This lead us to define our three research
questions. We then clearly defined our search terms, as to cover the topic of
adaptive gamification in education. More specifically we used the search query:
(gamif*) AND (learning OR education OR teaching) AND (adapt* OR tailor*
OR personali*)
Adaptive gamification in education 3
The first part of our query (i.e. gamif*) was used to capture all terms
that start with “gamif” (i.e. gamification, gamified etc.). Note that the queries
“gamif*” and “gamif” were used depending on the capabilities of the search en-
gines used as some allowed for wildcard characters and others not. After testing
different permutations of “teaching words” we settled on “Learning” “Educa-
tion” and “Teaching” (when we added alternatives such as “learn” or “learner”
the result count did not change, so we stuck with a more focused approach).
Finally for the adaptive part, we had a similar reasoning as with “gamif”. The
three base words (“adapt”, “tailor” and “personali”) allowed us to capture the
different keywords used to describe these works (and also allow for regional vari-
ants such as the British “personalised” versus the American ”personalized”).
2.2 Paper search & filter
We ran our search query on the major scientific digital libraries (ACM, IEEE,
Science Direct, Springer) and Google Scholar. Due to the fairly large nature of
our search query, we received a large number of initial hits (370 papers, see table
1), which lead to a rigorous filtration process in order to remove false hits.
Table 1. Number of papers before and after content filtering. The number of papers
excluded is given for each filtration step.
Filtration step Source
ACM IEEE Science Direct Springer Google Scholar
Keyword query 64 94 17 35 160
Removed - format 18 8 1 2 49
Removed - scope 41 79 13 26 74
Removed - duplicate 2 1 1 1 34
Final count 3 6 2 6 3
Papers were first reviewed by scanning the keywords and title, then the ab-
stract, and finally the full text if the paper was not excluded from the previous
two steps. Papers were then excluded for the following reasons:
Format: Results that were either abstracts, preview content, posters or work-
shop papers were removed. We made this decision so that we only studied
mature works. Finally, we also removed papers that were not in English
(many of the results from Google Scholar had English abstracts or titles,
but the rest of the paper is in another language).
Scope: Here we analysed the content discussed in the papers. Papers were
excluded due to scope because they did not specifically deal with adaptive
gamification in learning. For example papers that discussed adaptive gami-
fication for health or sport were removed.
Duplicates: A few references were found in multiple databases, as some of
the databases contain references to papers that are cited by papers that
4 S. Hallifax et al.
they publish. Furthermore some of the papers found were extended versions
of previous papers. The non extended versions were therefore excluded.
After this filtering, we were left with a final total of twenty papers that we
included in our final analysis.
3 Literature analysis
We analysed the papers through the lenses of each of our three research questions.
We first identify the type of the contribution (section 3.1) to identify the degree of
maturity of the research field. We then present the different adaptation systems
by identifying what they are based on, and what they adapt (section 3.2). We
more particularly distinguish static and dynamic adaptation as they rely on
different mechanisms and different kinds of information. Finally we review the
results of studies on the impact of the adaptation of game elements on learners’
motivation and performances (section 3.3).
3.1 Contributions: Recommendations, Architectures and Studies
We examined the degree of maturity of the research field in light of two criteria.
First, we identified the contribution type of each reviewed paper (table 2). Sec-
ond, we reviewed the vocabulary used to describe the adapted content in each
contribution.
Table 2. Type of each contribution: Recommendations, Architecture, or Study. These
types are described below.
Contribution
type
Recommendations 8 [2,4,6,9,10,11,21,23]
Architecture 2 [22,29]
Study 10 [18,19,20,25,28,30,31,34,35,36]
Regarding the first criterion, we classify the papers into three types of con-
tributions that emerged from the review:
Recommendations: identification of game elements that would be adapted to
different categories or classes of learners, based on literature review, or gen-
eral surveys (8 papers). These recommendations correspond to preliminary
research and they have not been implemented in a system yet.
Architectures: adaptation engines based on existing theoretical works, that
have not yet been tested in real world situations (2 papers).
Adaptation studies: an adaptation engine, based on recommendations to
adapt game elements to learners, tested with learners through a real world
study (the combination of an adaptation architecture, theoretical recommen-
dations, and a real world study) (10 papers).
Adaptive gamification in education 5
Recommendations: We found two major categories of papers: papers that
base their recommendations on literature surveys, and those that base their rec-
ommendations on user surveys, or feedback. In the first category, Borges et al. [4]
review literature on ”player types” (archetypal reasons why users seek out game
experiences) and link these to learner roles and different game elements based
on the motivational aspects they provide. Challco et al. [6] also link motiva-
tional aspects with player types and game elements. ˇ
Skuta et al. [23] also use
player types, but link them to higher level game principles. They then propose
a matrix that associates game elements to player types based on how well each
game element implements the linked game principles. In the second category,
Denden et al. present three user studies, two based on a feedback after using
a non adapted gamified tool [9,11], and one based on a user survey [10] where
participants rated statements based on game elements in order to determine
their preference. Knutas et al. [21] analysed videos and interviews with learners
in a software engineering project to create clusters of learners based on their
interactions. These clusters were then linked to Bartle player types and relevant
game elements. Barata et al. [2] used a similar approach, creating four types of
learners based on their strategies during an online course. They then propose
different goals that could be provided to each of the learner types. These studies
serve to provide valuable information about what game elements learners might
prefer, but still need to be implemented and tested in a real adaptation system.
Architectures: We found only two papers that describe adaptation engine
architectures without any associated study. They present what the engine takes
into account, what it adapts, and how it adapts it. Kuntas et al. [22] describe
their process for designing an algorithm based personalised gamification system.
They detail learner characteristics on which they base the adaptation of some
game elements and the algorithm used to link the two. Monterrat et al. [29]
describe an architecture that presents game elements as ”epiphytes”, completely
separate from the learning content. They can therefore swap out game elements
as needed. They also propose a module that tracks learner interactions in order
to more finely adapt the game elements. They use a learner model that contains
data on learner (gender, age, player type), usage data, and environment data.
Studies: Half of the reviewed papers present studies that rely on an adaptive
gamification system in an educational setting [18,19,20,25,28,30,31,34,35,36].
These papers provide valuable results about the impact of adaptive gamification
on learner motivation and performance. We present them in section 3.3.
Vocabulary: Regarding the second criterion, we observed that the papers re-
viewed have a general consensus about the vocabulary used to describe the gami-
fication elements. Twelve of the papers reviewed [2,4,6,9,10,11,18,19,20,23,34,35]
used the term ”game element” to describe the low level implementations they
6 S. Hallifax et al.
use, such as points, levels, leaderboards, progress. Four papers from the same
authors [25,28,29,30] use the term ”game features” to present the same level
of implementation. Knutas et al. [21,22] use the terms ”game like elements”.
Mora et al. [31] present different gamification ”situations” (that combine differ-
ent game elements). We can therefore observe that the papers reviewed generally
agree on the term ”game element” to designate what is adapted.
In summary, we find the field of adaptive gamification in education to be
emergent, as there is a relatively low number of papers, that cover a wide variety
of contribution types. Regarding the kind of contributions, twelve papers (two
architectures and ten studies) take advantage of the ground work that the eight
recommendations papers lay out. Furthermore, we found the vocabulary used to
describe what is adapted to be quite stable, pointing towards a general consensus
among authors.
3.2 Information used for adaptation and its effect on game elements
In this section we analyse both 1) what information is considered for adaptation
(learner profile or activity) and 2) what the effect of the adaptation is (a change
of the game element, or a modification of how the game element works). Our
review analysis also allowed us to identify two major types of systems: static
systems, and dynamic systems (see table 3). In a static system, the adaptation
occurs once, usually before the learners start using the learning environment. In
a dynamic system, the adaptation happens multiple times during the learning
activity. We present our analysis according to these two categories as information
considered for adaptation and its effect clearly depends on them.
Table 3. Classification of the papers according to the kind of information used for
adaptation (user profile and/or activity), its effect (game element change or modifi-
cation of its functioning) and the kind of adaptation (static or dynamic). The learner
activity concerns either context based performance, or general behaviours. Some
papers use multiple types of information, and are present on multiple rows.
Static Dynamic
Change Modification Change Modification
Profile
Player Type 8 [4,6,23,25,28,30,31,36] 0 2 [21,29] 0
Personality 4 [9,11,18,35] 0 0 0
Expertise 1 [4] 0 0 0
Other 2 [4,10] 0 1 [29] 1 [22]
Activity Performance 0 0 0 2 [19,20]
Behaviours 0 0 2 [21,29]4[2,22,34]
Adaptive gamification in education 7
Static adaptation Systems that use static adaptation all work in a similar
manner. They base their adaptation on a learner profile, and adapt by changing
game elements. Learners’ profiles are identified, learners are sorted into different
categories based on these profiles, and different game elements are given to each
of the different categories of learners.
For learner profiles, the static adaptation systems generally use player types
and more rarely learner personality. Player types are archetypal reasons or mo-
tivations that explain why players play games. The papers reviewed used either
the Bartle Player types [3] (used in two papers [6,23]), the Brainhex player satis-
faction model [32] (used in three papers [25,28,36]), the Hexad player types [37]
(used in one paper [31]), or the categories of players described by Ferro et al. [14]
(used in one paper [4]). These different categorisations of players types describe
the reasons why players prefer different games. For example the Hexad player
classification describes ”Achievers” as people who “like to prove themselves by
tackling difficult challenges” [37]. The papers that use these player types typically
use the definitions of the different categories as a basis for their adaptation rules,
for example the Hexad classification suggests using badges and levels (amongst
others) for Achievers. Brainhex and Hexad provide a questionnaire to determine
a player profile, i.e a set of values that define how well the player fits each type.
Generally studies adapt using the dominant player type, i.e. the type that scores
the highest for a given learner. However, Mora at al. [31] question the precision
of only using the dominant type and propose to consider several dimensions of
the profile to tailor gamification.
For the personality traits, two of the five papers [9,11] used the Big Five
Factors personality traits [15]. Two papers used a user motivation questionnaire:
Roosta et al. [35] used the framework presented by Elliot et al. [13]; Hassan et
al. [18] used the questionnaire developed by Chen et al. [7]. Only a few static
systems used other kinds of user characteristics, such as gender and gaming
frequency [10], or learner role (tutor or tutee) [4].
Dynamic adaptation In dynamic adaptation, systems use learner activity to
adapt game elements, either alone or in combination with a learner profile.
Systems that only use learner activity make adaptation by modifying the
functioning of the game element. Two papers adapt the goals presented to learn-
ers. Paiva et al. [34] categorise all learner actions as either collaborative, gamifi-
cation, individual or social interactions; the system adapts the kind of goals the
learner receives according to the kind of actions they perform. Barata et al. [2]
propose a system that varies the goals and rewards given to learners based on
their behaviours, by distinguishing four types of learners: achievers, disheart-
ened, underachievers, and late bloomers (a learner is not fixed into a specific
category, as their behaviour may vary over time). Jaguˇst et al. [19] present two
dynamic adaptation situations, both of them using learner activity. In the first
situation, learners are timed in a maths quiz. Each time the learner gets a ques-
tion right, they are given less time for the next question, essentially increasing
the difficulty based on the learner’s performance. In the second situation, the
8 S. Hallifax et al.
learners are shown a target score that changes depending on how they respond
to questions: the more correct answers they give, the more the target score in-
creases. Kickmeier-rust et al. [20] change the types of badges presented to, and
feedback received by the learner based on the mistakes they make.
Two systems use both learner activity and profile. Monterrat et al. [29,30]
aim to modify the learners’ profile based on their activity. The system then uses
previously established static adaptation rules. When the learners’ profile changes
significantly, a different game element is given to the learner. The learner pro-
file is based on the Ferro player types in earlier versions of their work [29], and
in more recent work [30] they propose to use the Brainhex model (in [29] they
also use gender and age for adaptation). This is a straight forward way of im-
plementing dynamic adaptive gamification using static adaptation rules. The
systems proposed by Knutas et al. [21,22] use an algorithm that also uses learn-
ers’ profile and interactions. In both systems, they use the Hexad player profile,
and in the more recent one [22] they also use learner skills. In [21] they analysed
videos of students during project meetings and classified their interactions and
propose different game elements based on a combination of profile and interac-
tion types. They lay the ground rules for a dynamic adaptation based on learner
activity, but do not offer a method to detect these actions in real time. In [22]
they use learner chat activity and profile to provide personalised goals.
In summary, adaptation of game elements is made using two major categories
of information: static adaptation mainly relies on learners’ profile (mainly their
preferences and motivations), dynamic adaptation is based on how learners per-
form with regards to the learning content, or how the learners interact with the
system in general. The majority of systems then use this information to select
which game elements would be the most appropriate for learners. Only a few
(five) adapt by modifying how the game elements function.
3.3 Impact of adapted gamification on learners
We examined the impact of adaptive systems reported in the ”study” papers
identified in section 3.1. We found that the results could be split into two cat-
egories (see Table 4) those that show a general positive impact on learner’s
motivation or performance, and 2) those that show more mitigated results. We
also split the studies based on 1) whether they used a static or dynamic adap-
tation, and 2) the duration to investigate whether these factors influence the
impact of adaptive gamification on learners. We identified short studies as those
lasting less than two weeks, and long studies as lasting more than two weeks
(with an experimental process that is closer to real world learning practices).
Short studies We found two studies that lasted less than two weeks [19,20],
with both of these studies using a dynamic adaptation. All of these studies
reported positive results on learners. In [20], learners used the adaptive system
over two sessions, for a total possible time of thirty minutes. According to the
Adaptive gamification in education 9
Table 4. Impact of the reviewed studies. The numbers show how many studies are
present in each category.
Duration Static Dynamic
Positive Mitigated Positive Mitigated
Short 0 0 2 [19,20] 0
Long 4 [18,28,31,35]2[25,30] 0 1 [34]
authors the personalised system reduced the amount of errors that learners made.
Learners with the adaptive situation showed a larger decrease in errors made
in the second session when compared to learners that used the non adaptive
situation. In [19] Jagust et al. test two adaptive situations that learners used for
15 minutes each. In the first situation, the time learners had to answer questions
changed depending on how quickly they answered the previous question. In the
second situation, a target score changed depending on group performance. In
both situations the authors report an increase in learner performance (learners
completed more tasks than compared to a non gamified situation), although the
first situation caused a larger increase than the second one.
Long studies Seven of the reviewed studies lasted more than three weeks [18,
25,28,30,31,34,35]. Four studies showed generally positive results [18,28,31,35].
Roosta et al. [35] presented learners with a different game element based on
their motivation type. Learners used an online tool for one month. The authors
find that learners who had game elements that were suited to their motivation
type showed significant differences in motivation, engagement, and quiz results
when compared to learners who had randomly assigned game elements. They
used learner participation rates in the online activities as a metric to gauge mo-
tivation and engagement. Monterrat et al. [28] split learners into three different
groups: one group received game elements adapted to their Brainhex player type,
one group received counter-adapted game elements, and the third group received
random game elements. Learners were then free to use the learning environment
as they wanted over a three week period. The authors found that learners with
the adapted game elements spent more time using the learning tool that those
with the counter adapted elements. Hassan et al. [18] also showed a widely pos-
itive result in their study: learners who used game elements adapted to their
learning style showed a higher course completion rate than those who used ran-
dom game elements. This impact was also observed with learners’ self-reported
motivation using a questionnaire. Finally Mora et al. [31] also report a general
positive impact from their adaptation, with an increase in behavioural and emo-
tional engagement in learners, reported using a questionnaire that was given to
learners after using the tool. In this study, university learners were sorted into
different groups based on their Hexad profile (the groups contained users that
had similar Hexad profiles) and used a learning tool over a period of 14 weeks,
with each of the different Hexad groups receiving different game elements. How-
10 S. Hallifax et al.
ever, the authors themselves point out that these results are not significant due
to the small sample size.
The other three studies showed more mitigated results [25,30,34]. In Monter-
rat et al. [30] learners used the learning environment during 3 structured learning
sessions, each lasting 45 minutes set over a three week period. The learners were
middle school students, and used the learning environment as normal part of
their lessons. The results show that learners with counter-adapted game ele-
ments found their game elements to be more fun and useful than learners with
adapted or random elements. The authors performed a similar study reported
in [25], with adults who used the learning tool voluntarily. Learners were free
to use the learning tool over three weeks. They found little to no difference
for the majority of learners.They found that adaptation had an influence only
on the more invested learners: learners with adapted game elements showed less
amotivation (calculated using a questionnaire [16]). They did not find any differ-
ence in learner enjoyment for those particular learners. Paiva et al. [34] analysed
the usage data during the month after the introduction of tailored goals in their
learning tool. Learners received personalised goals to encourage them to increase
the number of specific learning actions they performed (for example learners who
performed a low number of individual learning actions were shown goals designed
to increase their number of individual learning actions). The authors found that
the social and collaborative goals were effective in increasing the number of re-
lated actions. However this effect was not observed with individual learning goals
(they do not observe an increase in the number of individual learning actions).
In summary we can see that shorter studies tend to show positive results from
adaptive gamification, where as the longer ones show more mitigated results. The
two short studies compared the impact of the adaptive gamified situation to a
non adaptive gamified situation, this does not allow us to understand if the
impact on learners is due to the adaptive nature of the gamified system, or due
to the introduction of a novel gamified system itself. With the longer studies,
we can assume that the novelty effect wears off, thus leading to more mitigated
results, as the static adaptation tested in the longer studies may not be precise
enough to take learner variations into. This novelty effect was also identified by
Hamari et al. in [17]. Furthermore, we can see that there is some contradictory
results from the different papers. [28] and [18] both report an increase in learner
motivation for all learners in their studies, whereas [25] only show an increase in
the more invested learners. This could be due to the nature of the metrics used
to gauge learner motivation. In [18] they use a questionnaire to establish this,
but [25,28] both use the time learners spent using the tool.
4 Future research agenda
Adaptive gamification in education is a novel and cutting edge research field,
that has been gaining in popularity in the past few years. In order to better
understand the current state of research in this field we performed an in-depth
Adaptive gamification in education 11
literature review that included twenty papers. Our analysis highlights a strong
theoretical base, with eight papers that present recommendations for game el-
ements, two that propose architectures that use these recommendations, and
ten papers that test various adaptation engines in real world learning settings.
We observed a variety of information used as a basis for adaptation, with both
static and dynamic approaches to adaptation. This shows that this is a wide and
diverse research field. In order to guide future research, we present three needs
that emerge from our literature analysis that should be addressed in the future.
4.1 The need for richer learner models
As pointed out in 3.2, half of the reviewed papers use learner player types to
adapt game elements. Generally they use the dominant player type identified
to classify the learners. Mora et al. [31] question this in their study and show
promising results when adapting to more than the dominant player type (al-
though as the authors state, their results are not significant). Furthermore very
few systems (only two) take learning characteristics into account, such as learner
expertise [4] or learning styles [18]. We believe that the mitigated results identi-
fied in 3.3 could be partly due to the complex nature of learner preferences that
are not represented in these simplified learner classifications. We therefore firstly
advise taking into account more complex learner profiles, that include more spe-
cific learning data, such as learner expertise, learner skills as well as learner
player types. Furthermore, learner activity should also be better explored as a
means for adapting game elements.
4.2 The need to explore different adaptation methods
We identified in section 3.2 how adaptation of gamification may affect the gam-
ified learning environment by changing the game element itself, or by modify-
ing its functioning. In their current state, most adaptation systems work in a
static way. We highly believe that there is more to be explored in the domain
of dynamic adaptation. For example the question of how and when a dynamic
adaptation presents itself to a learner still has to be addressed. If the change
brought on by the adaptation is not explained or presented to the learner in
a clear and understandable manner this could confuse and could distract the
learner from his/her learning activity. In the field of user interface adaptation
Bouzit et al. [5] show that change needs to be observable, intelligible, predictable
and controllable for the user. We believe therefore that research needs to be done
into how these concepts can be applied to educational settings.
4.3 The need for longer and more structured studies
As identified in section 3.3, we advise that future adaptive gamification studies
should aim for longer durations, as the results from short studies may be affected
by the novelty effect of introducing gamification and not the adaptive nature of
12 S. Hallifax et al.
the gamified system. Furthermore, studies should compare the effectiveness of
the adaptive system to that of a non adaptive system, which would also help
with identifying if the impact on learners is due to gamification in general or
to the adaptive nature. We also observed two ways for studies to quantify the
effectiveness of the tested systems: either as an impact on learner performance
or learner motivation. For learner performance it is fairly straightforward, using
metrics such as course completion rate [18], or test results [20]. However, for
learner motivation, the process was some-what more complex, as studies used
ad-hoc metrics to infer learner motivation (for example [25] used time spent on
the learning tool, [30,34] used learner feedback). This makes the comparison
of the results from different studies difficult to make. We therefore advise that
more research be performed into a more structured manner to estimate learner
motivation levels.
5 Conclusion
In this paper we presented an in depth literature review in order to better under-
stand the field of adaptive gamification in education. We identified that the field
is emergent, with a theoretical base that several studies in real world learning
settings build upon, and a general consensus on the language used. There is still
room for this field to grow and develop, especially regarding dynamic adapta-
tion that has been studied only once on a long term. We listed three needs that
should be fulfilled in future research, based on the shortcomings we have iden-
tified. First, we highlighted the need for richer learner models that adaptation
systems can use for adaptation. Second, dynamic adaptation methods should
be deepened to better adapt to learner behaviour. Third, there is a need for
longer and more structured studies in order to better understand and be able to
compare the impact of adaptive systems on learners.
6 Acknowledgements
This work is a part of the LudiMoodle project financed by the e-FRAN Pro-
gramme d’investissement d’avenir, operated by the Caisse des D´epots.
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