Bartle Types' Response to a Gamified Menial Task

Response of Bartle types to a menial task
gamified using points and levels
Mentor Palokaj
Universiteit van Amsterdam
Spui 21
1012 WX Amsterdam
+31 (0)20 525 9111
mentor@palokaj.co
ABSTRACT
The Bartle types (Bartle, 1996) are a player types that stem from
Multi User Dungeon games. The test has since been applied far
outside its original context in the fields of game design and
gamification. This study examined the effect that Bartle types have
on susceptibility on the gamification intervention of giving points.
This in order to determine whether the Bartle types are a useful tool
in designing games and gamification interventions.
A system was built on a web technology stack (Bearnes, 2016) to
let participants do a Stroop test (Stroop, 1935) inspired task which
randomly assigned the gamification intervention.
The population (overall) gamified with a points reward system
showed with a 34% (p ≈ 0.015) difference in engagement as
measured by voluntary continued duration of the task. The
interaction levels as analyzed per Bartle type yielded a 20%
difference for Explorers (p ≈ 0.028) and a 47% difference for
Socializers (p ≈ 0.015). The Killer and Achiever samples did now
show a statistically significant difference.
The population on the ‘interacting with’ axis (combined Explorer
and Socializer populations) showed an overall mean difference of
53% (p ≈ 0.001). The types on the ‘acting on’ axis (combines Killer
and Achiever populations) showed a 28% difference (p ≈ 0.002),
but only in a normalized data set where outliers were removed.
The results indicate that points and levels are an intervention that
can be used to increase engagement, without alienating and thus
decreasing the engagement of Bartle types not traditionally thought
to respond to points and levels. Further research is needed to
determine if the same can be said for the response of Bartle types
to other forms of gamification.
In addition, Explorer and Socializer types on the ‘interacting with’
axis of the matrix have a greater response to points and levels than
the types on the ‘acting on’ axis. This challenges the idea that game
and gamification design all Bartle types need to be addressed with
separate game mechanics.
Categories and Subject Descriptors
K.8.0 [General]: Games; H.1.2 [User/Machine Systems]: software
psychology;
General Terms
Experimentation, Human Factors.
Keywords
Bartle, gamification, game design.
1. Introduction
This research project tests whether Bartle types engage to different
degrees with a task more than the overall population when this task
is gamified with an Achiever targeted intervention, specifically
reward through experience points and levels.
The Achiever type was the focus of this research project since it is
stereotypes to have a predisposition to respond well to points and
levels as a game mechanic. This mechanic is interesting since it can
be widely applied in commerce, education and other fields.
Customer loyalty programs for example can be argued to appeal to
Achiever types. Likewise changing the education grading system
from averages to points and levels may very well appeal better to
this Bartle type.
In essence the goal is to examine whether the Bartle matrix can be
used to divide subjects by magnitude of response to the
gamification intervention.
1.1.1 Hypothesis and goals
The hypothesis is that overall subjects engage longer when
gamified but that Achiever types stay engaged longest in a non-
game task when motivated with a game mechanic geared towards
their Bartle type, where the others show lesser effects.
One element that is a major concern is finding out not only if
achievers respond well, but whether the other types are alienated
by this approach. Preciously the possibility of changing the
educational grading system to points and levels was mentioned. If
this approach would alienate one of the Bartle types, this approach
would put a group of students at a disadvantage.
1.1.2 Methods: the gamified task
Specifically, subjects were asked to name the color of a printed
sentence, which was inspired by the Stroop test (Stroop, 1935). This
was gamified using experience points and levels, awarded for
continued engagement. Subjects are asked to start this repetitive
task and are allowed to quit whenever they desire. Engagement is
measured as the amount of time subjects spend performing the task.
1.1.3 Pre-testing significance
The research software was tested against a conveniently selected
group of participants to gauge whether any effects significant
enough to research can be found, and to examine bias between
types.
1.2 The Bartle test of gamer psychology
The Bartle test of gamer psychology classifies gamers as being part
of one of four gamer types (Bartle, 1996). Each type has a
preference for particular game dynamics.

1.2.1 The Bartle test groups players into four types
The Bartle types (figure 1) can be visualized in quadrants where:
• The vertical axis separates those who prefer interacting
with the game world or with other players
• The horizontal axis separates those who prefer acting on
something versus interacting with something
These types are not fully mutually exclusive. A player will have a
dominant type and a lesser influence of other types.
Figure 1: The Bartle matrix (Kyatric, 2013)
These four types have different preferences in games, as illustrated
by quotes from Bartle’s original paper on the matter (Bartle, 1996):
Table 1: Quotes describing Bartle types (Bartle, 1996)
Type Quote
Killers “Players use the tools provided by the game to
cause distress to (or, in rare circumstances, to
help) other players.”
Achievers “Players give themselves game-related goals, and
vigorously set out to achieve them.”
Socializers “Players use the game's communicative facilities,
and apply the role-playing that these engender”
Explorers “Players try to find out as much as they can about
the virtual world”
Bartle indicates a balanced MUD (multi-user dungeon) game to be
one where the game keeps in balance the amount of players in each
category over time.
1.2.2 The Bartle test was intended for MUD players
MUD (multi-user dungeon) games are real-time multiplayer game
worlds that started out as text based (Bartle, 2004). They are the
precursor to the now widespread game genre of Massively
Multiplayer Online Role Playing Games, known as MMORPGs for
short (Castronova, 2008; Stuart, 2007).
The Bartle test was created based on the paper "Hearts, Clubs,
Diamonds, Spades: Players Who suit MUDs" (Bartle, 1996) by
MUD community members Erwin Andreasen and Brandon
Downey (Andreasen, 2009; Bartle, 2004).
1.2.3 The test is often used in non-MUD fields
While intended for MUD games the Bartle test is popularized as a
classification tool for gamers and people in general. It has been
suggested as a tool to improve education (Edtechteacher, 2014),
gamification (Thegwailo, 2015), gamification of education (Hanus
& Fox, 2015) and many more.
1.2.4 Extending the Bartle test is criticized
When looking at the Bartle test as a MUD playing classifier it may
appear limited. Instead it is often applied as a set of player
archetypes. Bartle himself and others have criticized blind
application of the test (Bartle, 2012; Kyatric, 2013).
Specifically, on gamification Bartle himself cautions against
indiscriminately implementing the four Bartle types without
considering the underlying principles. One might stimulate
unintended behavior or alienate player types (Kyatric, 2013).
1.3 Gamification
Gamification is the application of game mechanics and game
psychology in non-game contexts (Deterding, Dixon, Khaled, &
Nacke, 2011; Huotari & Hamari, 2012).
1.3.1 Goal of gamification
There are a number or motivations to apply gamification to a task
or interaction. Most of these are to facilitate a form of behavioral
change such as engagement or motivation to complete a task
(Hamari, 2013, 2015). Examples of such behavioral changes
include applications in learning, productivity and realigning
motivational patterns (Scott, Ghinea, & Arachchilage, 2014;
Zichermann & Cunningham, 2011).
1.3.2 Case study: speed camera lottery
The latter can be exemplified by a speed camera in Sweden that
creates a raffle that partially distributes the fines of all drivers
violating the speed limit to one of those driving below the
prescribed speed (Volkswagen, 2010).
Figure 2: the speed camera lottery (Volkswagen, 2010).
The behavioral changes during this experiment were significant:
• Average speed before: 32 km/h
• Average speed during: 25 km/h
This experiment used effective reward and competition
mechanisms often found in games and turned sticking to the speed
limit into a competition.
1.3.3 When is something a game?
One might argue that the above example of nothing more than the
application of operant conditioning (Skinner, 1938), specifically
positive reinforcement, where the subject is rewarded for a certain
behavior which should increase this behavior.
It has been proposed that human beings, especially when younger,
use play as a way to explore the world (Hirsh-Pasek, Golinkoff, &
Eyer, 2004). Indeed some believe it is an essential part of human
culture and society (Huizinga, 1938).

When referring to game design elements in gamification these, in
this paper, mean to be psychological reward, punishment,
conditioning and the use thereof to induce a playful interaction.
What sets apart gamified activities and play & games as such is that
play is said to be “not serious” and “outside ordinary life”
(Huizinga, 1938). Many gamified tasks however are certainly
serious and part of ordinary life.
The line then between a game and gamified activity is the mode in
which one engages with the activity. Specifically, activities
engaged with for their own sake qualify as games.
2. Methods and result access
The test has three phases that respectively measure and/or poll 1)
demographics, 2) bartle type and 3) test engagement.
The system used for testing is a platform built using the web
technologies PHP, HTML5, CSS3, jQuery and MySQL. The full
code, history and results are available on GitHub (Palokaj, 2016).
An overview of all elements of the test system interface are
available in appendix 13.1.
Figure 3: ungamified test
Variables saved to the MySQL database:
• ID – a unique identification number for this entry
• bartle_type – the calculated bartle type
• gamified – whether or not this entry was gamified
• interactions – number of task interactions
• interactions_correct – number of correct responses
• Killer_quotient – percentage of Killer answers
• Achiever_quotient – percentage of Killer answers
• Explorer_quotient – percentage of Explorer answers
• Socializer_quotient – percentage of Socializer answers
• gender – reported gender
• age – reported age
• email – reported email (optional)
• timestamp – timestamp of the entry creation
3. Preliminary test
In order to gauge whether there are any biases in the color
categotization task there was a pre-test done from a pool of subjects
conveniently selected through the Facebook social medium.
3.1.1 Test setup
Participation entered the subjects into a raffle for 6 months of
Netflix or Spotify service. The request to participate was shared on
Facebook.
• Deployment code of the test: git commit hash
829f7371f5512861cca3857faf071baf9958c872
• Deployment URL: https://www.skillcollector.com/bartle
• Test timespan: April 24th
2016 – May 1st
2016

Figure 4: gamified test
3.1.2 Test results
The MySQL database supports data exports to Microsoft Excel
compatible CSV files. The rest results are available in CSV, JSON,
PDF and SQL formats on the aforementioned GitHub repository.
Table 2: Distribution of Bartle types, percentages rounded to
no decimals
Metric Value
Number of participants 153
Number of gamified entries 82 (54%)
Number of Killer types 24 (16%)
Number of Achiever types 17 (11%)
Number of Socializer types 35 (23%)
Number of Explorer types 77 (50%)
Mean engagements across types 86.5
3.1.3 Data analysis
This preliminary test uses mean data analysis to draw its
conclusions.
Means were generated with the SQL ‘AVG()’ function. Standard
deviations (σ) with the ‘STD()’ function.
Table 3: Engagement data of the ungamified entry sample
Metric (group: ungamified) Value σ
Number of Killer types 10 -
Number of Achiever types 6 -
Number of Socializer types 14 -
Number of Explorer 38 -
Mean engagements Killer types 71.9 32.4
Mean engagements Achiever types 54.8 26.7
Mean engagements Socializer types 90.9 82.1
Mean engagements Explorer types 67.3 63.2
Mean engagements across types 71.8 62.9

Table 4: Engagement data of the gamified entry sample
Metric (group: gamified) Value σ
Table 5: Engagement differences between gamified and
ungamified Bartle types rounded to 2 decimals
Bartle Type Difference in engagement as factor
Killer 2.44x
Achiever 1.70x
Socializer 1.06x
Explorer 1.13x
3.1.4 Interpretation of preliminary data
The data gathered from the pre test leads to a number of
observations and possible interpretations.
3.1.4.1 Software performance was stable
The software performed stable and as expected. There were
however three unexplained NULL entries. These could be the result
of many things, such as the use of older browsers that handle
JavaScript code different than expected.
The JavaScript randomization performed as expected, gamifying
54%, a result close to the theoretical statistic of 50%.
3.1.4.2 The biggest subject group was Explorer type
Half of the participants were typed as Explorers. The Achiever
group that is most of interest to the hypothesis was the smallest
group.
The next biggest group were the Socializers. Both these groups
fall in the part of the matrix concerned with interaction rather than
action. Possible reasons for this are that these types might be:
• More active users of Facebook
• Predisposed to clicking links found on Facebook
• Highly motivated to win Spotify or Netflix subscriptions
3.1.4.3 Achievers engage least in the task
When not motivated by points and levels the Achievers appear to
be predisposed to low engagement. In fact, of all groups the
Achievers had the lowest mean interactions, well below the mean
engagement across Bartle types.
Interestingly the Socializers engaged longest in the task when
ungamified.
3.1.4.4 Achievers engagement mean increases
While Achievers do not engage long in the ungamified task the
gamification intervention brings their engagement numbers well
above the ungamified mean and close to the gamified mean.
In this data the intervention increased engagement interactions by
a factor 1.7, which seems to be in line with the overall hypothesis.
3.1.4.5 Killers mean engagement increases more
While the hypothesis expects Achievers to engage more and longer
when motivated by points and levels, the Killer gamified and
ungamified groups showed the biggest difference.
In fact, the 2.44 factor difference is well above the 1.7 difference
observed in the Achievers. The standard deviation indicates that the
data points are relatively far apart though, so this observation is
very preliminary.
3.1.4.6 ‘Acting on’ vs ‘Interacting with’
The Killers and Achievers are both in the ‘acting on’ side of the
matrix rather than the ‘interacting with’ side.
The susceptibility of these types to gamification by points seems to
indicate that these types that are concerned with influencing the
(game) world around them respond well the have the results of their
actions visualized.
4. Final data collection
The final round of data collection was used to supplement the data
from the preliminary test.
4.1 Sample characteristics
Due to the nature of conveniently selected participants there are a
number of caveats to consider concerning the data.
4.1.1 Target number of participants
The pretest resulted in more entries for certain types than others.
The smallest group were the Achievers at 17 entries. It has been
suggested that valid statistical analysis requires between 10 and 30
subjects depending on the type of research (Corder & Foreman,
2009).
The target set for the final data collection round was a minimum of
30 entries per Bartle type, a criterion which was satisfied with the
smallest group being the Achievers at n=43.
4.1.2 Source of participants
The original intention was to use personal networks for the pretest
and the Amazon Mechanical Turk system for mass data collection
in the second round.
Due to restrictions placed by the Amazon company, using this
service outside the United States was not an option (Amazon,
2016).
Instead subjects were selected through two channels:
• A newly created Facebook page
• The email list of skillcollector.com
The Facebook page was promoted with a minor advertising budget
of €18. The email list contained 5,781 subscribers of which 4.3%
clicked the link to the survey.
4.1.3 Test setup
Participation entered the subjects into a raffle for 6 months of
Netflix or Spotify service. This was made clear on the Facebook
page and in the email sent to Skill Collector subscribers.

• Deployment code of the test: git commit hash
8cbb842f0e67e49eef4426238059d2468a278975
• Deployment URL: https://www.skillcollector.com/bartle
• Test timespan: May 11th
2016 – May 20st
2016
5. Analysis and results of final data
The rest results are available in CSV, JSON, PDF and SQL formats
on the aforementioned GitHub repository.
A Shapiro-Wilk analysis for all samples concluded:
• The distribution of data is not normal
• T-tests are irrelevant (they assume normal distribution)
• Non parametric analysis will need to be used
5.1 Complete data
For all samples a Shapiro-Wilk analysis was done, which found
significances of 3.9856E-29, 0.000003, 1.4534E-15, 4.2985E-14,
0.000001 for the full, achiever, explorer, killer and socializer
samples respectively. This in all cases a Mann-Whitney test was
used.
5.1.1 Full sample
Data integrity and histograms are available in the appendix section
11.2.
Table 6: Distribution of Bartle types, percentages rounded to
no decimals
Metric Value
Number of participants 369
Number of gamified entries 185 (50%)
Number of Killer types 53 (14%)
Number of Achiever types 43 (12%)
Number of Socializer types 73 (20%)
Number of Explorer types 200 (50%)
Mean engagements across types 83.4
Note that the discrepancy in percentages is an artifact of NULL
entries and rounding off.
5.1.2 Gamified vs ungamified overview
This analysis starts at a general level and drills down to more
advances statistical analysis.
This analysis focuses on a high level ‘at a glance’ overview of the
data based on overall means.
Means were generated with the SQL ‘AVG()’ function. Standard
deviations (σ) with the ‘STD()’ function.
Table 7: Engagement data of the ungamified entry sample
Metric (group: ungamified) Value σ
Table 8: Engagement data of the gamified entry sample
Metric (group: gamified) Value σ
Mean engagements Killer types 114 234.6
The statistical significance of the effect observed between the
gamified and ungamified groups was 0.001. This means the effect
of the intervention is statistically significant.
See appendix section 11.3 for detailed analysis results.
5.1.3 Achiever sample analysis
gamified and ungamified groups was 0.411, thus p ≤ 0.05 is not
true. This means the effect of the intervention is not statistically
significant within this group.
5.1.4 Explorer sample analysis
gamified and ungamified groups was 0.028, thus p ≤ 0.05. This
means the effect of the intervention is statistically significant within
this group.
5.1.5 Killer sample analysis
gamified and ungamified groups was 0.893, thus p ≤ 0.05 is false.
This means the effect of the intervention is not statistically
5.1.6 Socializer sample analysis
gamified and ungamified groups was 0.015, thus p ≤ 0.05. This
means the effect of the intervention is statistically significant within
this group.
5.2 Analysis of manipulated data by type
The dataset contained some extreme values that might skew results
for the smaller groups. The below analysis removes outliers as
defined per heading.
A Shapiro-Wilk analysis yielded a significance of 0.000126 and
0.643418 for the achiever and killer samples respectively. In both
cases a Mann-Whitney test was used.

5.2.1 Achiever manipulated data analysis
The outliers as found by SPSS exploration are four data entry points
with case numbers: 36, 26, 35, 11. These correspond to user ids:
294, 205, 291, 118. These are data points with an interaction
amount above 200.
In this analysis only entry points with under 200 interactions will
be considers so as to exclude the outliers.
See appendix section 11.4 for detailed outlier analysis.
5.2.2 Killer manipulated data analysis
The outliers as found by SPSS exploration are four data entry points
with case numbers: 12, 41, 18, 45, 27, 15. These correspond to user
ids: 78, 252, 106, 270, 179, 96. These are data points with an
interaction amount above 159.
See appendix section 11.4 for detailed outlier analysis.
5.3 Analysis of manipulated data by axis
The above data seems to indicate that the types on the ‘interacting
with’ axis of the Bartle matrix have a statistically significant
response, whereas the ‘acting on’ do not.
The below sections analyze the effects and significance as
separated by the axes. A Shapiro-Wilk analysis yielded a
significance of 7.6073E-18, 1.9851E-17 and 3.7576E-7 for the
‘acting on’, ‘interacting with’ and normalized ‘acting on’, samples
respectively. In all cases a Mann-Whitney test was used.
5.3.1 ‘Acting on’ data analysis
The acting on axis concerns the Killer and Achiever Bartle types.
5.3.2 ‘Interacting with’ data analysis
The acting on axis concerns the Explorer and Socializer Bartle
types.
gamified and ungamified groups was 0.001, thus p ≤ 0. This means
the effect of the intervention is statistically significant within this
group.
5.3.3 Normalized ‘acting on’ data analysis
The acting on axis concerns the Killer and Achiever Bartle types.
Normalization removed the outliers as identified by case ids: 147,
222, 4, 13, 193, 226, 52, 209, 80, 265, 167, 121, 263, 251, 255.
Which correspond to entry ids: 203, 306, 5, 15, 273, 310, 66, 290,
107, 362, 233, 168, 360, 342, 346. These are data points with an
interaction amount above 200.
true. This means the effect of the intervention is statistically
6. Interpretation of final data
The analysis allows for a number of interpretations and
observations.
6.1 Overview of significant effects
This table displays the results of comparing gamified and
ungamified samples of separate groups.
Table 9: Overview of statistically significant effects. Mean
interactions are for ungamified sample. Mean difference is the
factor of difference between ungamified and gamified groups.
Sample Significant
in
Mean
interactions
Mean
difference
Explorer Raw data 76 1.20
Socializer Raw data 67 1.47
‘Interacting with’ Raw data 72 1.53
‘Acting on’ Normalized 61 1.28
6.1.1 Overall significant effect on engagement
Overall the engagement means for the ungamified and gamified
samples when looking at the total population of all Bartle types
show a statistically significant result.
Specifically, the mean increase from 73.4 to 98.2 was significant at
a p value of 0.015.
6.1.2 Effects within raw data
When examining the statistical significance of the difference
between groups it was found that:
• Achiever data showed no significant effect
• Killer data showed no significant effect
• Explorer data did show a significant effect
• Socializer data did show a significant effect
When looking at the ‘acting on’ and ‘interacting with’ axes it was
found that:
• The ‘acting on’ axis showed no significant effect
• The ‘interacting with’ axis showed a significant effect
6.1.3 Effects within normalized data
When examining the statistical significance of the difference
between groups it was found that:
• Achiever data showed no significant effect
• Killer data showed no significant effect
When looking at the axes:
• The ‘acting on’ axis showed significant effect

6.2 Significant observations
Based on the data from this experiment one may observe a number
of trends.
6.2.1 Points and levels increase engagement
The overall data indicates that points and levels are a way to
significantly increase engagement in an otherwise nonproductive
task. This is the case for all statistically significant correlations
found.
6.2.2 Difference ‘acting on’ and ‘interacting with’
While populations on both sides of the horizontal axis of the Bartle
matrix respond, the ‘interacting with’ population shows a different
difference in engagement than the ‘acting on’ axis with 53% and
28% differences respectively.
6.2.3 Unexpected differences between types and axes
The hypothesis that Achievers respond best to points and levels as
a motivating game mechanic seems to be contradicted by the
significant correlations. There was no specific statistically
significant data for the Achievers, but the ‘acting on’ axis
engagement difference was lower that that of the ‘interacting with’,
even though traditionally points are thought to be more compatible
with ‘acting on’ types.
6.2.4 Outliers in the ‘acting on’ population
Without normalization the Killer, Achiever and ‘acting on’ groups
did not show statistical significance. A cause for this was the large
concentration of outliers in these groups. This could imply that the
response of these types is mediated by another factor.
7. Discussion
This study found a significant effect of gamification using points
on engagement times, and some statistically significant effects
when this data is dissected based on the Bartle matrix. There are
however a number of factors that need to be addressed in relation
to this study.
7.1 Practical applications
The results of this study seem to confirm the effectiveness of the
points and level game mechanic. Application in other fields is
indeed already widely present. Commercial rewards programs for
example often use a similar dynamic. This study can be interpreted
as a confirmation of the hypothetical effectiveness of this approach.
In short:
• Points and levels can be used to increase engagement
• Points and levels do not seem to alienate non ‘acting on’
Bartle types
• Achiever types do not respond significantly better than
other types
One may thus conclude that if the conclusions of this study can be
generalized to the human population in general the application of
points and levels can benefit all those the intervention is applied to
from the perspective of the Bartle types.
In addition, from a game design perspective one can not simply say
that all Bartle types need to be addressed with separate game
mechanics. Indeed Achievers while stereotypically being
associated with points and levels seem to respond less than
Explorer and Socializer types.
7.2 Confounding factors
There are a number of factors one might argue limit the conclusions
one can draw from the data of this particular study.
7.2.1 Subject bias
The subjects used in this study came from possibly highly biased
populations. The main sources were:
• Personal social medium (Facebook) profile
• Facebook advertising
• Personal blog email list
There is a convincing argument to be made that these channels have
inherent biases. It is for example the case that a large proportion of
the subjects fell in the Socializer and Explorer group. One might
hypothesize the social nature of the subject source is the cause of
this.
Likewise, the individuals from these data sources have been
selected over time by direct and indirect interaction with the
profile/blog owner, which most likely biased the type of person
participating in this study.
7.2.2 Sample size
It is possible that there is a threshold at which effects of the Bartle
matrix on engagement mediated by points become visible. This
study found significant overall effects in the study subjects, which
one could argue to be linked to the fact that the size of this group is
per definition larger than the sub groups.
7.2.3 Bartle type vs coefficient
The Bartle test scores subjects per type, and labels a participant
based on the type with the highest points. This is a rather binary
approach and does not take into account the fact that subjects
exhibit traits from all types.
7.2.4 Intervention and task bias
The possibility exists that the chosen intervention of points and
levels is a game mechanic that appeals to all Bartle types in such a
way that the effect matches the current data.
In addition, it is possible that the task appealed to a particular
subject group outside of their Bartle type hereby skewing the
results.
8. Future research
Although this data did not find a link between the Bartle matrix and
engagement response mediated by gamification, there are a number
of avenues that can be explored still.
8.1 With the current data set
The current data set collected from this experiment allows for
further research.
8.1.1 Demographic analysis
The current data set contains demographics data which is currently
unused. Effects of gender and age can be examined without the
need for extra data.
8.1.2 Separation based on Bartle coefficient
Likewise, the Bartle coefficient for all subjects has been recorded.
It is possible that the lack of a found effect in the Killer and
Achiever groups is due to the binary nature of the Bartle test in
discounting the traits that did not score highest. One could approach
this data set in a more complex manner and weigh a subject’s
interactions based on their multiple coefficients.
8.2 Outside the current data set
Aside from the current data set there are many possibilities for
research in this area.

8.2.1 Different sample size and source
While this study did not find a full correlation between Bartle types
and engagement, it is entirely possible that a bigger sample taken
from a more diverse source does show an effect. Replication of this
study on a bigger scale or using different subjects can present an
interesting research opportunity.
8.2.2 Different gamification mechanics
One interesting avenue is the researching of different gamification
interventions than points and levels. As discussed before it is
possible that all Bartle types respond well to points and levels,
therefore not showing significant effects between them. Using
different game mechanics and analyzing the responses of the Bartle
types may produce more significant effects.
8.2.3 Gamifying a different task
By applying game mechanics to a different task one might possibly
find that the Bartle matrix does separate groups of subjects with
statistically significantly differing engagement levels.
8.2.4 Using different subject classifications
The Bartle matrix may not be a useful tool to analyze gamification
related behavior at all. Instead there are many models that could be
used to correlate personality types with susceptibility to
gamification. Some common personality tests and/or traits that
could be used are:
• Introvert vs extrovert individuals
• Myer-Briggs Type Indicator (MBTI)
• MMORPG class preferences
Although many others are of course possible.
8.2.5 Using extrapolated classifications
From a business perspective it may be interesting to test
gamification effects based on subject traits that do not rely on an
explicit test. Examples of classifications that could be used are:
• Google Analytics Interest Groups
• Google Analytics Market Segments
• In game/application behavior
• Classifications based on self reported user profiles
The classifications above would not require breaking a user’s usual
experience, but can still determine susceptibility to certain
gamification mechanics if this classification turns out to have a
statistically significant effect.
9. Acknowledgements
My gratitude goes out to those who helped complete this research
project. Specifically, I thank the individuals below for their
invaluable help.
Liesbeth van den Berg, for support on statistics and general mental
support. Without her this research would not have arrived at the
conclusions that it did.
Daniel Buzzo, for helping shape this research while allowing me to
maintain full autonomy in the development, execution and
reporting. His feedback gave inspiration to where needed expand
or refocus the research efforts.
Frank Nack, for logistical support and insights that provided much
needed reassurance during the course of this project.
10. References
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Bartle, R. (2004). Designing virtual worlds. New Riders.
Bartle, R. (2012). Player Type Theory: Uses and Abuses | Richard
BARTLE. Retrieved March 18, 2016, from
https://www.youtube.com/watch?v=ZIzLbE-93nc
Bearnes, B. (2016). How To Install Linux, Apache, MySQL, PHP
(LAMP) stack on Ubuntu 16.04. Retrieved from
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Castronova, E. (2008). Synthetic worlds: The business and culture
of online games. University of Chicago press.
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Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011). From
game design elements to gamefulness: defining
gamification. In Proceedings of the 15th international
academic MindTrek conference: Envisioning future media
environments (pp. 9–15). ACM.
Edtechteacher. (2014). Use the Four Gamer Types to Help Your
Students Collaborate – from Douglas Kiang on Edudemic.
Hamari, J. (2013). Transforming homo economicus into homo
ludens: A field experiment on gamification in a utilitarian
peer-to-peer trading service. Electronic Commerce Research
and Applications, 12(4), 236–245.
Hamari, J. (2015). Do badges increase user activity? A field
experiment on the effects of gamification. Computers in
Human Behavior.
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gamification in the classroom: A longitudinal study on
intrinsic motivation, social comparison, satisfaction, effort,
and academic performance. Computers & Education, 80,
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never used flash cards: How our children really learn--and
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Huizinga, J. (1938). Homo ludens: proeve fleener bepaling van het
spel-element der cultuur. Haarlem: Tjeenk Willink.
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everything--gamedev-4173
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https://github.com/actuallymentor/bartle-platform/
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11. Appendix
A high resolution version of this article in PDF is available on
Github: https://github.com/actuallymentor/bartle-platform
11.1 Test software screenshots
Figure 3: ungamified test
Figure 4: gamified test
Figure 5: welcome screen for participants
Figure 6: instructions and demographics for participants

Figure 7: Bartle test interface
Figure 8: gamified pretest instructions
Figure 8: ungamified pretest instructions
Figure 9: final ‘thank you’ screen
11.2 Data integrity and distribution
11.2.1 Full data set
Using a t-test and preceding f-test we can determine whether the
gamification by points showed a significant effect. Over the entire
dataset:
• 11 were NULL entries
• 12 non NULL entries scored a ratio below 0.8 for
interactions versus correct interactions
To eliminate entries resulting from participants not actively
engaging in the task but answering randomly the entries below a
ratio of 0.8 were eliminated.
11.2.1.1 Data distribution
Diagram 1: Histogram for ungamified sample
Diagram 2: Histogram for gamified sample
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11.2.2 Achiever sample analysis
Since the main hypothesis centers on Achievers they are a main
focus of analysis. Of the 44 Achievers:
• All non NULL entries scored a ratio above 0.8 for
The NULL entries were discarded.
The ratio indicates that the remainder of subjects actively engaged
with the task. Simply hitting buttons randomly in the task would
have resulted in a ratio closer to 0.5.
Diagram 3: Histogram for ungamified Achiever types
Diagram 4: Histogram for gamified Achiever types
11.2.3 Killer sample analysis
The preliminary data analysis saw a relatively large mean
engagement increase for Killers. The Killer type is also on the
‘acting on’ axis of the Bartle matrix.
• 1 non NULL entry scored a ratio below 0.6 for
The NULL entries were discarded.
To eliminate entries resulting from participants not actively
engaging in the task but answering randomly the entries below a
ratio of 0.8 were eliminated.
Diagram 5: Histogram for ungamified Killer types
Diagram 6: Histogram for gamified Killer types
11.2.4 Explorer sample analysis
The preliminary data analysis indicated a relatively small
difference compared to the ‘acting on’ Bartle types.
The NULL entries were discarded. Entries below a ratio of 0.8 were
discarded also.
Diagram 7: Histogram for ungamified Explorer types
Diagram 8: Histogram for gamified Killer types
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11.2.5 Socializer data analysis
The preliminary data analysis indicated a relatively small
difference compared to the ‘acting on’ Bartle types.
The NULL entries were discarded. Entries below a ratio of 0.8 were
discarded also.
Diagram 9: Histogram for ungamified Socializer types
Diagram 10: Histogram for gamified Socializer types
11.3 Mann-Whitney Tests
The Mann-Whitney tests were executed using IBM SPSS software.
The diagrams and table sets below are screenshots of said software.
11.3.1 Overall sample
Table Set 1: Mann-Whitney test for overall sample
11.3.2 Achiever sample
Table Set 2: Mann-Whitney test for Achiever sample
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11.3.3 Explorer sample
Table Set 3: Mann-Whitney test for Explorer sample
11.3.4 Killer sample
Table Set 4: Mann-Whitney test for Killer sample
11.3.5 Socializer sample
Table Set 5: Mann-Whitney test for Socializer sample
11.4 Manipulated data analysis
The Mann-Whitney tests were executed using IBM SPSS software.
The diagrams and table sets below are screenshots of said software.
11.4.1 Achiever sample
Diagram 11: Boxplot of Achiever sample distribution

Table Set 6: Mann-Whitney test of Achiever sample without
outliers
11.4.2 Killer sample
Diagram 12: Boxplot of Killer sample distribution
Table Set 7: Mann-Whitney test of Killer sample without
outliers
11.5 Data analysis by axis
11.5.1 ‘Acting on’ axis
Table Set 8: Mann-Whitney test of ‘acting on’ sample

11.5.2 ‘Interacting with’ axis
Table Set 9: Mann-Whitney test of ‘interacting with’ sample
11.5.3 Normalised ‘acting on’ axis
Table Set 10: Mann-Whitney test of normalized ‘acting on’
sample

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