Player-Type-based Personalization of Gamification in Fitness Apps

Nadine Sienel, Patrick Münster and Gottfried Zimmermann

Responsive Media Experience Research Group, Hochschule der Medien, Stuttgart, Germany

Keywords: Gamification, Fitness Apps, Personalization.

Abstract: This paper examines the effect of personalized gamification on an individual’s motivation in the context of

fitness apps. In a first study, we evaluate the four categorization models "Bartle Player Types", "Big Five",

"Hexad User Types", and "BrainHex" on their ability to predict individual gamification preferences of users

and develop a new prediction model called “MoMo”. Bartle, BrainHex, and MoMo are validated empirically

in a second study, employing off-the-shelf fitness apps with gamification elements. The results of both studies

indicate that a prediction is possible using the categorization models. Among all models, MoMo performs

best in predicting individual gamification preferences, followed by BrainHex. Results of the second study

indicate that, although the models MoMo and BrainHex perform better in predicting the theoretical rating of

gamification elements than the random model, the prediction of the real motivation value in a specific fitness

app is more difficult. This may be due to the concrete implementation of the elements in the second study,

and due to the general problem of (theoretically) rating gamification elements without having experienced

them in a real application.

1 INTRODUCTION

Fitness apps aim to support users in enhancing their

health. One goal thereof is to increase the motivation

of the users to engage in sports. The present study,

therefore, examines the influence of individualized

gamification on the increase of motivation in the

context of fitness apps.

Gamification has its origins in the digital media

industry, where its first use is documented in 2008

(Deterding et al., 2011). The definition of Deterding

et al. (2011) is mostly used in literature, defining it as:

“the use of game design elements in non-game

contexts”. Later on, Werbach (2014) presents a

revised definition of the term gamification: In his

opinion, gamification should be defined as a process

of making activities playful. He justifies this with the

fact that not everything containing a game element

automatically generates gamification, but rather that

the entire experience in a system is important.

Whether the applied gamification of a system

achieves the desired effect, e. g. an increase in

motivation to eat a healthy diet or do sports, depends

on the personal preferences of each individual user

(Ferro, 2018). A possible approach for personalized

gamification is the categorization of users by means

www.hdm-stuttgart.de/home/gzimmermann

of categorization models like the Bartle Player Types

and a personalized gamification design that is derived

from these models. Some researchers have already

worked on the evaluation of categorization models for

the personalization of gamification. Kocadere and

Çağlar (2018) examined the influence of the Bartle

Player Types on gamification preferences; their

results show differences (albeit small) between the

different types. The effects of the Big Five

personalities on the preferences for game elements of

users have also been investigated in several studies,

such as Ferro (2018). However, their results indicate

that they have little impact. The Hexad User Types

are evaluated in several studies on the personalization

of gamification, for example by Mora et al. (2019)

who found correlations between the User Types and

gamification preferences. In contrast to the studies

mentioned above, Hallifax et al. (2019) do not focus

on a single model but compare the three models

BrainHex, Hexad, and Big Five in terms of their

influence on gamification preferences. The results of

the study show that Hexad is most suitable for

predicting preferences (Hallifax et al., 2019).

This previous research shows that categorization

by categorization models is promising with regard to

the prediction of personal preferences in gamification.

Sienel, N., Münster, P. and Zimmermann, G.

Player-Type-based Personalization of Gamiﬁcation in Fitness Apps.

DOI: 10.5220/0010230603610368

In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF, pages 361-368

ISBN: 978-989-758-490-9

361

However, most of these studies focus on only one of

the categorization models.

This paper focuses on the comparison of four

models (Bartle, Big Five, Hexad, and BrainHex), and

on developing a specific model of the motivational

value (MoMo for short) using a set of questions from

the four categorization models to predict preferences

in gamification elements. Besides, many of the

studies mentioned above were only conducted in the

laboratory and the participants had to rate the patterns

without “playing” with them. In our second study, we

asked the participants to rate the patterns before and

after the extensive use of real fitness apps

implementing gamification patterns.

The remainder of this paper is structured as

follows: Chapter 2 specifies a set of relevant

gamification elements, derived from literature.

Chapter 3 describes the four used categorization

models. Chapter 4 explains our methodological

approach, chapter 5 (study 1) and chapter 6 (study 2)

describe the methods and results of the two conducted

studies. Finally (chapter 7), we discuss the results and

possible implications.

2 GAMIFICATION ELEMENTS

To develop a model that can predict the gamification

preferences of users, it is necessary to first determine

which different gamification elements exist. For this

purpose, we developed a list of 30 gamification

elements based on gamification elements mentioned

in the literature (see Table 1).

3 CATEGORIZATION MODELS

To determine preferences, we applied categorization

models for player types. These serve as a basis for

differentiating between participants in order to

identify differences and similarities in their

preferences. Various categorization models are

discussed in the literature, of which four of the best-

known models are used in this study. These

categorization models are the following.

3.1 Bartle Player Types

The Bartle Player Types are known as one of the most

basic categorizations of players (Kocadere & Çağlar,

2018). They were developed by the British professor

Richard Bartle and are based on the Multi-User

Dungeons genre (Bartle, 1996). The Bartle Player

Types contain the following four player types:

Achiever (“Acting on the World”), Explorer

(“Interacting with the World”), Killer (“Acting on

other Players”), and Socializer (“Interacting with

other Players”) (Bartle, 1996).

To determine the Bartle Player Types, we used the

Bartle Test developed by the authors González

Mariño et al. (2018). As the questions of the test itself

are not mentioned in the paper González Mariño et al.

(2018), we requested them from the authors by e-

mail.

3.2 Big Five

The Big Five, also known as the OCEAN model, is a

widely used personality model (Suryapranata et al.,

2020). The five dimensions of the Big Five are

defined by Rammstedt et al. (2013) as follows:

Agreeableness (a person's behavior towards other

people), Conscientiousness (behavior of a person

during the completion of a task), Extraversion

(behavior of a person towards their environment),

Neuroticism (emotional stability of a person), and

Openness to experience (how interested a person is

in new things).

In this paper, for the determination of the Big

Five, the test “10 Item Big Five Inventory” (short:

BFI-10) by Rammstedt et al. (2013) is used.

Table 1: List of gamification elements with definitions. Mentions of the elements in the literature: 1 = Arango-López et al.

(2017), 2 = Chou (2016), 3 = Deterding et al. (2011), 4 = Ferro (2018), 5 = Hallifax et al. (2019), 6 = Kocadere and Çağlar

(2018), 7 = Raftopoulos et al. (2015), 8 = Suryapranata et al. (2020), 9 = Swacha and Muszyńska (2016).

Achievement Symbol [2, 3, 4, 5, 6, 7, 8] Discussionboard [5] Progress Bar [1, 2, 4, 5, 9]

Assessment [9] Feedback [2, 3, 8, 9] Record [6, 9]

Avatar [2, 4, 8, 9] Gift [1, 2, 4, 6, 9] Reward [1, 2, 4, 5, 6, 9]

Brag Button [2, 9] Leaderboard [1, 2, 3, 4, 5, 6, 7, 9] Schedule [2, 5, 9]

Challenge [1, 2, 3, 4, 7, 9] Level [2, 3, 4, 5, 6, 8, 9] Social Feedback [1]

Choice [2, 9] Number Limit [2] Social Graph [9]

Collection Set [2, 9] Performance Graph [2, 9] Team [2, 5, 6, 9]

Unlocking [2, 4, 6, 9] Permadeath [4] Time Limit [1, 2, 3, 4, 5]

Crowning [2, 7, 9] Points [1, 2, 4, 5, 6, 7, 9] Topic [2, 4, 6, 8, 9]

Difficulty Selection [4] Prize Pacing [2, 9] Torture Break [2, 9]

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3.3 Hexad User Types

The Hexad User Types were developed by Andrzej

Marczewski to provide a user-type model specifically

for gamification (Hallifax et al., 2019). Marczewski

(2016) describes the following user types: Achiever

(intrinsically motivated by mastery), Free Spirit

(intrinsically motivated by autonomy),

Philanthropist (intrinsically motivated by purpose

and meaning), Socializer (intrinsically motivated by

relatedness), Disruptor (extrinsically motivated by

change), and Player (extrinsically motivated by

rewards).

The test developed by Tondello et al. (2016) is the

official test for the determination of the Hexad User

Types and is used in this study.

3.4 BrainHex

The BrainHex was developed by the company

International Hobo Ltd. (2011). It was developed

based on existing research on players and knowledge

of underlying neurobiological mechanisms (Nacke et

al., 2011). The BrainHex types contain the following

types: Achiever (is motivated by long-term success),

Conqueror (does not want to win easily in a game),

Daredevil (seeks the thrill and the risk), Mastermind

(wants problems for which a strategy is needed),

Seeker (enjoys moments of wonder), Socializer (is

focused on the other people), and Survivor (enjoys

strongly negative experiences such as terror) (Nacke

et al., 2011).

The BrainHex types can be determined by a

questionnaire, which is available online (International

Hobo Ltd., 2019). The evaluation of the test was not

published completely. Therefore, we derived its

categorization logic by reverse engineering, using the

publication of Nacke et al. (2011) as well as the

displayed results on the website of International Hobo

Ltd. (2019).

4 METHODS

To develop and validate a model based on users’

categorization models, we first conducted a user

research (study 1) to determine the preferences in

gamification elements of users. Based on the results

of this first study, we compared the different

categorization models and developed subsequently

the motivational value model (MoMo) for predicting

preferences. Finally, we validated the prediction

power of MoMo, Bartle, and BrainHex for fitness in

a second study.

5 USER RESEARCH (STUDY 1)

5.1 Methods for User Research

Our first user research served for a better

understanding of the users’ preferences in

gamification patterns and their relationships to the

various models for categorizing users. Based on these

results, we developed models for predicting

preferences.

To collect the data of study 1, we developed a

questionnaire. It uses the tests mentioned above to

determine the Bartle Player Types, Big Five, Hexad

User Types, and BrainHex Types. Additionally, a few

demographic data are requested and the participants

are asked to rate the 30 elements on a 5-Likert scale

from “demotivates me very much” through “neutral”

to “motivates me very much”, supported by a

definition and a descriptive image. We tested the

questionnaire in a pilot study and subsequently

adjusted it, based on the results.

For the evaluation of the survey, we made a

distinction between players and non-players, by using

the question "How many hours do you play per

week?". We wanted to find out whether there are

differences between players and non-payers.

Participants who play more than 2 hours per week

were classified as "players", whereas those who play

two or fewer hours per week were classified as "non-

players".

We used linear regression analysis to calculate

regression models for predicting the preferences of

each gamification element. For this purpose, we

performed multiple regression per gamification

element and categorization model, using the method

"stepwise". Finally, we compared the average R

value and the number of predictable gamification

patterns between the five models. The resulting

regression models are used as the basis for the

calculation of preferences in study 2 (see section 4.4).

5.2 Creating the Model of Motivational

Value (MoMo)

To create the MoMo, we used a correlation matrix in

which all questions and results of the four

categorization models are correlated with the ratings

of the gamification elements. Such a correlation

matrix is created for players, non-players, and all

participants. Using the correlation matrixes, we

selected all questions and results of the models whose

significance value is less than 0.01 or, if not available,

less than 0.05, for multiple linear regression. For each

Player-Type-based Personalization of Gamiﬁcation in Fitness Apps

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gamification element, the regression model with the

highest R

value and the lowest significance value is

selected.

Afterward, we compared the regression models of

all participants, all players, and all non-players. This

makes it possible to determine whether there are

major differences between the models of players and

non-players. Furthermore, the best regression model

can be selected for each gamification element

individually by deciding whether a division into

player and non-player is appropriate.

5.3 Results

5.3.1 Description of the Sample

We collected the survey data in the period from May

5th, 2020 to May 20th, 2020. For this purpose, we

invited the participants via email to complete the

questionnaire online. A total of 122 participants fully

completed the survey. Of the participants, 56.6 %

were male and 43.4 % female, which makes the

distribution quite balanced. Less balanced, however,

is the distribution in age: the sample consisted of

persons aged 18-64 years, with almost three-quarters

of the respondents between 18 and 30 years old, 16.4

% of the participants between 31 and 40 years old,

and just under 11 % over 40 years old. The

categorization of players and non-players results in a

distribution of 59.1 % players and 40.9 % non-

players. 7 participants did not provide any

information about the playing time and therefore

could not be classified.

5.3.2 Differentiation of Players and

Non-Players

After analyzing the individual regression models for

MoMo for players, non-players, and all participants

in total, the regression models for all participants

performed worse than the regression models for

players and non-players separately. Therefore, we

carried out further evaluations separately, for players

and non-players only.

5.3.3 Comparison of the Models

We compared the four user categorization models as

well as the MoMo based on the R

value and the

number of significantly predictable gamification

elements, divided into players, non-players, and all

participants. Figure 1 shows that MoMo is the only

model that can predict all 30 gamification elements

for players, non-players, and all participants. Thus,

the MoMo scores best in this comparison, followed

by BrainHex and Hexad. For players, the Big Five are

the least suitable, with only 15 predictable elements.

For non-players and all participants, however, Bartle

scores the worst in this comparison. The opposite is

true when comparing the coefficient of determination,

where BrainHex is best for both players and non-

players. The coefficient of determination of the Big

Five and Bartle models is also opposite to the number

of predictable elements. For players, both models have

the same coefficient of determination. For non-players

and all participants, however, the coefficient of

determination of Bartle is greater than that of Big Five.

To validate the models in a subsequent study 2,

we included the two best models from study 1. These

are MoMo and BrainHex. Since MoMo covers all

questions of Bartle, Bartle is also implicitly included

in the validation.

6 VALIDATION (STUDY 2)

6.1 Methods for Validation

Based on the results of the user research, the two best

models are selected for validation: MoMo and

BrainHex. The goal is to validate whether the

prediction of preferences is possible with these two

models. For this purpose, we conducted a longitudinal

study over two weeks. Participants first filled out an

online questionnaire (questionnaire 1) that contains

the same questions as the questionnaire of study 1 but

shortened to the questions necessary for the

prediction. After completing the questionnaire, we

asked the participants to use a fitness app which they

should test for about one week. Two real fitness apps

were available for this purpose, both containing

various gamification elements. For the selection, we

first tested different fitness apps and evaluated them

according to the criteria of the checklist with the

quality criteria catalog of the Technical University of

Dortmund (Reh@pp-Quality, 2016). Due to the

relatively comparable range of functions, the same

quality evaluation, and different gamification

elements contained in the apps, MyFitnessPal and

Virtuagym were selected for validation. After the test,

the participants filled out a second questionnaire

(questionnaire 2) containing the same questions as the

first questionnaire of study 2, and additionally the

ratings of the gamification elements implemented by

the tested app and their influence on the motivation to

engage in sports. Furthermore, we conducted a semi-

structured interview with four participants to evaluate

the gamification elements tested. The participants

were randomly selected.

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Figure 1: Comparison of the five models Bartle, Big Five, Hexad, BrainHex and MoMo in the number and average coefficient

of determination of predictable elements.

To compare the participants’ preferences of

gamification elements with the predictions calculated

by the models, both the real and the calculated ratings

are categorized according to the following scheme:

 Min – 2.5 = “demotivated”

 2.5 – 3.5 = “neutral”

 3.5 – Max = “motivated”

The values resulting from the categorization can

then be compared by analyzing whether the

prediction and the rating are the same (“correct

prediction”) or not (“incorrect prediction”). To

compare the models and evaluate the quality of the

predictions, we determined the number of correctly

predicted scores per gamification element and

participant. For the evaluation of the prediction

quality, we compared the number of correct

predictions with a random model, derived from a

normal distribution of the three categorization

possibilities (“motivated”, “neutral”, “demotivated”).

Due to the three categorization possibilities, the

expected value of this random model is 1/3. This

results in a random model of 10 out of 30 correct

predicted gamification elements per participant.

Consequently, for 6 out of 19 participants, the

preference in a gamification element is correctly

predicted.

For the validation of the predictions in real apps,

however, the random model is calculated for each

gamification element individually, since some

participants had missed a gamification element in the

app and therefore could not rate it. With participants

not rating some elements, a fixed random model

could falsify the results. This falsification can be

prevented by calculating the random model in the

following way: multiplying the number of

participants who have seen the element in the app by

1/3.

6.2 Results

6.2.1 Description of the Sample

We collected the data in the period from July 20th,

2020 to August 7th, 2020, by inviting participants via

email to participate in the study. A total of 19

participants took part in the study. Of the participants,

57.9 % were female and 42.1 % male. The sample

consisted of persons aged 22-54 years, but more than

two-thirds of the participants were between 18 and 30

years old, 26.3 % were between 31 and 40 years old,

and only one person was over 40 years old.

Categorizing players and non-players results in a

distribution of 57.9 % players and 42.1 % non-

players.

6.2.2 Prediction Quality

The comparison in the number of correctly predicted

preferences per participant (before they used the app),

which is illustrated in Figure 2, shows that MoMo

performs best with a mean of 17.11 correct element

Player-Type-based Personalization of Gamiﬁcation in Fitness Apps

365

Figure 2: Comparison of the number of correct predictions per participant in questionnaire 1 of study 2 and mean for each

model with standard deviation.

predictions per participant, followed by BrainHex

(14.95) and Bartle (8.00). A t-test shows, that the

differences between all models are significant. The

mean value of MoMo and BrainHex is clearly above

the random model of 10 elements. In contrast, the

mean value of Bartle is significantly below the

random model.

Furthermore, these tendencies are also shown by

the comparison of the preference predictions and the

real rating of the gamification elements: MoMo with

an average of 57 % correct predictions in

questionnaire 1 has the best results, followed by

BrainHex with 50 %, and Bartle with 27 %.

Considering the prediction quality for each of the 30

gamification elements separately, the MoMo is above

or equal to the random model (6 out of 19 correct

predictions per element) for all elements. For

BrainHex, the predictions of 27 elements are better or

equal to the random model, and for Bartle 12

elements.

These tendencies are continued in the results of

questionnaire 2, where the MoMo is the best

performing and Bartle the worst for all evaluations.

However, percentages of correct predictions rise

slightly for all models: MoMo 60 %, BrainHex 52 %,

and Bartle 29 %. The same applies to the comparison

of correct predictions per participant, where the mean

values of each model increase slightly: MoMo 18.00,

BrainHex 15.74, and Bartle 8.26.

6.2.3 Validation of the Predictions in Real

Apps

The validation of the preferences with the ratings in

the real apps (after using the app) shows a different

result from the theoretical prediction quality (before

using the app). Nevertheless, the distribution of the

three models remains the same: Out of 43 elements

that were used in the real apps, the MoMo is the

strongest with 32 elements predicted correctly,

followed by BrainHex with 28 elements, and Bartle

with 19 elements. Note that the random model would

predict 2 elements correctly on average. The MoMo

can thus correctly predict 33 % of the preferences,

BrainHex 29 %, and Bartle 19 %. Since the random

model achieves 27 %, both MoMo and BrainHex are

slightly better than the random model.

7 DISCUSSION OF THE

RESULTS

The results of the user research (study 1) show that all

models are suitable to predict preferences for at least

some elements. However, the comparison of the

different models shows that there are considerable

differences in the quality and number of the

predictions. Of the four categorization models,

BrainHex scores best. Furthermore, the results for the

creation of the motivational value model (MoMo)

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show that by combining all four categorization

models, it is possible to create an even better model

for predicting preferences.

The quality evaluation of the predictions (study 2)

shows, that the users’ theoretical ratings of

gamification elements can be predicted well since

both, MoMo (57% correct predictions) and BrainHex

(50% correct predictions) perform better than the

random model, whereas Bartle (27% correct

predictions) scores worse.

Nevertheless, the comparison of the results of

questionnaire 1 and 2 shows variances in the

distribution of correct predictions per element, which

can be attributed to the varying ratings of the

gamification elements by the participants in

questionnaire 1 and 2: only 65 % of the elements in

questionnaire 1 and 2 were rated the same. In contrast,

the predictions differ less: 12 % of the predictions in

MoMo differ, 13 % in BrainHex, and 7 % in Bartle.

This suggests that answering the questionnaires of the

models is relatively stable, but a theoretical rating of

the gamification elements is difficult for the

participants. However, it is also possible that the

ratings may have changed due to experiences in the

apps. For this reason, it would be reasonable to test

whether the ratings stabilize over time in a long-term

study.

In contrast, the validation of the ratings of the

elements in the real apps (after having used the app)

shows a lower prediction quality compared to the

theoretical ratings (before using the app), while the

distribution of the models remains the same: The

MoMo (33 % correct predictions) is the strongest

followed by BrainHex (29 % correct predictions), and

Bartle (19 % correct predictions). The strong decrease

of percentages is based on the equally strong variation

in the ratings of the elements in theory and the real

apps: only 32 % of the ratings match between before

and after use of the apps. This may be due to two

reasons: First, as mentioned above, the theoretical

rating of the gamification elements may be difficult

for the participant, and therefore the data from the

regression analysis, which is based on the theoretical

data, may not match the real ratings. Second, it may

be caused by the implementation of the gamification

elements in the apps since a bad implementation is

rated worse than a good one, which may lead to

differences. This was partially confirmed by the

interviews in which it was apparent for some

elements that they would be motivating in principle

but did not influence the participants in the actual test

phase. For example, the element “Challenge” was

rated theoretically as motivating. However, since

there were no suitable challenges, it was rated rather

neutral after having used the real app. Furthermore,

the validation performed in this study, as well as the

data collection, was based on solely subjective ratings

and did not measure the objective increase or

decrease in motivation through certain gamification

elements.

Moreover, with 19 participants in the second

study, quantitative validation of the data was not

possible, which is why the validation should be

repeated by a study with a significantly larger sample

size. In addition, other quality features of the

predictions should be considered, since it cannot be

ensured that the ratings were normally distributed and

thus the 1/3 random model may not be applicable for

every element.

8 CONCLUSIONS

The results of this study indicate that gamification

preferences can be predicted using the Bartle, Big

Five, Hexad, and BrainHex categorization models. In

comparison, BrainHex scores best and Bartle scores

worst. The results also show that by combining the

four categorization models, a model (MoMo) could

be developed that can predict preferences even better

than the four individual categorization models. In the

validation, it becomes clear that the prediction of the

models for the theoretical rating of gamification

elements is significantly higher than the random

model for both BrainHex and MoMo. The prediction

of the motivational value after having experienced a

real app is much more difficult. Reasons for the rather

poor predictability of the preferences in the real apps

may be the concrete implementation of the elements

or the fact that the elements are difficult to rate

without having experienced them in a real app.

REFERENCES

Arango-López, J., Ruiz, S., Taborda, J. P., Vela, F. L. G.,

& Collazos, C. A. (2017). Gamification Patterns: A

Catalog to Enhance the Learning Motivation. In Actas

del V Congreso Internacional de Videojuegos y

Educación (CIVE'17).

Bartle, R. (1996). Hearts, Clubs, Diamonds, Spades:

Players Who Suit MUDs.

Chou, Y.‑K. (2016). Actionable gamification: Beyond

points, badges, and leaderboards. Octalysis Media.

Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011).

From game design elements to gamefulness. In A.

Lugmayr, H. Franssila, C. Safran, & I. Hammouda

(Eds.), Proceedings of the 15th International Academic

Player-Type-based Personalization of Gamiﬁcation in Fitness Apps

367

MindTrek Conference on Envisioning Future Media

Environments - MindTrek '11 (p. 9). ACM Press.

Ferro, L. S. (2018). An analysis of players’ personality type

and preferences for game elements and mechanics.

Entertainment Computing, 27, 73–81.

González Mariño, J. C., Cantú Gallegos, M. d. L., Camacho

Cruz, H. E., & osales Camacho, J. A. (2018).

Redesigning the Bartle Test of Gamer psychology for

its application in gamification processes of learning. In

N. C. Callaos (Ed.), The 12th International Multi-

Conference on Society, Cybernetics and Informatics:

July 8-11, 2018, Orlando, Florida, USA: Proc. (pp. 35–

40). IIIS.

Hallifax, S., Serna, A., Marty, J.‑C., Lavoué, G., & Lavoué,

E. (2019). Factors to Consider for Tailored

Gamification. In J. Arnedo, L. E. Nacke, V. Vanden

Abeele, & Z. O. Toups (Eds.), Proc. of the Annual

Symposium on Computer-Human Interaction in Play

(pp. 559–572). ACM.

International Hobo Ltd. (2011). Subclass Popularity.

https://blog.brainhex.com/

International Hobo Ltd. (2019). Welcome to the BrainHex

questionnaire! www.survey.ihobo.com/BrainHex/

Kocadere, S. A., & Çağlar, Ş. (2018). Gamification from

Player Type Perspective: A Case Study. In Educational

Technology & Society (pp. 12–22).

Marczewski, A. (2016). Even Ninja Monkeys like to play:

Gamification, Game Thinking and Motivational Design.

Mora, A., Tondello, G. F., Calvet, L., González, C.,

Arnedo-Moreno, J., & Nacke, L. E. (2019). The quest

for a better tailoring of gameful design. In Unknown

(Ed.), Proc. of the XX International Conference on

Human Computer Interaction - Interacción '19 (pp. 1–

8). ACM Press.

Nacke, L. E., Bateman, C., & Mandryk, R. L. (2011).

BrainHex: Preliminary Results from a Neurobiological

Gamer Typology Survey. In D. Hutchison, et al. (Eds.),

Lecture Notes in Computer Science. Entertainment

Computing – ICEC 2011 (Vol. 6972, pp. 288–293).

Springer Berlin Heidelberg.

Raftopoulos, M., Walz, S., & Greuter, S. (2015). How

enterprises play: Towards a taxonomy for enterprise

gamification. In Digital Games Research Association

DiGRA (Ed.), Proceedings of DiGRA 2015: Diversity

of play: Games – Cultures – Identities.

Rammstedt, B., Kemper, C. J., Klein, M. C., Beierlein, C.,

& Kovaleva, A. (2013). Eine kurze Skala zur Messung

der fünf Dimensionen der Persönlichkeit: 10 Item Big

Five Inventory (BFI-10). In Mannheim : GESIS (Ed.),

methoden, daten, analysen (7(2), pp. 233–249).

Reh@pp-Quality (Ed.). (2016). CHECK-Liste.

www.rehatechnologie.fk13.tu-dortmund.de/rehapp

Suryapranata, L. K. P., Kusuma, G. P., Heryadi, Y., &

Abbas, B. S. (2020). Adaptive Gamification

Framework With Proper Player Type Classification

And Effectiveness Evaluation. In ICIC International

(Ed.), ICIC ExpressLetters (Vol. 1, pp. 9–14).

Swacha, J., & Muszyńska, K. (2016). Design patterns for

gamification of work. In F. J. García-Peñalvo (Ed.),

Proceedings of the Fourth International Conference on

Technological Ecosystems for Enhancing

Multiculturality - TEEM '16 (pp. 763–769). ACM Press.

Tondello, G. F., Wehbe, R. R., Diamond, L., Busch, M.,

Marczewski, A., & Nacke, L. E. (2016). The

Gamification User Types Hexad Scale. In A. Cox, Z. O.

Toups, R. L. Mandryk, P. Cairns, V. Vanden Abeele, &

D. Johnson (Eds.), Proceedings of the 2016 Annual

Symposium on Computer-Human Interaction in Play -

CHI PLAY '16 (pp. 229–243). ACM Press.

Werbach, K. (2014). (Re)Defining Gamification: A Process

Approach. In D. Hutchison, T. Kanade, J. Kittler, J. M.

Kleinberg, A. Kobsa, F. Mattern, J. C. Mitchell, M.

Naor, O. Nierstrasz, C. Pandu Rangan, B. Steffen, D.

Terzopoulos, D. Tygar, G. Weikum, A. Spagnolli, L.

Chittaro, & L. Gamberini (Eds.), Lecture Notes in

Computer Science / Information Systems and

Applications, Incl. Internet/Web, and HCI: Vol. 8462.

Persuasive Technology - Persuasive (Vol. 8462, pp.

266–272). Springer.

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