=Paper=
{{Paper
|id=Vol-2497/paper5
|storemode=property
|title=Designing an Emotive Avatar for a Grammar Game - A Case Study of Engagement and Performance Development
|pdfUrl=https://ceur-ws.org/Vol-2497/paper5.pdf
|volume=Vol-2497
|authors=Kay Berkling,Engy Fawaz,Armin Zundel,Slim Abdennadher
|dblpUrl=https://dblp.org/rec/conf/gamilearn/BerklingFZA19
}}
==Designing an Emotive Avatar for a Grammar Game - A Case Study of Engagement and Performance Development==
https://ceur-ws.org/Vol-2497/paper5.pdf
Designing an Emotive Avatar for a Grammar Game - A Case
Study of Engagement and Performance Development
Kay Berkling Engy Fawaz Armin Zundel
Cooperative State University German University in Cairo Inline Internet Online Dienste
Karlsruhe, Germany Cairo, Egypt GmbH
kay.berkling@dhbw- engy_ahmedfawaz@hotmail.com Karlsruhe, Germany
karlsruhe.de Zundel@inline.de
Slim Abdennadher
German University in Cairo
Cairo, Egypt
slim.abdennadher@guc.edu.eg
ABSTRACT Game-Based Learning by Plass, Homer and Kinzer [14] pro-
This paper describes the design of a 3D running game with vide such a model with a comprehensive view encompassing
educational content. The goal of the game is to teach chil- affect, motivation, cognition and socio-cultural aspects of the
dren capitalization in German. Sentences are presented to the game as it is embedded into its application environment. In the
children in increasing order of difficulty, determined by the past, we have applied this model to an existing game [1]. But
syntactic structure of the sentences. Words within sentence key ideas to a new game were born during the reading of this
were presented decaptialized and the children had to select paper as outlined below. While there are many dimensions to
the words that should have been capitalized. The game design take into account in game design, this paper focuses on Henry
uses an emotional avatar along with speed factor in order to (the chicken runner) and Sam(i) the "feedback" avatar. Plass
motivate the children to play longer and improve their per- et al. offer four pillars of engagement in their model that have
formance. 20 children played the game with the avatar and served as a guideline during the design of Henry run.
16 children played the game without the avatar. A qualita-
Affect
tive feedback was collected via an online survey. In addition,
their performance profile was logged and analyzed. Based Affect relates to the emotional engagement of the player with
on the data, we report some trends that indicate increased en- the game, through visuals or music as an example. We chose
gagement and performance. These indicators can be used for exaggerated humor embodied in an avatar to get the player to
improving user-dependent, adaptive design for the player. laugh. Rather than sounding out a fail sound, the avatar should
get exasperated and even fall over backwards with horror at
the performance of the player. Emotion, fun, joy and even
ACM Classification Keywords frustration, can influence learning in a positive way [13, 9].
D.2.2. User interfaces: K.8.0. Games
Motivation
Author Keywords Gamification approaches to motivation often result in a level
Educational Game; Avatar; Emotion; Performance; and points system. Games on the other hand offer “fun death"
Adaptivity; Personalization; Interaction; Motivation; upon failure with the key being the ability to start over as many
Game-Based Learning times as the player wishes, the goal is to reach the next more
difficult level to get a bigger challenge. The storyline is that
the player helps the runner so that the avatar doesn’t have to
INTRODUCTION be annoyed anymore. The motivation of the player is therefore
When designing games, it is relevant to look at design frame- to make the avatar happy and the runner good. The reward
works that allow us to guide and judge design, motivation, and system follows a cognitivist model (good performance of a
learning. A research and practice model [8] or Foundations of friend) rather than a behaviorist model (points) [7].
Cognition
According to Plass et al., cognitive factors entail some of the
following:
Copyright © 2019 for this paper by its authors. Use permitted under Cre- • context of skills application
ative Commons License Attribution 4.0 International (CC BY 4.0).
In: J. Arnedo-Moreno, C.S. González, A. Mora (eds.): Proceedings of • skills meaningful outside of game and transferable
the 3rd International Symposium on Gamification and Games for Learning
(GamiLearn’19), Barcelona, Spain, 22-10-2019, published at http://ceur- • scaffolding through personalization
ws.org
• formative and immediate feedback complexity as they represent objects. The number of nouns
directly relates to the complexity of the task of identifying
• content representation them. A higher count of verbs directly indicates complex sen-
• mechanics aligned with learning goals tences. By squaring the value, we place exponential emphasis
on higher weighted areas. The sentences are then sorted by
• mapping gesture to features of content their difficulty level. Indirectly, the measure reflects sentence
length. We count the number of occurrences in a sentence
The skills (German orthogrphay) are directly meaningful out- of the following (weight of count in parenthesis): nouns (2),
side of the game. The personalisation results from the distance verbs (including auxiliary) (2), adjectives (1), adverbs (1), da-
and speed with which the runner travels. Feedback is both tives (1), genitives (1), accusatives (1), and nominatives (1). In
immediate and formative. Any mistakes in the game are imme- addition, the maximal distance across all nouns to the root of
diately corrected and available for the player to contemplate the sentence, counting the steps through the dependency tree
before the next task. Gestures in the game result in the direct to the root, is noted and is associated with weight value of 2.
capitalization of the word, even though the gesture itself is not The square of each count is then multiplied by the weight and
related directly to the meaning. summed up to result in the difficulty measure of the sentence.
Socio-cultural (This measure can be data-driven in future work.)
The final pillar of engagement leads to an often forgotten
DESIGN OF FEEDBACK
component of the world surrounding the game itself. In the
design we chose, even the runner in the game and the avatar The player is provided with feedback using three methods:
are already friends that the player is helping out. Future work Emotion, visuals, and information.
would have to spend more analysis on how the customization
Emotions
of the avatar and runner could help engagement though social
interaction with avatar and runner or other players. The current design of the game has a single environment which
is a forest scenery. The sentence is placed at the top of the
The goal of the presented work is to show the following: scene and originally the question was placed at the bottom.
The on-going score is added to the top left of the scene. The
1. Using an emotional avatar should engage players for longer
avatar "Sam" is added to the scene (see Figure 1.
2. Using an emotional avatar should result in returning players
3. Increased playtime should result in higher skill level
THE GAME
One of the most significant problems German school children
face is the concept of capitalization that does not exist in many
other languages. In German, nouns are capitalized. In addition,
even a verb or an adjective can become nominalized. Wrong
capitalization is one of four most frequent spelling errors that
persists even for adults [2]. The advent of careless spellings
Figure 1: Game Scene
in modern media as well as the use of automatic spell checks
may decrease children’s awareness even further.
The player uses a chicken "Henry" to move in the game and
Sam has five emotional expressions that are designed to pro-
pick answers in an endless run. When the correct word is
vide feedback to the player, namely: Idle, happiness, sadness,
picked, it is capitalized in the sentence shown. For example "fun death", and stop. The default emotion is in idle posi-
a sentence like "Auch jede möwe und jede biene ist da." is tion. On expressing happiness, a sound effect of kids cheering
shown and the player has to pick "möwe" and "biene" as they "yaay" is played. While on expressing sadness and stop, a
have to be capitalized (see Figure 1). The game contains 123 sound effect of a sudden buzz is played to indicate something
sentences sorted by difficulty. The difficulty reflects sentence wrong happened. "Fun death", includes a sound effect of
complexity. The goal of this game is to practice the concept hitting the ground due to Sam’s falling.
and automate it through speed and increasing difficulty of sen-
tences. In order to compute difficulty measures of a sentence When Henry makes a mistake, Sam expresses sadness. He
a dependency parser is needed. For this purpose, spaCy, an expresses it using both a facial expression and a gesture. He
open-source natural language processing (NLP) library written raises his eyebrows while his head moves downwards and his
in Python, was chosen. Given a sentence, it can return CoNLL- hand is moving towards his mouth. This indicates to the player
U formatted encoding of a sentence that includes tokenization, that something was wrong and the player might be encour-
Part-of-Speech (PoS) tagging and dependency parsing [10]. aged to fix the situation or check the sentence for the given
Given the above resulting information, the difficulty level can correction in order to avoid the same mistake later on. When
be computed. From past research, we know that sentences with Henry selects the correct answer, Sam jumps with happiness
embellishments such as adjectives and adverbs are difficult for and has a wide smile to transfer this feeling to the player (See
children [12]. In addition, the use of cases adds to sentence also Figure 2).The additional emotion of "fun death" is an
exaggerated emotion of the reaction "falls over backwards",
where Sam faints with exasperation. It occurs after repeated
mistakes committed by the player. The intend of fun death is
humor to increase engagement and focus while providing the
feedback that there is room for improvement in the player’s
performance. This emotion places two ’X’s in Sam’s eyes and
a tongue to indicate fainting as he falls to the ground. After
a short period he returns to upright position. Figure 3 shows
Sam in his execution of "fun death" manoeuvre.
Figure 4: Instruction panel with example sentence with a rule
to apply when checking for capitalization (In this example: “if
you can add an article to the word, then it should be capital-
pics/Screenshot(107).png
ized”)
red. The game speed depends on this performance bar. As a
result, the difficulty level is increased for high performers to
Figure 2: Sam’s sad and happy and stop expressions encourage skill automation. It also prevents boredom for good
players and faster movement towards more difficult sentences.
As such, the speed is individual and adaptive to the player.
Last but not least, a panel showing the final score and a button
to play again is shown when players stop the game.
Figure 5: Example word color change for a runner who might
have reached word "jede".
Figure 3: Sam’s Fun Death
UI ADJUSTMENTS
Initial informal evaluations of the UI focused on understand-
Finally, when Sam realizes (by number of mistakes made) that ing children’s first reaction to the game and verifying their
the player doesn’t know how to play, he stops Henry and shows interest and understanding of the humor. Feedback was col-
the player a panel of grammar instructions as depicted in Fig- lected regarding the game and design. Their improvement and
ure 4. After spending as much time as the players chooses with ideas were collected and dislikes noted. Furthermore, it was
the instructions, they can restart the game from the beginning. important to test their understanding of the game interface for
The panel contains the informative feedback, that explains the playing since there are no instructions at the beginning. The
rules explicitly to the player. In this case, the explanation has feedback was collected through observations as they played
grammatical rules for capitalization.The emotion chosen for and through questions that were answered directly while in-
Sam as he stops the player, is an exaggerated version of "stop, terviewing all of the participants together to encourage each
what in the world are you doing? Let me explain it to you other to respond thoroughly.
once again before you keep messing up even more" without so
many words. The face has an angry expression and the hand
is raised to stop the player from running around like a crazy
person with no idea of what they are doing. It is intended to
be humorous.
Visual and Informative Feedback
Both correct and incorrect answers are displayed in the sen-
tence at the top as the player runs past the words in the game:
Correct choices for words that should have been capitalized
are changed to capital and displayed in green. In contrast, any
missed or wrongly picked word turn red and their spelling is Figure 6: Avatar selection
not changed (see also Figure 5). A performance bar was added
to indicate how good the player’s performance is. It starts
with 50% at the start. It increases and decreases with correct Changes
and faulty answers respectively. As it falls below 30% it turns The following changes were made due to the feedback.
• Furthermore, as suggested, we have added the "Chicken
Dance" as it is popular worldwide to most of the kids. This
animation occurs when the player gets the correct answer
several times. We have also added a part of the song to it.
This animation plays fast so that makes it funnier too. The
fast speed is due to short timing between player’s choices
in game. This animation is also accompanied by a facial
expression of a wide smile to express great happiness to
motivate the player (see Figure 8).
Figure 7: Example screenshot from Game Introduction (trans- • The avatar’s idle status was substituted with a running back-
lated to English for this paper) wards animation that indicates that the avatar is running
with Henry. This animation increases the fun factor because
• A female avatar (Sami) was added as suggested by most Sam running backwards looks hilarious, especially as Henry
participants in all evaluations. The player chooses the avatar speeds up.
he/she wants (Figure 6).
• To personalize the avatar, more color and texture options
were provided for the T-shirt selection. The player chooses
between pink, blue, white and polka dots.
• The progress-bar constituted a source of misunderstand-
ing since it does not indicate progress but performance.
Since the performance coincides with speed, the caption
was changed to "speed X m/sec", where X denotes the cur-
rent value. The bar now visualized the speed and is easier
to interpret.
• Since the majority of the participants had requested a change Figure 8: Chicken Dance
in the instructions panel, the original long text instruction
was replace with two example buttons that the players can
choose to listen to as often as they want. On clicking the DATA ACQUISITION
examples buttons, an audio is played reading this example. Two versions of the game were deployed as browser games
with and without the avatar. Each version of the game is
• Some participants had difficulty to read both the sentence
followed by a questionnaire. The research question is whether
and its question. The question was moved directly below
the avatar will lead to longer playing times and an increased
the sentence at the top of the scene. The light green color
return rate to play more. The presence of the avatar would
previously employed was replaced with a much darker ver-
have a positive impact on learning, assuming longer play-time
sion to increase visibility, taking into account color blind
improves performance.
issues.
Additions w/o Sam w/ Sam
In addition to the above changes, new ideas were added. participants 16 24
age groups from 7 to 14 from 7 to 14
• Getting the players to feel the game and to know the story gender 6 males & 10 9 males & 15
behind, it is very important to increase motivation and en- females females
hance perception. A skip-able introduction supports the number of log files 6 11
story after the player chooses his avatar. Here, an exhausted sentences logged 37 151
Sam(i) is shown with Henry. Sam notices that the player has
arrived on the scene and asks the player why it took so much Table 1: Metadata of participants. All players where from
time to arrive. Afterwards, the avatar introduces himself Egypt, upper middle to higher class social standing and from
and that he is helping Henry the chicken to learn German private international schools.
but he is really tired from all the running and Henry’s mis-
takes. So he asks the player to help make Henry learn and
win. Notice the indirection built into the story of whose Data
performance is on the line. The players themselves are The data acquired for analysis were from the two surveys
here to help Henry the chicken rather than learn themselves. and from game logs. Each sentence and word that the player
The intend is to lower the pressure on the player. This ap- encountered was logged along with correctness. The partic-
proach has the potential to create a relationship with the ipants had different German background knowledge. The
avatar or Henry during a joint effort to be accomplished to participants who had more than average German knowledge
increase the motivation and engagement for the game (see were 50% in the without Sam evaluation and 62.5% in the
also Figure 7). with Sam evaluation. The number of log files are less than
number of participants because some participants encountered • I like to read.
internet connection problems and some closed their browsers
quickly that the logs were not posted. The score and time was • I’m good at German.
computed from the log files.
• My parents think I’m good at German.
ENGAGEMENT EVALUATION
In order to understand engagement, a survey was designed • My teachers think that I am good at German.
according to theoretical derivation of questionnaire sections
as described further in [1] and based on previous related work
by [14, 4]. A short review of the motivation behind each • I’m good at this game.
section of the survey is given here for completeness.
Cognition
It has been shown in various science fields that learning
through games boost the educational benefits in term of en-
hancing the learning progress [16, 17]. To be certain that
our game achieved this goal, we asked to define the extent of
agreement to the following statements in both surveys in form
of Likert scale statements.
Figure 9: Child-friendly Likert scale (jotforms) • I learn upper and lower case in this game.
• I make less mistakes in writing when I get good in this
As shown in Figure 9, the design of the surveys is child- game.
friendly for easier understanding and enjoyment of filling
it. We have added the answers to choose from in pictures and
• I talk to others about the questions in this game.
smiley faces for the Likert scale. Apart from meta data, the
following topics were sections in the survey. Together they Flow State
indicate engagement: Basic needs, self-efficacy, cognition, Flow is initiated when an individual reaches a state of effortless
flow, and affect. concentration and enjoyment and is exceedingly productive
Basic Needs while feeling happy. It happens when someone’s skills are
Basic needs are the essential fundamentals of the human be- completely focused on winning a manageable challenge [6,
ings for survival and well-being and an important prerequisite 11].
for learning [5]. It was shown that learning in an environment The questions are designed to capture flow with the following
where failure has disturbing consequences is burdensome and survey questions by asking about the four states of ability and
decreases efficiency of learning process [3]. For a learning difficulty, in other words, boredom, stress, ease and difficulty
environment, basic needs can be described as the need for and normalizing it against their feeling of fun.
basic safety not as the need for food and water. The learner
faults are accepted through the learning process as a normal • The game is fun.
path to achieving learning objectives. One of the advantages
of learning games over classroom learning is that they allow • The game is too easy for me.
friendly or funny failures.The purpose of the following ques-
tions is to ensure that the player enjoys playing the game and • The game is too difficult for me.
feels comfortable with making mistakes.
• It’s okay to make mistakes at school. • Henry has gotten better in the game.
• It’s okay to make mistakes in the game. • The game bored me.
• In school I like to take part in exercises.
• The game stressed me out.
• I enjoy learning in school.
• I enjoy learning in games. • I’ll play the game again.
Self-Efficacy • Were you good with upper and lower case before?
A person’s self-image is a result of combination of some or all
of the following four factors. Self perceptions also depends Affect of Game
on how other perceive us and our own interpretation of other’s Liking the game details is important so that players choose
opinion [15]. There is classically known direct positive corre- to play this game. So we asked about the agreement of the
lation between our self-perception and our performance [18]. statements in Table 2. And at the end of the surveys, we added
The questions are intended to capture self-efficacy with respect an open paragraph question to let the participants add their
to reading skill (an important partial skill of this game). comments and suggestions about the game.
Without Sam With Sam
• I like Henry the chicken. • I want to help Sam(i).
• I like the music. • I want to help Henry the
chicken.
• I want Henry the chicken
to win. • I wonder what Sam(i)
wants to do next.
• I’m enjoying the egg
race. • I was able to help Henry.
• Sam(i)was happy in the
end.
• Sam(i) is funny.
• Did you have the same
feelings as Sam(i)?
• Sam bothers me while I
play.
• Sam helped me.
Table 2: Affect Questions
Engagement Results Figure 10: Survey Results
For both games, the results are depicted in Figure 10. Games
motivate children more than regular class time. The only
answer where both groups differed significantly (p=.0382), is the correct identification of nouns only (ignoring correctly not
the question whether it’s ok to make a mistake in the game. picked items).
While the group with Sam was indifferent, the group playing
without Sam said that its ok to make mistakes with an average
of 2 on the Likert scale. In contrast, making mistakes in the Student thinks...
classroom was not significantly different from either group or Truth noun (picked) not noun (not picked)
when compared to the game-play within each group. Answers speed up +20 no effect on speed
to the Affect questions lie between 2.3 (I was able to help Word turns green
noun
Henry) and 3.0 (do you want Henry to win?) The set of Word is capitalized Word is capitalized
questions regarding the flow show similarities between the performance
two groups. But the results also show that both games seem to = picked/nouns[30]
produce a sort of flow. The game was neither too easy nor too not slow down −20 achievable in idle
hard. It was not stressful and it was not boring. The players noun Word turns red word remains white
feel they improved while having fun and would play again.
Table 3: Determining performance over window of last 30
After analyzing the results of both surveys, it is clear that the words seen, speed and visual feedback
participants like both versions of the game. There seems to
be more worry regarding making mistakes in the version with
Sam. On interviewing children, why they were worried more Performance Results
in the game with Sam, it seems that they cared when Sam got Figure 11 shows difficulty vs speed vs correct pick percentage.
sad and wanted to avoid his sadness. The red line represents the speed, the green line represents
the sentence difficulty (as defined earlier) and the blue line
PERFORMANCE EVALUATION represents the % correct pick of nouns. The graph indicates
what we call peaks of performance. We note the following:
Computing Performance • increasing speed produces dip in performance that can be
Computing performance can be tricky in a game. In this ver- recovered
sion, the student has to actively pick words that should be
capitalized. Doing nothing should not be rewarded. Table 3 • increasing difficulty produces dip in performance and speed
summarizes how speed and performance is computed. While that is recovered but more slowly
speed depends on the correct and wrong active picks the stu-
dent makes, the correct performance is computed based on Performance is measured at each peak.
EFFECTS OF REPEATED PLAY
It is important to see how repeated play affects performance.
Additional logs of multiple play for three players are observed.
Players 1-3 are girls from different international schools in
Cairo, Egypt in grades 2, 3 and 4 at ages 8, 9 and 10 years old,
respectively.
Figure 11: Sample learning curve of a 4th grader Data Description
Each time the performance peaks (as observed and explained
Duration of Play in Section 7.3), it is computed for the given difficulty level.
Based on the log files, the play time can be computed as Figure 12 depicts the amount of peaks by level for each round
summarized in Table 4 it can be seen that players with Sam of play. Figure 13 shows the duration of play with each new
have a tendency to play longer. Comparing the differences of round of play. It can be seen that each round of play leads
the two distributions a p-value of 0.0941 (> .05) is not quite to a longer session. It can also be seen that with each trial,
significant for a 95% confidence interval. Clearly one of the the child has been able to reach a higher level of difficulty.
problems is the small number of trials but there seems to be a The players were able to pass the point that they had been
tendency. “stuck" on in a previous trial and continue on to more difficult
sentences with new performance peaks.
Group w/o Sam w/ Sam
Mean 184.50 403.64
SD 210.37 255.74
SEM 85.89 77.11
N 6 11
Table 4: Comparing duration (in seconds) of play
According to the data, all players got faster in speed while
playing. High speed results in difficulty of handling the me-
chanics of the game and thinking about the cognitive task
simultaneously. This leads to one of three possibilities:
• The instruction panel pops up in case of continuous decreas- Figure 12: Difficulty Level Reached for Peak of Repeat Play-
ing performance that leads to zero valued speed and then ers
player restarts the run (A).
• The player gives up (B).
• The player continues, gets better and faster again (C).
Possibility No Sam Sam
A: Restarts 3 3.4
B: Gives up 4.6 2.6
C: Recovers 2.6 11.3
Table 5
Figure 13: Duration of Game Play for Repeat Players
In Table 5, the normalized numbers of occurrences for each
category are computed by dividing the number of occurrences
of each by total number of words seen. Given this data, both Learning Curve
versions show instruction panel the same number of times. As an example of a learning curve, Figure 14 plots perfor-
More often participants gave up in the game version without mance vs. words seen for Player 2. The blue line represents
Sam indicating a higher engagement and motivation with Sam. the first game run, the red line represents the second game
Moreover, the comparison of the numbers of occurrences of run and the green line represents the third game run. The
players recovering from the setbacks (C) supports their en- graph is plotting the correct pick performance vs words for the
durance with Sam at a much larger rate. three game runs. The learning curves for the other two players
have similar behaviour. From the learning curve exemplified 6. M. Csikszentmihalyi. 1997. The masterminds series.
in Figure 14, it can be seen that repeated playing results in Finding Flow: The Psychology Of Engagement With
improved performance and time of play increases. Everyday Life. Basic Books.
7. Jacquelynne S Eccles, Allan Wigfield, and Ulrich
Schiefele. 1998. Motivation to succeed. (1998).
8. Rosemary Garris, Robert Ahlers, and James E Driskell.
2002. Games, motivation, and learning: A research and
practice model. Simulation & gaming 33, 4 (2002),
441–467.
9. Hans-Georg Häusel. 2016. Brain view: warum Kunden
kaufen. Vol. 143. Haufe-Lexware.
Figure 14: Player 2 learning curve
10. Matthew Honnibal and Mark Johnson. 2015. An
improved non-monotonic transition system for
dependency parsing. In Proceedings of the 2015
CONCLUSION AND FUTURE WORK Conference on Empirical Methods in Natural Language
Results are preliminary studies on a first prototype of the game Processing. 1373–1378.
that serves as a data exploration effort to gain clarity on log
file collection and interpretation. A large number of players 11. S. A. Jin. 2012. “Toward Integrative Models of Flow”:
is needed to show the impact of the learning content on skill Effects of Performance, Skill, Challenge, Playfulness,
acquisition and improvement that transfers into writing skills. and Presence on Flow in Video Games. Journal of
We plan to deploy the game with undergraduate students and Broadcasting and Electronic Media 56(2),169–186
school children learning German for this large-scale analysis. (2012).
12. Rémi Lavalley and Kay M. Berkling. 2014. Data
ACKNOWLEDGMENTS Exploration of Sentence Structures and Embellishments
We would like to thank all the children who have participated in German texts: Comparing Children’s Writing vs
in the preliminary evaluation of this game and Sam(i) the Literature. In Proceedings of the 12th Edition of the
avatar. Konvens Conference, Hildesheim, Germany, October
8-10, 2014. 241–247.
REFERENCES
13. Jan L Plass, Steffi Heidig, Elizabeth O Hayward, Bruce D
1. Kay Berkling and Roger Gilabert Guerrero. 2019. Homer, and Enjoon Um. 2014. Emotional design in
Designing a comprehensive evaluation method for multimedia learning: Effects of shape and color on affect
learning games - a general approach with specific and learning. Learning and Instruction 29 (2014),
application to iRead. In Proceedings of the 3rd 128–140.
International GamiFIN Conference, Levi, Finland, April
8-10, 2019. 94–105. 14. Jan L Plass, Bruce D Homer, and Charles K Kinzer. 2015.
http://ceur-ws.org/Vol-2359/paper9.pdf Foundations of game-based learning. Educational
Psychologist 50, 4 (2015), 258–283.
2. Kay Berkling and Rémi Lavalley. 2018. Automatic
orthographic error tagging and classification for German 15. T. B. Rogers, N. A. Kuiper, and W. S Kirker. 1977.
texts. Computer Speech & Language 52 (2018), 56–78. Self-Reference and the Encoding of Personal Information.
https://doi.org/10.1016/j.csl.2017.11.002 Journal of Personality and Social Psychology
35(9):677-–688 (1977).
3. K. Berkling and C. Thomas. 2013. Gamification of a
software engineering course and a detailed analysis of the 16. D. Shaffer, K. Squire, R. Halverson, and J. P Gee. 2005.
factors that lead to it’s failure. Interactive Collaborative Video Games and the Future of Learning. Phi Delta
Learning (ICL), International Conference IEEE 525–530 Kappan 87(2),105-–111 (2005).
(2013). 17. E. Zhi Feng Liu and P. Chen. 2013. The Effect of
4. Cyril Brom, Filip Děchtěrenko, Vít Šisler, Zdenek Game-Based Learning on Students’ Learning
Hlávka, and Jiri Lukavsky. 2018. Does Motivation Performance in Science Learning – A Case of
Enhance Knowledge Acquisition in Digital Game-Based “Conveyance Go”. Procedia - Social and Behavioral
and Multimedia Learning? A Review of Studies from Sciences 103,1044–1051 (2013).
One Lab. 18. B.J. Zimmerman, A. Bandura, and M. Martinez-Pons.
1992. Self-motivation for academic attainment: The role
5. E. Chew, N. Jones, and D. Turner. 2008. Critical review
of self-efficacy beliefs and personal goal setting.
of the blended learning models based on maslows and
American educational research journal 29(3),663–676
vygotskys educational theory. International Conference
(1992).
on Hybrid Learning and Education, Springer 40–53
(2008).