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This study investigates the possibility of using Bartle’s player types for gamification
in the context of language learning apps. By taking user preferences into
account, this might assist in selecting the most suitable game elements. Learning
apps are gaining popularity as an innovative method for obtaining an independent
and flexible learning experience. Gamification keeps users motivated and involved
with the content.
After the research on the usage of gamification and its effects on the user, a language
learning app prototype was created. The evaluation consisted of a user test with
interview questions and the short User Experience Questionnaire (UEQ). The Bartle
test of gamer psychology was used to determine the player types of the participants.
The results show that, while player type and gamification preference can partially
coincide, there are too many deviations to confidently say it can be transferred into
gamification contexts. We conclude that game elements should not be chosen based
on a user’s Bartle player type and are more effectively used by incorporating a variety
of different gamification components.
The capabilities of Artificial Intelligence (AI) are utilized increasingly
in today‘s world. The autonomous and adaptive characteristics
allow applications to be more effective and efficient. A certain
subfield of Artificial Intelligence, Machine Learning, is enabling
services to be tailored to a user‘s specific needs. This could prove to
be useful in an information-heavy field such as Statistics. As design
research from SPSS Statistics, a legacy statistical application, has
indicated, statistics beginners struggle to tackle the challenge of
preparing a statistical research study. They turn to several sources
of information in an attempt to find help and answers but are not
always successful. This leads to them being unconfident before
they have even started to execute the statistical study. The adaptive
features of Artificial Intelligence could help support students
in this case, if designed according to established principles. This
thesis investigated the question whether an AI-powered solution
could elevate the users‘ confidence in statistical research studies.
In order to find the answer, a prototype with exemplary User Experience
was designed and implemented. Preceding research determined
the domain and market offer. User research was conducted
to ensure a human-centered outcome. The prototype was evaluated
with real test users and the results answered the question in
the affirmative.