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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.
Im Zusammenhang mit dem Projekt „BGM Vital“ der Charité Universitätsmedizin Berlin, wird erforscht, mit welchen Methoden das betriebliche Gesundheitsmanagement ge-fördert werden kann.
Hierfür wurde sich im Rahmen dieser Arbeit mit der Entwicklung einer App befasst, wel-che dazu dient, die Anwender spielerisch dazu zu motivieren, sich sportlich zu betätigen. Hierbei sollen sie nicht nur ihr Bestes geben und versuchen an ihre Grenzen zu gehen, sondern es soll auch analysiert werden, ob es der Fitness App gelingen würde, den Benutzer nach einem durchgeführten Training zu einem erneuten Training mit dieser App zu motivieren.
Es wird beschrieben, wie die App entwickelt wurde. Von der Anforderungsliste über die genutzten Technologien und dem Ablauf der App, bis zu der eigentlichen Entwicklung.
Des Weiteren wird der Testablauf näher beleuchtet. Hierbei wird auf den Testaufbau, die Testpersonen, die zu absolvierenden Trainingsübungen und die Durchführung des Tests eingegangen. In einer abschließenden Diskussion wurden die Testergebnisse interpretiert und bewertet.