Designing a Data Mining Framework for Quantitative UX Analysis
Dilbaz, Göktug (2024)
2024
Master's Programme in Computing Sciences
Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences
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Hyväksymispäivämäärä
2024-05-07
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202404213997
https://urn.fi/URN:NBN:fi:tuni-202404213997
Tiivistelmä
In today's world, user experience is one of the essential factors for digital product creators. UX research methods are becoming even more important with companies investing in them to improve the usability of their products. This study approaches the subject through quantitative methods. Analytics, an outstanding quantitative method, has emerged as a powerful tool for understanding and enhancing UX. This study focuses on product analytics to understand user interaction and common usage patterns. However, there is a gap in research on how to use analytics for application types other than web or mobile.
This thesis aims to address this gap by utilizing product analytics effectively to observe its capabilities and value. By investigating how this solution can be integrated with SICA-GUI to enhance user experience, this work contributes to the field by demonstrating the effectiveness of the analytics method for a C++/Qt-based Linux application.
Numerous challenges arise in this path such as most of the available analytics solutions being specialized for web and mobile, the application operating in offline conditions, lack of support for C++ as the embedded area is recently adapting to this solution. This study provides a practical solution to the mentioned problems using fundamental knowledge about software engineering and data engineering.
This thesis aims to address this gap by utilizing product analytics effectively to observe its capabilities and value. By investigating how this solution can be integrated with SICA-GUI to enhance user experience, this work contributes to the field by demonstrating the effectiveness of the analytics method for a C++/Qt-based Linux application.
Numerous challenges arise in this path such as most of the available analytics solutions being specialized for web and mobile, the application operating in offline conditions, lack of support for C++ as the embedded area is recently adapting to this solution. This study provides a practical solution to the mentioned problems using fundamental knowledge about software engineering and data engineering.