This report holds a subset of an integrated thesis involving the development and testing of an educational tool named ”Playlist,” aimed at improving the learning experience of Computer Science & Engineering bachelor students at the TU Delft. The Playlist tool was developed as a feature for the existing tool called Skill Circuits, supporting students in creating personalized sequences of learning activities for their study sessions. The integrated research contributes to the complexities of modern digital learning environments and the challenges students face in navigating vast amounts of educational content.  The study uses a design-based research approach, which involves iterative development and research cycles and including students in the evaluation phases. This report focuses on the description and design of these phases. For each phase, it then reports on how the design of the Playlist tool utilized user feedback gathered through surveys and focus group sessions with Computer Science students.  Key findings in the integrated thesis indicate that playlists can improve student engagement, motivation, and overall learning experience by supporting students in finding learning activities aligned with their learning preferences and goals. The research highlights the importance of providing students with tools that improve navigating the learning environment and support them in self-directed learning. The Playlist feature not only aids students in organizing their learning activities but also promotes a holistic approach to education by incorporating supporting activities such as reflection and taking breaks.  The integrated thesis makes two contributions: it serves as an example of the design and implementation of personalization tools in higher education and provides insights into integrating such tools into an existing educational system. The thesis aims to demonstrate the potential of designing tools in an iterative manner to personalize learning and support students in achieving their learning goals.

Political content reaches civilians via social media more and more nowadays. This content is often biased and filtered depending on the user’s connections on social media. However, almost nobody watches actual debates or reads reports from the actual Dutch House of Representatives (DHoR), where the most influential decisions are made. With our Bachelor’s thesis we aim to give users an insight into the language used by Representatives in the DHoR. This Bachelor thesis can be divided into two parts, the first part is the research phase, the second part the development of the Data Analysis Tool. During the research phase we searched for ways that would allow us to describe the use of language in the DHoR, and the resources necessary to perform corresponding calculations. We defined several non-topical text features to perform the description: complexity, sentiment analysis and the rate of femininity / masculinity. For each one of these features, we will calculate their respective values based on the particular language of a Representative. Besides these non-topical text features, we also introduce a topical text feature. As opposed to the first, topical text features actually focus on the content of a text, rather than the language used. Some politicians are known to act differently regarding certain circumstances. By introducing filters, we can explore the effects or influences these circumstances have on the use of language (text features) of Representatives. Finally, we examined the available data from the DHoR and how to access it. The second part of this thesis is the development of our Data Analysis Tool (DAT). In order to calculate scores for the text features, we used reported data from debates and discussions in the DHoR from the past five years. We developed a DAT that can be used to explore the use of language of Representatives by: – analysing individual Representative text feature scores – comparing the average text feature scores of all Representatives – allowing to plot the text feature scores of Representatives over time and filter on time