Context. Learning platforms, especially Intelligent Tutoring Systems (ITS), are increasingly used to assist with tasks such as distributing and collecting materials, offering opportunities for students to work independently, and conducting assignments and quizzes. Problem. However, it has been shown that student motivation decreases rapidly, even over short periods of time. This is a serious issue, as it can lead to students not reaching their goals or even dropping out of university. To address this, gamification has been integrated into learning platforms and ITS to help maintain motivation. Yet, the effectiveness of gamification elements varies significantly among students due to differing individual preferences. To maximize the benefits, these elements must be tailored to each individual. While personalization based on player types — such as Bartle’s four player types — has already demonstrated improvements, more advanced models and additional personalization metrics offer further potential. Objective. The objective of this thesis is to personalize gamification elements within an ITS to help sustain student motivation over time. From the beginning, it was planned to use the more state-of-the-art HEXAD player type model, offering a more suited classification than Bartle’s model. Furthermore, to achieve an even better personalization, additional metrics beyond player type are considered to adapt the gamification elements. Learning progress was selected for this purpose, aiming to enhance personalization by incorporating two different metrics at the same time. Method. The thesis is structured into four parts. First, an extensive literature review was conducted to identify suitable metrics and formulate hypotheses regarding their use for personalization. Learning progress was solidified as a complementary metric to player type. Second, a conceptual approach was developed, focusing on adapting gamification elements based on HEXAD player types combined with learning progress. Third, a prototype implementing the concept was created using Figma, offering a concrete, interactive proof of concept. Finally, a user study was conducted to evaluate the effectiveness of the adapted gamification elements. Result. The study revealed that participants reported significantly higher levels of selfassessed motivation when interacting with gamification elements personalized through the combination of HEXAD player type and learning progress, compared to non-adapted elements. Conclusion. The results demonstrate that combining a player type model like HEXAD with another metric such as learning progress enables an effective personalization of gamification elements. This approach can significantly contribute to maintaining and increasing student motivation on learning platforms and ITS.