Agent-based simulation models are rising in popularity recently due to their ability to model real-world problems in a wide range of domains. Inherent to these types of simulations is the fact that an enormous amount of data can be generated, which needs to be analysed in order to make the simulation useful. At the moment, the available tools to perform this analysis are limited for most platforms in terms of functionality or usability. Model designers are often not experienced programmers, so processing and analysing the data from these experiments in Python or R can be challenging. The goal of this master thesis is to determine the best way to make data mining methods accessible to model designers so they can easily execute and analyse model experiments for NetLogo. In order to achieve this goal, the limitations of the currently available tools will be established by communicating with the target group. When there is a clear overview of what exists and what still needs to be done, we will attempt to develop an accessible tool that addresses the current limitations. The tool that was created consists of a GUI that allows the user to design experiments, run simulations and visualise results. With this GUI, the coding aspect of data analysis is taken completely out of the users hands, so anyone with NetLogo experience should be able to conduct extensive analysis of their model and its results. To determine whether the tool achieved its goal of being easy to use while still providing advanced analysis options, a usability study will be conducted with the target group. In this study, the users will execute a certain number of tasks with the tool and the results are recorded. After the tasks are completed, the user will fill in a questionnaire to get more opinion based data to give a complete overview of the usability. This usability study will allow us to determine whether our tool is indeed an improvement for the field and something that people could see themselves actually using during model development. Every measurement goal that was set before the usability study was achieved, most of them with wide margins. The ASQ questionnaires obtained an average result of 4.69 out of 5, where the target was set at 4. Furthermore, the SUS questionnaire obtained an average result of 85 out of 100, with the target being 75. These results show that developing an accessible data mining tool is possible and thus that the goal of this thesis was achieved. This research can be used as a basis for future development in order to provide the agent-based simulation community with more tools for the analysis of models.

This report concludes the project 'Predictive Learning Analytics' for FeedbackFruits. It includes all the work that has been done in a ten week span at FeedbackFruits. FeedbackFruits is a company inspired to help teachers shape learning activities and spark students' active thinking, by creating an online platform on which teachers can create interactive learning activities. The goal for this project was to extend the analytics tools for interactive presentations that are offered to teachers who use the platform. This hopefully results in giving them more insight in the participation of the class, understanding of the course material and the effort that students put in their school work.