Entrance tickets for cultural venues are often sold through online retailers. Internally, these retailers have access to demand insights of a large amount of globally distributed venues. Using Tiqets as our industry partner, we were able to access data from one of the largest retailers with global reach. This thesis explores the possibility of leveraging prices to adjust the demand for a venue. In finding the relation between price and demand from historical sales records we encounter several obstacles. This thesis explores three different ways of minimizing the negative effects of using observational data. By introducing a decomposable time-series regression model to predict demand we can isolate the effects of trend, seasonality and recurring events without having to introduce new data. Introducing price as a module enables us to control for the desired future demand. Secondly, we explore exogeneity in the variation observed from historical price changes and introduce ways of ensuring exogeneity on future data. Finally, we look for an automated way of finding similarity across venues using the associated historical sales data and use this to estimate the variation in error when grouping venues in a controlled experiment.

FeedbackFruits is a company that provides tools for educators to organize their courses. The company is currently working on aiding teachers in aligning course material and assessment. Aligning the two provides students with clear expectations and can lead to an increase in learning [1]. Aligning course material and assessment is usually done by comparing what the students are taught to how students are assessed. When a student is assessed by an exam consisting of questions, the alignment process involves classifying these questions according to the cognitive process categories needed to answer them[2].
This process can be time consuming if an exam contains many questions it, and it can be easy to lose oversight of whether the questions in the assessment are representative of what is taught in the course material. The task of classifying questions into categories that represent the cognitive processes needed to answer them can be facilitated by providing a classification tool. This tool also gives educators insight by displaying a summary of the different question categories present in a set of questions. As part of the solution to the problem of course alignment, FeedbackFruits requested the development of a question
classifier which classifies questions according to the cognitive process required to answer them. Bloom’s revised taxonomy (subsection 2.1.1) is a taxonomy that categorizes questions and learning objectives into six distinct classes in the cognitive process domain. We propose a software solution that uses machine learning techniques to classify a courses’ questions and provides a clear overview of the classes in Bloom’s revised taxonomy present in these courses. To achieve this, we built a training set and test set by combining a preexisting labeled dataset from Anwar Ali Yahya, Addin Osama, et al. [5] and a self labeled
dataset of over 1500 samples. We engineered a set of features specific to short text samples and questions. We adopted an experimental approach in selecting the classifier model: we tested several different models throughout the project and picked the best performing models as final step. When looking up Bloom’s taxonomy it is often presented with lists of class specific keywords. We replicated a study [3] that makes use of keywords that are indicative of the class in Bloom’s taxonomy to set a baseline to compare our model to. We ran our model and the model of the baseline study on the same test set. Our model scored an accuracy of 75% compared to the baseline model which scored an accuracy of 40%.