Retrieval of Relevant Data for Measuring the Impact of Spaced-Repetition Algorithms on the Learning Success in Mobile Learning Games

This paper presents an approach on how to retrieve relevant data from a huge set of game-data in order to find evidence for the impact of using spaced-repetition algorithms on the learning success in a mobile learning game. After having collected approximately 12 million sets of playing data, the database needs to be preprocessed before analyzing it in order to filter out any data that is irrelevant or useless for our analysis or may even dilute its results. One structured and established way to do this is to follow the KDD process, which includes several consecutive steps of consolidating the available data, with preprocessing it being one of them. In order to be able to define the data were are looking for, we set up some proposals about how the relevant data should look like and how to retrieve it from our database.