Respecting Data Privacy in Educational Data Mining: An Approach to the Transparent Handling of Student Data and Dealing with the Resulting Missing Value Problem

Learning is becoming increasingly digital, which leads to an increasing amount of data originating from educational environments. Research fields, such as educational data mining are investigating algorithms that can use this data to better understand students and the settings which they learn in. In recent years, this has repeatedly led to problems between companies that want to analyze the data, and students, parents, and schools, which do not agree with a non-transparent use of their data. In this work, we propose an approach that can lead to transparency, and thus confidence, in the use of educational data mining. Every student should decide for himself which of his data may be passed on to third parties and be used by them. In the form of opt-in checklists, the features or feature groups are provided for selection. Since not every student will allow everything, datasets with missing values are created. This requires algorithms or strategies to deal with such data. We simulate missing values on a student dataset and evaluate an approach to dealing with missing data for several prediction tasks of different difficulty levels. Depending on the amount of missing data in a dataset, and the predictive task, this approach provides useful results.