Analysis of studyability by means of prediction and simulation models

Outcome indicators of studyability can be operationalized as parameters relevant to study success and thus modeled and predicted. This paper shows how choosing the appropriate machine learning method enables both the prediction of individual studyability with an accuracy of almost 90% and the analysis of factors influencing individual studyability. Furthermore, a conceptual intersection of the prediction model with a simulation model is discussed in order to analyse the structural dimension of studyability.