One of the problems of individualized classes which adapt contents and methods of teaching to students of different cognitive capabilities is early and widely available detection of students gifted in certain educational fields. The paper proposes models which are based on stream mining and which can detect students gifted in Mathematics solely on the basis of their interaction with the m-learning system using educational computer games and with no access to any other feature except for student age. Classification accuracy and time-efficiency of different feature selection methods are examined in order to make the models more interpretable, hence less complex. Stream mining classification accuracy in the utilized models is evaluated on new (yet unseen) records, while the concept drift detection analyses at which point of time new models should be built.
