Open AccessDetecting Students Gifted in Mathematics with Stream Mining and Concept Drift Based M-Learning Models Integrating Educational Computer Games
Author(s) -
Petar Jurić,
Marija Brkić Bakarić,
Maja Matetić
Publication year - 2021
Publication title -
international journal of emerging technologies in learning/international journal: emerging technologies in learning
Language(s) - English
Resource type - Journals
eISSN - 1868-8799
pISSN - 1863-0383
DOI - 10.3991/ijet.v16i12.21925
Subject(s) - concept drift , computer science , point (geometry) , feature (linguistics) , artificial intelligence , machine learning , feature selection , mathematics education , educational data mining , data stream , data mining , data stream mining , mathematics , telecommunications , linguistics , philosophy , geometry
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 should new models be built.