Cluster-based prediction of mathematical learning patterns.

Autor/inn/en	Käser, Tanja; Busetto, Alberto Giovanni; Solenthaler, Barbara; Kohn, Juliane; Aster, Michael von; Gross, Markus
Institution	International Artificial Intelligence in Education Society
Titel	Cluster-based prediction of mathematical learning patterns.
Quelle	Aus: Lane, H. Chad (Hrsg.); Yacef, Kalina (Hrsg.); Mostow, Jack (Hrsg.); Pavlik, Philip (Hrsg.): Artificial Intelligence in Education. 16th International Conference, AIED 2013, Memphis, TN, USA, July 9-13, 2013. Proceedings. Berlin u.a.: Springer (2013) S. 389-399 PDF als Volltext Link als defekt meldenVerfügbarkeit
Reihe	Lecture Notes in Computer Science. 7926
Beigaben	Literaturangaben
Zusatzinformation	Forschungsdaten, Studiendetails und Erhebungsinstrumente
Sprache	englisch
Dokumenttyp	online; gedruckt; Sammelwerksbeitrag
ISBN	978-3-642-39111-8; 978-3-642-39112-5
DOI	10.1007/978-3-642-39112-5_40
Schlagwörter	Lernprozess; Mathematische Kompetenz; Computerunterstütztes Verfahren; Trainingsprogramm
Abstract	This paper introduces a method to predict and analyse students' mathematical performance by detecting distinguishable subgroups of children who share similar learning patterns. We employ pairwise clustering to analyse a comprehensive dataset of user interactions obtained from a computer-based training system. The available data consist of multiple learning trajectories measured from children with developmental dyscalculia, as well as from control children. Our online classification algorithm allows accurate assignment of children to clusters early in the training, enabling prediction of learning characteristics. The included results demonstrate the high predictive power of assignments of children to subgroups, and the significant improvement in prediction accuracy for short- and long-term performance, knowledge gaps, overall training achievements, and scores of further external assessments. (Orig.).
Erfasst von	DIPF \| Leibniz-Institut für Bildungsforschung und Bildungsinformation, Frankfurt am Main
Update	2020/3