Using Educational Data Mining Methods to Assess Field-Dependent and Field-Independent Learners' Complex Problem Solving

Autor/inn/en	Angeli, Charoula; Valanides, Nicos
Titel	Using Educational Data Mining Methods to Assess Field-Dependent and Field-Independent Learners' Complex Problem Solving
Quelle	In: Educational Technology Research and Development, 61 (2013) 3, S.521-548 (28 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1042-1629
DOI	10.1007/s11423-013-9298-1
Schlagwörter	College Students; Computer System Design; Cognitive Style; Immigration; Problem Solving; Data; Information Retrieval; Computer Software; Data Analysis; Hidden Figures Test + Suchen Sie Ihr Suchwort? Collegestudent; Cognitive styles; Kognitiver Stil; Problemlösen; Daten; Auswertung
Abstract	The present study investigated the problem-solving performance of 101 university students and their interactions with a computer modeling tool in order to solve a complex problem. Based on their performance on the hidden figures test, students were assigned to three groups of field-dependent (FD), field-mixed (FM), and field-independent (FI) learners, and were instructed to use integrated-format materials and Model-It[R] in order to solve a problem about immigration policy. The results showed that there were significant differences among the three groups of learners in terms of their problem-solving performance. Consequently, the study employed educational data mining (EDM) methods in order to examine how FD and FI learners actually interacted with Model-It[R] in order to solve the problem. The EDM methods provided rich analytical information and details about learners' interactions with the computer tool. Implications for designing effective joint cognitive systems are discussed. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10