Detecting Strengths and Weaknesses in Learning Mathematics through a Model Classifying Mathematical Skills

Autor/inn/en	Karagiannakis, Giannis N.; Baccaglini-Frank, Anna E.; Roussos, Petros
Titel	Detecting Strengths and Weaknesses in Learning Mathematics through a Model Classifying Mathematical Skills
Quelle	In: Australian Journal of Learning Difficulties, 21 (2016) 2, S.115-141 (27 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Baccaglini-Frank, Anna E.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1940-4158
DOI	10.1080/19404158.2017.1289963
Schlagwörter	Mathematics Skills; Mathematical Aptitude; Skill Analysis; Learning Disabilities; Cluster Grouping; Mathematics Activities; Mathematics Tests; Grade 5; Grade 6; Factor Analysis; Models; Educational Diagnosis; Correlation; Evidence; Performance Based Assessment; Mathematics Achievement; Nonverbal Ability; Number Concepts; Memory; Thinking Skills; Literature Reviews; Foreign Countries; Intelligence Tests; Statistical Analysis; Goodness of Fit; Greece (Athens); Raven Progressive Matrices + Suchen Sie Ihr Suchwort? Mathmatics achievement; Mathematics ability; Mathematische Kompetenz; Learning handicap; Lernbehinderung; Eingruppierung; School year 05; 5. Schuljahr; Schuljahr 05; School year 06; 6. Schuljahr; Schuljahr 06; Faktorenanalyse; Analogiemodell; Pedagogical diagnostics; Pädagogische Diagnostik; Korrelation; Evidenz; Leistungsermittlung; Mathmatics sikills; Mathematical ability; Number concept; Zahlbegriff; Gedächtnis; Denkfähigkeit; Ausland; Intelligence test; Intelligenztest; Statistische Analyse
Abstract	Through a review of the literature on mathematical learning disabilities (MLD) and low achievement in mathematics (LA) we have proposed a model classifying mathematical skills involved in learning mathematics into four domains (Core number, Memory, Reasoning, and Visual-spatial). In this paper we present a new experimental computer-based battery of mathematical tasks designed to elicit abilities from each domain, that was administered to a sample of 165 typical population 5th and 6th grade students (MLD = 9 and LA = 17). Explanatory and confirmatory factor analysis were conducted on the data obtained, together with K-means cluster analysis. Results indicated strong evidence for supporting the solidity of the model, and clustered the population into six distinguishable performance groups with the MLD and LA students distributed within five of the clusters. These findings support the hypothesis that difficulties in learning mathematics can have multiple origins and provide a means for sketching students' mathematical learning profiles. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01