Details
Originalsprache | Englisch |
---|---|
Seitenumfang | 17 |
Fachzeitschrift | Higher Learning Research Communications |
Jahrgang | 9 |
Ausgabenummer | 1 |
Frühes Online-Datum | 25 Mai 2019 |
Publikationsstatus | Veröffentlicht - Juni 2019 |
Abstract
Based on a cognitive–motivational modeling of construct relations, this study analyzed the role of prior statistics experiences within a framework of domain-specific self-concept and value variables to predict education science students’ statistics anxiety. Data were analyzed from two independent samples comprising 113 and 87 participants—using the passing of a statistics exam as the experience measure in each case. In both samples, the results of three-way analyses of variance demonstrated students’ statistics anxiety to be substantially explained by their negative utility value and self-concept but only to a minor extent by their prior statistics experiences. Students’ statistics anxiety appeared to be dependent on value and self-concept scores across all experience levels. Though producing somewhat varying effect patterns, the findings from both samples led to similar effects, indicating the crucial role of self-belief variables. Results are discussed in terms of conceptual, methodological, and instructional implications.
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in: Higher Learning Research Communications, Jahrgang 9, Nr. 1, 06.2019.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Predicting Education Science Students’ Statistics Anxiety
T2 - The Role of Prior Experiences Within a Framework of Domain-Specific Motivation Constructs
AU - Faber, Günter
AU - Drexler, Heike
PY - 2019/6
Y1 - 2019/6
N2 - Based on a cognitive–motivational modeling of construct relations, this study analyzed the role of prior statistics experiences within a framework of domain-specific self-concept and value variables to predict education science students’ statistics anxiety. Data were analyzed from two independent samples comprising 113 and 87 participants—using the passing of a statistics exam as the experience measure in each case. In both samples, the results of three-way analyses of variance demonstrated students’ statistics anxiety to be substantially explained by their negative utility value and self-concept but only to a minor extent by their prior statistics experiences. Students’ statistics anxiety appeared to be dependent on value and self-concept scores across all experience levels. Though producing somewhat varying effect patterns, the findings from both samples led to similar effects, indicating the crucial role of self-belief variables. Results are discussed in terms of conceptual, methodological, and instructional implications.
AB - Based on a cognitive–motivational modeling of construct relations, this study analyzed the role of prior statistics experiences within a framework of domain-specific self-concept and value variables to predict education science students’ statistics anxiety. Data were analyzed from two independent samples comprising 113 and 87 participants—using the passing of a statistics exam as the experience measure in each case. In both samples, the results of three-way analyses of variance demonstrated students’ statistics anxiety to be substantially explained by their negative utility value and self-concept but only to a minor extent by their prior statistics experiences. Students’ statistics anxiety appeared to be dependent on value and self-concept scores across all experience levels. Though producing somewhat varying effect patterns, the findings from both samples led to similar effects, indicating the crucial role of self-belief variables. Results are discussed in terms of conceptual, methodological, and instructional implications.
KW - Mathematics self-concept
KW - Prior statistics experiences
KW - Statistics anxiety
KW - Utility value
UR - http://www.scopus.com/inward/record.url?scp=85068831906&partnerID=8YFLogxK
U2 - 10.18870/hlrc.v9i1.435
DO - 10.18870/hlrc.v9i1.435
M3 - Article
AN - SCOPUS:85068831906
VL - 9
JO - Higher Learning Research Communications
JF - Higher Learning Research Communications
IS - 1
ER -