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Learning Style Preference and Studen...
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Caswell, Donna.
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Learning Style Preference and Student Achievement.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Learning Style Preference and Student Achievement./
作者:
Caswell, Donna.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2016,
面頁冊數:
149 p.
附註:
Source: Dissertation Abstracts International, Volume: 78-05(E), Section: A.
Contained By:
Dissertation Abstracts International78-05A(E).
標題:
Education. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10196153
ISBN:
9781369342772
Learning Style Preference and Student Achievement.
Caswell, Donna.
Learning Style Preference and Student Achievement.
- Ann Arbor : ProQuest Dissertations & Theses, 2016 - 149 p.
Source: Dissertation Abstracts International, Volume: 78-05(E), Section: A.
Thesis (Ph.D.)--Grand Canyon University, 2016.
This nonexperimental, causal-comparative study investigated how the Preliminary Scholastic Aptitude Test (PSAT) mathematics scores differ by dominant learning style preference as identified by the Kolb learning style inventory, version 3 (LSI3). An additional focus was to identify whether variations in PSAT math scores exist as a function of learning style and gender. The Friedman test indicated there was a statistically significant difference between the type of learning style and the PSAT score (chi2 (1) = 54.000, p =.000). The post hoc analysis showed that the observed differences between learning style preferences and mean PSAT math scores were significant. Thus the null hypothesis was rejected and it was assumed that high mean PSAT math scores were associated with certain learning styles (z = 6.396, p < .001). The contrasts between PSAT math scores and learning styles (Z = -6.396, p = .000, r = -.87), PSAT math scores and gender (Z = -6.395, p = .000, r = -.87), and gender and learning style (Z = -5.965, p = .000, r = -.81) were found to be statistically significant. In both cases, the statistically significant contrasts indicated that the high PSAT math score means were more significant between learning style preference and gender than between PSAT math score mean and gender. The conclusions of the research discussed the rationale for the identification of learning style preferences, achievement on standardized test measures, and how educators can enhance student performance through the use of flex style learning to understand students' cognitive strengths and weaknesses.
ISBN: 9781369342772Subjects--Topical Terms:
516579
Education.
Learning Style Preference and Student Achievement.
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This nonexperimental, causal-comparative study investigated how the Preliminary Scholastic Aptitude Test (PSAT) mathematics scores differ by dominant learning style preference as identified by the Kolb learning style inventory, version 3 (LSI3). An additional focus was to identify whether variations in PSAT math scores exist as a function of learning style and gender. The Friedman test indicated there was a statistically significant difference between the type of learning style and the PSAT score (chi2 (1) = 54.000, p =.000). The post hoc analysis showed that the observed differences between learning style preferences and mean PSAT math scores were significant. Thus the null hypothesis was rejected and it was assumed that high mean PSAT math scores were associated with certain learning styles (z = 6.396, p < .001). The contrasts between PSAT math scores and learning styles (Z = -6.396, p = .000, r = -.87), PSAT math scores and gender (Z = -6.395, p = .000, r = -.87), and gender and learning style (Z = -5.965, p = .000, r = -.81) were found to be statistically significant. In both cases, the statistically significant contrasts indicated that the high PSAT math score means were more significant between learning style preference and gender than between PSAT math score mean and gender. The conclusions of the research discussed the rationale for the identification of learning style preferences, achievement on standardized test measures, and how educators can enhance student performance through the use of flex style learning to understand students' cognitive strengths and weaknesses.
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