Claims

DGS instruction was most effective for recognizing transformations and stating their features, where it beat both manipulatives and traditional instruction

Claim · All claims
Evidence · 1 study (2 entries) · causal r2 · q3 · i3 large
Currency · newest study 2015 · not yet checked against its sources · not reviewed by a person

Subclaims

Reading the evidence codes

q is the design tier, i the impact, n the sample, and the kind with its r the rigour judged within that kind: what each code means.

This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.

q3 i3 On the recognition section, ANCOVA showed Computer > Manipulatives > Traditional (F(2,111) = 53.53, η2 = .49). → Gül Kaleli Yılmaz 2015 q3 i3 On the features section, ANCOVA showed the same ordering (F(2,111) = 52.07, η2 = .48). → Gül Kaleli Yılmaz 2015 (2)

Evidence

Gül Kaleli Yılmaz 2015

Gül Kaleli Yılmaz. (2015). The Effect of Dynamic Geometry Software and Physical Manipulatives on Candidate Teachers’ Transformational Geometry Success. Educational Sciences: Theory & Practice. https://doi.org/10.12738/estp.2015.5.2610

q3 · i3 · causal · r2

ANCOVA on the recognition section with its pre-test as covariate; Bonferroni comparisons: CG vs. MG mean difference = 1.63, CG vs. TG = 3.83, MG vs. TG = 2.20, all p < .001. The article credits Cabri's visual features and dynamism for instant viewing of transformations.

"there was a significant mean difference in the gain scores of students in the Computer Group, Manipulatives Group, and Traditional Group with respect to the recognition section of the test [ F(2,111) = 53.53, p < .05, η 2 = .49]"

Gül Kaleli Yılmaz 2015 (2)

Gül Kaleli Yılmaz. (2015). The Effect of Dynamic Geometry Software and Physical Manipulatives on Candidate Teachers’ Transformational Geometry Success. Educational Sciences: Theory & Practice. https://doi.org/10.12738/estp.2015.5.2610

q3 · i3 · causal · r2

ANCOVA on the features section with its pre-test as covariate; Bonferroni comparisons: CG vs. MG = 1.58, CG vs. TG = 3.47, MG vs. TG = 1.89, all p < .001. The article explains Computer Group students could measure sides and area on screen to check features.

"There was a significant mean difference in the gain scores of students in the Computer Group, Manipulatives Group, and Traditional Group with respect to the features section of the test [F(2,111) = 52.07, p < .001, η2 = .48]"

Discussion

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