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In this model, math and science scores predicted problem solving scores at the student level.
Discrepancies between math and problem solving scores are discussed in relation to quality of education.
In the first equation, problem solving scores across the 41 countries were regressed on math-science coherence.
Indeed, those participants who employed more novice-like categorization features (Table 1) displayed lower problem solving scores.
Likewise, despite a diversity of novice explanation and card sort patterns (as noted above), experts displayed significantly higher evolution problem categorization scores and evolution problem solving scores.
r(M,S): correlation between math and science performance; R 2 : explanation of variance in problem solving scores by math and science scores.
When sixth grade participants were considered independently, participants in the most supported drawing condition also obtained higher problem solving scores than those who drew without support.
Leutner et al. ([2012]) suggest that discrepancies between math and problem solving scores are indicative of the relative effectiveness of math education (OECD [2004]).
Interestingly, countries' level of coherence in math-science education was substantially related to their problem solving scores (r = .76, p < .001).001
Figure 3 The relation between math-science coherence and the capability under-utilisation index for science and problem solving scores across the 41 countries.
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