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The role of metacognitive skills in learning to solve problems

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Study II: learn<strong>in</strong>g revisited 93<br />

1 = correct answer), the alpha is equivalent <strong>to</strong> the Kuder-Richardson<br />

20 (KR20). Because <strong>in</strong>itially the reliability coefficients were rather low,<br />

several items were deleted. <strong>The</strong> deleted items were distributed randomly<br />

over the learn<strong>in</strong>g goals. After deletion version A <strong>of</strong> KMQUESTions conta<strong>in</strong>s<br />

27 items for declarative knowledge (was 29) and 22 items for procedural<br />

knowledge (was 22). Version B <strong>of</strong> KMQUESTions then conta<strong>in</strong>s<br />

27 items for declarative knowledge (was 29) and 20 items for procedural<br />

knowledge (was 22). In table 6.6 the results are shown. <strong>The</strong> reliability<br />

<strong>of</strong> both versions is moderate <strong>to</strong> sufficient 2 .<br />

Version A KR20 N<br />

Declarative .76 16<br />

Procedural .64 16<br />

Version B KR20 N<br />

Declarative .70 15<br />

Procedural .56 15<br />

Table 5.1. KR20 coefficients for the items <strong>of</strong> the subsets declarative and procedural<br />

knowledge <strong>in</strong> both versions <strong>of</strong> KMQUESTions after deletion <strong>of</strong> items.<br />

Pre-test<br />

Post-test<br />

Mean SD N Mean SD N<br />

Group AB Declarative .45 .15 16 .63 .14 16<br />

Group AB Procedural .47 .15 16 .71 .15 16<br />

Group BA Declarative .43 .18 15 .54 .14 15<br />

Group BA Procedural .48 .12 15 .57 .11 15<br />

Declarative <strong>to</strong>tal sample .44 .16 31 .58 .14 31<br />

Procedural <strong>to</strong>tal sample .46 .14 31 .64 .14 31<br />

Table 5.2. Mean and standard deviation <strong>of</strong> the pre- and post-test parallel versions<br />

<strong>of</strong> KMQUESTions, measured <strong>in</strong> proportion <strong>of</strong> correct answers.<br />

2 Although the generally accepted value <strong>of</strong> 0.8 is appropriate for cognitive tests, the value<br />

<strong>of</strong> 0.7 can be used for ability tests. However, when the construct be<strong>in</strong>g measured is ‘vague’<br />

or ill-def<strong>in</strong>ed (such as KM), even lower coefficients can be expected (Kl<strong>in</strong>e, 1993) A rule <strong>of</strong><br />

thumb is that <strong>in</strong> general when coefficients are higher than 0.5 the ability measured dom<strong>in</strong>ates<br />

the measurement error or noise <strong>in</strong> the data.

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