Applying evaluation criteria to a data solution
Test the solution against a criterion, record the result (met, partly met or not met) and justify the rating with evidence. Recommend improvements if it is not fully met.
Understanding Applying evaluation criteria to a data solution in VCE Data Analytics U4O1
What it does: The rating is backed by specific evidence, comparing the solution's result with the source data. The recommendation is a specific, actionable fix that would address the gap found when applying the criterion. 3 seconds is under the 5 second limit, so the criterion is fully met. Most charts pass but two fail, so the criterion is only partly met. With no validation rules, there is no evidence of any progress towards the criterion, so it is not met. A strong justification cites specific evidence such as counts, names or test results. When a criterion is not fully met, a good evaluation recommends an improvement to fix it.
Common mistakes: The evidence shows an average of 3.2 s, which is under 5 s, so the criterion is met. The result should be 'Result: Met'. 4 IDs are blank, so the criterion 'No blank IDs' is not fully achieved. Most IDs are filled, so the rating should be 'Result: Partly met'.
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