On Characterization of Norm-Referenced Achievement Grading Schemes toward Explainability and Selectability

<table class="algorithm-group"><tr><td><table class="algorithm" id="alg1"><tr><td> </td><td><b>Input</b></td></tr><tr><td> </td><td>  S: vector of learners’ scores</td></tr><tr><td> </td><td>  GS: set of ranked eligible grade symbols</td></tr><tr><td> </td><td><b>Output</b></td></tr><tr><td> </td><td>  G: vector of learners’ grades</td></tr><tr><td> </td><td><b>Local variable</b></td></tr><tr><td> </td><td>  cnt: number of eligible grades</td></tr><tr><td> </td><td>  SG: vector of score gaps</td></tr><tr><td> </td><td>  R: vector of score ranges</td></tr><tr><td> </td><td><b>Begin</b></td></tr><tr><td>(1)</td><td>  S ← sort(S);</td></tr><tr><td>(2)</td><td>  cnt ← countEligibleGrades(GS);</td></tr><tr><td>(3)</td><td>  SG ← calculateAllScoreGaps(S);</td></tr><tr><td>(4)</td><td>  SG ← descendingSort(SG);</td></tr><tr><td>(5)</td><td>  SG ← selectWidestGaps(SG, cnt – 1);</td></tr><tr><td>(6)</td><td>  R ← defineScoreRangesFromGaps(SG);</td></tr><tr><td>(7)</td><td>  G ← grades(S, R);</td></tr><tr><td> </td><td><b>End</b></td></tr></table></td></tr></table>

Applied Computational Intelligence and Soft Computing

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Algorithm 1

Algorithm 1: On Characterization of Norm-Referenced Achievement Grading Schemes toward Explainability and Selectability 