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Similarity analyses in the process of summative evaluation

Leading article: 2017. February (MIAU No. 222.)
(Previous article: MIAU No. 221.)

Keywords: everyone-can-be-the-same-principle, objectivity, automated evaluation

Abstract: The summative evaluation is a fundamental part of the school systems. In this paper, the author considers the summative evaluation a wide-spread practice (e.g. mid-term or end-of-year grading) Teachers, trying to reach the maximum objectivity of their grading system, may use various weighting methods to calculate the appropriate averages of the students’ previous grades. These weighting methods can be righteous but must be inevitably subjective as well. Using the similarity analysis software (COCO), there are other possibilities and this paper focuses on the introduction of some of them. The similarity analysis uses staircase functions to evaluate each unique value of every attribute instead of the global weight-coefficients. The anti-discriminative (Y0) method of the similarity analyses has a special “null hypothesis” considering every object (in this case: student) to be equal in the final summative evaluation. If a fitting set of staircase functions can not be found during the evaluation process, the confirmed “best” and “worst” students’ performance will be revealed. Being aware of these confirmed extremities of the students’ performance allows teachers to make their evaluation system more objective and to manage their available capacities more efficiently. More (PDF)

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