“…Given the ordinality characteristic of the educational grading scales, it is well known that arithmetic mean is not an appropriate statistic (Stevens, 1951(Stevens, , 1955Ieta, 2004). However, in our recent work (Ieta et al, 2004) we have demonstrated a theorem for the selection of equivalent ordinal scales. Comparison of arithmetic mean (ranks) originating on different ordinal scales is appropriate for such scales.…”
Section: Introductionmentioning
confidence: 76%
“…Some brief remarks will be made regarding a possible application of arithmetic mean ranks, as defined and studied so far (Ieta et al, 2004), to a candidate selection process. A two-stage candidate selection process will be considered.…”
Section: Selection Proceduresmentioning
confidence: 99%
“…It has been demonstrated (see B.2 in Ieta, 2004) that the rank of average of a concatenated set lies between the rank of averages of individual sets. This ordinal property does no longer hold if the (rank of) average is calculated on a scale S X non-equivalent to S i (and to S G ).…”
Section: Weaknesses Of Decisions Made On Rank Of Arithmetic Meanmentioning
confidence: 99%
“…The school also uses S 12 for calculating averages that include sets of grades originating on S 100 . Analysis and observations related to scales S 4 , S 12 , and S 100 can be found in Ieta et al, (2004). The conversion of grades from S 12 (or S 4 ) to S 100 fails to make all the fine distinctions between subranks possible on S 100 .…”
Section: Non-uniform Use Of An Arithmetic Mean Criterionmentioning
confidence: 99%
“…Those having grades on both S 100 and S 4 , whose average is evaluated on S 12 (equivalent to S 4 ) where the average, as a letter grade, will usually be diminished relative to its l.g. equivalent on S 100 (Ieta et al, 2004). A student studying at School2 and having a mixture of grades from S 4 and S 100 will likely be at a disadvantage relative to his/her peers if the overall average of the grades is needed in certain types of selection processes.…”
Section: Non-uniform Use Of An Arithmetic Mean Criterionmentioning
“…Given the ordinality characteristic of the educational grading scales, it is well known that arithmetic mean is not an appropriate statistic (Stevens, 1951(Stevens, , 1955Ieta, 2004). However, in our recent work (Ieta et al, 2004) we have demonstrated a theorem for the selection of equivalent ordinal scales. Comparison of arithmetic mean (ranks) originating on different ordinal scales is appropriate for such scales.…”
Section: Introductionmentioning
confidence: 76%
“…Some brief remarks will be made regarding a possible application of arithmetic mean ranks, as defined and studied so far (Ieta et al, 2004), to a candidate selection process. A two-stage candidate selection process will be considered.…”
Section: Selection Proceduresmentioning
confidence: 99%
“…It has been demonstrated (see B.2 in Ieta, 2004) that the rank of average of a concatenated set lies between the rank of averages of individual sets. This ordinal property does no longer hold if the (rank of) average is calculated on a scale S X non-equivalent to S i (and to S G ).…”
Section: Weaknesses Of Decisions Made On Rank Of Arithmetic Meanmentioning
confidence: 99%
“…The school also uses S 12 for calculating averages that include sets of grades originating on S 100 . Analysis and observations related to scales S 4 , S 12 , and S 100 can be found in Ieta et al, (2004). The conversion of grades from S 12 (or S 4 ) to S 100 fails to make all the fine distinctions between subranks possible on S 100 .…”
Section: Non-uniform Use Of An Arithmetic Mean Criterionmentioning
confidence: 99%
“…Those having grades on both S 100 and S 4 , whose average is evaluated on S 12 (equivalent to S 4 ) where the average, as a letter grade, will usually be diminished relative to its l.g. equivalent on S 100 (Ieta et al, 2004). A student studying at School2 and having a mixture of grades from S 4 and S 100 will likely be at a disadvantage relative to his/her peers if the overall average of the grades is needed in certain types of selection processes.…”
Section: Non-uniform Use Of An Arithmetic Mean Criterionmentioning
Grading techniques for tuning student and faculty performanceNew faculty are highly qualified in their own field, where they have accumulated some research experience and where they can bring fair amounts of enthusiasm. This article discusses grading techniques that help match student performance and instructor interest. Grading as a tool for evaluating student performance has been considered mainly from the student perspective. Anybody new to teaching rarely has proper training on grading techniques, which often are of least concern relative to teaching content. Nevertheless, grading as perceived by students may greatly impact their attitude towards the course and its instructor. It has been proven that students are very sensitive to grades and inaccurate evaluation of their perceived performance can also alter their future performance as well as their evaluation of teaching, which may adversely affect the instructor. Often, scaling of raw scores is used in grading engineering tests. There are no official standards on how this operation should be performed, hence the wide variation in the common procedures used. This work compares a few common-sense scaling procedures and shows how the final outcome may vary when determined from the same raw scores. Such grading variations significantly affect what the evaluation of the students' performance represents. This article offers recommendations on use of scaling methods so that the negative impacts of grading techniques and grade distortions can be minimized and lead to enhanced and efficient evaluation standards. Since grading and grading techniques are of general interest to instructors, this article may be of service to many instructors, especially to the new and relatively new faculty willing to review some of their own grading procedures.
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