Evaluation
Evaluation

 

Point Distribution


The final grade will be based on the number of points achieved over the following :

      Work Points
      Homework/Quizzes
      20%
      Midterm(s)
      20%
      Lecture Final
      20%
      Programs 40%

    These are tentative weightings and subject to change. Refer to posted grades and announcements for the final weight distribution. Note: A passing grade must be obtained on both the programming and examination portions of this course in order to receive a passing grade,

Grade Scale



      Grade
      Range
       
      Score
      Scale
      100 - 93
      A
      92 - 90
      A-
      89 - 87
      B+
      86 - 83
      B
      82 - 80
      B-
      79 - 77
      C+
      76 - 73
      C
      72 - 70
      C-
      69 - 67
      D+
      66 - 63
      D
      62 - 60
      D-
      59 - 0
      F

 

Krider Curve


The Krider Curve was developed by Dr. Daniel W. Krider, Professor of Mathematics, Concord College, Athens, WV. The curve coerces grades into forming a distribution which more closely resembles a Normal Distribution. This is a partial solution to the problem of class grades tending to be skewed toward the lower values. The Krider Curve forces symmetrical clustering about the mean (property of central tendency) and smaller variance within the scores (less dispersion).


Mathematical Derivation

Given the following:

The following formula is applied to each individual student grade:

This is equivalent to averaging two X values with one grade of a 100. The lower scores receive a larger curve than the upper scores. An appropriate action when one considers that poorer students require more help. The curve ensures that a student will never surpass another student who scores higher.

Alpha can be set to achieve a desired class mean.

Where:

The formula for deriving alpha's value for a desired mean:

Computer Science 3114 Data Structures and Algorithms
D. Barnette