In standards-based learning, students typically receive multiple scores for each learning target as they are assessed and re-assessed throughout the grading period. In order to figure out their overall score for the learning target, admins must decide which of several calculation modes to use.
The calculation mode is chosen when creating or editing a standards-based scale at
Main > Grading Scales > Create Standards-based Scale or
Main > Grading Scales > edit
The standards-based grading scale calculation modes are:
- Median of Recent Scores
- Decaying Weights
- Power Law
- Mode of Recent Scores
- Average of Recent Scores
Except for Power Law and Highest, each mode requires that admins select how many of the most recent scores are included in the data set for the calculation. This helps ensure students are not negatively impacted by low scores when they are first introduced to a topic.
This mode uses the median, or middle score, of the sorted most recent scores. For example, if the number of recent scores to consider is 9, and a student has received at least nine scores for a learning target, then these scores are sorted high to low, and the middle value, the 5th score, is selected as the standards-based grade for this learning target.
|Example: 4, 4, 3, 3, 3, 2, 2, 2, 1|
|The student receives a 3 because 3 is in the middle of the data set.|
If there are fewer scores than the number of scores to consider, then the median of the available scores is selected. For example, if there were only 7 scores for a learning target, but the number of recent scores to be considered is 9, then they would be ordered high to low, and the 4th score would be selected as the standards-based grade for the learning target.
|Example: 4, 3, 3, 2, 2, 2, 1|
|The student receives a 2 because two is in the middle of the data set.|
When there is an even number of scores, the median is the average of the two middle scores. For example, if the student receives 8 scores for a learning target, then the median is the average of the 4th and 5th score.
|Example: 4, 3, 3, 3, 2, 2, 1, 1|
|The student would receive a 2.5 (the average between 2 and 3).|
This mode orders recent scores from newest to oldest, and calculates a score, based on assigned weights. Weights are assigned by age allowing for more recent student performance to factor more greatly into the overall score, if desired. The algorithm used is (Score1*Weight1)+(Score2*Weight2)... divided by (Weight1 + Weight2…)
|Example: For scores 3, 2, 3, 2, 1 with corresponding weights 40, 20, 17, 13, and 10|
|The student’s score is calculated as 247/100=2.47.|
The algorithm can work if fewer scores are available than the number set. For example, if the same setup is used, and a student only has two assignments on a target, the most recent would be worth 40/60, the next would be worth 20/60.
|Example: For score 3, 2 with corresponding weights 40, 20, 17, 13, and 10|
|The students score is calculated as 160/60 = 2.67.|
This mode is similar to Decaying Weights, but instead of having an admin choose the weights of each score, a statistical formula determines the weights based on the total number of assignments. Similar to any algorithms that use the most recent scores, students are not negatively impacted by low scores early on. The major drawback of power law is that it is very difficult to explain to students, parents, and staff. Parents can view a list of scores but will be unable to determine how the score for a particular learning target was calculated.
|Example: 1, 2, 2, 3|
|The student receives a 2.76 based on the algorithm.|
This mode generates a list of the student’s most recent scores; of that set, the score the student has received the most often will be their score for the learning target.
|Example: 3, 3, 2, 2, 2|
|The student will receive a 2 because the student received three 2s and two 3s. 2 is the most frequent score, or the mode.|
If multiple scores have the same frequency, the admin must decide whether the most recent or the highest score should be selected.
|Example: 2, 3, 3, 2, 1 (Listed most recent to oldest)|
|If the most recent score is prioritized, the student receives a 2. If the highest score is prioritized, the student receives a 3.|
This mode should be selected with caution. If a student is assessed with scores 3, 2, 2, 1, 1, 1, they would receive a 1, even though they have shown clear improvement in their recent scores.
This mode uses the highest earned score out of the data set. This mode may be selected based on the idea that once a student demonstrates a certain level, then he or she has achieved that level. However, a single data point may act as an outlier and not give a good representation of a student’s true level.
|Example: 2, 2, 2, 4, 2|
|The student receives a 4 even though they have mostly been assessed at level 2.|
This mode calculates a simple average (mathematical mean) of the recent scores. The scores are added together and divided by the total number of scores being considered. This mode is susceptible to outliers and may disproportionately punish students who struggle early on but later show improvement.
|Example: 3, 2, 3, 2, 1|
|The student’s score is calculated as (3+2+3+2+1)/5 = 2.2.|