# Make differences between students when assessing groups in assigments

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#### Details

• Type: Improvement
• Status: Open
• Priority: Minor
• Resolution: Unresolved
• Affects Version/s: 3.7
• Fix Version/s: None
• Component/s:
• Labels:
• Affected Branches:
MOODLE_37_STABLE

#### Description

The assignment allows  assessing groups globally, which is fine, but in some cases there are differences between the work done by the different team members. Some members may work less than the others or not at all.

In some cases the students themselves claim against the lazy peers. Or the teacher when doing the supervision detects that some of them have a weak knowledge of the work done.
When the grading is done by means of a rubric the teacher has no control on the final score and is difficult to adjust the score to each student.

I propose to be able to score the groups (not one of the students and then copy the score to the other team members). And within each group show the members.
Once the work for the group has been scored, then the teacher can include a weighting for each member. This weighting could be done in two ways:

1. The teacher rates each student with  a percentage and the student gets a final score that is a percentage of the group's score
2. The teacher rates each student with a percentage and the global score is distributed between the students so that the sum is: the global score x number of students in the team.  Here some students may reach the maximum score and some "points" may be lost

So for example, a group of three members has  global score of 80 and the teacher  weights the three members as: 100%, 100%  and 50%.
Computing the grading using the first option, the final scores would be: 80, 80 and 40
Computing the grading using the second option, the final scores would be:
80*3 = 240 points to be distributed  so the first and second get  240*100/250=96
while the third students gets 240*50/250=48

#### People

• Assignee:
Unassigned
Reporter:
Joan Codina
Participants:
Component watchers:
Damyon Wiese, Adrian Greeve, Mihail Geshoski, Peter Dias
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