"Usage"
,
as we’ll be uploading and analyzing usage data in this section.
usage_data.json
file.
tab1
to Usage
.
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group by
above the email
field.
This will group each log on a student-by-student basis,
each of which can be unfolded to see the raw data.
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mean
is plotted for the reduction metrics,
but this can be changed at the bottom of the table.
Let’s change the metric to count
instead,
and let’s order the groups in ascending count order.
It doesn’t matter which column we use for the group sorting,
they all have the same count
value,
which refers to the number of logs (rows) in the group.
In this case we’ll just go with the timestamp count.
With this configuration applied,
we have the students making most use shown at the top,
and those making the least use shown at the bottom.
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student/email
for the x
axis (unique student identifier)
and student/email -> count
for the y
axis.
A separate bar is then plotted for each unique email address,
with the number of logs on the y axis.
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time()
function on our datetime column.
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mean
as our reduction statistic, and group by gender
,
we’ll then get the average time of day when males vs female are on the platform.
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x
axis,
and then select the number of buckets we’d like to use.
Let’s go with 50 buckets.
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performance
for the ratio of marks acheived out of the total available,
and paper_question_num
for a unique paper + question value.
Let’s create these derived columns.
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MarkingAssistant
project
and select our Usage
tab,
we see the same configuration we previously saved.
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