3. Course 230 chain
Based on how students
complete graded
activities, we can
compute the course
Markov Chain which
shows clearly how
most student follow the
course sequentially.
3
4. green track: sequential, nothing missed
blue track: skipped one
red track: skipped two (more than two in yellow)
Student participation map
START 2..08
2..14 2..15
3..19 4..23
6..34 7..38
8..40 9..43
12..57 13..60
123
16 4
1
1
2
2
2
2
11..5210..48
28
76
0
35
70
105
140
2..08 2..14 2..15 3..19 4..23 6..34 7..38 8..40 9..43 10..48 11..52 12..57 13..60 END
16
2
61
2111
4
63
1
2332
6
9
17
2
9
9121511
911
64
77
66
77
90
81847882
9095
105
123
as soon as skipping is feasible, skipping appears
8. Sequences to WS8
8
What can we say
about lookbacks?
Gabadinho, A., Ritschard, G., Müller, N.S. & Studer, M. (2011), Analyzing and visualizing
state sequences in R with TraMineR, Journal of Statistical Software. Vol. 40(4), pp. 1-37.
9. Diligent students
Looking locally
does not really tell
us how diligent a
student is over the
whole course.
We only know that
the red track will
detract at least 2
points and the blue
track will detract 1.
9
11. Diligence and gaps
The means of the
loopback degrees
for each student, in
each target activity
are generally lower
for diligent
students:
they show less
gaps in their
retention.
11
More generally, study which activities require the most recall
16. Vision
Charting the tracks of
the students, traced
according to some
property, on a Riemann
surface so that we can
help them navigate in
their learning flow.
16