Trajectories

TrajectoriesMy research question is in the context of an 8-week pure online delivery.

If one plots the student user trajectory for the first three weeks, with what kind of reliability can one predict the trajectory for the next five weeks and their final outcome in week eight?

Is the indication of the first three weeks sufficiently strong (clear, reliable) to justify the cost (time, effort) of intervention?

What do you plot? Activity? Results of formative quizzes?

How practical would it be to code an add-in to the standard Moodle that flags up those students whose trajectories suggest they are at risk?

AttributionStudent trajectories were first introduced to me by Graeme Cornwell of NMIT in about 2008, and I would like to acknowledge his contribution to my understanding.
I have found them very useful and remarkably reliable as a prediction instrument. The more you plot and study, and the more that you reflect upon them, the more you start seeing patterns. But, like the Titanic dataset, it's uncertain outcomes ... a student who goes along just fine at the start can suddenly flatline, a student who starts poorly may pick up. And the profile belongs to them, not to the course... or does it partially belong to the course too?

(A) Where the trajectory is thought of as belonging to the student

Reasons a student may flatline:

(B) Where the trajectory is thought of as being partly attributable to the course

Usually signalled by 50% or more of the students flatlining.

Reasons students may be flatlining:

You can have a go on some real student user data if you want.

Last update: SL 2011-07-05