Process mining for self-regulated learning assessment in e-learning
R. Cerezo A. Bogarin M. Esteban C. Romero
Content assessment has broadly improved in e-learning scenarios in recent
decades. However, the eLearning process can give rise to a spatial and temporal
gap that poses interesting challenges for assessment of not only content, but
also students' acquisition of core skills such as self-regulated learning. Our
objective was to discover students' self-regulated learning processes during an
eLearning course by using Process Mining Techniques. We applied a new algorithm
in the educational domain called Inductive Miner over the interaction traces
from 101 university students in a course given over one semester on the Moodle
2.0 platform. Data was extracted from the platform's event logs with 21629
traces in order to discover students' self-regulation models that contribute to
improving the instructional process. The Inductive Miner algorithm discovered
optimal models in terms of fitness for both Pass and Fail students in this
dataset, as well as models at a certain level of granularity that can be
interpreted in educational terms, which are the most important achievement in
model discovery. We can conclude that although students who passed did not
follow the instructors' suggestions exactly, they did follow the logic of a
successful self-regulated learning process as opposed to their failing
classmates. The Process Mining models also allow us to examine which specific
actions the students performed, and it was particularly interesting to see a
high presence of actions related to forum-supported collaborative learning in
the Pass group and an absence of those in the Fail group.