Mouse Movement and Probabilistic Graphical Models Based E-Learning
  Activity Recognition Improvement Possibilistic Model
  Automatically recognizing the e-learning activities is an important task for
improving the online learning process. Probabilistic graphical models such as
hidden Markov models and conditional random fields have been successfully used
in order to identify a Web users activity. For such models, the sequences of
observation are crucial for training and inference processes. Despite the
efficiency of these probabilistic graphical models in segmenting and labeling
stochastic sequences, their performance is adversely affected by the imperfect
quality of data used for the construction of sequences of observation. In this
paper, a formalism of the possibilistic theory will be used in order to propose
a new approach for observation sequences preparation. The eminent contribution
of our approach is to evaluate the effect of possibilistic reasoning during the
generation of observation sequences on the effectiveness of hidden Markov
models and conditional random fields models. Using a dataset containing 51 real
manipulations related to three types of learners tasks, the preliminary
experiments demonstrate that the sequences of observation obtained based on
possibilistic reasoning significantly improve the performance of hidden Marvov
models and conditional random fields models in the automatic recognition of the
e-learning activities.
