  We present a general method for using a competences map, created by defining
generalization/specialization and inclusion/part-of relationships between
competences, in order to build an overlay student model in the form of a
dynamic Bayesian network in which conditional probability distributions are
defined per relationship type. We have created a competences map for a subset
of the transversal competences defined as educational goals for the Mexican
high school system, then we have built a dynamic Bayesian student model as said
before, and we have use it to trace the development of the corresponding
competences by some hypothetical students exhibiting representative
performances along an online course (low to medium performance, medium to high
performance but with low final score, and two terms medium to high
performance). The results obtained suggest that the proposed way for
constructing dynamic Bayesian student models on the basis of competences maps
could be useful to monitor competence development by real students in online
course.
