  A data-driven model where individual learning behavior is a linear
combination of certain stylized learning patterns scaled by learners'
affinities is proposed. The absorption of stylized behavior through the
affinities constitutes "building blocks" in the model. Non-negative matrix
factorization is employed to extract common learning patterns and their
affinities from data ensuring meaningful non-negativity of the result. The
empirical learning patterns resulting from the actual course interaction data
of 111 students are connected to a learning style system. Bootstrap-based
inference allows to check the significance of the pattern coefficients.
Dividing the learners in two groups "failed" and "passed" and considering their
mean affinities leads to a bootstrap-based test on whether the course structure
is well-balanced regarding the learning preferences.
