  With the increasing popularity of online learning, intelligent tutoring
systems are regaining increased attention. In this paper, we introduce adaptive
algorithms for personalized assignment of learning tasks to student so that to
improve his performance in online learning environments. As main contribution
of this paper, we propose a a novel Skill-Based Task Selector (SBTS) algorithm
which is able to approximate a student's skill level based on his performance
and consequently suggest adequate assignments. The SBTS is inspired by the
class of multi-armed bandit algorithms. However, in contrast to standard
multi-armed bandit approaches, the SBTS aims at acquiring two criteria related
to student learning, namely: which topics should the student work on, and what
level of difficulty should the task be. The SBTS centers on innovative reward
and punishment schemes in a task and skill matrix based on the student
behaviour.
  To verify the algorithm, the complex student behaviour is modelled using a
neighbour node selection approach based on empirical estimations of a students
learning curve. The algorithm is evaluated with a practical scenario from a
basic java programming course. The SBTS is able to quickly and accurately adapt
to the composite student competency --- even with a multitude of student
models.
