Assessment of virtual spaces learning processes using state machines

This paper presents an innovative methodology to assess student learning in virtual spaces using Deterministic Finite Automata (DFA). The methodology is supported on a model that integrates the DFA into the Blackboard platform. The DFA is based on Adaptive Systems (AS) and Learning Objects (LO). The AS and LO allow the students to adjust their learning rate through the DFA. The proposed methodology was implemented for a virtual course on “Algorithms and Complexity”. The implementation showed that there is a significant improvement in performance for students learning through the DFA.