Validation of indicators for implementing an adaptive platform for MOOCs

Abstract

Personalization techniques are a classic solution recommended by many experts for improving learning. Information and communication technologies and online courses have helped reduce the difficulties teachers face with a diversity of student profiles and a large number of students in a classroom. When these factors are extreme, like in a Massive Open Online Course (MOOC), those techniques may be the solution. However, even the most sophisticated technologies have not solved all the challenges posed by personalized learning, and in cases where teachers are not skilled in the technology they must use, the adaptive systems have only complicated the implementation of online courses. Therefore, this paper proposes a construct of adaptivity for MOOCs to identify some specific personalizing indicators. These indicators are chosen as a result of previous work done and are based on two aspects of learning: self-regulation and cooperation. This construct presents a consistent scale. A study is conducted to find the indicators that are most acceptable to participants in a MOOC, and it considers whether the performance or completion of other MOOCs previously influences the participant's perception of the value of the proposed construct.