Towards an approach for measuring different types and intensities of interaction in massive open online courses (moocs)

Abstract

In the Interaction Equivalency Theorem (IET), Anderson proposed that students are likely to achieve different levels of satisfaction depending on the interaction intensity inside a course (student-student, student-content, or student-teacher interaction). Although higher interaction intensity could lead to a more satisfying learning experience, this may not be as cost- or time effective as less interactive learning sequences. However, the research done so far about the IET has not validated this thesis statement. Therefore, we aim to define a methodological research approach which would allow us to identify different types of interaction and its intensity inside Massive Open Online Courses (MOOCs), in order to confirm Anderson’s thesis. As MOOCs are by definition massive, i.e. many people enrol in them, they provide an opportunity for exploration of hypotheses that can prove to be more challenging to research in other environments, such as the IET. Furthermore, as virtually all student behaviour is recorded, measuring different types of interaction is easier and can be much more systematic, as the same approach can then be applied across myriads of online courses. Since it is challenging to determine what can be considered a high, medium or low level of interaction, we also aim to define an approach that will allow us to do that.