1 UniversitĂ  degli Studi di Napoli Federico II (ITALY)
2 UniversitĂ  degli Studi di Cassino e del Lazio Meridionale (ITALY)
About this paper:
Appears in: INTED2019 Proceedings
Publication year: 2019
Pages: 6694-6700
ISBN: 978-84-09-08619-1
ISSN: 2340-1079
doi: 10.21125/inted.2019.1629
Conference name: 13th International Technology, Education and Development Conference
Dates: 11-13 March, 2019
Location: Valencia, Spain
The Massive Open Online Courses (MOOCs) phenomenon, emerged with much popularity in recent years, raises questions about why some learners reach their achievement while others do not. Online learning environments differ from traditional forms of learning, because of their limited time and location restrictions, then online student engagement and learning needs a different considerations than the other forms of learning. We can see engagement as the investment of time, energy, and effort in learning, that is a degree of participation, individual or collective, to learning process. In educational literature, there are three distinct domains of engagement: behavioural, cognitive, and emotional. Behavioural engagement is intuitively defined as all the actions taken by learners to achieve their own goals. In learning analytics literature there are many analysis relating to how MOOC learners’ behaviours determine performance and reaching online course achievements, using a wide range of statistical instruments. On the basis of this literature it is possible to make hypothesis about how construct learning and engagement as latent variables and the relationship between these two concepts using a structural equation model. Furthermore, considered the complex nature of MOOCs that offers different forms of learning (videos, texts, discussion forums, quizzes, etc.) it is necessary to consider engagement and learning in MOOCs context as multidimensional variables, measurable using a structural equation model.

Since there is no theoretical model relating learning through MOOCs, it is necessary to compare different theoretically plausible alternative models. Statistical literature offers many goodness of fit index and information criteria to choose the best theoretical model.
Engagement, learning, PLS-PM, structural equation modeling, latent variables.