Modelling students’ performance in MOOCs: a multivariate approach

ABSTRACT

Massive Open Online Courses, universally labelled as MOOCs, become more and more relevant in the era of digitalization of higher education. The availability of free education resources without access restrictions for a plenty of potential users has changed the learning market in a way unthinkable only few decades ago. This form of web-based education allows to track all the actions of the students, thus providing an information base to understand how students' behaviour can influence their performance. The paper proposes a structural equation model in the framework of the component-based approach to measure which are the main factors affecting students' performance (Partial Least Squares Path Modelling). The novelty of the approach is the simultaneous analysis of more than one factor that exerts an impact on the performance. The analysis is carried out on the log data of a course available on the edX MOOCs platform named FedericaX.

KEYWORDS:

• Learning analytics
• multivariate modelling
• learning
• engagement

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://www.federica.eu/.

2 https://www.edx.org/xseries/federicax-introduction-to-political-science#why-this-program.

3 The statistical significance of the coefficients is evaluated using the classical t-test where standard errors are estimated using a bias-corrected bootstrap approach with 500 samples.

4 Permutations for multigroup analyses have been realized according to Chin and Dibbern (2010).

Additional information

Funding

This paper was written thanks to the contribution of Federica Weblearning Centre for Innovation and Dissemination of Distance Education, University of Naples Federico II, who provided the data and the research project under the POR FESR 2014-2020 Asse 2 Obiettivo Specifico 2.3 Azione 2.3.1 ‘La fabbrica digitale’.