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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Analysing Event Transitions to Discover Student Roles and Predict Grades in MOOCs
verfasst von : Ángel Pérez-Lemonche, Gonzalo Martínez-Muñoz, Estrella Pulido-Cañabate
Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2017
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Abstract
When interacting with a MOOC, students can perform different kinds of actions such as watching videos, answering exercises, participating in the course forum, submitting a project or reviewing a document. These actions represent the dynamism of student learning paths, and their preferences when learning in an autonomous mode. In this paper we propose to analyse these learning paths with two goals in mind. The first one is to try to discover the different roles that students may adopt when interacting with an online course. By applying k-means, six of these roles are discovered and we give a qualitative interpretation of them based on student information associated to each cluster. The other goal is to predict academic performance. In this sense, we present the results obtained with Random Forest and Neural Networks that allow us to predict the final grade with around 10% of mean absolute error.