Early detection of serious but struggling learners in MOOCs using ensemble deep learning

Abstract

ABSTRACT This study works on the early identification of serious but struggling students in Massive Open Online Courses (MOOCs), which facilitates a transition to a hybrid learning format. Firstly, we exclusively utilize basic behavior log features that are universally available across all MOOC platforms. Secondly, we encode each learning activity with a distinct identifier, which helps us utilize the current advances in NLP to address our problem. Thirdly, we seamlessly integrate our LSTM model with a Logistic Regression model, configured as a single dense layer with a sigmoid activation function, within the broader framework of deep learning.