# Dropout Prediction over Weeks in MOOCs by Learning Representations of Clicks and Videos

arXiv: https://arxiv.org/abs/2002.01955 · HTML: https://mlsanity.com/paper/2002.01955/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2020.

## Built on by (most cited first, 1 of 1)

- [Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach](https://mlsanity.com/paper/2111.00901/) (2021)

## Abstract (arXiv)

This paper addresses a key challenge in MOOC dropout prediction, namely to build meaningful representations from clickstream data. While a variety of feature extraction techniques have been explored extensively for such purposes, to our knowledge, no prior works have explored modeling of educational content (e.g. video) and their correlation with the learner's behavior (e.g. clickstream) in this context. We bridge this gap by devising a method to learn representation for videos and the correlation between videos and clicks. The results indicate that modeling videos and their correlation with clicks bring statistically significant improvements in predicting dropout.