Related papers: Dropout Prediction over Weeks in MOOCs by Learning Representations of Clicks and Videos

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

URL: http://arxiv.org/abs/2002.01955v1
Date: Wed, 5 Feb 2020 19:10:01 GMT
Title: Dropout Prediction over Weeks in MOOCs by Learning Representations of Clicks and Videos
Authors: Byungsoo Jeon, Namyong Park
Abstract summary: We develop 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.
Score: 6.030785848148107
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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