Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dropout Prediction over Weeks in MOOCs by Learning Representations of
  Clicks and Videos   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.

10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
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The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
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The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat