  With the rapid emergence of K-12 online learning platforms, a new era of
education has been opened up. It is crucial to have a dropout warning framework
to preemptively identify K-12 students who are at risk of dropping out of the
online courses. Prior researchers have focused on predicting dropout in Massive
Open Online Courses (MOOCs), which often deliver higher education, i.e.,
graduate level courses at top institutions. However, few studies have focused
on developing a machine learning approach for students in K-12 online courses.
In this paper, we develop a machine learning framework to conduct accurate
at-risk student identification specialized in K-12 multimodal online
environments. Our approach considers both online and offline factors around
K-12 students and aims at solving the challenges of (1) multiple modalities,
i.e., K-12 online environments involve interactions from different modalities
such as video, voice, etc; (2) length variability, i.e., students with
different lengths of learning history; (3) time sensitivity, i.e., the dropout
likelihood is changing with time; and (4) data imbalance, i.e., only less than
20\% of K-12 students will choose to drop out the class. We conduct a wide
range of offline and online experiments to demonstrate the effectiveness of our
approach. In our offline experiments, we show that our method improves the
dropout prediction performance when compared to state-of-the-art baselines on a
real-world educational dataset. In our online experiments, we test our approach
on a third-party K-12 online tutoring platform for two months and the results
show that more than 70\% of dropout students are detected by the system.
