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An Educational System for Personalized Teacher Recommendation in K-12 Online Classrooms

2021-07-15 05:04:28
Jiahao Chen, Hang Li, Wenbiao Ding, Zitao Liu
arXiv_AI
       
Abstract

In this paper, we propose a simple yet effective solution to build practical teacher recommender systems for online one-on-one classes. Our system consists of (1) a pseudo matching score module that provides reliable training labels; (2) a ranking model that scores every candidate teacher; (3) a novelty boosting module that gives additional opportunities to new teachers; and (4) a diversity metric that guardrails the recommended results to reduce the chance of collision. Offline experimental results show that our approach outperforms a wide range of baselines. Furthermore, we show that our approach is able to reduce the number of student-teacher matching attempts from 7.22 to 3.09 in a five-month observation on a third-party online education platform.

Abstract (translated)

URL

https://arxiv.org/abs/2107.07124

PDF

https://arxiv.org/pdf/2107.07124.pdf

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