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

TL;DR

This paper introduces a practical teacher recommendation system for K-12 online classrooms that improves matching efficiency, promotes new teachers, and ensures diverse recommendations, outperforming existing methods in offline tests and real-world deployment.

Contribution

The paper presents a novel teacher recommender system with modules for reliable training labels, ranking, novelty boosting, and diversity, significantly enhancing matching efficiency and recommendation quality.

Findings

Outperforms baseline methods in offline experiments.

Reduces student-teacher matching attempts from 7.22 to 3.09.

Effective in real-world online education platform deployment.

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.