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

arXiv: https://arxiv.org/abs/2107.07124 · HTML: https://mlsanity.com/paper/2107.07124/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 15 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Automatic Dialogic Instruction Detection for K-12 Online One-on-one Classes](https://mlsanity.com/paper/2006.01204/) (2020)
- [Identifying At-Risk K-12 Students in Multimodal Online Environments: A Machine Learning Approach](https://mlsanity.com/paper/2003.09670/) (2020)
- [A Multimodal Alerting System for Online Class Quality Assurance](https://mlsanity.com/paper/1909.11765/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Design of A Simple Yet Effective Exercise Recommendation System in K-12 Online Learning](https://mlsanity.com/paper/2206.12291/) (2022)

## Abstract (arXiv)

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.