102 citations · 153 across the 8 of their papers we have counts for
8 papers
Getting too personal(ized): The importance of feature choice in online adaptive algorithms
ZhaoBin Li, Luna Yee, Nathaniel Sauerberg +3
Digital educational technologies offer the potential to customize students' experiences and learn what works for which students, enhancing the technology as more students interact…
Using Adaptive Experiments to Rapidly Help Students
Angela Zavaleta-Bernuy, Qi Yin Zheng, Hammad Shaikh +4
Adaptive experiments can increase the chance that current students obtain better outcomes from a field experiment of an instructional intervention. In such experiments, the probabi…
Increasing Students' Engagement to Reminder Emails Through Multi-Armed Bandits
Fernando J. Yanez, Angela Zavaleta-Bernuy, Ziwen Han +3
Conducting randomized experiments in education settings raises the question of how we can use machine learning techniques to improve educational interventions. Using Multi-Armed Ba…
How can Email Interventions Increase Students' Completion of Online Homework? A Case Study Using A/B Comparisons
Angela Zavaleta-Bernuy, Ziwen Han, Hammad Shaikh +5
Email communication between instructors and students is ubiquitous, and it could be valuable to explore ways of testing out how to make email messages more impactful. This paper ex…
Reinforcement Learning for Education: Opportunities and Challenges
Adish Singla, Anna N. Rafferty, Goran Radanovic +1
This survey article has grown out of the RL4ED workshop organized by the authors at the Educational Data Mining (EDM) 2021 conference. We organized this workshop as part of a commu…
Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments
Joseph Jay Williams, Jacob Nogas, Nina Deliu +4
Multi-armed bandit algorithms have been argued for decades as useful for adaptively randomized experiments. In such experiments, an algorithm varies which arms (e.g. alternative in…
Encodings of Source Syntax: Similarities in NMT Representations Across Target Languages
Tyler A. Chang, Anna N. Rafferty
We train neural machine translation (NMT) models from English to six target languages, using NMT encoder representations to predict ancestor constituent labels of source language w…
An Overview of Machine Teaching
Xiaojin Zhu, Adish Singla, Sandra Zilles +1
In this paper we try to organize machine teaching as a coherent set of ideas. Each idea is presented as varying along a dimension. The collection of dimensions then form the proble…