24 citations · 43 across the 9 of their papers we have counts for
10 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…
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…
Sequential Explanations with Mental Model-Based Policies
Arnold YS Yeung, Shalmali Joshi, Joseph Jay Williams +1
The act of explaining across two parties is a feedback loop, where one provides information on what needs to be explained and the other provides an explanation relevant to this inf…
RiPPLE: A Crowdsourced Adaptive Platform for Recommendation of Learning Activities
Hassan Khosravi, Kirsty Kitto, Joseph Jay Williams
This paper presents a platform called RiPPLE (Recommendation in Personalised Peer-Learning Environments) that recommends personalized learning activities to students based on their…