no code implementations • 18 Oct 2023 • Ilya Musabirov, Angela Zavaleta-Bernuy, Pan Chen, Michael Liut, Joseph Jay Williams
In adaptive experiments, as different arms/conditions are deployed to students, data is analyzed and used to change the experience for future students.
no code implementations • 13 Oct 2023 • Harsh Kumar, Ilya Musabirov, Mohi Reza, Jiakai Shi, Xinyuan Wang, Joseph Jay Williams, Anastasia Kuzminykh, Michael Liut
Personalized chatbot-based teaching assistants can be crucial in addressing increasing classroom sizes, especially where direct teacher presence is limited.
1 code implementation • 13 Oct 2023 • Harsh Kumar, Tong Li, Jiakai Shi, Ilya Musabirov, Rachel Kornfield, Jonah Meyerhoff, Ananya Bhattacharjee, Chris Karr, Theresa Nguyen, David Mohr, Anna Rafferty, Sofia Villar, Nina Deliu, Joseph Jay Williams
Digital mental health (DMH) interventions, such as text-message-based lessons and activities, offer immense potential for accessible mental health support.
1 code implementation • 29 Sep 2023 • Mohi Reza, Nathan Laundry, Ilya Musabirov, Peter Dushniku, Zhi Yuan "Michael" Yu, Kashish Mittal, Tovi Grossman, Michael Liut, Anastasia Kuzminykh, Joseph Jay Williams
Exploring alternative ideas by rewriting text is integral to the writing process.
no code implementations • 6 Sep 2023 • ZhaoBin Li, Luna Yee, Nathaniel Sauerberg, Irene Sakson, Joseph Jay Williams, Anna N. Rafferty
We explore these issues in the context of using multi-armed bandit (MAB) algorithms to learn a policy for what version of an educational technology to present to each student, varying the relation between student characteristics and outcomes and also whether the algorithm is aware of these characteristics.
no code implementations • 22 Nov 2022 • Susan Athey, Undral Byambadalai, Vitor Hadad, Sanath Kumar Krishnamurthy, Weiwen Leung, Joseph Jay Williams
We design and implement an adaptive experiment (a ``contextual bandit'') to learn a targeted treatment assignment policy, where the goal is to use a participant's survey responses to determine which charity to expose them to in a donation solicitation.
no code implementations • 10 Aug 2022 • Angela Zavaleta-Bernuy, Qi Yin Zheng, Hammad Shaikh, Jacob Nogas, Anna Rafferty, Andrew Petersen, Joseph Jay Williams
Adaptive experiments can increase the chance that current students obtain better outcomes from a field experiment of an instructional intervention.
no code implementations • 10 Aug 2022 • Fernando J. Yanez, Angela Zavaleta-Bernuy, Ziwen Han, Michael Liut, Anna Rafferty, Joseph Jay Williams
We highlight problems with these adaptive algorithms - such as possible exploitation of an arm when there is no significant difference - and address their causes and consequences.
no code implementations • 4 Mar 2022 • Nina Deliu, Joseph Jay Williams, Bibhas Chakraborty
In recent years, reinforcement learning (RL) has acquired a prominent position in health-related sequential decision-making problems, gaining traction as a valuable tool for delivering adaptive interventions (AIs).
no code implementations • 22 Mar 2021 • Joseph Jay Williams, Jacob Nogas, Nina Deliu, Hammad Shaikh, Sofia S. Villar, Audrey Durand, Anna Rafferty
We therefore use our case study of the ubiquitous two-arm binary reward setting to empirically investigate the impact of using Thompson Sampling instead of uniform random assignment.
no code implementations • 17 Jul 2020 • Arnold YS Yeung, Shalmali Joshi, Joseph Jay Williams, Frank Rudzicz
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 information.
no code implementations • 14 Apr 2018 • Avi Segal, Yossi Ben David, Joseph Jay Williams, Kobi Gal, Yaar Shalom
We present a new computational approach to this problem called MAPLE (Multi-Armed Bandits based Personalization for Learning Environments) that combines difficulty ranking with multi-armed bandits.