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Found 12 papers, 2 papers with code
Opportunities for Adaptive Experiments to Enable Continuous Improvement that Trades-off Instructor and Researcher Incentives
In adaptive experiments, as different arms/conditions are deployed to students, data is analyzed and used to change the experience for future students.
Impact of Guidance and Interaction Strategies for LLM Use on Learner Performance and Perception
Personalized chatbot-based teaching assistants can be crucial in addressing increasing classroom sizes, especially where direct teacher presence is limited.
Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health
Digital mental health (DMH) interventions, such as text-message-based lessons and activities, offer immense potential for accessible mental health support.
ABScribe: Rapid Exploration & Organization of Multiple Writing Variations in Human-AI Co-Writing Tasks using Large Language Models
Exploring alternative ideas by rewriting text is integral to the writing process.
Getting too personal(ized): The importance of feature choice in online adaptive algorithms
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.
Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learning
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.
Using Adaptive Experiments to Rapidly Help Students
Adaptive experiments can increase the chance that current students obtain better outcomes from a field experiment of an instructional intervention.
Increasing Students' Engagement to Reminder Emails Through Multi-Armed Bandits
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.
Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions
In recent years, reinforcement learning (RL) has acquired a prominent position in the space of health-related sequential decision-making, becoming an increasingly popular tool for delivering adaptive interventions (AIs).
Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments
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.
Sequential Explanations with Mental Model-Based Policies
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.
Combining Difficulty Ranking with Multi-Armed Bandits to Sequence Educational Content
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.
