Search Results for author:
Found 17 papers, 14 papers with code
Educator Attention: How computational tools can systematically identify the distribution of a key resource for students
Educator attention is critical for student success, yet how educators distribute their attention across students remains poorly understood due to data and methodological constraints.
Tutor CoPilot: A Human-AI Approach for Scaling Real-Time Expertise
Following a preregistered analysis plan, we find that students working with tutors that have access to Tutor CoPilot are 4 percentage points (p. p.)
Mathfish: Evaluating Language Model Math Reasoning via Grounding in Educational Curricula
To ensure that math curriculum is grade-appropriate and aligns with critical skills or concepts in accordance with educational standards, pedagogical experts can spend months carefully reviewing published math problems.
Backtracing: Retrieving the Cause of the Query
While information retrieval (IR) systems may provide answers for such user queries, they do not directly assist content creators -- such as lecturers who want to improve their content -- identify segments that _caused_ a user to ask those questions.
Edu-ConvoKit: An Open-Source Library for Education Conversation Data
We introduce Edu-ConvoKit, an open-source library designed to handle pre-processing, annotation and analysis of conversation data in education.
Bridging the Novice-Expert Gap via Models of Decision-Making: A Case Study on Remediating Math Mistakes
We evaluate state-of-the-art LLMs on our dataset and find that the expert's decision-making model is critical for LLMs to close the gap: responses from GPT4 with expert decisions (e. g., "simplify the problem") are +76% more preferred than without.
SIGHT: A Large Annotated Dataset on Student Insights Gathered from Higher Education Transcripts
To overcome this challenge, we propose a set of best practices for using large language models (LLMs) to cheaply classify the comments at scale.
Is ChatGPT a Good Teacher Coach? Measuring Zero-Shot Performance For Scoring and Providing Actionable Insights on Classroom Instruction
In doing so, we propose three teacher coaching tasks for generative AI: (A) scoring transcript segments based on classroom observation instruments, (B) identifying highlights and missed opportunities for good instructional strategies, and (C) providing actionable suggestions for eliciting more student reasoning.
Solving Math Word Problems by Combining Language Models With Symbolic Solvers
Automatically generating high-quality step-by-step solutions to math word problems has many applications in education.
Evaluating Human-Language Model Interaction
To evaluate human-LM interaction, we develop a new framework, Human-AI Language-based Interaction Evaluation (HALIE), that defines the components of interactive systems and dimensions to consider when designing evaluation metrics.
Know Thy Student: Interactive Learning with Gaussian Processes
The teacher must interact and diagnose the student, before teaching.
Calibrate your listeners! Robust communication-based training for pragmatic speakers
We propose a method that uses a population of neural listeners to regularize speaker training.
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
Too many cooks: Bayesian inference for coordinating multi-agent collaboration
Underlying the human ability to collaborate is theory-of-mind, the ability to infer the hidden mental states that drive others to act.
Model-based Reinforcement Learning for Decentralized Multiagent Rendezvous
We propose hierarchical predictive planning (HPP), a model-based reinforcement learning method for decentralized multiagent rendezvous.
R-MADDPG for Partially Observable Environments and Limited Communication
There are several real-world tasks that would benefit from applying multiagent reinforcement learning (MARL) algorithms, including the coordination among self-driving cars.
