61 citations · 182 across the 14 of their papers we have counts for
15 papers
Million Tutoring Moves (MTM): An Open Multimodal Dataset for the Science of Tutoring
René Kizilcec, Kirk Vanacore, Zhuqian Zhou +8
We introduce the Million Tutoring Moves (MTM) project, an open dataset initiative aimed at advancing the science of tutoring through large-scale, reusable, and multimodal interacti…
An Integrated Platform for Studying Learning with Intelligent Tutoring Systems: CTAT+TutorShop
Vincent Aleven, Conrad Borchers, Yun Huang +6
Intelligent tutoring systems (ITSs) are effective in helping students learn; further research could make them even more effective. Particularly desirable is research into how stude…
TRESTLE: A Model of Concept Formation in Structured Domains
Christopher J. MacLellan, Erik Harpstead, Vincent Aleven +1
The literature on concept formation has demonstrated that humans are capable of learning concepts incrementally, with a variety of attribute types, and in both supervised and unsup…
Beyond Repetition: The Role of Varied Questioning and Feedback in Knowledge Generalization
Gautam Yadav, Paulo F. Carvalho, Elizabeth A. McLaughlin +1
This study examines the effects of question type and feedback on learning outcomes in a hybrid graduate-level course. By analyzing data from 32 students over 30,198 interactions, w…
How Can I Get It Right? Using GPT to Rephrase Incorrect Trainee Responses
Jionghao Lin, Zifei Han, Danielle R. Thomas +4
One-on-one tutoring is widely acknowledged as an effective instructional method, conditioned on qualified tutors. However, the high demand for qualified tutors remains a challenge,…
How Can I Improve? Using GPT to Highlight the Desired and Undesired Parts of Open-ended Responses
Jionghao Lin, Eason Chen, Zeifei Han +5
Automated explanatory feedback systems play a crucial role in facilitating learning for a large cohort of learners by offering feedback that incorporates explanations, significantl…
Improving Assessment of Tutoring Practices using Retrieval-Augmented Generation
Zifei FeiFei Han, Jionghao Lin, Ashish Gurung +5
One-on-one tutoring is an effective instructional method for enhancing learning, yet its efficacy hinges on tutor competencies. Novice math tutors often prioritize content-specific…
Using Large Language Models to Assess Tutors' Performance in Reacting to Students Making Math Errors
Sanjit Kakarla, Danielle Thomas, Jionghao Lin +2
Research suggests that tutors should adopt a strategic approach when addressing math errors made by low-efficacy students. Rather than drawing direct attention to the error, tutors…
Improving Student Learning with Hybrid Human-AI Tutoring: A Three-Study Quasi-Experimental Investigation
Danielle R. Thomas, Jionghao Lin, Erin Gatz +8
Artificial intelligence (AI) applications to support human tutoring have potential to significantly improve learning outcomes, but engagement issues persist, especially among stude…
How to Teach Programming in the AI Era? Using LLMs as a Teachable Agent for Debugging
Qianou Ma, Hua Shen, Kenneth Koedinger +1
Large Language Models (LLMs) now excel at generative skills and can create content at impeccable speeds. However, they are imperfect and still make various mistakes. In a Computer…
Comparative Analysis of GPT-4 and Human Graders in Evaluating Praise Given to Students in Synthetic Dialogues
Dollaya Hirunyasiri, Danielle R. Thomas, Jionghao Lin +2
Research suggests that providing specific and timely feedback to human tutors enhances their performance. However, it presents challenges due to the time-consuming nature of assess…
Using Large Language Models to Provide Explanatory Feedback to Human Tutors
Jionghao Lin, Danielle R. Thomas, Feifei Han +4
Research demonstrates learners engaging in the process of producing explanations to support their reasoning, can have a positive impact on learning. However, providing learners rea…
Is AI the better programming partner? Human-Human Pair Programming vs. Human-AI pAIr Programming
Qianou Ma, Tongshuang Wu, Kenneth Koedinger
The emergence of large-language models (LLMs) that excel at code generation and commercial products such as GitHub's Copilot has sparked interest in human-AI pair programming (refe…
Decomposed Inductive Procedure Learning
Daniel Weitekamp, Christopher MacLellan, Erik Harpstead +1
Recent advances in machine learning have made it possible to train artificially intelligent agents that perform with super-human accuracy on a great diversity of complex tasks. How…
Learning Cognitive Models using Neural Networks
Devendra Singh Chaplot, Christopher MacLellan, Ruslan Salakhutdinov +1
A cognitive model of human learning provides information about skills a learner must acquire to perform accurately in a task domain. Cognitive models of learning are not only of sc…