18 citations · 81 across the 30 of their papers we have counts for
31 papers
Towards Breaking the Learning System Wall Using Multimodal Tutoring Transcriptions
Danielle R. Thomas, Marie Cynthia Abijuru Kamikazi, Ashish Gurung +5
Past research using log data has faced the "learning system wall," whereby few methods exist for generalizing models of student learning across platforms. Increasingly, online lear…
Framing the 5% Problem: Teachers' Perspectives on Persistence in Educational Technology
Conrad Borchers
Adaptive K-12 mathematics platforms often show low sustained student use, a pattern termed the 5% problem. Although prior work has developed analytics and interventions to identify…
AI-Driven Assessment of Human Tutors: Linking Training Performance to Real-Life Practice
Danielle R. Thomas, Marie Cynthia Abijuru Kamikazi, Clara Brandt +2
There exist numerous tutor training platforms. However, few provide AI-driven training and evaluation for human tutors based on real-life performance. We present an AI-driven syste…
Self-Efficacy and Favorability Shape Learning from Tutoring Systems and Paper Practice
Xinfei Cen, Vincent Aleven, Kenneth R. Koedinger +2
Motivational factors such as self-efficacy and how favorably students feel toward practice play a crucial role in shaping learning, particularly in technology-supported environment…
Measurement of dijet transverse momentum imbalance and azimuthal acoplanarity in + collisions at GeV with the sPHENIX detector
sPHENIX Collaboration, M. I. Abdulhamid, U. Acharya +326
This Letter reports on measurements of dijet transverse momentum () imbalance and azimuthal acoplanarity in proton-proton collisions at ~GeV, using da…
Modernizing Ground Truth: Four Shifts Toward Improving Reliability and Validity in AI in Education
Danielle R. Thomas, Conrad Borchers, Kirk P. Vanacore +2
Generative Artificial Intelligence (GenAI) is now widespread in education, yet the efficacy of GenAI systems remains constrained by the quality and interpretation of the labeled da…
Evaluating a Data-Driven Redesign Process for Intelligent Tutoring Systems
Qianru Lyu, Conrad Borchers, Meng Xia +4
Past research has defined a general process for the data-driven redesign of educational technologies and has shown that in carefully-selected instances, this process can help make…
Sticky Help, Bounded Effects: Session-by-Session Analytics of Teacher Interventions in K-12 Classrooms
Qiao Jin, Conrad Borchers, Ashish Gurung +6
Teachers' in-the-moment support is a limited resource in technology-supported classrooms, and teachers must decide whom to help and when during ongoing student work. However, less…
Brief but Impactful: How Human Tutoring Interactions Shape Engagement in Online Learning
Conrad Borchers, Ashish Gurung, Qinyi Liu +3
Learning analytics can guide human tutors to efficiently address motivational barriers to learning that AI systems struggle to support. Students become more engaged when they recei…
Comparing RAG and GraphRAG for Page-Level Retrieval Question Answering on a Math Textbook
Eason Chen, Chuangji Li, Eric Li +3
Large language models (LLMs) show promise as educational aids but often lack alignment with specific course materials. We investigate Retrieval-Augmented Generation (RAG) and Graph…
Beyond Agreement: Rethinking Ground Truth in Educational AI Annotation
Danielle R. Thomas, Conrad Borchers, Kenneth R. Koedinger
Humans can be notoriously imperfect evaluators. They are often biased, unreliable, and unfit to define "ground truth." Yet, given the surging need to produce large amounts of train…
Optimizing Mastery Learning by Fast-Forwarding Over-Practice Steps
Meng Xia, Robin Schmucker, Conrad Borchers +1
Mastery learning improves learning proficiency and efficiency. However, the overpractice of skills--students spending time on skills they have already mastered--remains a fundament…
Leveraging LLMs to Assess Tutor Moves in Real-Life Dialogues: A Feasibility Study
Danielle R. Thomas, Conrad Borchers, Jionghao Lin +6
Tutoring improves student achievement, but identifying and studying what tutoring actions are most associated with student learning at scale based on audio transcriptions is an ope…
Detecting LLM-Generated Short Answers and Effects on Learner Performance
Shambhavi Bhushan, Danielle R Thomas, Conrad Borchers +5
The increasing availability of large language models (LLMs) has raised concerns about their potential misuse in online learning. While tools for detecting LLM-generated text exist…
LLM-Generated Feedback Supports Learning If Learners Choose to Use It
Danielle R. Thomas, Conrad Borchers, Shambhavi Bhushan +3
Large language models (LLMs) are increasingly used to generate feedback, yet their impact on learning remains underexplored, especially compared to existing feedback methods. This…
Combining Log Data and Collaborative Dialogue Features to Predict Project Quality in Middle School AI Education
Conrad Borchers, Xiaoyi Tian, Kristy Elizabeth Boyer +1
Project-based learning plays a crucial role in computing education. However, its open-ended nature makes tracking project development and assessing success challenging. We investig…
Starting Seatwork Earlier as a Valid Measure of Student Engagement
Ashish Gurung, Jionghao Lin, Zhongtian Huang +4
Prior work has developed a range of automated measures ("detectors") of student self-regulation and engagement from student log data. These measures have been successfully used to…
VTutor for High-Impact Tutoring at Scale: Managing Engagement and Real-Time Multi-Screen Monitoring with P2P Connections
Eason Chen, Xinyi Tang, Aprille Xi +5
Hybrid tutoring, where a human tutor supports multiple students in learning with educational technology, is an increasingly common application to deliver high-impact tutoring at sc…
Can Large Language Models Match Tutoring System Adaptivity? A Benchmarking Study
Conrad Borchers, Tianze Shou
Large Language Models (LLMs) hold promise as dynamic instructional aids. Yet, it remains unclear whether LLMs can replicate the adaptivity of intelligent tutoring systems (ITS)--wh…
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…
Augmenting Human-Annotated Training Data with Large Language Model Generation and Distillation in Open-Response Assessment
Conrad Borchers, Danielle R. Thomas, Jionghao Lin +2
Large Language Models (LLMs) like GPT-4o can help automate text classification tasks at low cost and scale. However, there are major concerns about the validity and reliability of…
Toward Sufficient Statistical Power in Algorithmic Bias Assessment: A Test for ABROCA
Conrad Borchers
Algorithmic bias is a pressing concern in educational data mining (EDM), as it risks amplifying inequities in learning outcomes. The Area Between ROC Curves (ABROCA) metric is freq…
Combining Large Language Models with Tutoring System Intelligence: A Case Study in Caregiver Homework Support
Devika Venugopalan, Ziwen Yan, Conrad Borchers +2
Caregivers (i.e., parents and members of a child's caring community) are underappreciated stakeholders in learning analytics. Although caregiver involvement can enhance student aca…
How Learner Control and Explainable Learning Analytics on Skill Mastery Shape Student Desires to Finish and Avoid Loss in Tutored Practice
Conrad Borchers, Jeroen Ooge, Cindy Peng +1
Personalized problem selection enhances student practice in tutoring systems. Prior research has focused on transparent problem selection that supports learner control but rarely e…