59 citations · 117 across the 18 of their papers we have counts for
18 papers
Cross-Subject Predictive Validity for Learning Outcomes of Delayed Start Behavior
Jordan Gutterman, Ashish Gurung, Lee Branstetter +2
Behavioral detectors provide valuable insights into learner motivation and self-regulation. Among these, delayed start, a new session-level detector, has shown great promise as a v…
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…
Simulating Learners' Task-Selection Strategies and System Constraints in Mastery Learning
Haley Noh, Aarna Chowdhary, Jeroen Ooge +2
Intelligent Tutoring Systems often grant learners shared control over skill and problem selection. This choice brings motivational and metacognitive benefits. At the same time, pas…
From Heuristics to Analytics: Forecasting Effort and Progress in Online Learning
Eric S. Qiu, Danielle R. Thomas, Boyuan Guo +2
Sustained effort is essential for realizing the benefits of intelligent tutoring systems (ITS), yet many learners disengage or underuse available practice time. We introduce engage…
Improving Hybrid Human-AI Tutoring by Differentiating Human Tutor Roles Based on Student Needs
Ashish Gurung, Ge Gao, Jordan Gutterman +6
Hybrid human-AI tutoring, where technology and humans jointly facilitate student learning, can be more beneficial than AI-only tutoring. However, preliminary evidence suggests that…
Coasting Through Class: Learning Opportunity Loss from Practice Avoidance During Individual Seatwork
Ashish Gurung, Jordan Gutterman, Danielle R. Thomas +3
Measures of disengagement provide insights into unproductive use of learning opportunities. Although measures of active disengagement, such as gaming the system and mind-wandering,…
Balancing Teacher and Student Agency: Co-Orchestration Tool Design Supporting Real-Time Dynamic Pairing
Kexin Bella Yang, Menghan Liu, Liyi Xu +2
In human-AI interaction, respecting user agency is essential for fostering trust and sustaining effective use of technology. In educational settings, dynamically integrating indivi…
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…
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…
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…
Improving Automated Feedback Systems for Tutor Training in Low-Resource Scenarios through Data Augmentation
Chentianye Xu, Jionghao Lin, Tongshuang Wu +2
Tutoring is an effective instructional method for enhancing student learning, yet its success relies on the skill and experience of the tutors. This reliance presents challenges fo…
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…
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,…
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…
Revealing Networks: Understanding Effective Teacher Practices in AI-Supported Classrooms using Transmodal Ordered Network Analysis
Conrad Borchers, Yeyu Wang, Shamya Karumbaiah +3
Learning analytics research increasingly studies classroom learning with AI-based systems through rich contextual data from outside these systems, especially student-teacher intera…
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…