59 citations · 86 across the 14 of their papers we have counts for
15 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…
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…
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,…
Representation Learning to Study Temporal Dynamics in Tutorial Scaffolding
Conrad Borchers, Jiayi Zhang, Ashish Gurung
Adaptive scaffolding enhances learning, yet the field lacks robust methods for measuring it within authentic tutoring dialogue. This gap has become more pressing with the rise of r…
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…
Let Me Try Again: Examining Replay Behavior by Tracing Students' Latent Problem-Solving Pathways
Shan Zhang, Siddhartha Pradhan, Ji-Eun Lee +2
Prior research has shown that students' problem-solving pathways in game-based learning environments reflect their conceptual understanding, procedural knowledge, and flexibility.…
Automatic Large Language Models Creation of Interactive Learning Lessons
Jionghao Lin, Jiarui Rao, Yiyang Zhao +6
We explore the automatic generation of interactive, scenario-based lessons designed to train novice human tutors who teach middle school mathematics online. Employing prompt engine…
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…
Automated Feedback in Math Education: A Comparative Analysis of LLMs for Open-Ended Responses
Sami Baral, Eamon Worden, Wen-Chiang Lim +4
The effectiveness of feedback in enhancing learning outcomes is well documented within Educational Data Mining (EDM). Various prior research has explored methodologies to enhance t…
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…
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…