most citedCombining Large Language Models with Tutoring System Intelligence: A Case Study in Caregiver Homework Support

18 citations · 69 across the 10 of their papers we have counts for

collaborators

10 papers

cs.HC2025

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…

cs.HC202418 cited

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…

cs.HC20247 cited

Do Tutors Learn from Equity Training and Can Generative AI Assess It?

Danielle R. Thomas, Conrad Borchers, Sanjit Kakarla +5

Equity is a core concern of learning analytics. However, applications that teach and assess equity skills, particularly at scale are lacking, often due to barriers in evaluating la…

cs.HC202413 cited

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…

cs.HC20246 cited

Does Multiple Choice Have a Future in the Age of Generative AI? A Posttest-only RCT

Danielle R. Thomas, Conrad Borchers, Sanjit Kakarla +5

The role of multiple-choice questions (MCQs) as effective learning tools has been debated in past research. While MCQs are widely used due to their ease in grading, open response q…

cs.LG20248 cited

Evaluating the Impact of Data Augmentation on Predictive Model Performance

Valdemar Švábenský, Conrad Borchers, Elizabeth B. Cloude +1

In supervised machine learning (SML) research, large training datasets are essential for valid results. However, obtaining primary data in learning analytics (LA) is challenging. D…