arXiv subjects

Lee Branstetter

Publications and source records attributed to Lee Branstetter.

3 recordsLinked to original sources

arXiv2026

Cross-Subject Predictive Validity for Learning Outcomes of Delayed Start Behavior

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 valid behavioral measure that generalizes well across systems. In this paper, we examine cross-subject predictive validity of delayed start behavior. Using iReady data from 711 grade 7 students, we find delayed starts during Math practice are predictive of standardized test performance in both Math ($β$=.07 SD, p=.02) and English ($β$=.10 SD, p=<.001). Additionally, using mixture modeling and sensitivity analyses, we use a data-driven strategy to operationalize the identification of delayed starters in practice. We identify two underlying sub-groups of interest: "early starters" (<5 minute average delay, 20% of students) and "chronic delayers" (>13 minutes average delay, 20% of students). Relative to students in neither sub-group, early starters experienced greater growth (Math $β$=.11 SD, p=.07; ELA $β$=.15 SD, p=.02), while chronic delayers had the opposite trends (Math $β$=-.13 SD, p=0.05; ELA $β$=-.11 SD, p=0.11). Session-level measures provide a new opportunity for content-independent detectors, adding a behavioral component to the traditional usage and progress based on student engagement with content. This work aims to bridge education research with classroom practice by developing interpretable measures that align with behavioral cues teachers already use during classwork sessions to monitor and support students.

cs.CY↗

arXiv2026

Improving Hybrid Human-AI Tutoring by Differentiating Human Tutor Roles Based on Student Needs

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 lower-performing students derive greater benefit from human-AI tutoring than higher-performing students. As such, this study evaluates whether a differentiated tutoring policy can effectively support both groups: human tutors initiate support for lower-performing students, while higher-performing students receive reactive, on-demand support. Using their within-grade median state test scores, we assigned 635 students (grades 5-8) to receive proactive (< median) or reactive ($\geq$ median) tutoring. Using a DiDC design, we compare outcomes across two time periods: fall (AI-only tutoring) and spring (proactive-reactive human-AI tutoring). This quasi-experimental design isolates the effects of proactive-reactive tutoring approaches by comparing the discontinuity in spring outcomes to the fall, where no such discontinuity existed. Using data around the cutoff (Imbens-Kalyanaraman criterion), we find significant overall improvements from human-AI tutoring compared to AI-only baseline: 25% increase in time on task, 36% in skill proficiency, and 61% in academic growth (standardized MAP test). Between proactive and reactive tutoring, we find comparable improvements in time-on-task and skill proficiency. However, proactive tutoring, on average, showed marginally higher MAP growth (75%, p = .065) than reactive tutoring, i.e., proactive tutoring was more beneficial to students farther below the cutoff and helped narrow achievement gaps. Our findings provide evidence that differentiated human-AI tutoring addresses the needs of both groups, offering a practical and cost-effective strategy for scaling hybrid instruction.

cs.CY↗

arXiv2023

Improving Student Learning with Hybrid Human-AI Tutoring: A Three-Study Quasi-Experimental Investigation

Artificial intelligence (AI) applications to support human tutoring have potential to significantly improve learning outcomes, but engagement issues persist, especially among students from low-income backgrounds. We introduce an AI-assisted tutoring model that combines human and AI tutoring and hypothesize that this synergy will have positive impacts on learning processes. To investigate this hypothesis, we conduct a three-study quasi-experiment across three urban and low-income middle schools: 1) 125 students in a Pennsylvania school; 2) 385 students (50% Latinx) in a California school; and 3) 75 students (100% Black) in a Pennsylvania charter school, all implementing analogous tutoring models. We compare learning analytics of students engaged in human-AI tutoring compared to students using math software only. We find human-AI tutoring has positive effects, particularly in student's proficiency and usage, with evidence suggesting lower achieving students may benefit more compared to higher achieving students. We illustrate the use of quasi-experimental methods adapted to the particulars of different schools and data-availability contexts so as to achieve the rapid data-driven iteration needed to guide an inspired creation into effective innovation. Future work focuses on improving the tutor dashboard and optimizing tutor-student ratios, while maintaining annual costs per students of approximately $700 annually.

cs.HC↗