Jordan Gutterman

Ashish Gurung, Jordan Gutterman, Danielle R. Thomas et al.

Measures of disengagement provide insights into unproductive use of learning opportunities. Although measures of active disengagement, such as gaming the system and mind-wandering, are well studied, loss of practice time due to outright task avoidance remains relatively understudied. The current study addresses this gap by extending existing within-task measures (idle time) with two new session-level measures (delayed start and early stop) to capture loss of practice time due to task avoidance. We characterize the combined lost time as coasted time and the associated behavior as coasting behavior. Using ASSISTments logs (N = 1,425), we find that students dedicate only 40% of available classwork time to math practice and coast through the remaining 60%. Of the coasted time, 36% resulted from delayed starts, 2% from mid-practice idling, and 62% from stopping early. Delayed start and early stop showed moderate temporal stability (G = 0.73 and 0.71, respectively), suggesting that coasting is a consistent behavioral pattern. Even after excluding early stops attributable to assignment completion (i.e., early stop = 0), coasted time remained substantial at 32%. While we observe significant differences in coasting by gender and IEP status, we do not observe them by other demographic factors or school locale. Critically, students who continued working beyond the first assignment completion ("extra effort") performed significantly better on standardized tests. For research, coasting offers a new lens on opportunity loss by combining session-level disengagement with within-task disengagement. For practitioners, our results highlight the need for platform affordances that support sustained engagement and more productive use of available practice time.

Ashish Gurung, Ge Gao, Jordan Gutterman et al.

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.