TutorTrace dataset of IDE telemetry, behavioral segments, and observable metrics from 480 learners across four introductory Python deployments

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Evidence · 2 claims (2 for) · 3 studies (1 causal, 1 associational, 1 design), q2 · 0 of 3 report an effect size

Description

TutorTrace is a publicly released dataset created by its authors that combines IDE telemetry, AI interaction logs, task outcomes, and expert-validated learner behavioral classifications for studying AI-assisted programming help-seeking.

Components

Claims

• Behavior-aware AI-tutor prompting was associated with a decrease in Passive inter-query windows from 50.0% to 20.7% in a nonrandomized between-deployment comparison [+M]
• Observable metrics computed from IDE telemetry improved held-out prediction of query imminence (AUROC .726) and guided versus dependent help-seeking (AUROC .717) over raw telemetry [+M]

Related Products and Programmes

• BILearn-CS

Key Sources

• David Barron, Xiaohang Tang, Rezky Dwisantika, Minsun Kim, David H. Smith IV, Jiaming Cui, and Yan Chen. 2026. TutorTrace: A Dataset and Taxonomy for Classifying Learner Behavioral States during AI-Assisted Programming Education. https://arxiv.org/abs/2608.26184

Page metadata

Field	Value
type	product
id	tutortrace-dataset-of-ide-telemetry-behavioral-segments-and-observable-metrics-from-480-le
title	TutorTrace dataset of IDE telemetry, behavioral segments, and observable metrics from 480 learners across four introductory Python deployments
description	TutorTrace is a publicly released dataset created by its authors that combines IDE telemetry, AI interaction logs, task outcomes, and expert-validated learner behavioral classifications for studying AI-assisted programming help-seeking.
status	review
product_kind	dataset
generated	by process:settle-candidates on 2026-10-12
trust	unverified — no one has confirmed this page's content

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