Detecting Target Leakage by Label Reconstruction: A Diagnostic with a Learning-Analytics Case Study
Year: 2026
Type: preprint
Abstract: Data and R code for "Detecting Target Leakage by Label Reconstruction: A Diagnostic with a Learning-Analytics Case Study." The case study uses 118 de-identified peer reviews written by 43 students in an undergraduate visual analytics course (spring 2025). Each review scores a classmate's visualization on an eight-item design rubric and adds free-text comments. The "active reviewer" label (more words than the median review) is a deterministic function of the review's word counts. The paper uses this case to show how such target leakage can be detected.
Source: OSF Preprints (OSF Preprints)
Author: Alon Friedman
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225321588