• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar

information for practice

news, new scholarship & more from around the world


advanced search
  • gary.holden@nyu.edu
  • @ Info4Practice
  • Archive
  • About
  • Help
  • Browse Key Journals
  • RSS Feeds

Multimodal Data Fusion to Detect Preknowledge Test-Taking Behavior Using Machine Learning

Educational and Psychological Measurement, Ahead of Print.
In various fields, including college admission, medical board certifications, and military recruitment, high-stakes decisions are frequently made based on scores obtained from large-scale assessments. These decisions necessitate precise and reliable scores that enable valid inferences to be drawn about test-takers. However, the ability of such tests to provide reliable, accurate inference on a test-taker’s performance could be jeopardized by aberrant test-taking practices, for instance, practicing real items prior to the test. As a result, it is crucial for administrators of such assessments to develop strategies that detect potential aberrant test-takers after data collection. The aim of this study is to explore the implementation of machine learning methods in combination with multimodal data fusion strategies that integrate bio-information technology, such as eye-tracking, and psychometric measures, including response times and item responses, to detect aberrant test-taking behaviors in technology-assisted remote testing settings.

Read the full article ›

Posted in: Journal Article Abstracts on 10/09/2023 | Link to this post on IFP |
Share

Primary Sidebar

Categories

Category RSS Feeds

  • Calls & Consultations
  • Clinical Trials
  • Funding
  • Grey Literature
  • Guidelines Plus
  • History
  • Infographics
  • Journal Article Abstracts
  • Meta-analyses - Systematic Reviews
  • Monographs & Edited Collections
  • News
  • Open Access Journal Articles
  • Podcasts
  • Video

© 1993-2025 Dr. Gary Holden. All rights reserved.

gary.holden@nyu.edu
@Info4Practice