An Ensemble Learning Approach Based on TabNet and Machine Learning Models for Cheating Detection in Educational Tests
Published 2023 View Full Article
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Title
An Ensemble Learning Approach Based on TabNet and Machine Learning Models for Cheating Detection in Educational Tests
Authors
Keywords
-
Journal
EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT
Volume -, Issue -, Pages -
Publisher
SAGE Publications
Online
2023-08-22
DOI
10.1177/00131644231191298
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Related references
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- An Unsupervised‐Learning‐Based Approach to Compromised Items Detection
- (2021) Yiqin Pan et al. JOURNAL OF EDUCATIONAL MEASUREMENT
- Tabular data: Deep learning is not all you need
- (2021) Ravid Shwartz-Ziv et al. Information Fusion
- Detecting Examinees With Item Preknowledge in Large-Scale Testing Using Extreme Gradient Boosting (XGBoost)
- (2019) Cengiz Zopluoglu EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT
- Beyond one-hot encoding: Lower dimensional target embedding
- (2018) Pau Rodríguez et al. IMAGE AND VISION COMPUTING
- Ensemble learning: A survey
- (2018) Omer Sagi et al. Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery
- A Feature-Scaling-Based $k$ -Nearest Neighbor Algorithm for Indoor Positioning Systems
- (2016) Dong Li et al. IEEE Internet of Things Journal
- Detecting Test Tampering Using Item Response Theory
- (2015) James A. Wollack et al. EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT
- Using Deterministic, Gated Item Response Theory Model to Detect Test Cheating due to Item Compromise
- (2013) Zhan Shu et al. PSYCHOMETRIKA
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