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Title: Evaluating Gaming Detector Model Robustness Over Time
Research into "gaming the system" behavior in intelligent tutoring systems (ITS) has been around for almost two decades, and detection has been developed for many ITSs. Machine learning models can detect this behavior in both real-time and in historical data. However, intelligent tutoring system designs often change over time, in terms of the design of the student interface, assessment models, and data collection log schemas. Can gaming detectors still be trusted, a decade or more after they are developed? In this research, we evaluate the robustness/degradation of gaming detectors when trained on old data logs and evaluated on current data logs. We demonstrate that some machine learning models developed using past data are still able to predict gaming behavior from student data collected 16 years later, but that there is considerable variance in how well different algorithms perform over time. We demonstrate that a classic decision tree algorithm maintained its performance while more contemporary algorithms struggled to transfer to new data, even though they exhibited better performance on both new and old data alone. Examining the feature importances provides some explanation for the differences in performance between models, and offers some insight into how we might safeguard against detector rot over time.  more » « less
Award ID(s):
2000638
NSF-PAR ID:
10352945
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Proceedings of the 15th International Conference on Educational Data Mining, International Educational Data Mining Society
Page Range / eLocation ID:
398-405
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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