Publication details

Item Ordering Biases in Educational Data



Year of publication 2019
Type Article in Proceedings
Conference International Conference on Artificial Intelligence in Education
MU Faculty or unit

Faculty of Informatics

Keywords intelligent tutoring system; data collection; explore-exploit tradeoff; simulation
Description Data collected in a learning system are biased by order in which students solve items. This bias makes data analysis difficult and when not properly addressed, it may lead to misleading conclusions. We provide clear illustrations of the problem using simulated data and discuss methods for analyzing the scope of the problem in real data from a learning system. We present the data collection problem as a variant of the explore-exploit tradeoff and analyze several algorithms for addressing this tradeoff.
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