Item Ordering Biases in Educational Data
| Authors | |
|---|---|
| Year of publication | 2019 |
| Type | Article in Proceedings |
| Conference | International Conference on Artificial Intelligence in Education |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1007/978-3-030-23204-7_5 |
| 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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