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Back to the Basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation
[article]
2016
arXiv
pre-print
Estimating student proficiency is an important task for computer based learning systems. We compare a family of IRT-based proficiency estimation methods to Deep Knowledge Tracing (DKT), a recently proposed recurrent neural network model with promising initial results. We evaluate how well each model predicts a student's future response given previous responses using two publicly available and one proprietary data set. We find that IRT-based methods consistently matched or outperformed DKT
arXiv:1604.02336v2
fatcat:v77xnwzf5fc55fx6n3zn3vl5dm