the corresponding assessments. Then, we train a deep bidirectional Transformer encoder based assessment model to predict the masked assessments conditioned on surrounding interactions. After pre-training, we replace the last layer of the model with a layer corresponding to each downstream task, and all parameters of the model are then fine-tuned to the downstream tasks.

We empirically evaluate assessment modeling as pre-training tasks on *EdNet* [@choi2019ednet], a large-scale dataset collected by an active mobile education application, *Santa*, which has 1M users as well as 72M response data points gathered since 2016. The results show that assessment modeling provides a substantial performance gain in downstream AIEd tasks. In particular, we obtain an improvement of $14.16$ mean absolute error and $0.014$ accuracy from the previous state-of-the-art model for exam score and review correctness prediction respectively.

In summary, our contributions are follows:

-   We propose assessment modeling, fundamental pre-training tasks for general IESs. Assessment modeling as pre-training tasks allows the model to hold relevance to the label-scarce educational problems while reducing irrelevant noises.

-   We give formal definitions of knowledge tracing and assessment modeling in a form that is quantifiable and objective from a particular IES design.

-   Inspired by the recent success of 