the assessment model with a layer appropriate for each downstream task and fine-tune whole parameters in the model to predict labels in the downstream tasks. We provide detailed descriptions of our proposed assessment model in the following subsections.

### Input Representation

The first layer in the assessment model maps each interaction to an embedding vector. Firstly, we embed the following attributes:

-   `exercise_id`: We assign a latent vector unique to each exercise.

-   `exercise_category`: Each exercise has its own category tag that represents the type of the exercise. We assign a latent vector to each tag.

-   `position`: The relative position $t$ of the interaction $I_t$ in the input sequence. We use the learned positional embedding from [@gehring_2017] instead of the sinusoidal positional encoding that used in [@vaswani_2017].

As shown in Example \[ex:kt\], IES collects diverse interactive features that can potentially be used for assessment modeling. However, using all possible interactive features for assessment modeling is computationally intractable and does not guarantee the best results when the assessment model is fine-tuned on downstream tasks. For experimental studies, we narrow down the scope of interactive features to the ones available from an exercise-response pair, the simplest but widely considered interaction in 