tracing is typically instantiated as response correctness prediction, where the interaction $I_i = \{e_i, r_i\}$ consists of a consumed exercise $e_i$ and a corresponding response correctness $r_i$ [@piech2015deep; @zhang_2017; @liu_2019; @pandey2019self; @lee2019creating]. In this setup, only the response correctness $r_T$ of the last interaction $I_T$ is predicted and features related to predicted student response $r_T$ are masked. Following our definition of knowledge tracing, the task can be extended further to predict diverse interactive features such as:

-   `offer_selection`: Whether a student accepts to study the offered learning items.

-   `start_time`: The time when a student starts to solve an exercise.

-   `inactive_time`: The duration a student being inactive in a learning session.

-   `platform`: Whether a student responds to each exercise on web browser or mobile app.

-   `payment`: Whether a student purchases charged items.

-   `event` : Whether a student participates in application events.

-   `longest_answer`: Whether a student selected the answer choice with longest description.

-   `response_correctness`: Whether a student responds correctly to a given exercise.

-   `timeliness`: Whether a student responds to each exercise under the time limit recommended by domain experts.

-   `course_dropout` : Whether a student drops out of the entire class.

-   `elapsed_time`: The 