the assessment value `response_`$\texttt{correctness}_t$ is the recorded user response and $\texttt{timeliness}_t$ is calculated as 1 if the user responded under the time limits recommended by TOEIC experts (Table \[tab:timelimit\]). We exclude the interactions of users involved in any of the downstream tasks for pre-training to avoid learning the downstream data beforehand. After processing, the data consists of 251,989 users with a total of 49,944,086 interactions.

### Dataset for Label-Scarce Educational Problems

For the exam score prediction, we aggregate the real TOEIC scores reported by users of *Santa*. The reports are scarce in number because a user have to register, take the exam and report the score at his own expense. *Santa* offered corresponding reward to the users who have reported their score. A total of 2,594 score reports have been obtained through 6 months, which is comparably less than the number of 72M exercise responses. For our experiment, the data is divided into training set (1,302 users, 1815 labels), validation set (244 users, 260 labels), and test set (466 users, 519 labels).

For the review correctness prediction, we look over each student’s timeline and find exercises that have been solved at least twice. That is, if an exercise $e$ appears 