for each exercise provides strong evidence for the mastery of concepts required to solve the exercise properly. Also, `timeliness` also serves as assessment since a student responding to each exercise within limited time is expected to have high proficiency in skills and knowledge necessary to solve the exercise. Figure \[fig:am\_def\] depicts the relationship between assessments and general knowledge tracing features.

Assessment Modeling as Pre-training Tasks
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![Not all interactive features are collected equally often. For example, `payment`, `event`, `course_dropout` and `review_correctness` are interactive features obtained more sporadically than other features. Among the sporadic interactive features, `course_dropout` and `review_correctness` are assessments.[]{data-label="fig:am_freq"}](assessment_freq.pdf){width="45.00000%"}

![Assessment modeling is highly appropriate pre-training method for the label-scarce educational problems. First, most of assessments are available in large amount as they are automatically collected from student-system interactions (Interactive). Also, since assessments share educational aspects, narrowing down the scope of prediction targets to assessments reduces noise irrelevant to the problems (Educational).[]{data-label="fig:am_alt"}](assessment_alt.pdf){width="50.00000%"}

![Possible pre-train/fine-tune scenarios. We may pre-train a model to predict `start_time`, `response_correctness`, `timeliness` and `review_correctness` for estimating `exam_score` (red). Likewise, a model pre-trained to predict `start_time` and `response_correctness` can be trained to predict `review_correctness` (green). However, pre-training a non-educational interactive feature like `start_time` is not effective for the 