in learning new concepts than reviewing previously studied materials. `course_dropout` and `review_correctness` are examples of sporadic assessments (Figure \[fig:am\_freq\]).

To overcome the lack of aforementioned labels, we consider the pre-train/fine-tune paradigm that leverages the patterns of data available in large amount. In this paradigm, a model is first trained in an auxiliary task relevant to the tasks of interest with large-scale data. With pre-trained parameters as initial values, the model is slightly modified and finally trained in main tasks. This approach has been successful in AI fields like NLP, computer vision and speech recognition [@devlin2018bert; @studer2019comprehensive; @schneider2019wav2vec]. Accordingly, existing methods in AIEd pre-train the contents of learning materials like the word sequence of a passage, but such methods do not capture student behaviors and only utilize a single feature from the data.

Instead, one may pre-train different features automatically collected by IESs (see Figure \[fig:am\_alt\]). However, training every available features is computationally intractable and may introduce irrelevant noise. To this end, assessment modeling narrows down the prediction targets to assessments, the interactive features that are also educational. Since multiple assessments are available, a wide variety of pre-train/fine-tune pairs can be explored for effective assessment modeling (see Figure \[fig:am\_pre\_fine\]). This raises 