are large in scale, thus power data-driven approaches for many Artificial Intelligence in Education (AIEd) tasks and improve the quality of IESs further. However, there are important tasks with insufficient data that prevent relevant models from attaining the full potential. A possible case is when the task depends on labels external to IESs that is rare in number. For example, the labels for a grade prediction model in an in-class IES are collected at the end of class. In some cases, the data points are collected by IESs but simply lacks in number because the events occur sporadically in practice. For instance, the number of actions reviewing previously solved exercises may be outnumbered by number of actions solving unseen exercises, as it is likely for a student to invest more time to solve new exercises than to review.

To circumvent the lack of data, transfer learning has been studied in AIEd literature [@hunt2017transfer; @ding2019transfer]. However, the method works only when the available data and the task to complete have the same form. Instead, the pre-train/fine-tune paradigm can be considered. In this paradigm, a model is first pre-trained in an unsupervised auxiliary task with abundant data. Then, the model is modified 