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abstract: |
    Interactive Educational Systems (IESs) have developed rapidly in recent years to address the issue of quality and affordability of education. Analogous to many other domains in Artificial Intelligence (AI), there are specific tasks of AI in Education (AIEd) for which labels are scarce and expensive. For instance, labels like exam score and grade are considered important in educational and social context. However, unlike interactive features automatically collected by IESs, obtaining the labels is costly as they require student actions taken outside the system. Likewise, while student events like course dropout and review correctness are automatically recorded by IESs, they are few in number as the events occur sporadically in practice. A common way of circumventing the label-scarcity problem is the pre-train/fine-tine method, where a model is trained in a relevant auxiliary task with a large amount of data before the main task. Accordingly, existing works pre-train a model to learn representations of contents in learning items (e.g. exercises). However, such methods fail to utilize the student interaction data available and model student learning behavior.

    To this end, we propose assessment modeling, fundamental pre-training tasks for general IESs. An assessment is a feature of student-system interactions which can 