Interactive Educational Systems (IESs) have developed rapidly in recent years to address the issue of quality and affordability of education. Analogous to other domains in AI , there are specific tasks of AIEd for which labels are scarce . For instance, labels like exam score and grade are considered important in educational and social context. However, obtaining the labels is costly as they require student actions taken outside the system. Likewise, while student events like course dropout and review correctnessare 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. Accordingly, existing works pre-train a model to learn representations of contents in learning items. 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 IESs . An assessment is a feature of student-system interactions which can act as pedagogical evaluation, such as student response correctness or timeliness . Assessment modeling is the prediction of assessments conditioned on the surrounding context of interactions. Although it is natural to pre-train  interactive features available in large amount, narrowing down the prediction targets to assessments holds relevance to the label-scarce educational problems while reducing irrelevant noises. To the best of our knowledge, this is the first work investigating appropriate pre-training method of predicting educational features from student-system interactions. While the effectiveness of different combinations of assessments is open for exploration, we suggest assessment modeling as a guiding principle for selecting proper pre-training tasks for the label-scarce educational problems.
Like many other domains in Artificial Intelligence (AI) , there are specific tasks in the field of AI in Education (AIEd) for which labels are scarce and expensive, such as predicting exam score or review correctness . A common way of circumventing label-scarce problems is pre-training a model to learn representations of the contents of learning items. However, such methods fail to utilize the full range of student interaction data available and do not model student learning behavior. To this end, we propose Assessment Modeling, a class of fundamental pre-training tasks for general interactive educational systems . An assessment is a feature of student-system interactions which can serve as a pedagogical evaluation. Examples include the correctness and timeliness of a student's answer. Assessment Modeling is the prediction of assessments conditioned on the surrounding context of interactions. Although it is natural to pre-train on interactive features available in large amounts, limiting the prediction targets to assessments focuses the tasks' relevance to the label-scarce educational problems and reduces less-relevant noise. While the effectiveness of different combinations of assessments is open for exploration, we suggest Assessment Modeling as a first-order guiding principle for selecting proper pre-training tasks for label-scarce educational problems.
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