Neural network approach to classifying alarming student responses to online assessment
This addresses a critical safety issue in educational technology by automating the detection of concerning student responses, though it is an incremental application of existing neural network methods to a new domain.
The paper tackles the problem of identifying alarming student responses in online assessments, such as those indicating self-harm or abuse, using neural network models to flag these for human review, achieving a system that maximizes detection within a fixed review capacity.
Automated scoring engines are increasingly being used to score the free-form text responses that students give to questions. Such engines are not designed to appropriately deal with responses that a human reader would find alarming such as those that indicate an intention to self-harm or harm others, responses that allude to drug abuse or sexual abuse or any response that would elicit concern for the student writing the response. Our neural network models have been designed to help identify these anomalous responses from a large collection of typical responses that students give. The responses identified by the neural network can be assessed for urgency, severity, and validity more quickly by a team of reviewers than otherwise possible. Given the anomalous nature of these types of responses, our goal is to maximize the chance of flagging these responses for review given the constraint that only a fixed percentage of responses can viably be assessed by a team of reviewers.