Supervised Hierarchical Classification for Student Answer Scoring
This addresses the problem of automated educational assessment for teachers and students, but appears to be an incremental improvement on existing hierarchical classification methods.
The paper tackles automated student answer scoring by developing a hierarchical classification system that predicts labels sequentially through a binary tree structure, delaying more easily confused labels to later stages. The approach breaks down the classification task into binary subtasks, though no concrete performance numbers are provided in the abstract.
This paper describes a hierarchical system that predicts one label at a time for automated student response analysis. For the task, we build a classification binary tree that delays more easily confused labels to later stages using hierarchical processes. In particular, the paper describes how the hierarchical classifier has been built and how the classification task has been broken down into binary subtasks. It finally discusses the motivations and fundamentals of such an approach.