Supervised Hierarchical Classification for Student Answer Scoring

TL;DR

This paper presents a hierarchical classification system for automated student answer scoring, which predicts labels sequentially using a binary tree structure to improve accuracy in distinguishing similar responses.

Contribution

It introduces a novel hierarchical binary tree approach for student answer scoring, breaking down the classification into simpler binary subtasks to enhance performance.

Findings

Hierarchical classifier improves label prediction accuracy.

Binary tree structure effectively handles confusing labels.

Method demonstrates potential for automated grading systems.

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.