Anglia Ruskin Research Online (ARRO)
Browse
Ng_2023.pdf (2.5 MB)

Automated short answer grading with computer-assisted grading example acquisition based on active learning

Download (2.5 MB)
journal contribution
posted on 2023-09-01, 15:15 authored by Andrew Kwok-Fai Lui, Sin-Chun Ng, Stella Wing-Nga Cheung
The technology of automated short answer grading (ASAG) can efficiently process answers according to human-prepared grading examples. Computer-assisted acquisition of grading examples uses a computer algorithm to sample real student responses for potentially good examples. The process is critical for optimizing the grading accuracy of machine learning models given a budget of human effort and the appeal of ASAG to online learning providers. This paper presents a novel method called short answer grading with active learning (SAGAL) that features a unified formulation comprising the heuristics for identifying potentially optimal examples of representative answers, borderline answers, and anomalous answers. The method is based on active learning, which iteratively samples good examples and queries for annotation to increase the sampling accuracy. SAGAL has been evaluated with three different public datasets of distinctive characteristics. The results show that the resulting models generally outperform the baseline semi-supervised learning methods on the same number of grading examples.

History

Refereed

  • Yes

Page range

1-18

Publication title

Interactive Learning Environments

ISSN

1744-5191

Publisher

Informa UK Limited

File version

  • Accepted version

Language

  • eng

Legacy posted date

2023-02-24

Legacy creation date

2023-02-24

Legacy Faculty/School/Department

Faculty of Science & Engineering

Usage metrics

    ARU Outputs

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC