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GradeAid: a framework for automatic short answers grading in educational contexts-design, implementation and

Emiliano Del Gobbo1, Alfonso Guarino2, Barbara Cafarelli1

  • 1Department of Economics, Management and Territory, University of Foggia, Via da Zara, 11, 71121 Foggia, FG Italy.

Knowledge and Information Systems
|June 26, 2023
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Summary

GradeAid is a new framework for automatic short answer grading (ASAG) that analyzes lexical and semantic features. It achieves comparable performance to existing systems and supports non-English datasets.

Keywords:
Automatic short answer gradingLearning analyticsNatural language processing

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Area of Science:

  • Natural Language Understanding
  • Learning Analytics
  • Artificial Intelligence in Education

Background:

  • Automatic short answer grading (ASAG) is crucial for managing large student cohorts in higher education.
  • Existing ASAG solutions face limitations, particularly with non-English datasets and robust validation.

Purpose of the Study:

  • To introduce GradeAid, a novel framework for ASAG.
  • To address limitations in current ASAG research by incorporating lexical and semantic analysis.
  • To provide a robustly validated baseline for future ASAG developments.

Main Methods:

  • Developed GradeAid, a framework utilizing joint analysis of lexical and semantic features.
  • Employed state-of-the-art regressors for answer scoring.
  • Conducted validation and benchmarking on all publicly available datasets and a new dataset.

Main Results:

  • GradeAid demonstrates performance comparable to existing ASAG systems, with root-mean-squared errors as low as 0.25.
  • The framework successfully handles non-English datasets.
  • The study provides a comprehensive validation and benchmarking of the GradeAid framework.

Conclusions:

  • GradeAid offers a robust and versatile solution for automatic short answer grading.
  • The framework's ability to handle diverse datasets and its strong performance establish it as a valuable baseline.
  • Further research can build upon GradeAid to enhance intelligent tutoring systems and feedback mechanisms.