TODO
Please, to cite this package, cite the paper:
del Gobbo, E., Guarino, A., Cafarelli, B. et al. GradeAid: a framework for automatic short answers grading in educational contexts—design, implementation and evaluation. _Knowl Inf Syst_ 65, 4295–4334 (2023). https://doi.org/10.1007/s10115-023-01892-9
Here you can find the citation in BibTeX format:
@Article{delGobbo2023,
author={del Gobbo, Emiliano
and Guarino, Alfonso
and Cafarelli, Barbara
and Grilli, Luca},
title={GradeAid: a framework for automatic short answers grading in educational contexts---design, implementation and evaluation},
journal={Knowledge and Information Systems},
year={2023},
month={Oct},
day={01},
volume={65},
number={10},
pages={4295-4334},
issn={0219-3116},
doi={10.1007/s10115-023-01892-9},
url={https://doi.org/10.1007/s10115-023-01892-9}
}
Here the citation in RIS format:
TY - JOUR
AU - del Gobbo, Emiliano
AU - Guarino, Alfonso
AU - Cafarelli, Barbara
AU - Grilli, Luca
PY - 2023
DA - 2023/10/01
TI - GradeAid: a framework for automatic short answers grading in educational contexts—design, implementation and evaluation
JO - Knowledge and Information Systems
SP - 4295
EP - 4334
VL - 65
IS - 10
AB - Automatic short answer grading (ASAG), a hot field of natural language understanding, is a research area within learning analytics. ASAG solutions are conceived to offload teachers and instructors, especially those in higher education, where classes with hundreds of students are the norm and the task of grading (short)answers to open-ended questionnaires becomes tougher. Their outcomes are precious both for the very grading and for providing students with “ad hoc” feedback. ASAG proposals have also enabled different intelligent tutoring systems. Over the years, a variety of ASAG solutions have been proposed, still there are a series of gaps in the literature that we fill in this paper. The present work proposes GradeAid, a framework for ASAG. It is based on the joint analysis of lexical and semantic features of the students’ answers through state-of-the-art regressors; differently from any other previous work, (i) it copes with non-English datasets, (ii) it has undergone a robust validation and benchmarking phase, and (iii) it has been tested on every dataset publicly available and on a new dataset (now available for researchers). GradeAid obtains performance comparable to the systems presented in the literature (root-mean-squared errors down to 0.25 based on the specific tuple $$\langle $$dataset-question$$\rangle $$). We argue it represents a strong baseline for further developments in the field.
SN - 0219-3116
UR - https://doi.org/10.1007/s10115-023-01892-9
DO - 10.1007/s10115-023-01892-9
ID - delGobbo2023
ER -
This work is licensed under a [Creative Commons Attribution 4.0 International License][cc-by].
The artworks (library logo) have Proprietary license and cannot be used for other projects. If you fork the project, you MUST indicate it is a derivate project and not use the logo to identify the project.
Copyright (C) 2022-2024 Emiliano del Gobbo, Alfonso Guarino
Emiliano del Gobbo emidelgo@gmail.com Alfonso Guarino alfonso.guarino@unifg.it