Automated Waterloo Rubric for Concept Map Grading

Concept mapping is a well-known pedagogical tool to help students organize, represent, and develop an understanding of a topic. The grading of concept maps is typically manual, time-consuming, and tedious, especially for a large class. Existing research mostly focuses on topological scoring based-on...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Shresht Bhatia, Sajal Bhatia, Irfan Ahmed
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Concept map automatic grading cybersecurity education Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/bbe2dda27c1942959e7df63facd2ccb2
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:bbe2dda27c1942959e7df63facd2ccb2
record_format	dspace
spelling	oai:doaj.org-article:bbe2dda27c1942959e7df63facd2ccb22021-11-18T00:10:32ZAutomated Waterloo Rubric for Concept Map Grading2169-353610.1109/ACCESS.2021.3124672https://doaj.org/article/bbe2dda27c1942959e7df63facd2ccb22021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9597515/https://doaj.org/toc/2169-3536Concept mapping is a well-known pedagogical tool to help students organize, represent, and develop an understanding of a topic. The grading of concept maps is typically manual, time-consuming, and tedious, especially for a large class. Existing research mostly focuses on topological scoring based-on structural features of concept maps. However, the scoring does not achieve comparable accuracy to well-defined rubrics for manual analysis on the quality of content in a concept map. This paper presents <monospace>Kastor</monospace>, a new method to automate the Waterloo Rubric of scoring concept maps by quantifying the rubric’s quality assessment parameters. The evaluation is performed on a publicly-available dataset of 39 concept maps of two cybersecurity courses, i.e., digital forensics, and supervisory control and data acquisition (SCADA) system security. The evaluation results show that <monospace>Kastor</monospace> achieves the accuracy of around 84% and 95% (at accurate and close-to-accurate levels) for SCADA and forensics concept maps, respectively. Furthermore, <monospace>Kastor</monospace>’s comparison with a topological scoring method shows improvement by around 32% and 79% on SCADA and forensics concept maps, respectively.Shresht BhatiaSajal BhatiaIrfan AhmedIEEEarticleConcept mapautomatic gradingcybersecurity educationElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 148590-148598 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Concept map automatic grading cybersecurity education Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Concept map automatic grading cybersecurity education Electrical engineering. Electronics. Nuclear engineering TK1-9971 Shresht Bhatia Sajal Bhatia Irfan Ahmed Automated Waterloo Rubric for Concept Map Grading
description	Concept mapping is a well-known pedagogical tool to help students organize, represent, and develop an understanding of a topic. The grading of concept maps is typically manual, time-consuming, and tedious, especially for a large class. Existing research mostly focuses on topological scoring based-on structural features of concept maps. However, the scoring does not achieve comparable accuracy to well-defined rubrics for manual analysis on the quality of content in a concept map. This paper presents <monospace>Kastor</monospace>, a new method to automate the Waterloo Rubric of scoring concept maps by quantifying the rubric’s quality assessment parameters. The evaluation is performed on a publicly-available dataset of 39 concept maps of two cybersecurity courses, i.e., digital forensics, and supervisory control and data acquisition (SCADA) system security. The evaluation results show that <monospace>Kastor</monospace> achieves the accuracy of around 84% and 95% (at accurate and close-to-accurate levels) for SCADA and forensics concept maps, respectively. Furthermore, <monospace>Kastor</monospace>’s comparison with a topological scoring method shows improvement by around 32% and 79% on SCADA and forensics concept maps, respectively.
format	article
author	Shresht Bhatia Sajal Bhatia Irfan Ahmed
author_facet	Shresht Bhatia Sajal Bhatia Irfan Ahmed
author_sort	Shresht Bhatia
title	Automated Waterloo Rubric for Concept Map Grading
title_short	Automated Waterloo Rubric for Concept Map Grading
title_full	Automated Waterloo Rubric for Concept Map Grading
title_fullStr	Automated Waterloo Rubric for Concept Map Grading
title_full_unstemmed	Automated Waterloo Rubric for Concept Map Grading
title_sort	automated waterloo rubric for concept map grading
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/bbe2dda27c1942959e7df63facd2ccb2
work_keys_str_mv	AT shreshtbhatia automatedwaterloorubricforconceptmapgrading AT sajalbhatia automatedwaterloorubricforconceptmapgrading AT irfanahmed automatedwaterloorubricforconceptmapgrading
_version_	1718425188577574912

Automated Waterloo Rubric for Concept Map Grading

Ejemplares similares