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...
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| Auteurs principaux: | , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
IEEE
2021
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/bbe2dda27c1942959e7df63facd2ccb2 |
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| Résumé: | 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. |
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