Abstract
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 Kastor , 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 Kastor achieves the accuracy of around 84% and 95% (at accurate and close-to-accurate levels) for SCADA and forensics concept maps, respectively. Furthermore, Kastor ’s comparison with a topological scoring method shows improvement by around 32% and 79% on SCADA and forensics concept maps, respectively.
Highlights
Concept mapping is a process of representing a student’s knowledge on a topic in a graph-like structure referred to as concept map [1]
Existing research mainly focuses on topological scoring of concept maps which are not very effective as compared to the Waterloo Rubric, a welldefined grading rubric for manual analysis
This paper proposed a new method, Kastor that automates the quality assessment parameters for concept maps in the Waterloo Rubric
Summary
Concept mapping is a process of representing a student’s knowledge on a topic in a graph-like structure referred to as concept map [1]. It is a cognitively intensive task making the students recall their concepts on a subject and organize and relate them in a graphical representation. A concept map begins with an abstract/broad concept (mainly the topic being addressed). It adds circles and connecting links at different levels to identify more specific concepts and their relationships with each other as the map proceeds deeper into the hierarchy
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