Date of Award
Summer 8-2018
Degree Type
Thesis
Degree Name
M.S.
Degree Program
Computer Science
Department
Computer Science
Major Professor
Irfan Ahmed
Second Advisor
N. Adlai A. DePano
Third Advisor
Minhaz Zibran
Abstract
Concept Maps (CMs) are considered a well-known pedagogy technique in creating curriculum, educating, teaching, and learning. Determining comprehension of concepts result from comparisons of candidate CMs against a master CM, and evaluate "goodness". Past techniques for comparing CMs have revolved around the creation of a subjective rubric. We propose a novel CM scoring scheme called MAnanA based on a Fuzzy Similarity Scaling (FSS) score to vastly remove the subjectivity of the rubrics in the process of grading a CM. We evaluate our framework against a predefined rubric and test it with CM data collected from the Introduction to Computer Security course at the University of New Orleans (UNO), and found that the scores obtained via MAnanA captured the trend that we observed from the rubric via peak matching. Based on our evaluation, we believe that our framework can be used to objectify CM analysis.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Recommended Citation
Blake Gatto, Sharon Elizabeth, "MAnanA: A Generalized Heuristic Scoring Approach for Concept Map Analysis as Applied to Cybersecurity Education" (2018). University of New Orleans Theses and Dissertations. 2526.
https://scholarworks.uno.edu/td/2526
Included in
Artificial Intelligence and Robotics Commons, Information Security Commons, Theory and Algorithms Commons
Rights
The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.