Riordan Brennan
ABSTRACT
Concept maps are used in education to illustrate ideas and relationships among them. Instructors employ such maps to evaluate a student’s knowledge on a subject. Collective concept maps have been recently proposed as a tool to graphically summarize a group’s rather than an individual’s understanding on a topic. In this paper, we present a methodology that automatically generates collective concept maps, which relies on grouping similar ideas into node-clusters. We present a novel clustering algorithm that is shown to produce more informational maps compared to Markov clustering. We evaluate the collective map framework by applying it to sets of a total of 56 individual maps created by teachers (grades 2-12) and students (grades 6-11) during a week-long cybersecurity camp. Finally, we discuss how collective concept maps can support longitudinal research studies on program and student outcomes by providing a novel format for knowledge exchange. We have made our tool implementation publicly available.
- Alla Anohina and Janis Grundspenkis. 2007. A Concept Map Based Intelligent System for Adaptive Knowledge Assessment. In Proceedings of the 2007 Conference on Databases and Information Systems IV: Selected Papers from the Seventh International Baltic Conference DBIS’2006. IOS Press, NLD, 263–276.Google Scholar
- Marco Carmosino and Mia Minnes. 2020. Adaptive Rubrics. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education (Portland, OR, USA) (SIGCSE ’20). Association for Computing Machinery, New York, NY, USA, 549–555. https://doi.org/10.1145/3328778.3366946Google ScholarDigital Library
- [3] CLARK Center.Last accessed August 13, 2021. https://www.clark.center/.Google Scholar
- Binglin Chen, Sushmita Azad, Rajarshi Haldar, Matthew West, and Craig Zilles. 2020. A Validated Scoring Rubric for Explain-in-Plain-English Questions. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education(Portland, OR, USA) (SIGCSE ’20). Association for Computing Machinery, New York, NY, USA, 563–569. https://doi.org/10.1145/3328778.3366879Google ScholarDigital Library
- [5] Teach Cyber.Last accessed August 13, 2021. https://teachcyber.org/.Google Scholar
- Paul Denny, Sathiamoorthy Manoharan, Ulrich Speidel, Giovanni Russello, and Angela Chang. 2019. On the Fairness of Multiple-Variant Multiple-Choice Examinations. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (Minneapolis, MN, USA) (SIGCSE ’19). Association for Computing Machinery, New York, NY, USA, 462–468. https://doi.org/10.1145/3287324.3287357Google ScholarDigital Library
- Pranita Deshpande and Irfan Ahmed. 2019. Topological Scoring of Concept Maps for Cybersecurity Education. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (Minneapolis, MN, USA) (SIGCSE ’19). Association for Computing Machinery, New York, NY, USA, 731–737. https://doi.org/10.1145/3287324.3287495Google ScholarDigital Library
- Glenn W Ellis, A Rudnitsky, and Becky Silverstein. 2004. Using concept maps to enhance understanding in engineering education. International Journal of Engineering Education 20, 6 (2004), 1012–1021.Google Scholar
- IHMC. 2019. CmapTools. https://cmap.ihmc.us/Google Scholar
- [10] RING: Regions Investing in Next Generation.Last accessed August 13, 2021. https://www.caecommunity.org/initiative/k12-ring.Google Scholar
- Joy M. Jacobs-Lawson and Douglas A. Hershey. 2002. Concept Maps as an Assessment Tool in Psychology Courses. Teaching of Psychology 29, 1 (2002), 25–29.Google ScholarCross Ref
- Ville Kivimäki, Joonas Pesonen, Jani Romanoff, Heikki Remes, and Petri Ihantola. 2019. Curricular Concept Maps as Structured Learning Diaries: Collecting Data on Self-Regulated Learning and Conceptual Thinking for Learning Analytics Applications. Journal of Learning Analytics 6, 3 (Dec. 2019), 106–121. https://doi.org/10.18608/jla.2019.63.13Google ScholarCross Ref
- Abe Leite and Saúl A. Blanco. 2020. Effects of Human vs. Automatic Feedback on Students’ Understanding of AI Concepts and Programming Style. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education (Portland, OR, USA) (SIGCSE ’20). Association for Computing Machinery, New York, NY, USA, 44–50. https://doi.org/10.1145/3328778.3366921Google ScholarDigital Library
- Evan Maicus, Matthew Peveler, Stacy Patterson, and Barbara Cutler. 2019. Autograding Distributed Algorithms in Networked Containers. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (Minneapolis, MN, USA) (SIGCSE ’19). Association for Computing Machinery, New York, NY, USA, 133–138. https://doi.org/10.1145/3287324.3287505Google ScholarDigital Library
- John R. McClure, Brian Sonak, and Hoi K. Suen. 1999. Concept map assessment of classroom learning: Reliability, validity, and logistical practicality. Journal of Research in Science Teaching 36, 4 (1999), 475–492.Google ScholarCross Ref
- Joseph D Novak and Alberto J Cañas. 2006. The theory underlying concept maps and how to construct them. Technical Report IHMC CmapTools 1, 1 (2006), 1–31.Google Scholar
- Debbie Perouli, Akshay Verma, and Marta Magiera. 2021. Assessing a Group’s Understanding of Cybersecurity through Collective Concept Maps. Association for Computing Machinery, New York, NY, USA, 1293. https://doi.org/10.1145/3408877.3439673Google ScholarDigital Library
- [18] Cyber.org K-12 Cybersecurity Learning Standards.Last accessed August 13, 2021. https://cyber.org/standards.Google Scholar
- Stijn van Dongen. 2000. A cluster algorithm for graphs. Information Systems [INS]R 0010 (2000).Google Scholar
- Jim Vanides, Yue Yin, Miki Tomita, and Maria Araceli Ruiz-Primo. 2005. Concept maps. Science Scope 28, 8 (2005), 27–31.Google Scholar
- Valdemar Švábenský, Jan Vykopal, and Pavel Čeleda. 2020. What Are Cybersecurity Education Papers About? A Systematic Literature Review of SIGCSE and ITiCSE Conferences. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education (Portland, OR, USA) (SIGCSE ’20). Association for Computing Machinery, New York, NY, USA, 2–8. https://doi.org/10.1145/3328778.3366816Google ScholarDigital Library
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