skip to main content
10.1145/3290605.3300258acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
research-article
Public Access

MindDot: Supporting Effective Cognitive Behaviors in Concept Map-Based Learning Environments

Published:02 May 2019Publication History

ABSTRACT

While prior research has revealed the promising impact of concept mapping on learning, few have comprehensively modeled different cognitive behaviors during concept mapping. In addition, existing concept mapping tools lack effective feedback to support better learning behaviors. This work presents MindDot, a concept map-based learning environment that facilitates the cognitive process of comparing and integrating related concepts via two forms of support. A hyperlink support and an expert template. Study results suggested that both types of support had positive impact on the development of comparative strategies and that hyperlink support enhanced learning. We further evaluated the cognitive learning progress at a fine-grained level with two forms of visualizations. We then extracted several behavioral patterns that provided insights about the cognitive progress in learning. Lastly, we derive design recommendations that we hope will inspire future intelligent tutoring systems that automatically evaluate students' learning behaviors and foster them in developing effective learning behaviors

Skip Supplemental Material Section

Supplemental Material

paper028p.mp4

mp4

1 MB

References

  1. Lynne, Anderson-Inman, Leslie A. Ditson, and Mary T. Ditson. 1998. Computerbased concept mapping: Promoting meaningful learning in science for students with disabilities. Information Technology and Disabilities, 5(1--2).Google ScholarGoogle Scholar
  2. John R. Anderson, Albert T. Corbett, Kenneth R. Koedinger, and Ray Pelletier. 1995. Cognitive tutors: Lessons learned. The journal of the learning sciences, 4(2), pp.167--207.Google ScholarGoogle Scholar
  3. Thushari Atapattu, Katrina Falkner, and Nickolas Falkner. 2015, June. Educational question answering motivated by question-specific concept maps. In International Conference on Artificial Intelligence in Education (pp. 13--22). Springer, Cham.Google ScholarGoogle ScholarCross RefCross Ref
  4. Alberto J. Cañas, Greg Hill, Roger Carff, Niranjan Suri, James Lott, Gloria Gómez, Thomas C. Eskridge, Mario Arroyo, and Rodrigo Carvajal. 2004. CmapTools: A knowledge modeling and sharing environment.Google ScholarGoogle Scholar
  5. Kuo-En Chang, Yao-Ting Sung, and Sung-Fang Chen. 2001. Learning through computer-based concept mapping with scaffolding aid. Journal of computer assisted learning, 17(1), pp.21--33.Google ScholarGoogle ScholarCross RefCross Ref
  6. Pasana Chularut, and Teresa K. DeBacker. 2004. The influence of concept mapping on achievement, self-regulation, and self-efficacy in students of English as a second language. Contemporary Educational Psychology, 29(3), pp.248--263.Google ScholarGoogle ScholarCross RefCross Ref
  7. Martin Davies. 2011. Concept mapping, mind mapping and argument mapping: what are the differences and do they matter?. Higher education, 62(3), pp.279--301.Google ScholarGoogle Scholar
  8. Paul Denny, Fiona McDonald, Ruth Empson, Philip Kelly, and Andrew Petersen. 2018, April. Empirical Support for a Causal Relationship Between Gamification and Learning Outcomes. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (p. 311). ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Jeanie C. Dumestre. 2004, September. Using CmapTools software to assist in performing job task analysis. In Concept Maps: Theory, Methodology, Technology. Proceedings of the First International Conference on Concept Mapping. Pamplona: Universidad Pública de Navarra.Google ScholarGoogle Scholar
  10. Brittany Garcia, Sharon Lynn Chu, Beth Nam, and Colin Banigan. 2018, April. Wearables for Learning: Examining the Smartwatch as a Tool for Situated Science Reflection. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (p. 256). ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Tsukasa Hirashima, Kazuya Yamasaki, Hiroyuki Fukuda, and Hideo Funaoi. 2011, June. Kit-build concept map for automatic diagnosis. In International conference on artificial intelligence in education(pp. 466--468). Springer, Berlin, Heidelberg. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Phillip B. Horton, Andrew A. McConney, Michael Gallo, Amanda L. Woods, Gary J. Senn, and Denis Hamelin. 1993. An investigation of the effectiveness of concept mapping as an instructional tool. Science Education, 77(1), pp.95--111.Google ScholarGoogle ScholarCross RefCross Ref
  13. Gwo-Jen Hwang, Chih-Hsiang Wu, and Kuo Fan-Ray. 2013. Effects of touch technology-based concept mapping on students' learning attitudes and perceptions. Journal of Educational Technology & Society, 16(3), p.274.Google ScholarGoogle Scholar
  14. Ian M. Kinchin. 2001. If concept mapping is so helpful to learning biology, why aren't we all doing it?. International Journal of Science Education, 23(12), pp.12571269.Google ScholarGoogle ScholarCross RefCross Ref
  15. Leelawong, Krittaya, and Gautam Biswas. Designing learning by teaching agents: The Betty's Brain system. International Journal of Artificial Intelligence in Education, 18(3), pp.181--208. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Kyu Yon Lim, Hyeon Woo Lee, and Barbara Grabowski. 2009. Does concept? mapping strategy work for everyone? The levels of generativity and learners' self? regulated learning skills. British Journal of Educational Technology, 40(4), pp.606618.Google ScholarGoogle ScholarCross RefCross Ref
  17. Ching Liu, Juho Kim, and Hao-Chuan Wang. 2018, April. ConceptScape: Collaborative Concept Mapping for Video Learning. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (p. 387). ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Sarah Manlove, Ard W. Lazonder, and Ton de Jong. 2007. Software scaffolds to promote regulation during scientific inquiry learning. Metacognition and Learning, 2(2--3), pp.141--155.Google ScholarGoogle Scholar
  19. Gary Marchionini. 1997. Information seeking in electronic environments (No. 9). Cambridge university press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Roberto Martínez Maldonado, Judy Kay, and Kalina Yacef. 2010, November. Collaborative concept mapping at the tabletop. In ACM International Conference on Interactive Tabletops and Surfaces (pp. 207--210). ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. George Miller, and Scott Spoolman. 2011. Living in the environment: principles, connections, and solutions. Nelson Education.Google ScholarGoogle Scholar
  22. Mind Vector. 2018. https://www.mindvectorweb.comGoogle ScholarGoogle Scholar
  23. MindMaple. 2018. http://www.mindmaple.comGoogle ScholarGoogle Scholar
  24. B. Moon, A. J. Pino, and C. A. Hedberg. 2006. Studying Transformation: The Use of Cmap-Tools in Surveying the Integration of Intelligence and Operations. In Proc. 2nd Int'l Conf. Concept Mapping (pp. 527--533).Google ScholarGoogle Scholar
  25. Joseph D. Novak and Alberto J. Cañas. "The theory underlying concept maps and how to construct and use them." (2008).Google ScholarGoogle Scholar
  26. Peter Akinsola Okebukola. 1992. Can Good Concept Mappers be Good Problem Solvers in Science'. Research in Science & Technological Education, 10(2), pp.153--170.Google ScholarGoogle ScholarCross RefCross Ref
  27. Hector R. Ponce and Richard E. Mayer. 2014. Qualitatively different cognitive processing during online reading primed by different study activities. Computers in human behavior, 30, pp.121--130. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Sadhana Puntambekar and Agni Stylianou. 2005. Designing navigation support in hypertext systems based on navigation patterns. Instructional science, 33(5--6), pp.451--481.Google ScholarGoogle Scholar
  29. Steve Sizmur and Jonathan Osborne. 1997. Learning processes and collaborative concept mapping. International Journal of Science Education, 19(10), pp.11171135.Google ScholarGoogle ScholarCross RefCross Ref
  30. C.C. Tsai, Sunny SJ Lin, and Shyan-Ming Yuan. 2001. Students' use of web-based concept map testing and strategies for learning. Journal of Computer Assisted Learning, 17(1), pp.72--84.Google ScholarGoogle ScholarCross RefCross Ref
  31. Kurt Vanlehn. 2006. The behavior of tutoring systems. International journal of artificial intelligence in education, 16(3), pp.227--265. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Shang Wang, Erin Walker, and Ruth Wylie. 2017, June. What Matters in Concept Mapping? Maps Learners Create or How They Create Them. In International Conference on Artificial Intelligence in Education (pp. 406--417). Springer, Cham.Google ScholarGoogle ScholarCross RefCross Ref
  33. Romain Zeiliger, Thérèse Reggers, and Robert Peeters. 1996. Concept-map based navigation in educational hypermedia: a case study. In Proceedings of ED-MEDIA (Vol. 96).Google ScholarGoogle Scholar

Index Terms

  1. MindDot: Supporting Effective Cognitive Behaviors in Concept Map-Based Learning Environments

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Conferences
          CHI '19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
          May 2019
          9077 pages
          ISBN:9781450359702
          DOI:10.1145/3290605

          Copyright © 2019 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 2 May 2019

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article

          Acceptance Rates

          CHI '19 Paper Acceptance Rate703of2,958submissions,24%Overall Acceptance Rate6,199of26,314submissions,24%

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        HTML Format

        View this article in HTML Format .

        View HTML Format