skip to main content
10.1145/3306500.3306520acmotherconferencesArticle/Chapter ViewAbstractPublication Pagesic4eConference Proceedingsconference-collections
research-article

Investigating the influence of students' learning-style preferences on user intentions regarding illustration software

Authors Info & Claims
Published:10 January 2019Publication History

ABSTRACT

With repaid development of web techniques, the Internet brings great innovation to the world. Visualization tools have especially attracted attention from researchers because visual representations such as diagrams and charts can easily express data to receivers. Computer assisted visualization systems are presently used in such industries as medicine, service, and finances, so students equipped with fundamental knowledge of illustration software may enhance their core competencies. In addition, learning style refers to an individual's approach to learning based on preferences, strengths, and weaknesses. This study investigated the influence of learning-style preferences on learners' intentions to use illustration software. The experiment's result showed that different learning styles could affect learners' preferences in illustration software and differences in user satisfaction between "active-reflective" and "global-sequential" learning styles.

References

  1. Friendly, M., & Denis, D. J. 2001. Milestones in the history of thematic cartography, statistical graphics, and data visualization. http://www.datavis.ca/milestones.Google ScholarGoogle Scholar
  2. Ainsworth, S., Prain, V., & Tytler, R. 2011. Drawing to learn in science. representations, 3, 5.Google ScholarGoogle Scholar
  3. Van Meter, P., & Garner, J. 2005. The promise and practice of student-generated drawing: Literature review and synthesis. Educational Psychology Review, 17(4), 285--325.Google ScholarGoogle ScholarCross RefCross Ref
  4. Stern, E., Aprea, C., & Ebner, H. G. 2003. Improving cross-content transfer in text processing by means of active graphical representation. Learning and Instruction, 13(2), 191--203.Google ScholarGoogle ScholarCross RefCross Ref
  5. Latham, A. M., Crockett, K. A., McLean, D. A., Edmonds, B., & O'Shea, K. 2010. Oscar: An intelligent conversational agent tutor to estimate learning styles. In Proceedings of the IEEE world congress on computational intelligence 2010 (pp. 2533--2540). Spain: Barcelona.Google ScholarGoogle ScholarCross RefCross Ref
  6. Avgoustinov, N. 2000. VRML as means of expressive 4D illustration in CAM education. Future Generation Computer Systems, 17(1), 39--48. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Ieronutti, L., & Chittaro, L. 2007. Employing virtual humans for education and training in X3D/VRML worlds. Computers & Education, 49(1), 93--109. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Keefe, D. F., Acevedo, D., Miles, J., Drury, F., Swartz, S. M., & Laidlaw, D. H. 2008. Scientific sketching for collaborative VR visualization design. IEEE Transactions on Visualization and Computer Graphics, 14(4), 835--847. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Leenaars, F. A., van Joolingen, W. R., & Bollen, L. 2013. Using self - made drawings to support modelling in science education. British journal of educational technology, 44(1), 82--94.Google ScholarGoogle Scholar
  10. Harle, M., & Towns, M. H. 2013. Students' understanding of primary and secondary protein structure: Drawing secondary protein structure reveals student understanding better than simple recognition of structures. Biochemistry and Molecular Biology Education, 41(6), 369--376.Google ScholarGoogle ScholarCross RefCross Ref
  11. Van Joolingen, W. R., Bollen, L., Leenaars, F., & Gijlers, H. (2012, January). Drawing-Based modeling for early science education. In Intelligent Tutoring Systems (pp. 689--690). Springer Berlin Heidelberg. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Diaz, D. P., & Cartnal, R. B. 1999. Students' learning styles in two classes: Online distance learning and equivalent on-campus. College teaching, 47(4), 130--135.Google ScholarGoogle Scholar
  13. Graf, S., & Liu, T. C. 2010. Analysis of learners' navigational behaviour and their learning styles in an online course. Journal of Computer Assisted Learning, 26(2), 116--131.Google ScholarGoogle ScholarCross RefCross Ref
  14. Graf, S., Viola, S. R., Leo, T. & Kinshuk 2007. In-depth analysis of the felder-silverman learning style dimensions. Journal of Research on Technology in Education, 40(1), 79--93.Google ScholarGoogle ScholarCross RefCross Ref
  15. Felder, R. M., & Silverman, L. K. 1988. Learning and teaching styles in engineering education. Engineering education, 78(7), 674--681.Google ScholarGoogle Scholar
  16. Felder, R. & Spurlin, J. 2005. Applications, Reliability, and Validity of the Index of Learning Styles, International Journal of Engineering Education 21, 1, pp. 103--112Google ScholarGoogle Scholar
  17. Felder, R. M. and Soloman, B. A. 2003. Index of Learning Styles Questionnaire, Available online at http://www.ncsu.edu/felder-public/ILSdir/ilsweb.html.Google ScholarGoogle Scholar
  18. Parvez, S. M., & Blank, G. D. 2007. A pedagogical framework to integrate learning styles into intelligent tutoring systems. Journal of Computing Sciences in Colleges, 22(3), 183--189. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. MeeSoft (2014). Available online at http://logicnet.dk/DiagramDesigner/Google ScholarGoogle Scholar
  20. Download.com Editors, 2009, Available online at http://download.cnet.com/Diagram-Designer/3000-2191_410429024.htmGoogle ScholarGoogle Scholar
  21. Waterloo University, 2012. Understanding your learning styles: the Soloman-Felder index of learning styles (Retrieved from cte.uwaterloo.ca/teaching_resources/tips/understanding_your_learning_style.html.)Google ScholarGoogle Scholar
  22. Huang, E. Y., Lin, S. W., & Huang, T. K. 2012. What type of learning styles leads to online participation in the mixed-mode e-learning environment? A study of software usage instruction. Computers & Education, 58(1), 338--349. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Deterding, S., Sicart, M., Nacke, L., O'Hara, K., & Dixon, D. (2011, May). Gamification. using game-design elements in non-gaming contexts. In CHI'11 Extended Abstracts on Human Factors in Computing Systems (pp. 2425--2428). ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Raymer, R., & Design, E. L. 2011. Gamification: Using Game Mechanics to Enhance eLearning. Elearn Magazine, 2011(9), 3. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Jain, S., & Stephan, F. 2010. Query-based learning. In Encyclopedia of Machine Learning (pp. 820--822). Springer US.Google ScholarGoogle Scholar
  26. Chang, R. I., Lin, S. Y., & Hung, Y. 2012. Particle swarm optimization with query-based learning for multi-objective power contract problem. Expert Systems with Applications, 39(3), 3116--3126. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Investigating the influence of students' learning-style preferences on user intentions regarding illustration software

    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 Other conferences
      IC4E '19: Proceedings of the 10th International Conference on E-Education, E-Business, E-Management and E-Learning
      January 2019
      469 pages
      ISBN:9781450366021
      DOI:10.1145/3306500

      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: 10 January 2019

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
    • Article Metrics

      • Downloads (Last 12 months)5
      • Downloads (Last 6 weeks)1

      Other Metrics

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader