skip to main content
10.1145/2556288.2557294acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
research-article

Information-building applications: designing for data exploration and analysis by elementary school students

Authors Info & Claims
Published:26 April 2014Publication History

ABSTRACT

The propagation of Inquiry Based Learning has lead to many more elementary students interacting with authentic scientific tools and practices. However, the more problematic realities of scientific data collection, such as noise and large data sets, are often deliberately hidden from students. Students will need to confront these realities and be able to make skillful data scoping decisions in order to make sense of ever more prevalent large datasets. We dub software designed to support these activities Information-Building Applications (IBAs). This paper presents the design considerations that went into building an exemplar IBA, PhotoMAT (Photo Management and Analysis Tool), a brief user study to show how the solutions enacted by following these principles are taken up by actual students, and a discussion of how the design considerations identified by our work might be applied to another IBA.

References

  1. Bjork, R. A. (1994). Memory and metamemory considerations in the training of human beings. In J. Metcalfe and A. Shimamura (Eds.) Metacognition: Knowing about knowing. (pp.185--205). Cambridge, MA: MIT Press.Google ScholarGoogle Scholar
  2. Chi, M. T., Glaser, R., & Rees, E. (1981). Expertise in problem solving. Pittsburgh: Learning Research and Development Center, University of PittsburgGoogle ScholarGoogle Scholar
  3. Czeskis, A., Dermendjieva, I., & Yapit, H. (2010). Parenting from the Pocket: Value Tensions and Technical Directions for Secure and Private Parent-Teen Mobile. SOUPS: Symposium on Usable Privacy and Security. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Duschl, R. A., & Gitomer, D. H. (1991). Epistemological perspectives on conceptual change: Implications for educational practice. Journal of research in science teaching, 28(9), 839--858.Google ScholarGoogle ScholarCross RefCross Ref
  5. Edelson, D. C., & Reiser, B. J. (2006). Making authentic practices accessible to learners: Design challenges and strategies. Cambridge handbook of the learning sciences, 335--354.Google ScholarGoogle Scholar
  6. Fegraus, E. H. , Lin, K., Ahumada, J. A., Baru, C., Chandra, S., Youn, C. Data acquisition and management software for camera trap data: A case study from the TEAM Network, Ecological Informatics, Volume 6, Issue 6, November 2011, Pages 345--353.Google ScholarGoogle Scholar
  7. Gallagher, J. (2002). The learning theory of Piaget and Inhelder. iUniverse.Google ScholarGoogle Scholar
  8. Gentner, D., & Toupin, C. (1988). Systematicity and surface similarity in the development of analogy. Cognitive Science, 10, 277--300.Google ScholarGoogle ScholarCross RefCross Ref
  9. Hammerman, J. K., & Rubin, A. (2004). Strategies for managing statistical complexity with new software tools. Statistics Education Research Journal, 3(2), 17--41.Google ScholarGoogle Scholar
  10. Hancock, C., Kaput, J. J., & Goldsmith, L. T. (1992). Authentic enquiry with data: Critical barriers to classroom implementation. Educational Psychologist, 27(3), 337--364.Google ScholarGoogle ScholarCross RefCross Ref
  11. Heer, J., & Shneiderman, B. (2012). Interactive dynamics for visual analysis. Communications of the ACM, 55(4), 45--55. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Konold, C., & Higgins, T. L. (2002). Highlights of related research. In S. J. Russell, D. Schifter & V. Bastable (Eds.), Developing mathematical ideas (DMI): Working with data casebook, (pp. 165--201). Parsippany, NY: Dale Seymour Publications.Google ScholarGoogle Scholar
  13. Konold, C., Higgins, T. L., Russell, S. J., & Khalil, K. (2003). Data seen through different lenses. Unpublished manuscript, Amherst, MA.Google ScholarGoogle Scholar
  14. Konold, C., & Miller, C. (2004). TinkerPlotsTM Dynamic Data Exploration (Version Beta 1.0). Emeryville, CA: Key Curriculum Press.Google ScholarGoogle Scholar
  15. Krajcik, J., Blumenfeld, P., Marx, R. and Soloway, E. (2000). Instructional, curricular, and technological supports for inquiry in science classrooms. In J. Minstrell and E. H. van Zee (eds), Inquiring into Inquiry Learning and Teaching in Science (Washington, DC: American Association for the Advancement of Science), 283--315.Google ScholarGoogle Scholar
  16. Lee, H. and Songer, N. (2003). Making authentic science accessible to students, International Journal of Science Education, 25(8), 923--948.Google ScholarGoogle ScholarCross RefCross Ref
  17. Linn, M. C., Clark, D., & Slotta, J. D. (2003). WISE design for knowledge integration. Science Education, 87, 517--538.Google ScholarGoogle ScholarCross RefCross Ref
  18. Marx, R. W., Blumenfeld, P. C., Krajcik, J. S., Fishman, B., Soloway, E., Geier, R., & Tal, R. T. (2004). Inquiry-based science in the middle grades: Assessment of learning in urban systemic reform. Journal of Research in Science Teaching, 41(10), 1063--1080.Google ScholarGoogle ScholarCross RefCross Ref
  19. Metz, K. E. (2004). Children's Understanding of Scientific Inquiry: Their Conceptualization of Uncertainty in Investigations of Their Own Design. Cog. and Instruction, 22(2), 219--290.Google ScholarGoogle ScholarCross RefCross Ref
  20. Miller, J., Friedman, B., & Jancke, G. (2007). Value tensions in design: the value sensitive design, development, and appropriation of a corporation's groupware system. GROUP '07 Proceedings of the 2007 international ACM conference on Supporting group work, 281--290. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Moher, T., Brown, J., Reiser, B. J., Silva, A., Shelley, T., Lyons, L. (2013). The Urban Game Park: A Pilot Study of Student Investigations of Animal Behavior Using Motion-Detecting Cameras. American Educational Research Association Annual Meeting 2013.Google ScholarGoogle Scholar
  22. Piaget, J. (1977). Problems of equilibration. In Topics in cognitive development (pp. 3--13). Springer US.Google ScholarGoogle Scholar
  23. Reiser, B. J., Tabak, I., Sandoval, W. A., Smith, B. K., Steinmuller, F., & Leone, A. J. (2001). BGuILE: Strategic and conceptual scaffolds for scientific inquiry in biology classrooms. In S. M. Carver & D. Klahr (Eds.), Cognition and instruction: Twenty-five years of progress (pp. 263--305). Mahwah, NJ: Erlbaum.Google ScholarGoogle Scholar
  24. Resnick, M., Berg, R., & Eisenberg, M. (2000). Beyond black boxes: Bringing transparency and aesthetics back to scientific investigation. The Journal of the Learning Sciences, 9(1), 7--30.Google ScholarGoogle ScholarCross RefCross Ref
  25. Silva , A., Dasgupta, C., Shelley, T., Lopez Silva, B. A., Lyons, L., Moher, T. (2014). Shaping the Construction of Learner Questions. In review: American Educational Research Association Annual Meeting 2014.Google ScholarGoogle Scholar
  26. Simons, D. J., & Keil, F. C. (1995). An abstract to concrete shift in the development of biological thought: the insides story. Cognition, 56(2), 129--163.Google ScholarGoogle ScholarCross RefCross Ref
  27. Songer, N. (2006). BioKIDS: An Animated Conversation on the Development of Curricular Activity Structures for Inquiry Science. In R. K. Sawyer (Ed.), The Cambridge Handbook of the Learning Sciences. New York, NY: Cambridge University Press.Google ScholarGoogle Scholar
  28. Tobler, M. (2013). Camera Base. http://www.atriumbiodiversity.org/tools/camerabase/.Google ScholarGoogle Scholar
  29. Wellman, H. M., & Gelman, S. A. (1992). Cognitive development: foundational theories of core domains. Annual Review of Psychology, 43, 337--375.Google ScholarGoogle ScholarCross RefCross Ref
  30. Yoo, D., Huldtgren, A., & Woelfer, J. (2013). A value sensitive action-reflection model: evolving a co-design space with stakeholder and designer prompts. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 419--428. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Information-building applications: designing for data exploration and analysis by elementary school students

    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 '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
      April 2014
      4206 pages
      ISBN:9781450324731
      DOI:10.1145/2556288

      Copyright © 2014 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 the author(s) 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: 26 April 2014

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      CHI '14 Paper Acceptance Rate465of2,043submissions,23%Overall Acceptance Rate6,199of26,314submissions,24%

      Upcoming Conference

      CHI '24
      CHI Conference on Human Factors in Computing Systems
      May 11 - 16, 2024
      Honolulu , HI , USA

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader