Author(s):
- Lee, Victor R.
- Drake, Joel R.
- Thayne, Jeffrey L.
Abstract:
Wearable activity tracking devices associated with the Quantified Self movement have potential benefit for educational settings because they produce authentic and granular data about activities and experiences already familiar to youth. This article explores how that potential could be realized through explicit acknowledgment of and response to tacit design assumptions about how such technologies will be used in practice and strategic design for use in a classroom. We argue that particular practical adaptations that we have identified serve to ensure that the classroom and educational use cases are appropriately considered. As an example of how those adaptations are realized in actual elementary classrooms, we describe an effort to provide fifth-grade students each with their own Fitbit activity trackers in the context of a multi-week unit exploring core ideas in elementary statistics. Observational descriptions and transcript excerpts of students and teachers discussing their own Fitbit data are presented to illustrate what opportunities exist to leverage youth familiarity with daily activities in a way that targets development of statistical thinking. Quantitative written test results showing learning gains and differences between traditional and wearable device-enhanced instruction are also presented. Improvement on several statistical thinking constructs is identified, including in the areas of data display, conceptions of statistics, modeling variability, and informal inference.
Document:
https://doi.org/10.1109/TLT.2016.2597142
References:
1. V. R. Lee, “The quantified self (QS) movement and some emerging opportunities for the educational technology field”, Educ. Technol., vol. 53, no. 6, pp. 39, Nov./Dec. 2013. Google Scholar
2. V. R. Lee and J. Drake, “Quantified recess: Design of an activity for elementary students involving analysis of their own movement data”, Proc. 12th Int. Conf. Interaction Des. Children, pp. 273-276, 2013. Show Context Access at ACM Google Scholar
3. E. K. Choe, N. B. Lee, B. Lee, W. Pratt and J. A. Kientz, “Understanding quantified-selfers’ practices in collecting and exploring personal data”, Proc. 32nd Annu. ACM Conf. Human Factors Comput. Syst., pp. 1143-1152, 2014. Show Context Access at ACM Google Scholar
4. I. Li, A. Dey and J. Forlizzi, “A stage-based model of personal informatics systems”, Proc. SIGCHI Conf. Human Factors Comput. Syst., pp. 557-566, 2010. Show Context Access at ACM Google Scholar
5. C. C. Ching and S. Schaefer, “Identities in motion identities at rest: Engaging bodies and minds in fitness gaming research and design” in Learning Technologies and the Body: Integration and Implementation in Formal and Informal Learning Environments, Abingdon-on-Thames, U.K.:Routledge, pp. 201-219, 2014. Show Context Google Scholar
6. ” Next Generation Science Standards ” in , Washington, D.C., 2013. Show Context
7. ” Common Core State Standards ” in , Washington, D.C., 2010. Show Context
8. L. Lyons, “Exhibiting data: Using body-as-interface designs to engage visitors with data visualizations” in Learning Technologies and the Body: Integration and Implementation in Formal and Informal Learning Environments, Abingdon-on-Thames, U.K.:Routledge, pp. 185-200, 2014. Show Context Google Scholar
9. V. Lee and J. Thomas, “Integrating physical activity data technologies into elementary school classrooms”, Educ. Technol. Res. Dev., vol. 59, no. 6, pp. 865-884, Dec. 2011. Show Context CrossRef Google Scholar
10. V. R. Lee, J. Drake, J. Thayne and R. Cain, “Opportunistic uses of the traditional school day through student examination of Fitbit activity tracker data”, Proc. Interaction Des. Children, pp. 209-218, 2015. Show Context Access at ACM Google Scholar
11. T. White, A. Booker, C. C. Ching and L. Martin, “Integrating digital and mathematical practices across contexts: A manifesto for mobile learning”, Int. J. Learn. Media, vol. 3, no. 3, pp. 7-13, Jul. 2011. Show Context CrossRef Google Scholar
12. H. Beyer and K. Holtzblatt, Contextual Design Defining Customer-Centered Systems, San Francisco, CA, USA:Morgan Kaufmann, 1998. Show Context Google Scholar
13. K. Bødker, F. Kensing and J. Simonsen, Participatory it Design Designing for Business and Workplace Realities, Cambridge, MA, USA:MIT Press, 2004. Show Context Google Scholar
14. J. P. Smith, A. A. diSessa and J. Roschelle, “Misconceptions reconceived: A constructivist analysis of knowledge in transition”, J. Learn. Sci., vol. 3, no. 2, pp. 115-163, 1994. Show Context CrossRef Google Scholar
15. V. R. Lee and M. Briggs, “Lessons learned from an initial effort to bring a quantified self ‘Meetup’ experience to a new demographic”, Proc. ACM Int. Joint Conf. Pervasive Ubiquitous Comput. Adjunct Publication, pp. 707-710, 2014. Show Context Access at ACM Google Scholar
16. J. Cai, J. J. Lo and T. Watanabe, “Intended treatments of arithmetic average in U.S. and Asian school mathematics textbooks”, School Sci. Math., vol. 102, no. 8, pp. 391-404, Dec. 2002. Show Context CrossRef Google Scholar
17. W.-M. Roth, M. K. McGinn and G. M. Bowen, “How prepared are preservice teachers to teach scientific inquiry? Levels of performance in scientific representation practices”, J. Sci. Teach. Educ., vol. 9, no. 1, pp. 25-48, Feb. 1998. Show Context Google Scholar
18. J. M. Watson and J. B. Moritz, “The development of concepts of average”, Focus Learn. Problems Math., vol. 21, no. 4, pp. 15-39, 1999. Show Context Google Scholar
19. J. Mokros and S. J. Russell, “Children’s concepts of average and representativeness”, J. Res. Math. Educ., vol. 26, no. 1, pp. 20-39, Jan. 1995. Show Context CrossRef Google Scholar
20. V. Dubreil-Frémont, C. Chevallier-Gaté and N. Zendrera, “Students’ conceptions of average and standard deviation”, Proc. 9th Int. Conf. Teaching Statist., 2014. Show Context Google Scholar
21. A. Pollatsek, S. Lima and A. D. Well, “Concept or computation: Students’ understanding of the mean”, Educ. Studies Math., vol. 12, no. 2, pp. 191-204, May 1981. Show Context CrossRef Google Scholar
22. S. Strauss and E. Bichler, “The development of children’s concepts of the arithmetic average”, J. Res. Math. Educ., vol. 19, no. 1, pp. 64-80, Jan. 1988. Show Context CrossRef Google Scholar
23. A. J. Petrosino, R. Lehrer and L. Schauble, “Structuring error and experimental variation as distribution in the fourth grade”, Math. Thinking Learn., vol. 5, pp. 131-56, 2003. Show Context CrossRef Google Scholar
24. C. Konold and A. Pollatsek, “Data analysis as the search for signals in noisy processes”, J. Res. Math. Educ., vol. 33, no. 4, pp. 259-289, Jul. 2002. Show Context CrossRef Google Scholar
25. R. Lehrer, “Learning to reason about variability and chance by inventing measures and models”, Moore Lecture Series Friday Institute for Educational Innovation College of Education NC State University, Nov. 2007. Show Context Google Scholar
26. B. Hug and K. L. McNeill, “Use of first-hand and second-hand data in science: Does data type influence classroom conversations?”, Int. J. Sci. Educ., vol. 30, no. 13, pp. 1725-1751, Oct. 2008. Show Context CrossRef Google Scholar
27. U. Wilensky and W. M. Stroup, “Networked gridlock: Students enacting complex dynamic phenomena with the HubNet architecture”, Proc. 4th Annu. Int. Conf. Learn. Sci., pp. 282-289, 2000. Show Context Google Scholar
28. D., Abrahamson and U. Wilensky, “S.A.M.P.L.E.R.: Collaborative interactive computer-based statistics learning environment”, Proc. 10th Int. Congr. Math, 2004. Show Context Google Scholar
29. P. Cobb and C. Tzou, “Learning about data analysis” in Mathematical Representation at the Interface of Body and Culture, Charlotte, NC, USA:IAP, pp. 135-170, 2009. Show Context Google Scholar
30. C. Hancock, J. J. Kaput and L. T. Goldsmith, “Authentic inquiry with data: Critical barriers to classroom implementation”, Educ. Psychol., vol. 27, no. 3, pp. 337-364, Jun. 1992. Show Context CrossRef Google Scholar
31. C. Konold and C. D. Miller, TinkerPlots, Emeryville, CA, USA:Key Curriculum Press, 2005. Show Context Google Scholar
32. C. Konold, “Designing a data analysis tool for learners” in Thinking with Data, New York, NY, USA:Lawrence Erlbaum Associates, pp. 267-292, 2007. Show Context Google Scholar
33. W. Finzer, Fathom Dynamic Data Software, Emeryville, CA, USA:Key Curriculum Press, 2005. Show Context Google Scholar
34. J. Takacs, C. L. Pollock, J. R. Guenther, M. Bahar, C. Napier and M. A. Hunt, “Validation of the Fitbit one activity monitor device during treadmill walking” in J. Sci. Med. Sport Sports Med. Australia, vol. 17, no. 5, pp. 496-500, Sep. 2014. Show Context CrossRef Google Scholar
35. C. McKay and K. A. Peppler, “MakerCart: A mobile fab lab for the classroom”, Interaction Design for Children, 2013. Show Context Google Scholar
36. R. Lehrer, M.-J. Kim, E. Ayers and M. Wilson, “Toward establishing a learning progression to support the development of statistical reasoning” in Learning over Time: Learning Trajectories in Mathematics Education, Charlotte, NC, USA:Information Age Publishers, 2014. Show Context Google Scholar
37. R. A. Duschl, H. A. Schweingruber and A. W. Shouse, Taking Science to School: Learning and Teaching Science in Grades K-8, Washington, D.C., USA:National Academies Press, 2007. Show Context Google Scholar
38. R. G. Duncan and C. E. Hmelo-Silver, “Learning progressions: Aligning curriculum instruction and assessment”, J. Res. Sci. Teach., vol. 46, no. 6, pp. 606-609, 2009. Show Context CrossRef Google Scholar
39. V. R. Lee and M. DuMont, “An exploration into how physical activity data-recording devices could be used in computer-supported data investigations”, Int. J. Comput. Math. Learn., vol. 15, no. 3, pp. 167-189, Dec. 2010. Show Context CrossRef Google Scholar
40. A. A. diSessa, “Metarepresentation: Native competence and targets for instruction”, Cogn. Instr., vol. 22, no. 3, pp. 293-331, Sep. 2004. Show Context CrossRef Google Scholar
41. V. R. Lee, J. Drake and K. Williamson, “Let’s get physical: K-12 students using wearable devices to obtain and learn about data from physical activities”, TechTrends, vol. 59, no. 4, pp. 46-53, Jun. 2015. Show Context CrossRef Google Scholar