Advertisement

Digital Knowledge and Digital Research: What does eResearch Offer Education and Social Policy?

  • Lina Markauskaite
Chapter
Part of the Methodos Series book series (METH, volume 9)

Abstract

This chapter discusses conceptual and practical links and tensions between research for education and social policy and technology-enhanced research, called eResearch. It argues that significant methodological progress could be made by harnessing the increasing volume and density of digital data and by exploiting opportunities for technology-enhanced research collaboration in educational, social work and social policy research. The chapter introduces key notions relating to digital knowledge and eResearch and explores the roles of digital technologies in the methodological apparatus of social research. To illustrate eResearch applications, the chapter discusses selected examples of data mining and video analysis in educational and social policy research. After a discussion of challenges for eResearch uptake, the chapter suggests that, as a first step, researchers should try to embrace data-driven research approaches and new models of research dissemination.

Keywords

Digital Video Methodological Tradition Educational Data Mining Social Policy Research Epistemic Challenge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. ACDE. (2009). Data repository for teacher education scoping study. Australia: The Australian Council of Deans of Education.Google Scholar
  2. ACLS. (2006). Our cultural commonwealth: The final report of the American Council of Learned Societies Commission on Cyberinfrastructure for the humanities and social sciences: American Council of Learned Societies Commission on Cyberinfrastructure for the Humanities and Social Sciences.Google Scholar
  3. Anderson, T., & Kanuka, H. (2003). E-research: Methods, strategies and issues. Boston, CA: Pearson Education Inc.Google Scholar
  4. Araque, F., Roldán, C., & Salguero, A. (2009). Factors influencing university drop out rates. Computers and Education, 53, 563–574.CrossRefGoogle Scholar
  5. Armstrong, V., & Curran, S. (2006). Developing a collaborative model of research using digital video. Computers and Education, 46(3), 336–347.CrossRefGoogle Scholar
  6. Atkins, D. E., Droegemeier, K. K., Feldman, S. I., Garcia-Molina, H., Klein, M. L., Messerschmitt, D. G., et al (2003). Revolutionizing science and engineering through Cyberinfrastructure. Report of the National Science Foundation blue-ribbon advisory panel on Cyberinfrastructure. Arlington, VA: Directorate for Computer and Information Science and Engineering, National Science Foundation.Google Scholar
  7. Bentley, T., & Gillinson, S. (2007). A D&R system for education. UK: Innovation unit.Google Scholar
  8. Bereiter, C. (2002). Design research for sustained innovation. Cognitive Studies. Bulletin of the Japanese Cognitive Science Society, 9(3), 321–327.Google Scholar
  9. Blanke, T., Hedges, M., & Dunn, S. (2009). Arts and humanities e-science – current practices and future challenges. Future Generation Computer Systems, 25(4), 474–480.CrossRefGoogle Scholar
  10. Borgman, C. L. (2007). Scholarship in the digital age: Information, infrastructure, and the internet. Cambridge, MA: The MIT Press.Google Scholar
  11. Borgman, C. L., Abelson, H., Dirks, L., Johnson, R., Koedinger, K. R., Linn, M. C., et al (2008). Fostering learning in the networked world: The cyberlearning opportunity and challenge, a 21st century agenda for the National Science Foundation. Arlington, VA: NSF Task Force on Cyberlearning.Google Scholar
  12. Bourne, P. E., Fink, J. L., & Gerstein, M. (2008). Open access: Taking full advantage of the content. PLoS Computational Biology, 4(3), e1000037, doi:1000010.1001371/journal.pcbi.1000037.CrossRefGoogle Scholar
  13. Carmichael, P. (2007). Introduction: Technological development, capacity building and knowledge construction in education research. Technology, Pedagogy and Education, 16(3), 235–247.CrossRefGoogle Scholar
  14. de Laat, M., Lally, V., Lipponen, L., & Simons, R.-J. (2007). Investigating patterns of interaction in networked learning and computer-supported collaborative learning: A role for social network analysis. International Journal of Computer-Supported Collaborative Learning, 2(1), 87–103.CrossRefGoogle Scholar
  15. De Roure, D., Baker, M. A., Jennings, N. R., & Shadbolt, N. R. (2003). The evolution of the grid. In F. Berman, G. Fox, & A. J. G. Hey (Eds.), Grid computing: Making the global infrastructure a reality (pp. 65–100). West Sussex: Wiley.Google Scholar
  16. De Roure, D., & Frey, J. (2007). Three perspectives on collaborative knowledge acquisition in e-science. Paper presented at the workshop on Semantic Web for Collaborative Knowledge acquisition (SWeCKa), Hyderabad, India.Google Scholar
  17. De Roure, D., Jennings, N. R., & Shadbolt, N. R. (2005). The semantic grid: Past, present, and future. Proceedings of the IEEE, 93(3), 669–681.CrossRefGoogle Scholar
  18. Edwards, P., Farrington, J. H., Mellish, C., Philip, L. J., Chorley, A. H., Hielkema, F., et al (2009). E-social science and evidence-based policy assessment: Challenges and solutions. Social Science Computer Review, 27(4), 553–568.CrossRefGoogle Scholar
  19. Eisner, E. W. (1997). The promise and perils of alternative forms of data representation. Educational Researcher, 26(6), 4–10.Google Scholar
  20. Ercikan, K., & Roth, W.-M. (2006). What good is polarizing research into qualitative and quantitative? Educational Researcher, 35(5), 14–23.CrossRefGoogle Scholar
  21. Freebody, P. (2003). Qualitative research in education: Interaction and practice. London: Sage Publications.Google Scholar
  22. Givvin, K. B., Hiebert, J., Jacobs, J. K., Hollingsworth, H., & Gallimore, R. (2005). Are there national patterns of teaching? Evidence from the TIMSS 1999 video study. Comparative Education Review, 49(3), 311–343.CrossRefGoogle Scholar
  23. Goodyear, P., & Steeples, C. (1998). Creating shareable representations of practice. Association for Learning Technology Journal, 6(3), 16–23.Google Scholar
  24. Greenhow, C., Robelia, B., & Hughes, J. E. (2009). Learning, teaching, and scholarship in a digital age: Web 2.0 and classroom research: What path should we take now? Educational Researcher, 38(4), 246–259.CrossRefGoogle Scholar
  25. Halfpenny, P., & Procter, R. (2009). Special issue on e-social science. Social Science Computer Review, 27(4), 459–466.CrossRefGoogle Scholar
  26. Herzog, S. (2006). Estimating student retention and degree-completion time: Decision trees and neural networks vis-à-vis regression. New Directions for Institutional Research, 131, 17–33.CrossRefGoogle Scholar
  27. Herzog, S. (2007). The ecology of learning: The impact of classroom features and utilization on student academic success. New Directions for Institutional Research, 135, 81–106.CrossRefGoogle Scholar
  28. Hey, T., Tansley, S., & Tolle, K. (Eds.). (2009). The fourth paradigm: Data-intensive scientific discovery. Redmond, WA: Microsoft Research.Google Scholar
  29. Hine, C. (2000). Virtual ethnography. London: Sage.Google Scholar
  30. Hine, C. (Ed.). (2006). New infrastructures for knowledge production: Understanding e-science. Hershey, PA: Information Science Publishing.Google Scholar
  31. Jankowski, N. W. (Ed.). (2009). E-research: Transformation in scholarly practice. New York, NY: Routledge.Google Scholar
  32. Knoll, S., & Stigler, J. W. (1999). Management and analysis of large-scale video surveys using the software vPrism. International Journal of Educational Research, 31(8), 725–734.Google Scholar
  33. Liamputtong, P. (Ed.). (2006). Health research in cyberspace. New York: Nova Science Publishers.Google Scholar
  34. Markauskaite, L., & Reimann, P. (2008a). Enabling teacher-led research and innovation: A conceptual design of an inquiry framework for ICT-enhanced teacher innovation. In Proceedings of the World Conference on Educational Multimedia, Hypermedia and Telecommunications. ED-MEDIA 2008, June 30–July 4. (pp. 3484–3493). Austria, Vienna. Chesapeake, VA: AACE.Google Scholar
  35. Markauskaite, L., & Reimann, P. (2008b). Enhancing and scaling-up design-based research: The potential of e-research. In Proceedings of the International Conference of Learning Sciences. ICLS 2008. July 24–28. Utrecht, The Netherlands.Google Scholar
  36. Markham, A. N., & Baym, N. K. (Eds.). (2009). Internet inquiry: Conversations about method. Los Angeles, CA: Sage.Google Scholar
  37. Onwuegbuzie, A., Leech, N., & Whitcome, J. (2008). A framework for making quantitative educational research articles more reader-friendly for practitioners. Quality and Quantity, 42(1), 75–87.CrossRefGoogle Scholar
  38. NRC. (2002). Community and quality of life: Data needs for informed decision making. Committee on Identifying Data Needs for Place-Based. Decision Making, Committee on Geography, National Research Council. Washington, DC: National Academy Press.Google Scholar
  39. Pea, R., Lindgren, R., & Rosen, J. (2008). Cognitive technologies for establishing, sharing and comparing perspectives on video over computer networks. Social Science Information, 47(3), 353–370.CrossRefGoogle Scholar
  40. Poschl, U. (2004). Interactive journal concept for improved scientific publishing and quality assurance. Learned Publishing, 17, 105–113.CrossRefGoogle Scholar
  41. Rahman, H. (Ed.). (2009). Social and political implications of data mining: Knowledge management in e-government. Hershey, PA: IGI Global.Google Scholar
  42. Randolph, J. J. (2007). Multidisciplinary methods in educational technology research and development. Haneenlinna: HAMK Press.Google Scholar
  43. Romero, A. C., & Ventura, S. (Eds.). (2006). Data mining in e-learning. Southampton: WITpress.Google Scholar
  44. Romero, A. C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33, 135–146.CrossRefGoogle Scholar
  45. Sawyer, R. K. (2005). Social emergence: Societies as complex systems. New York: Cambridge University Press.CrossRefGoogle Scholar
  46. Schroeder, R., & Fry, J. (2007). Social science approaches to e-science: Framing an agenda. Journal of Computer-Mediated Communication, 12(2), article 11.Google Scholar
  47. SciVee. (2009). SciVee: Making science visible. Retrieved December 23, 2009, from http://www.scivee.tv
  48. Sinnott, R. O. (2009). Grid security. In L. Wang, W. Jie, & J. Chen (Eds.), Grid computing: Infrastructure, service, and applications (pp. 307–334). Boca Raton, FL, USA: CRC Press.CrossRefGoogle Scholar
  49. Smeyers, P., & Depaepe, M. (2007). Educational research: Networks and technologies. The Netherlands: Springer.CrossRefGoogle Scholar
  50. Sujitparapitaya, S. (2006). Considering student mobility in retention outcomes. New Directions for Institutional Research, 131, 35–51.CrossRefGoogle Scholar
  51. Tinto, V. (1994). Leaving college: Rethinking the causes and cures of student attrition (2nd ed.). Chicago: The University of Chicago Press.Google Scholar
  52. Voithofer, R. (2005). Designing new media education research: The materiality of data, representation, and dissemination. Educational Researcher, 34(9), 3–14.CrossRefGoogle Scholar
  53. Wells, M. I. (2006). Dreams deferred but not deterred: A qualitative study on undergraduate nursing student attrition. Journal of College Student Retention, 8(4), 439–456.Google Scholar
  54. Whitty, G. (2006). Education(al) research and education policy making: Is conflict inevitable? British Educational Research Journal, 32(2), 159–176.CrossRefGoogle Scholar
  55. Woolgar, S. (2004). Social shaping perspectives on e-science and e-social science: The case for research support. A consultative study for the Economic and Social Research Council (ESRC) on a strategy for supporting research on the social shaping of e-science and of e-social science. Oxford: University of Oxford.Google Scholar
  56. Wouters, P. (2005). The virtual knowledge studio for the humanities and social sciences. Paper presented at the First international Conference on E-Social Science, June 22–24. Manchester, UK.Google Scholar
  57. Zhao, C.-M., & Luan, J. (2006). Data mining: Going beyond traditional statistics. New Directions for Institutional Research, 131, 7–16.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Faculty of Education and Social WorkThe University of SydneySydneyAustralia

Personalised recommendations