New Learning—Old Methods? How E-research Might Change Technology-Enhanced Learning Research



That computer have changed how students learn and teachers teach—for better or worse—is well documented, amongst other sources by research reported in this book. In this chapter, we look at how computers (more generally, information technology) are beginning to change how learning research itself is conducted. An important driver of this change is the nature of the data that learning researchers analyze. While data used to be textual or numerical, they are becoming increasingly multimodal. And while data used to be gathered at one point in time or at a few distinct measurement points, learning researchers have now access to continuous streams of data. This raises for instance the basic question of when a research project is “finished.” Furthermore, IT is changing the methods of data analysis, the social orchestration of research, and the dissemination of research results—what used to be called publishing. For instance, research on a single project can now be distributed easily to involve many people with multiple specializations in many locations. Technology affords also to blur the distinction between “subjects” and researchers, enabling all kinds of new forms of participatory research. The ease of recording and storing data is the main driver of all these changes, but this convenience is a mixed blessing: an immediate problem is data deluge. Already today, only a small percentage of the data recorded in the modal design-based research study gets analyzed. Based on a review of how IT has been used to harness the deluge of data in other sciences, we describe new approaches to deal with massive amounts of complex data in the learning sciences (stemming, e.g., from log file recordings or from video recordings). Then, we will outline some aspects of research dissemination that are particularly relevant and important for the learning science research. We will conclude by discussing how new methods might affect the notion of theory in learning research and by outlining key challenges and a research agenda for the application of e-research methods in the learning sciences.


Cloud Computing Educational Innovation Provenance Information Collaboration Script Educational Data Mining 
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.


  1. Aalst, W. M. Pv. d, & Günther, C. W. (2007). Finding structure in unstructured processes: The case of process mining. In T. Basten, G. Juhas & S. Shukla (Eds.), Proceedings the 7th International Conference on Applications of Concurrency to System Design (ACSD 2007; Bratislava, Slovak Republic) (pp. 3–12). Los Alamitos, CA: IEEE Computer Society Press.CrossRefGoogle Scholar
  2. Abell, P. (2004). Narrative explanations: An alternative to variable-centered explanation? Annual Review of Sociology, 30, 287–310.CrossRefGoogle Scholar
  3. Barjak, F., Lane, J., Kertcher, Z., Poschen, M., Procter, R., & Robinson, S. (2009). Case studies of e-infrastructure adoption. Social Science Computer Review, Online first, published on April 8, 2009.Google Scholar
  4. Beardsley, L., Cogan-Drew, D., & Olivero, F. (2007). VideoPaper: Bridging research and practice for preservice and experienced teachers. In R. Goldman, R. Pea, B. Barron & S. Derry (Eds.), Video research in the learning sciences (pp. 479–493). Mahwah, NJ: Erlbaum.Google Scholar
  5. Beaulieu, A. (2004). Mediating ethnography: reflections on ethnographic practices and the study of the internet. Social Epistemology 18(2-3), 139–163.Google Scholar
  6. Bentley, T., & Gillinson, S. (2007). A D&R system for education. London: Innovation Unit.Google Scholar
  7. Bereiter, C. (2002a). Design research for sustained innovation. Cognitive Studies Bulletin of the Japanese Cognitive Science Society, 9(3), 321–327.Google Scholar
  8. Bereiter, C. (2002b). Education and mind in the knowledge age. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  9. Berman, F., Fox, G., & Hey, A. J. G. (2003). The grid: Past, present, future. In F. Berman, G. Fox & A. J. G. Hey (Eds.), Grid computing: Making the global infrastructure a reality (pp. 9–50). West Sussex: Wiley.Google Scholar
  10. Borgman, C. (2006). What can studies of e-learning teach us about collaboration in e-research? Some findings from digital library studies. Computer supported cooperative work, 15(4), 359–383.CrossRefGoogle 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. Report of the NSF Task Force on Cyberlearning. June 24, 2008.Google Scholar
  12. Bose, R., & Frew, J. (2005). Lineage retrieval for scientific data processing: A survey. ACM Computing Surveys, 37(1), 1–28.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. Chambel, T., Zahn, C., & Finke, M. (2004). Hypervideo design and support for contextualized learning. Proceedings of the IEEE international conference on advanced learning technologies (ICALT’04) (pp. 345–349).Google Scholar
  15. Chorley, A., Edwards, P., Preece, A., & Farrington, J. (2007, October). Tools for tracing evidence in social science. Paper presented at the third international conference on e-social science, Ann Arbor, MI, USA.Google Scholar
  16. Christie, D., Cassidy, C., Skinner, D., Coutts, N., Sinclair, C., Rimpilainen, S., et al. (2007). Building collaborative communities of enquiry in educational research. Educational Research and Evaluation, 13(3), 263–278.CrossRefGoogle Scholar
  17. Cochran-Smith, M., & Lytle, S. L. (1999). The teacher research movement: A decade later. Educational Researcher, 28(7), 15–25.Google Scholar
  18. Cohen, L. (2007). Social scholarship on the rise. Retrieved August 11, 2009, from
  19. Cohen, D. K., Raudenbush, S. W., & Loewenberg Ball, D. (2003). Resources, instruction, and research. Educational Evaluation and Policy Analysis, 25(2), 119–142.CrossRefGoogle Scholar
  20. Dana, N. F., & Silva, D. Y. (2003). The reflective educator’s guide to classroom research: Learning to teach and teaching to learn through practitioner inquiry. Thousand Oaks, CA: Corwin Press.Google Scholar
  21. 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
  22. 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
  23. 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
  24. dePaula, R., Fischer, G., & Ostwald, J. (2001). Courses as seeds: Expectations and realities. Proceedings of the Second European Conference on Computer-Supported Collaborative Learning (Euro-CSCL 2001) (pp. 494–501). Maastricht, Netherlands.Google Scholar
  25. Foray, D., & Hargreaves, D. (2003). The production of knowledge in different sectors: a model and some hypotheses. London Review of Education, 1(1), 7–19.CrossRefGoogle Scholar
  26. Foster, I., Kesselman, C., & Tuecke, S. (2001). The anatomy of the grid. Enabling scalable virtual organizations. International Journal of Supercomputer Applications, 15(3), 200–222.CrossRefGoogle Scholar
  27. Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P., & Trow, M. (1994). The new production of knowledge: The dynamics of science and research in contemporary societies. London: Sage.Google Scholar
  28. Goldman, R., Pea, R., Barron, B., & Sharon, S. D. (2007). Video research in the learning sciences. Mahwah, NJ: Erlbaum.Google Scholar
  29. Greenhow, C., Robelia, B., & Hughes, J. E. (2009). Web 2.0 and classroom research: What path should we take now? Educational Researcher, 38(4), 246–259.CrossRefGoogle Scholar
  30. Groth, P., Jiang, S., Miles, S., Munroe, S., Tan, V., Tsasakou, S., et al. (2006). An architecture for provenance systems. Retreived from
  31. Hargreaves, D. (2003). Education epidemic: Transforming secondary schools through innovation networks. London: Demos.Google Scholar
  32. Hotho, A., Jaeschke, R., Schmitt, C., & Stumme, G. (2006). Information retrieval in folksonomies: Search and ranking. The semantic web: Research and applications (pp. 411–426). Berlin: Springer.CrossRefGoogle Scholar
  33. Huppertz, P., Massler, U., & Plötzner, R. (2005). v-share—Video-based analysis and reflection of teaching experiences in virtual groups. Proceedings of the International Conference on Computer Support for Collaborative Learning. Mahwah, NJ: Erlbaum.Google Scholar
  34. Jacbos, D. (2005, July/August). Enterprise software as service. Queue, 3(6), 36–42.CrossRefGoogle Scholar
  35. Jordan, B., & Henderson, A. (1995). Interaction analysis: Foundations and practice. The Journal of the Learning Sciences, 4(1), 39–103.Google Scholar
  36. Kelly, A. E. (2004). Design research in education: Yes, but is it methodological? The Journal of the Learning Sciences, 13(1), 115–128.CrossRefGoogle Scholar
  37. Kollar, I., Fischer, F., & Hesse, F. W. (2006). Collaboration scripts – a conceptual analysis. Educational Psychological Review, 18, 159–185.CrossRefGoogle Scholar
  38. Lagemann, E. C. (2002). Usable knowledge in Education: a memorandum for the Spencer Foundation Board of Directors (Memorandum). Retrieved from
  39. Langley, A. (1999). Strategies for theorizing from process data. Academy of Management Review, 24(4), 691–710.Google Scholar
  40. Laterza, V., Carmichael, P., & Procter, R. (2007). The doubtful guest? A virtual research environment for education. Technology, Pedagogy and Education, 16(3), 249–267.CrossRefGoogle Scholar
  41. Markauskaite, L., & Reimann, P. (2008). Enabling teacher-led research and innovation: A conceptual design of an inquiry framework for ICT-enhanced teacher innovation. Paper presented at the World Conference on Educational Multimedia, Hypermedia and Telecommunications 2008.Google Scholar
  42. Maxwell, J. A. (2004). Causal explanation, qualitative research, and scientific inquiry in education. Educational Researcher, 33(2), 3–11.CrossRefGoogle Scholar
  43. Olson, G. M., Herbsleb, J. D., & Rueter, H. H. (1994). Characterizing the sequential structure of interactive behaviors through statistical and grammatical techniques. Human-Computer Interaction, 9(3/4), 427–472.CrossRefGoogle Scholar
  44. Pea, R., & Lindgren, R. (2008). Video collaboratories for research and education: An analysis of collaboration design patterns. IEEE Transactions on Learning Technologies, 1(4), 235–247.CrossRefGoogle Scholar
  45. Pea, R., Mills, M., Rosen, J., Dauber, K., & Effelsberg, W. (2004). The DIVER project: Interactive digital video repurposing. IEEE Multimedia, 11, 54–61.CrossRefGoogle Scholar
  46. Perera, D., Kay, J., Koprinska, I., Yacef, K., & Zaiane, O. (2008). Clustering and sequential pattern mining of online collaborative learning data. IEEE Transactions on Knowledge and Data Engineering, 21(6), 759–772.CrossRefGoogle Scholar
  47. Philip, L., Chorley, A., Farrington, J., & Edwards, P. (2007). Data provenance, evidence-based policy assessment, and e-social science. Paper presented at the Third international conference on e-social science, October 15, 2007, Ann Arbor, MI.Google Scholar
  48. Procter, R. (2007). Collaboration, coherence and capacity-building: the role of DSpace in supporting and understanding the TLRP. Technology, Pedagogy and Education, 16(3), 269–288.CrossRefGoogle Scholar
  49. Reimann, P. (2009). Time is precious: Variable- and event-centred approaches to process analysis in CSCL research. International Journal of Computer-Supported Collaborative Learning 4, 239–257.CrossRefGoogle Scholar
  50. Reimann, P., Frerejean, J., & Thompson, K. (2009). Using process mining to identify models of group decision making processes in chat data. In C. O’Malley, D. Suthers, P. Reimann & A. Dimitracopoulou (Eds.), Computer-supported collaborative learning practices: CSCL2009 conference proceedings (pp. 98–107). Rhodes, Greece: International Society for the Learning Sciences.CrossRefGoogle Scholar
  51. Rich, P. J., & Hannafin, M. (2009). Video annotation tools. Journal of Teacher Education, 60(1), 52–67.CrossRefGoogle Scholar
  52. Romero, C., & Ventura, S. (2005). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), 135–146.Google Scholar
  53. Schleyer, T., Spallek, H., Butler, B. S., Subramanian, S., Weiss, D., Poythress, L., et al. (2008). Facebook for scientists: Requirements and services for optimizing how scientific collaborations are established. Journal of Medical Internet Research, 10(3). Retrieved from
  54. Simmhan, Y. L., Plale, B., & Gannon, D. (2005). A survey of data provenance in e-science. SIGMOD Record, 34(3), 31–36.CrossRefGoogle Scholar
  55. Srikant, R., & Agrawal, R. (1996). Mining sequential patterns: Generalizations and performance improvements. Proceedings of Fifth International Conference on Extending Database Technology (EDBT), Avignon, France.Google Scholar
  56. Stevens, R., Robinson, A., & Goble, C. (2003). My-grid: Personalised bioinformatics on the information grid. Bioinformatics, 19(1), 302–304.CrossRefGoogle Scholar
  57. Strijbos, J.-W., & Fischer, F. (2007). Methodological challenges for collaborative learning research. Learning and Instruction, 17(4), 389–393.CrossRefGoogle Scholar
  58. Taylor, K., Essex, J. W., Frey, J. G., Mills, H. R., Hughes, G., & Zaluska, E. J. (2006). The semantic grid and chemistry: Experiences with CombeChem. Journal of Web Semantics, 4(2), 84–101.CrossRefGoogle Scholar
  59. Ure, J., Procter, R., Lin, Y. -W., Hartswood, M., & Ho, K. (2007). Aligning technical and human infrastructures in the semantic web: A socio-technical perspective. Paper presented at the third international conference on e-social science, October 7–9, 2007, Ann Arbor, MI, USA.Google Scholar
  60. Van der Aalst, W. M. P., & Günther, C. W. (2007). Finding structure in unstructured processes: the case of process mining. In T. Basten, G. Juhas & S. Shukla (Eds.), Proceedings the 7th International Conference on Applications of Concurrency to System Design (ACSD 2007; Bratislava, Slovak Republic) (pp. 3–12). Los Alamitos, CA: IEEE Computer Society Press.Google Scholar
  61. Van der Aalst, W. M. P., & Weijters, A. J. M. M. (2005). Process mining. In M. Dumas, W. M. P. van der Aalst & A. H. M. ter Hofstede (Eds.), Process-aware information systems: Bridging people and software through process technology (pp. 235–255). New York: Wiley.CrossRefGoogle Scholar
  62. Wasserman, S., & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge, UK: Cambridge University Press.Google Scholar
  63. Wilson, A., Rimpiläinen, S., Skinner, D., Cassidy, C., Christie, D., Coutts, N., et al. (2007). Using a virtual research environment to support new models of collaborative and participative research in Scottish education. Technology, Pedagogy and Education, 16(3), 289–304.CrossRefGoogle Scholar
  64. Wong, W. Y., & Reimann, P. (2009). Web based educational video teaching and learning platform with collaborative annotation. In I. Aedo, N.-S. Chen, K. D. Sampson & L. Zaitseva (Eds.), The Ninth IEEE International Conference on Advanced Learning Technologiies (ICALT 2009) (pp. 696–700). Riga, Latvia: IEEE.CrossRefGoogle Scholar
  65. Ye, N. (Ed.). (2003). The handbook of data mining. Mahwah, NJ: Lawrence Erlbaum.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

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

Personalised recommendations