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Technology, Knowledge and Learning

, Volume 22, Issue 3, pp 385–400 | Cite as

Inferring Learning from Big Data: The Importance of a Transdisciplinary and Multidimensional Approach

  • Jason M. Lodge
  • Sakinah S. J. Alhadad
  • Melinda J. Lewis
  • Dragan Gašević
Original research
First Online:

Abstract

The use of big data in higher education has evolved rapidly with a focus on the practical application of new tools and methods for supporting learning. In this paper, we depart from the core emphasis on application and delve into a mostly neglected aspect of the big data conversation in higher education. Drawing on developments in cognate disciplines, we analyse the inherent difficulties in inferring the complex phenomenon that is learning from big datasets. This forms the basis of a discussion about the possibilities for systematic collaboration across different paradigms and disciplinary backgrounds in interpreting big data for enhancing learning. The aim of this paper is to provide the foundation for a research agenda, where differing conceptualisations of learning become a strength in interpreting patterns in big datasets, rather than a point of contention.

Keywords

Learning analytics Inference Predictive modelling Transdisciplinarity 
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Notes

Acknowledgements

This work was supported by the Australian Research Council (JML) [Grant Number SR120300015].

References

  1. Ackoff, R. L. (1989). From data to wisdom. Journal of Applied Systems Analysis, 16, 3–9.Google Scholar
  2. Anderson, J. R., Reder, L. M., & Simon, H. A. (1996). Situated learning and education. Educational Researcher, 25, 5–11.CrossRefGoogle Scholar
  3. Arum, R., & Roksa, J. (2011). Academically adrift: Limited learning on college campuses. Chicago, IL: University of Chicago Press.Google Scholar
  4. Baker, R. S. (2016). Stupid tutoring systems, intelligent humans. International Journal of Artificial Intelligence in Education, 26(2), 600–614.CrossRefGoogle Scholar
  5. Barnett, R. (2004). Learning for an unknown future. Higher Education Research & Development, 23(3), 247–260. doi: 10.1080/0729436042000235382.CrossRefGoogle Scholar
  6. Basler, M. H. (2009). Utility of the McNamara fallacy. BMJ, 339, b3141. doi: 10.1136/bmj.b3141.CrossRefGoogle Scholar
  7. Berger, J. O., & Berry, D. A. (1988). Statistical analysis and the illusion objectivity. American Scientist, 76, 159–165.Google Scholar
  8. Blackman, K. C. A., Zoellner, J., Berrey, L. M., Alexander, R., Faning, J., Hill, J. L., et al. (2013). Assessing the internal and external validity of mobile health physical activity promotion interventions: A systematic literature review using the RE_AIM framework. Journal of Medical Internet Research, 15(10), e224. doi: 10.2196/jmir.2745.CrossRefGoogle Scholar
  9. Buckingham Shum, S., & Crick, R. D. (2016). Learning analytics for 21st century competencies. Journal of Learning Analytics, 3(2), 6–21.CrossRefGoogle Scholar
  10. Cohen, J. (1994). The earth is round (p < .05). American Psychologist, 49(12), 997–1003.CrossRefGoogle Scholar
  11. Datnow, A., & Hubbard, L. (2016). Teacher capacity for and beliefs about data-driven decision making: A literature review of international research. Journal of Educational Change, 17(1), 7–28.CrossRefGoogle Scholar
  12. De Houwer, J., Barnes-Holmes, D., & Moors, A. (2013). What is learning? On the nature and merits of a functional definition of learning. Psychonomic Bulletin & Review, 20(4), 631–642. doi: 10.3758/s13423-013-0386-3.CrossRefGoogle Scholar
  13. Dover, A. G., & Schultz, B. D. (2016). Troubling the edTPA: Illusions of objectivity and rigor. The Educational Forum, 80(1), 95–106.CrossRefGoogle Scholar
  14. Dunning, T. (2008). Improving causal inference: Strengths and limitations of natural experiments. Political Research Quarterly, 61, 282–293.CrossRefGoogle Scholar
  15. Farrell, C. C., & Marsh, J. A. (2016). Contributing conditions: A qualitative comparative analysis of teachers’ instructional responses to data. Teaching and Teacher Education, 60, 398–412.CrossRefGoogle Scholar
  16. Ferguson, R., Hoel, T., Scheffel, M., & Drachsler, H. (2016). Guest editorial: Ethics and privacy in learning analytics. Journal of Learning Analytics, 3(1), 5–15.CrossRefGoogle Scholar
  17. Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. Tech Trends, 59(1), 64–71.CrossRefGoogle Scholar
  18. Green, L. W., & Glasgow, R. E. (2006). Evaluating the relevance, generalization, and applicability of research: Issues in external validation and translation methodology. Evaluation & The Health Professionals, 29(1), 126–153.CrossRefGoogle Scholar
  19. Hauben, M., Reich, L., Gerrits, C. M., & Younus, M. (2007). Illusion of objectivity and a recommendation for reporting data mining results. European Journal of Clinical Pharmacology, 63, 517–521.CrossRefGoogle Scholar
  20. Holland, J., Holyoak, K., Nisbett, R., & Thagard, P. (1986). Induction: Processes of inference, learning, and discovery. Cambridge, MA: MIT Press.Google Scholar
  21. Horvath, J. C., & Donoghue, G. M. (2016). A bridge too far–revisited: Reframing Bruer’s neuroeducation argument for modern science of learning practitioners. Frontiers in Psychology, 7, 377.CrossRefGoogle Scholar
  22. Horvath, J. C., & Lodge, J. M. (2017). A framework for organizing and translating science of learning research. In J. C. Horvath, J. M. Lodge, & J. A. C. Hattie (Eds.), From the laboratory to the classroom: Translating learning sciences for teachers. Abingdon: Routledge.Google Scholar
  23. Horvath, J. C., Lodge, J. M., & Hattie, J. A. C. (2017). From the laboratory to the classroom: Translating science of learning for teachers. Abingdon: Routledge.Google Scholar
  24. Jacobson, M. J., Kapur, M., & Reimann, P. (2016). Conceptualizing debates in learning and educational research: Toward a complex systems conceptual framework of learning. Educational Psychologist, 51(2), 210–218. doi: 10.1080/00461520.2016.1166963.CrossRefGoogle Scholar
  25. Jonassen, D., & Land, S. (2012). Theoretical foundations of learning environments. Abingdon, UK: Routledge.Google Scholar
  26. Jones, C. & Kennedy, G. (2011). Stepping beyond the paradigm wars: pluralist methods for research in learning technology. In ALTC 2011 Proceedings of the 18th international conference of the Association for Learning Technology (pp. 18–28). Leeds: University of Leeds, September 6–8, 2011.Google Scholar
  27. Kitson, A. L., Harvey, G., & McCormack, B. (1998). Enabling the implementation of evidence based practice: A conceptual framework. Quality in Health Care, 7, 149–159.CrossRefGoogle Scholar
  28. Kitson, A. L., Rycroft-Malone, J., Harvey, G., McCormack, B., Seers, K., & Titchen, A. (2008). Evaluating the successful implementation of evidence into practice using the PARiHS framework: Theoretical and practical challenges. Implementation Science. doi: 10.1186/1748-5908-3-1.Google Scholar
  29. Knight, S., Buckingham Shum, S., & Littleton, K. (2014). Epistemology, assessment, pedagogy: Where learning meets analytics in the middle space. Journal of Learning Analytics, 1(2), 23–47.CrossRefGoogle Scholar
  30. Krathwohl, D. R. (2002). A revision of Bloom’s taxonomy: An overview. Theory into Practice, 41(4), 212–218. doi: 10.1207/s15430421tip4104_2.CrossRefGoogle Scholar
  31. Laranjo, L., Arguel, A., Neves, A. L., Gallagher, A. M., Kaplan, R., Mortimer, N., et al. (2014). The influence of social networking sites on behavior change: A systematic review and meta-analysis. Journal of the American Medical Informatics Association, 22(1), 243–256.CrossRefGoogle Scholar
  32. Lewis, M. J., & Lodge, J. M. (2016). Keep calm and credential on: Linking learning, life and work practices in a complex world. In D. Ifenthaler, N. Bellin-Mularski, & D-K. Mah (Eds.), Foundation of digital badges and micro-credentials (pp. 41–54). Cham: Springer International Publishing. doi: 10.1007/978-3-319-15425-1_3.
  33. Liu, D. Y.-T., Rogers, T., & Pardo, A. (2015). Learning analytics—Are we at risk of missing the point? In T. Reiners, B. R. von Konsky, D. Gibson, V. Chang, L. Irving & K. Clarke (Eds.), Globally connected, digitally enabled. Proceedings ascilite 2015 in Perth.Google Scholar
  34. Lodge, J. M., & Lewis, M. J. (2012). Pigeon pecks and mouse clicks: Putting the learning back into learning analytics. In M. Brown, M. Hartnett, & T. Stewart (Eds.), Future challenges, sustainable futures. Proceedings ascilite Wellington 2012.Google Scholar
  35. Macfadyen, L. P., Dawson, S., Pardo, A., & Gašević, D. (2014). Embracing big data in complex educational systems: The learning analytics imperative and the policy challenge. Research & Practice in Assessment, 9(2), 17–28.Google Scholar
  36. Maxwell, S. E., Lau, M. Y., & Howard, G. S. (2015). Is psychology suffering from a replication crisis? What does “failure to replicate” really mean? American Psychologist, 70(6), 487.CrossRefGoogle Scholar
  37. Milligan, S., (2015, March). Crowd-sourced learning in MOOCs: Learning analytics meets measurement theory. In Proceedings of the Fifth International Conference on Learning Analytics and Knowledge (pp. 151–155). New York, NY: ACM.Google Scholar
  38. Mislevy, R., & Gitomer, D. (1996). The role of probability-based inference in an intelligent tutoring system. User-Mediated and User-Adapted Interaction, 5, 253–282. doi: 10.1037/e652102011-001.CrossRefGoogle Scholar
  39. Nathan, M. J., & Alibali, M. W. (2010). Learning sciences. Wiley Interdisciplinary Reviews: Cognitive Science, 1(3), 329–345.Google Scholar
  40. Newell, A. (1990). Unified theories of cognition. Cambridge, MA: Harvard University Press.Google Scholar
  41. Nichols, S. L., & Berliner, D. C. (2007). Collateral damage: How high-stakes testing corrupts America’s schools. Cambridge, MA: Harvard Education Press.Google Scholar
  42. Nicolescu, B. (2002). Manifesto of transdisciplinarity. Albany, Ny: State University if New York Press.Google Scholar
  43. Palghat, K., Horvath, J. C., & Lodge, J. M. (2017). The hard problem of ‘educational neuroscience’. Trends in Neuroscience & Education, 6(1), 204–210. doi: 10.1016/j.tine.2017.02.001.CrossRefGoogle Scholar
  44. Pardos, Z. A., Baker, R. S., Gowda, S. M., & Heffernan, N. T. (2011). The sum is greater than the parts: Ensembling models of student knowledge in educational software. SIGKDD Explorations, 13(2), 37–44.CrossRefGoogle Scholar
  45. Powers, J. T., Cook, J. E., Purdie-Vaughns, V., Garcia, J., Apfel, N., & Cohen, G. L. (2016). Changing environments by changing individuals: The emergent effects of psychological intervention. Psychological Science, 27(2), 150–160.CrossRefGoogle Scholar
  46. Prinsloo, P., & Slade, S. (2016). Student vulnerability, agency, and learning analytics: An exploration. Journal of Learning Analytics, 3(1), 159–182.CrossRefGoogle Scholar
  47. Reeves, T. (2006). Design research from a technology perspective. In J. V. D. Akker, K. Gravemeijer, S. McKenney, & N. Nieveen (Eds.), Educational design research (pp. 52–66). New York: Routledge.Google Scholar
  48. Reimann, P. (2016). Connecting learning analytics with learning research: The role of design-based research. Learning: Research & Practice, 2(2), 130–142.Google Scholar
  49. Rogers, T. (2015). Critical realism and learning analytics research: Epistemological implications of an ontological foundation. In Proceedings of the fifth international conference on learning analytics and knowledge (pp. 223–230). New York, NY: ACM.Google Scholar
  50. Rowntree, D. (1987). Assessing students: How shall we know them?. London: Kogan Page.Google Scholar
  51. Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380–1400.CrossRefGoogle Scholar
  52. Siemens, G., & Baker, R. S. (2012). Learning analytics and educational data mining: Towards communication and collaboration. In S. Buckingham Shum, D. Gašević, & R. Ferguson (Eds.), Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 252–254). New York, NY: ACM.Google Scholar
  53. Siemens, G., & Gašević, D. (2012). Guest editorial: Learning and knowledge analytics. Educational Technology & Society, 15(3), 1–2.Google Scholar
  54. Smoliar, S. W. (1987). Book review: “Induction: Processes of inference, learning, and discovery”. IEEE Expert, 2(3), 92–93.CrossRefGoogle Scholar
  55. Soderstrom, N. C., & Bjork, R. A. (2015). Learning versus performance: An integrative review. Perspectives on Psychological Science, 10(2), 176–199.CrossRefGoogle Scholar
  56. Strickland, J. S. (2014). Predictive modelling and analytics. Colorado: Lulu Press.Google Scholar
  57. Wise, A. F., & Shaffer, D. W. (2015). Why theory matters more than ever in the age of big data. Journal of Learning Analytics, 2, 5–13.CrossRefGoogle Scholar
  58. Yarkoni, T. (2012). Psychoinformatics: New horizons at the interface of the psychological and computing sciences. Current Directions in Psychological Science, 21(6), 391–397. doi: 10.1177/0963721412457362.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.Melbourne Centre for the Study of Higher EducationUniversity of MelbourneMelbourneAustralia
  2. 2.Learning FuturesGriffith UniversityBrisbaneAustralia
  3. 3.Learning Academy, Division of Student LearningCharles Sturt UniversityBathurstAustralia
  4. 4.School of Education and InformaticsUniversity of EdinburghEdinburghScotland, UK

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