Advertisement

A Futures Perspective on Information Technology and Assessment

  • Jason M. LodgeEmail author
Reference work entry
Part of the Springer International Handbooks of Education book series (SIHE)

Abstract

Assessment is perhaps the area, like no other, where the utility of information technology in education is tested. The possibilities for assessing using these technologies are expanding rapidly. In particular, new technologies afford possibilities for focusing assessment on learning as an ongoing developmental process, rather than on performance. Building on notions of assessment grounded in measurement theory, there are prospects for assessing students continuously while they learn in a developmental way through the use of data and analytics. The resulting picture of student development will then allow for a more holistic and systemic approach to assessment in the years ahead. While it is often problematic to make predictions about the future, in this chapter, I will attempt to draw on current developments to provide suggestions about where the intersections of assessment and information technologies are likely headed. That future is likely to entail more continuous, personalized forms of assessment that focus heavily on helping students to make better judgments about their own learning and development.

Keywords

Educational technology Performance Measurement Continuous assessment 

References

  1. Allen, M. J., & Yen, W. M. (2001). Introduction to measurement theory. Long Grove: Waveland Press.Google Scholar
  2. Amin, T. G., & Levrini, O. (2017). Converging perspectives on conceptual change: Mapping an emerging paradigm in the learning sciences. Abingdon: Routledge.Google Scholar
  3. Arguel, A., Lockyer, L., Lipp, O., Lodge, J. M., & Kennedy, G. (2017). Inside out: Ways of detecting learners confusion for successful e-learning. Journal of Educational Computing Research, 55(4), 526–551.  https://doi.org/10.1177/0735633116674732. (pre-press version).CrossRefGoogle Scholar
  4. Bakr, M. M., Massey, W., & Alexander, H. (2013). Evaluation of Simodont® Haptic 3D virtual reality dental training simulator. International Journal of Dental Clinics, 5(4), 1–6.Google Scholar
  5. Black, P., Harrison, C., Lee, C., Marshall, B., & Wiliam, D. (2003). Assessment for learning: Putting it into practice. Buckingham: Open University Press.Google Scholar
  6. Black, P., McCormick, R., James, M., & Pedder, D. (2006). Learning how to learn and assessment for learning: A theoretical inquiry. Research Papers in Education, 21(2), 119–132.  https://doi.org/10.1080/02671520600615612.CrossRefGoogle Scholar
  7. Blikstein, P., Worsley, M., Piech, C., Sahami, M., Cooper, S., & Koller, D. (2014). Programming pluralism: Using learning analytics to detect patterns in the learning of computer programming. The Journal of the Learning Sciences, 23(4), 561–599.CrossRefGoogle Scholar
  8. Buckingham Shum, S., SÁndor, Á., Goldsmith, R., Wang, X., Bass, R., & McWilliams, M. (2016). Reflecting on reflective writing analytics: Assessment challenges and iterative evaluation of a prototype tool. In 6th international learning analytics & knowledge conference. New York: ACM.  https://doi.org/10.1145/2883851.2883955.
  9. Caramazza, A., & Mahon, B. Z. (2003). The organization of conceptual knowledge: The evidence from category-specific semantic deficits. Trends in Cognitive Science, 7, 354–361.CrossRefGoogle Scholar
  10. Clement, J. (1982). Students preconceptions in introductory mechanics. American Journal of Physics, 50, 66–71.CrossRefGoogle Scholar
  11. D’Mello, S., Lehman, B., Pekrun, R., & Graesser, A. (2014). Confusion can be beneficial for learning. Learning and Instruction, 29, 153–170.  https://doi.org/10.1016/j.learninstruc.2012.05.003.CrossRefGoogle Scholar
  12. Dalgarno, B., Kennedy, G., & Bennett, S. (2014). The impact of students’ exploration strategies on discovery learning using computer-based simulations. Educational Media International, 51(4), 310–329.  https://doi.org/10.1080/09523987.2014.977009.CrossRefGoogle Scholar
  13. Dann, R. (2014). Assessment as learning: Blurring the boundaries of assessment and learning for theory, policy and practice. Assessment in Education: Principles, Policy & Practice, 21(2), 149–166.CrossRefGoogle Scholar
  14. 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, 24(4), 631–642.  https://doi.org/10.3758/s13423-013-0386-3.CrossRefGoogle Scholar
  15. Gartner Inc. (2015). Research methodologies: Hype cycles. Stamford: Gartner. Retrieved on 10 Oct 2016, from http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp.Google Scholar
  16. Graesser, A., Chipman, P., Haynes, B., & Olney, A. (2005). AutoTutor: An intelligent tutoring system with mixed-initiative dialogue. IEEE Transactions on Education, 48(4), 612–618.CrossRefGoogle Scholar
  17. Hays, R. T., Jacobs, J. W., Prince, C., & Salas, E. (1992). Flight simulator training effectiveness: A meta-analysis. Military Psychology, 4(2), 63–74.CrossRefGoogle Scholar
  18. 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
  19. 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.  https://doi.org/10.1080/00461520.2016.1166963.CrossRefGoogle Scholar
  20. Jonsson, A., & Svingby, G. (2007). The use of scoring rubrics: Reliability, validity and educational consequences. Educational Research Review, 2(2), 130–144.CrossRefGoogle Scholar
  21. Kim, Y. J., Almond, R. G., & Shute, V. J. (2016). Applying evidence-centered design for the development of game- based assessments in physics playground. International Journal of Testing, 16(2), 142–163.CrossRefGoogle Scholar
  22. Leighton, J. P., & Gierl, M. J. (Eds.). (2007). Cognitive diagnostic assessment for education: Theories and applications. Cambridge, UK: Cambridge University Press.Google Scholar
  23. Lodge, J. M., & Corrin, L. (2017). What data and analytics can and do say about effective learning. Nature npj Science of Learning, 2(1), 4–5.  https://doi.org/10.1038/s41539-017-0006-5.CrossRefGoogle Scholar
  24. Lodge, J. M., & Horvath, J. C. (2017). Science of learning and digital learning environments. 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
  25. Lodge, J. M., & Kennedy, G. E. (2015). Prior knowledge, confidence and understanding in interactive tutorials and simulations. In T. Reiners, B. R. Von Konsky, D. Gibson, V. Chang, L. Irving, & K. Clarke (Eds.), Globally connected, digitally enabled (pp. 178–188). Proceedings ascilite 2015 in Perth. ASCILITE, Tugun, Qld.Google Scholar
  26. Lodge, J. M., Kennedy, G., & Hattie, J. A. C. (2018). Understanding, assessing and enhancing student evaluative judgement in digital environments. In D. Boud, R. Ajjawi, P. Dawson, & J. Tai (Eds.), Developing evaluative judgement in higher education: Assessment for knowing and producing quality work. Abingdon: Routledge.Google Scholar
  27. Mason, M. (2008). Complexity theory and the philosophy of education. Educational Philosophy and Theory, 40(1), 4–18.CrossRefGoogle Scholar
  28. Meyer, R. H. (1997). Value added indicators of school performance: A primer. Economics of Education Review, 16, 283–301.CrossRefGoogle Scholar
  29. Milligan, S., & Griffin, P. (2016). Understanding learning and learning design in MOOCs: A measurement-based interpretation. Journal of Learning Analytics, 3(2), 88–115.  https://doi.org/10.18608/jla.2016.32.5.CrossRefGoogle Scholar
  30. Nitko, A. (1995). Curriculum-based continuous assessment: A framework for concepts, procedures and policy. Assessment in Education, 2(3), 321–337.CrossRefGoogle Scholar
  31. Pekrun, R., & Linnenbrink-Garcia, L. (Eds.). (2014). International handbook of emotions in education. New York: Routledge.Google Scholar
  32. Pieterse, V. (2013). Automated assessment of programming assignments. ACM Transactions on Graphics, 28(4), 1–12.  https://doi.org/10.1145/1559755.1559763.CrossRefGoogle Scholar
  33. Piromchai, P., Ioannou, I., Wijewickrema, S., Kasemsiri, P., Lodge, J. M., Kennedy, G., & O'Leary, S. (2017). The effects of anatomical variation on trainee performance in a virtual reality temporal bone surgery simulator – A pilot study. The Journal of Laryngology & Otology, 131(S1), S29–S35.  https://doi.org/10.1017/S0022215116009233.CrossRefGoogle Scholar
  34. Putnam, A. L., Nestojko, J. F., & Roediger, H. L. (2017). Improving student learning: Two strategies to make it stick. 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
  35. Roediger, H. L., & Karpicke, J. D. (2006). Test-enhanced learning: Taking memory tests improves long-term retention. Psychological Science, 17, 249–255.  https://doi.org/10.1111/j.1467-9280.2006.01693.x.CrossRefGoogle Scholar
  36. Roediger, H. L., & Marsh, E. J. (2005). The positive and negative consequence of multiple-choice testing. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 1155–1159.Google Scholar
  37. Selwyn, N. (2014). Distrusting educational technology: Critical questions for changing times. New York: Routledge.Google Scholar
  38. Shute, V. J. (2011). Stealth assessment in computer-based games to support learning. Computer Games and Instruction, 55(2), 503–524.Google Scholar
  39. Shute, V. J., & Kim, Y. J. (2014). Formative and stealth assessment. In J. M. Spector, M. D. Merrill, J. Elen, & M. J. Bishop (Eds.), Handbook of research on educational communications and technology (4th ed.). New York: Springer.Google Scholar
  40. Shute, V. J., & Ventura, M. (2013). Measuring and supporting learning in games: Stealth assessment. Cambridge, MA: MIT Press.Google Scholar
  41. Soderstrom, N. C., & Bjork, R. A. (2015). Learning versus performance: An integrative review. Perspectives on Psychological Science, 10(2), 176–199.  https://doi.org/10.1177/1745691615569000.CrossRefGoogle Scholar
  42. van der Linden, W. J., & Hambleton, R. K. (1997). Handbook of modern item response theory. New York: Springer.CrossRefGoogle Scholar
  43. Webb, M., Gibson, D. C., & Forkosh-Baruch, A. (2013). Challenges for information technology supporting educational assessment. Journal of Computer Assisted Learning, 29(5), 451–462.  https://doi.org/10.1111/jcal.12033.CrossRefGoogle Scholar
  44. Wiliam, D. (2011). What is assessment for learning? Studies in Educational Evaluation, 37(1), 3–14.CrossRefGoogle Scholar
  45. Woolf, B. P. (2009). Building intelligent interactive tutors. Burlington: Morgan Kaufmann.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Education and Science of Learning Research CentreUniversity of QueenslandBrisbaneAustralia

Section editors and affiliations

  • Mary Webb
    • 1
  • Dirk Ifenthaler
    • 2
  1. 1.King's College LondonLondonUK
  2. 2.University of MannheimMannheimGermany

Personalised recommendations