The Future Value of Serious Games for Assessment: Where Do We Go Now?

The Future Value of Serious Games for Assessment: Where Do We Go Now?

Authors

  • Sebastiaan de Klerk
     University of Twente/eX:plain, Enschede
  • Pamela M. Kato
     Coventry University, Coventry

Keywords:

Evidence-Centered Design, Game-Based Assessment, Psychometrics, Serious Game, Training and Assessmen.t

Abstract

Game-based assessments will most likely be an increasing part of testing programs in future generations because they provide promising possibilities for more valid and reliable measurement of students’ skills as compared to the traditional methods of assessment like paper-and-pencil tests or performance-based assessments. The current status of serious games for assessment has been highlighted from several angles in the previous articles of this special issue. Here, we will synthesize the findings from the individual papers to demonstrate how to best benefit from the advantages of game-based assessment and how to address the many challenges that still remain. In the first part we will once more discuss how game-based assessments advantages can play a role in future testing, and in the second part we will address one of the most daring challenges: the psychometrics behind the game. In a short conclusion section we will discuss how research and practice should shape a future generation of game-based assessment.

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Published

2017-10-03

How to Cite

de Klerk, S., & Kato, P. M. (2017). The Future Value of Serious Games for Assessment: Where Do We Go Now?. Journal of Applied Testing Technology, 18(S1), 32–37. Retrieved from http://www.jattjournal.net/index.php/atp/article/view/118674

Issue

Volume 18, Special Issue 1, 2017

Section

Articles

References

Bauer, M., Wylie, C. Jackson, T., Mislevy, R. J., Hoffman-John, E., & John, E. (2017). Journal of Applied Testing Technology.

Clark, R. C., & Mayer, R. E. (2011). E-learning and the science of instruction. San Francisco: Pfeiffer. https://doi.org/10.1002/9781118255971

De Klerk, S., Eggen, T. J. H. M., & Veldkamp, B. P. (2014). A blending of computer-based assessment and performancebased assessment: Multimedia-Based Performance Assessment (MBPA). The introduction of a new method of assessment in Dutch Vocational Education and Training (VET). Cadmo, 22(1), 39-56. https://doi.org/10.3280/ CAD2014-001006

De Klerk, S., Veldkamp, B. P., & Eggen, T. J. H. M. (2015). Psychometric analysis of the performance data of simulationbased assessment: A systematic review and a Bayesian network example. Computers & Education, 85, 23–34. https://doi.org/10.1016/j.compedu.2014.12.020

DiCerbo, K. (2017). Building the evidentiary argument in game-based assessment. Journal of Applied Testing Technology.

Elliot, A. J., & McGregor, H. (1999). Test anxiety and the hierarchical model of approach. Contemporary Educational Psychology, 19, 430–446.

Gobert, J. D., Sao Pedro, M. A., Baker, R. S. J. D., Toto, E., & Montalvo, O. (2012). Leveraging educational data mining for real-time performance assessment of scientific inquiry skills within microworlds. Journal of Educational Data Mining, 4, 111–143.

Iseli, M. R., Koenig, A. D., Lee, J. J., & Wainess, R. (2010). Automated assessment of complex task performance in games and simulations (CRESST Research rep. No. 775).

Los Angeles: National Center for Research on Evaluation, Standards, Student Testing. Retrieved from: http:// www.cse.ucla.edu/products/reports/R775.pdf PMCid:PMC3389788

Kato, P. M. (2012). Evaluating efficacy and validating health games. Games for Health: Research, Development, and Clinical Applications, 1(1), 74–76. https://doi.org/10.1089/ g4h.2012.1017 PMid:26196436

Kato, P. M. (2010). Video games in health care: Closing the gap. Review of General Psychology, 14(2), 113–121. https://doi.org/10.1037/a0019441

Kerr, D., & Chung, G. K. W. K. (2012). Identifying key features of student performance in educational video games and simulations through cluster analysis. Journal of Educational Data Mining, 4(1).

Levy, R. (2013). Psychometric and evidentiary advances, opportunities, and challenges for simulation-based assessment. Educational Assessment, 18(3), 182–207. https:// doi.org/10.1080/10627197.2013.814517

Mislevy, R. J., Steinberg, L. S., & Almond, R. G. (2003). Focus article: On the structure of educational assessments. Measurement: Interdisciplinary Research and Perspectives, 1(1), 3–62. https://doi.org/10.1207/S15366359MEA0101_02

Mislevy, R. J., Behrens, J. T., DiCerbo, K., & Levy, R. (2012). Design and discovery in educational assessment: evidencecentered design, psychometrics, and data mining. Journal of Educational Data Mining, 4, 11–48.

Mislevy, R.J., Oranje, A., Bauer, M., von Davier, A.A., Hao, J., Corrigan, S., Hoffman, E., DiCerbo, K., & John, M. (2014). Psychometric considerations in game-based assessment. New York, NY: Institute of Play.

Neapolitan, R.E. (2003). Learning Bayesian networks. New York, NY: Prentice-Hall.

Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Francisco, CA: Morgan Kaufmann. https://doi.org/10.1016/B978-0-08051489-5.50012-6

Nelson, B., Nugent, B., & Rupp, A. A. (2012). On instructional utility, statistical methodology, and the added value of ECD: Lessons learned from the special issue. Journal of Educational Data Mining, 7(4), 224–230.

Quellmalz, E. S., Timms, M. J., Silberglitt, M. D., & Buckley, B. C. (2012). Science assessments for all: Integrating science simulations into balanced state science assessment systems. Journal of Research in Science Teaching, 49(3), 363–393. https://doi.org/10.1002/tea.21005

Rupp, A. A., DiCerbo, K. E., Levy, R., Benson, M., Sweet, S., Crawford, A., et al. (2012). Putting ECD into practice: the interplay of theory and data in evidence models within a digital learning environment. Journal of Educational Data Mining, 4, 49–110.

Rupp, A. A., Levy, R., DiCerbo, K., Sweet, S. J., Crawford, A. V., Calico, T., Benson, M., Fay, D., Kunze, K. L., Mislevy, R. J., & Behrens, J. T. (2012). Putting ECD into practice: The interplay of theory and data in evidence models within a digital learning environment. Journal of Educational Data Mining, 4(1), 49–110.

Shute, V. J., Ventura, M., Bauer, M. I., & Zapata-Rivera, D. (2009). Melding the power of serious games and embedded assessment to monitor and foster learning: Flow and grow. In U. Ritterfeld, M. J. Cody, & P. Vorderer (Eds.). Serious games: Mechanisms and effects (pp. 295-321). Mahwah, NJ: Routledge.

Shute, V. J. (2011). Stealth assessment in computer-based games to support learning. In S. Tobias and J.D. Fletcher (Eds.), Computer Games and Instruction (pp. 503-523). Charlotte, NC: Information Age Publishing.

Straatemeier, M. (2014). Math garden: A new educational and scientific instrument. Doctoral dissertation, UvA, The Netherlands.