Abstract
In an interactive digital game or gamified e-learning experience, mapping a learner’s progress, problem-solving attempts, self-expressions and social communications can entail highly detailed and time-sensitive computer-based traces that capture the context, actions, processes and products. New educational measurement and analysis considerations are needed to address the challenges of finding patterns and making inferences concerning what someone knows and can do. Methods based in data-mining, machine learning, model-building and complexity theory are discussed as theoretical foundations for dealing with time sensitivity, spatial relationships, multiple layers of aggregations at different scales, and the dynamics of complex performance spaces. Examples of these considerations in game-based learning analytics are presented and discussed in the hope of making a contribution to the development and refinement of theoretical approaches to measurement.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Barab, S. A., & Duffy, T. (2000). From Practice fields to communities of practice. In D. H. Jonassen, & S. M. Land (Eds.) Theoretical foundations of learning environments, 1(1), 25–55. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.162.3127
Bennett, R. (2001). How the internet will help large-scale assessment reinvent itself. Education Policy Analysis Archives, 9(5), 1–23.
Bennett, R., Persky, H., Weiss, A., & Jenkins, F. (2007). Problem solving in technology-rich environments: A report from the NAEP technology-based assessment project. The Nation’s Report Card (p. 180). Retrieved from http://nces.ed.gov/nationsreportcard/pdf/studies/2007466.pdf
Brooks, R. (1986). A robust layered control system for a mobile robot. Journal of Robotics and Automation, 2, 14–23.
Brooks, R. (1999). Cambrian intelligence: The early history of the new AI. Cambridge, MA: MIT Press.
Brown, A. L., Ash, D., Rutherford, M., Nakagawa, K., Gordon, A., & Campione, J. (1993). Distributed expertise in the classroom. In G. Salomon (Ed.), Distributed cognitions: Psychological and education considerations. New York: Cambridge University Press.
Cangelosi, A., & Parisi, D. (2002). Computer Simulation : A New Scientific Approach to the Study of Language Evolution. In A. Cangelosi & D. Parisi (Eds.), Simulating the evolution of language (pp. 3–28). London: Springer.
Choi, Y., Rupp, A., Gushta, M., & Sweet, S. (2010). Modeling learning trajectories with epistemic network analysis: An investigation of a novel analytic method for learning progressions in epistemic games. In National Council on Measurement in Education (pp. 1–39).
Clarke, J., & Dede, C. (2010). Assessment, technology, and change. Journal of Research in Teacher Education, 42(3), 309–328.
Clarke-Midura, J., Code, J., Dede, C., Mayrath, M., & Zap, N. (2012). Thinking outside the bubble: Virtual performance assessments for measuring complex learning. In M. C. Mayrath, J. Clarke-Midura, & D. Robinson (Eds.), Technology-based assessments for 21st Century skills: Theoretical and practical implications from modern research (pp. 125–148). Charlotte, NC: Information Age Publishers.
Clarke-Midura, J., Mayrath, M., & Dede, C. (2010). Measuring inquiry: New methods, promises & challenges. Library, 2, 89–92.
Feyerabend, P. (1970). Problems of empiricism. Cambridge: Cambridge University Press.
Gee, J. P. (2007). Learning and Identity: What does it mean to be a Half-Elf? What Video Games Have to Teach Us About Learning and Literacy, 45–69.
Gibson, D., & Clarke-Midura, J. (in press). Some Psychometric and Design Implications of Game-Based Learning Analytics. In D. Ifenthaler, J. Spector, P. Isaias, & D. Sampson (Eds.), E-Learning Systems, Environments and Approaches: Theory and Implementation. London: Springer.
Gibson, D., & Jakl, P. (2013). Data challenges of leveraging a simulation to assess learning. West Lake Village, CA. Retrieved from http://www.curveshift.com/images/Gibson_Jakl_data_challenges.pdf
Gibson, D., & Knezek, G. (2011). Game changers for teacher education. In P. Mishra, & M. Koehler (Eds.), Proceedings of Society for Information Technology & Teacher Education International Conference 2011 (pp. 929–942). Chesapeake, VA: AACE.
Gigerenzer, G., Todd, P., & ABC Research Group. (1999). Simple heuristics that make us smart (p. 416). Oxford: Oxford University Press.
Holland, J. (1995). Hidden order: How adaptation builds complexity. Cambridge, MA: Helix Books/Perseus Books.
Holland, J., Holyoak, K., Nisbett, R., & Thagard, P. (1986). Induction: Processes of inference, learning, and discovery. Cambridge, MA: MIT Press.
IBM. (n.d.). Big data. Retrieved from http://www-01.ibm.com/software/au/data/bigdata/
Koch, M. J., & DeLuca, C. (2012). Rethinking validation in complex high-stakes assessment contexts. Assessment in Education: Principles, Policy & Practice, 19(1), 99–116.
Lincoln, Y. S., Guba, E. G., Lincoln, E., & Guba, Y. (1985). Naturalistic inquiry (p. 416). Newbury Park, CA: Sage Publications.
Mayrath, M., Clarke-Midura, J., & Robinson, D. (2012). Introduction to technology-based assessments for 21st century skills. In M. Mayrath, J. Clarke-Midura, D. Robinson, & G. Schraw (Eds.), Technology-based assessments for 21st century skills: Theoretical and practical implications from modern research (p. 386). Charlotte, NC: Information Age Publishers.
Mislevy, R., Behrens, J., Dicerbo, K., Frezzo, D., & West, P. (2012). Three things game designers need to know about assessment. In D. Ifenthaler, D. Eseryel, & X. Ge (Eds.), Assessment in game-based learning: Foundations, innovations and perspectives (pp. 59–81). New York: Springer Science & Business Media B.V.
Nicolis, G. (1989). Exploring complexity: An introduction. New York: WH Freeman.
Pirnay-Dummer, P., Ifenthaler, D., & Spector, M. (2010). Highly integrated model assessment technology and tools. Educational Technology Research and Development, 58(1), 3–18.
Quellmalz, E., Timms, M., Buckley, B., Davenport, J., Loveland, M., & Silberglitt, M. (2012). 21st century dynamic assessment. In M. Mayrath, J. Clarke-Midura, & D. Robinson (Eds.), Technology-based assessments for 21st Century skills: Theoretical and practical implications from modern research (pp. 55–90). Charlotte, NC: Information Age Publishers.
Reimer, T., & Rieskamp, J. (2006, November). Fast and frugal heuristics. Encyclopedia of Social Psychology. doi:10.1016/j.psychsport.2006.06.002
Rupp, A., Gushta, M., Mislevy, R., & Shaffer, D. (2010). Evidence-centered design of epistemic games: Measurement principles for complex learning environments. Journal of Technology, Learning, and Assessment, 8(4), 1–45.
Schmidt, M., & Lipson, H. (2009). Symbolic regression of implicit equations. Genetic Programming Theory and Practice (Vol. 7 (Chap 5), pp. 73–85).
Shaffer, D. (2007). Epistemic games. Innovate, 1(6). Retrieved from http://innovateonline.info/pdf/vol1_issue6/Epistemic_Games.pdf.
Shaffer, D., Hatfield, D., Svarovsky, G., Nash, P., Nulty, A., Bagley, E. et al. (2009). Epistemic network analysis: A prototype for 21st-century assessment of learning. International Journal of Learning and Media, 1(2), 33–53. Retrieved from http://www.mitpressjournals.org/doi/abs/10.1162/ijlm.2009.0013
Sharkasi, N. (2010). The doctor will be you now: A case study on medical ethics and role. In K. Schrier & D. Gibson (Eds.), Ethics and game design (pp. 275–290). Hershey, PA: IGI Global.
Stecher, B. (2010). Performance assessment in an era of standards-based educational accountability (p. 46). Standford, CA: Stanford University.
Sugihara, G., May, R., Ye, H., Hsieh, C., Deyle, E., Fogarty, M., et al. (2012). Detecting causality in complex ecosystems. Science, 338(6106), 496–500. doi:10.1126/science.1227079.
Tobias, S., & Fletcher, J. D. (2011). Computer games and instruction. Charlotte, NC: Information Age Publishing.
USDOE. (2010). Learning powered by technology: Transforming American Education. Educational Technology (p. 88).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Gibson, D., Jakl, P. (2015). Theoretical Considerations for Game-Based e-Learning Analytics. In: Reiners, T., Wood, L. (eds) Gamification in Education and Business. Springer, Cham. https://doi.org/10.1007/978-3-319-10208-5_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-10208-5_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10207-8
Online ISBN: 978-3-319-10208-5
eBook Packages: Business and EconomicsEconomics and Finance (R0)