Abstract
Games can be rich environments for learning and can elicit evidence of students’ conceptual understanding and inquiry processes. Illuminating students’ content-specific gameplay decisions, or methods of completing game tasks related to a certain domain, requires a context that is open-ended enough for students to make choices that demonstrate their thinking. Doing this also requires rich log data and methods of Game Learning Analytics (GLA) that are granular enough to look at the specific choices most relevant to that context and domain. This paper presents research done on student exploration of high school level Mendelian genetics in a multiplayer online game called The Radix Endeavor. The study uses three approaches to identify content-specific gameplay decisions and distinguish players utilizing different methods, looking at actions and tool use, play patterns and player types, and tool input patterns. In the context of the selected game quest, the three approaches were found to yield insights into different ways that students complete tasks in genetics, suggesting the potential for a set of more generalized guiding questions in the GLA field that could be adopted by learning games designers and data scientists to convey information about content-specific gameplay decisions in learning games.
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References
Alonso-Fernández, C., Calvo, A., Freire, M., Martinez-Ortiz, I., & Fernandez-Manjon, B. (2017). Systematizing game learning analytics for serious games. Paper presented at the—2017 IEEE Global Engineering Education Conference (EDUCON) (pp. 1111–1118). https://doi.org/10.1109/EDUCON.2017.7942988
Alonso-Fernández, C., Calvo-Morata, A., Freire, M., Martínez-Ortiz, I., & Fernández-Manjón, B. (2019). Applications of data science to game learning analytics data: A systematic literature review. Computers and Education, 141, 103612. https://doi.org/10.1016/j.compedu.2019.103612.
Awang-Kanak, F., Masnoddin, M., Matawali, A., Daud, M. A., & Jumat, N. R. (2016). Difficulties experience by science foundation students on basic mendelian genetics topic: A preliminary study. Transactions on Science and Technology, 3(1–2), 283–290.
Aznar, M. M., & Orcajo, T. I. (2005). Solving problems in genetics. International Journal of Science Education, 27(1), 101–121. https://doi.org/10.1080/09500690410001673801.
Bado, N. (2019). Game-based learning pedagogy: A review of the literature. Interactive Learning Environments. https://doi.org/10.1080/10494820.2019.1683587.
Bahar, M., Johnstone, A. H., & Hansell, M. H. (1999). Revisiting learning difficulties in biology. Journal of Biological Education, 33(2), 84–86. https://doi.org/10.1080/00219266.1999.9655648.
Butler, E., & Banerjee, R. (2014). Visualizing progressions for education and game design (white paper). Retrieved from https://www.ericbutler.net/assets/papers/unpub2014_visualization.pdf.
Chaudy, Y., Connolly, T., & Hainey, T. (2014). Learning analytics in serious games: A review of the literature. Paper presented at the ECAET 2014: European Conference in the Applications of Enabling Technologies. United Kingdom
Cheng, M.-T. (2020). The radix endeavor dataset. figshare. https://doi.org/10.6084/m9.figshare.12442469.v1.
Chu, Y., & Reid, N. (2012). Genetics at school level: Addressing the difficulties. Research in Science and Technological Education, 30(3), 285–309. https://doi.org/10.1080/02635143.2012.732059.
Collins, A., & Stewart, J. H. (1989). The knowledge structure of mendelian genetics. The American Biology Teacher, 51(3), 143–149.
Cooper, S., Khatib, F., Treuille, A., Barbero, J., Lee, J., Beenen, M., & players, F. (2010). Predicting protein structures with a multiplayer online game. Nature, 466(7307), 756–760. https://doi.org/10.1038/nature09304.
Duncan, R. G., & Reiser, B. J. (2007). Reasoning across ontologically distinct levels: Students’ understandings of molecular genetics. Journal of Research in Science Teaching, 44(7), 938–959. https://doi.org/10.1002/tea.20186.
Etobro, A. B., & Banjoko, S. O. (2017). Misconceptions of genetics concepts among pre-service teachers. Global Journal of Educational Research, 16(2), 121–128.
Freire, M., Serrano-Laguna, Á, Manero, B., Martínez-Ortiz, I., Moreno-Ger, P., & Fernández-Manjón, B. (2016). Game learning analytics: Learning analytics for serious games. Learning, design, and technology (pp. 1–29). AG, Switzerland: Springer Nature.
Institute of play. (2013). Playtime online 21: Digging into data with SimCityEDU. Retrieved from https://www.youtube.com/watch?v=Y5_7Y7wKE6A&feature=share.
Johnstone, A. H. (1991). Why is science difficult to learn? things are seldom what they seem. Journal of Computer Assisted Learning, 7(2), 75–83.
Juul, J. (2011). Half-real: Video games between real rules and fictional worlds. . Cambridge: MIT Press.
Karagoz, M., & Cakir, M. (2011). Problem solving in genetics: Conceptual and procedural difficulties. Educational Sciences Theory and Practice, 11(3), 1668–1674.
Ke, F. (2016). Designing and integrating purposeful learning in game play: A systematic review. Educational Technology Research and Development, 64(2), 219–244. https://doi.org/10.1007/s11423-015-9418-1.
Ketelhut, D. J. (2007). The impact of student self-efficacy on scientific inquiry skills: An exploratory investigation in river city, a multi-user virtual environment. Journal of Science Education and Technology, 16(1), 99–111. https://doi.org/10.1007/s10956-006-9038-y.
Klopfer, E., Haas, J., Osterweil, S., & Rosenheck, L. (2018). Resonant games. Cambridge: MIT Press.
Knippels, M. P. J., & Waarlo, A. J. (2018). Development, uptake, and wider applicability of the yo-yo strategy in biology education research: A reappraisal. Education Sciences, 8(3), 129.
Knippels, M. P. J., Waarlo, A. J., & Boersma, K. T. (2005). Design criteria for learning and teaching genetics. Journal of Biological Education, 39(3), 108–112. https://doi.org/10.1080/00219266.2005.9655976.
Lewis, J., & Kattmann, U. (2004). Traits, genes, particles and information: Re-visiting students’ understandings of genetics. International Journal of Science Education, 26(2), 195–206. https://doi.org/10.1080/0950069032000072782.
LiuKangLiuZouHodson, M. J. S. W. J. (2017). Learning analytics as an assessment tool in serious games: A review of literature. In M. A. MaOikonomou (Ed.), Serious games and edutainment applications. (pp. 537–563). Springer.
Loh, C. S., Sheng, Y., & Ifenthaler, D. (2015a). Serious games analytics: Theoretical framework. In C. S. Loh, Y. Sheng, & D. Ifenthaler (Eds.), Serious game analytics: Methodologies for performance measurement, assessment, and improvement Springer.
Loh, C. S., Sheng, Y., & Ifenthaler, D. (2015b). Serious games analytics: Methodologies for performance measurement, assessment, and improvement. . Springer.
NHGRI. (n.d.). Phenotype. Retrieved from https://www.genome.gov/genetics-glossary/Phenotype
Orcajo, T. I., & Aznar, M. M. (2005). Solving problems in genetics II: Conceptual restructuring. International Journal of Science Education. 27(12), 1495–1519. https://doi.org/10.1080/09500690500186584.
O'Rourke, E., Butler, E., Liu, Y., Ballweber, C., & Popovic, Z. (2013). The effects of age on player behavior in educational games. Paper presented at the The 8th International Conference on the Foundations of Digital Games (pp. 158–165)
Owen, V. E., & Baker, R. S. (2019). Learning analytics for games. In J. L. Plass, R. E. Mayer, & B. D. Homer (Eds.), Handbook of game-based learning. (pp. 513–536). MIT.
Qian, M., & Clark, K. R. (2016). Game-based learning and 21st century skills: A review of recent research. Computers in Human Behavior, 63, 50–58. https://doi.org/10.1016/j.chb.2016.05.023.
Rosenheck, L., Gordon-Messer, S., Clarke-Midura, J., & Klopfer, E. (2016). Design and implementation of an MMO: Approaches to support inquiry learning with games. In D. Russell, & J. M. Laffey (Eds.), Handbook of research on gaming trends in P-12 education (pp. 33-54). Hershey, PA: IGI Global
Rowe, E., Asbell-Clarke, J., & Baker, R. S. (2015). Serious games analytics to measure implicit science learning. In C. S. Loh, Y. Sheng & D. Ifenthaler (Eds.), Serious games analytics: Methodologies for performance measurement, assessment, and improvement (pp. 343-360). Cham: Springer International Publishing. doi:https://doi.org/10.1007/978-3-319-05834-4_15 Retrieved from https://doi.org/https://doi.org/10.1007/978-3-319-05834-4_15
Shaw, K. R. M., Van Horne, K., Zhang, H., & Boughman, J. (2008). Essay contest reveals misconceptions of high school students in genetics content. Genetics, 178(3), 1157–1168. https://doi.org/10.1534/genetics.107.084194.
Squire, K. D., & Jan, M. (2007). Mad city mystery: Developing scientific argumentation skills with a place-based augmented reality game on handheld computers. Journal of Science Education and Technology, 16(1), 5–29.
Steinkuehler, C. A., & Duncan, S. (2008). Scientific habits of mind in virtual worlds. Journal of Science Education and Technology, 17(6), 530–543. https://doi.org/10.1007/s10956-008-9120-8.
Tsui, C., & Treagust, D. (2010). Evaluating secondary students’ scientific reasoning in genetics using a two-tier diagnostic instrument. International Journal of Science Education, 32(8), 1073–1098. https://doi.org/10.1080/09500690902951429.
Wilensky, U., & Resnick, M. (1999). Thinking in levels: A dynamic systems approach to making sense of the world. Journal of Science Education and Technology, 8(1), 3–19.
Acknowledgement
We would like to thank the members of the Radix team at MIT who made this game and research possible, including Angie Tung, Susannah Gordon-Messer, and Jody Clarke-Midura.
Funding
This research was funded by the Bill and Melinda Gates Foundation and the Ministry of Science and Technology (MOST), Taiwan, under grant contract no. 104-2918-I-018-008 and 105-2511-S-018-015-MY5.
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Rosenheck, L., Cheng, MT., Lin, CY. et al. Approaches to illuminate content-specific gameplay decisions using open-ended game data. Education Tech Research Dev 69, 1135–1154 (2021). https://doi.org/10.1007/s11423-021-09989-0
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DOI: https://doi.org/10.1007/s11423-021-09989-0