Game Learning Analytics: Learning Analytics for Serious Games
Video games have become one of the largest entertainment industries, and their power to capture the attention of players worldwide soon prompted the idea of using games to improve education. However, these educational games, commonly referred to as serious games, face different challenges when brought into the classroom, ranging from pragmatic issues (e.g., a high development cost) to deeper educational issues, including a lack of understanding of how the students interact with the games and how the learning process actually occurs. This chapter explores the potential of data-driven approaches to improve the practical applicability of serious games. Existing work done by the entertainment and learning industries helps to build a conceptual model of the tasks required to analyze player interactions in serious games (game learning analytics or GLA). The chapter also describes the main ongoing initiatives to create reference GLA infrastructures and their connection to new emerging specifications from the educational technology field. Finally, it explores how this data-driven GLA will help in the development of a new generation of more effective educational games and new business models that will support their expansion. This results in additional ethical implications, which are discussed at the end of the chapter.
KeywordsSerious games Game learning analytics Learning analytics Game analytics Educational standards
This work has been partially funded by Regional Government of Madrid (eMadrid S2013/ICE-2715), by the Complutense University of Madrid (GR3/14-921340), by the Ministry of Education (TIN2013-46149-C2-1-R and FPU12/04310), by the RIURE Network (CYTED 513RT0471), and by the European Commission (RAGE H2020-ICT-2014-1-644187, BEACONING H2020-ICT-2015-687676).
- Advanced Distributed Learning, U. S. D. O. D. (2014). Training and learning architecture (TLA): Experience API (xAPI) https://github.com/adlnet/xAPI-Spec/blob/master/xAPI.md. Accessed 10 May 2016.
- Advanced Distributed Learning, U. S. D. O. D. (2015). SCORM to TLA roadmap http://adlnet.github.io/SCORM-to-TLA-Roadmap/. Accessed 10 May 2016.
- Aldrich, C. (2004). Simulations and the future of learning: An innovative (and perhaps revolutionary) approach to e-learning. San Francisco, CA: Pfeiffer.Google Scholar
- Arnold, K. E., & Pistilli, M. D. (2012). Course signals at purdue: Using learning analytics to increase student success. In Proceedings of the 2nd international conference on learning analytics & knowledge. New York, NY: ACM.Google Scholar
- Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In Learning analytics (pp. 61–75). New York, NY: Springer.Google Scholar
- Barfield, W., & Caudell, T. (2001). Fundamentals of wearable computers and augmented reality. Mahwah, NJ: CRC Press.Google Scholar
- Bellotti, F., Kapralos, B., Lee, K., Moreno-Ger, P., & Berta, R. (2013). Assessment in and of serious games: An overview. Advances in Human-Computer Interaction, 2013, 1–11. doi:10.1155/2013/136864 (Article ID 136864).Google Scholar
- Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. Draft paper submitted to the Office of Educational Technology, U.S. Department of Education.Google Scholar
- del Blanco, Á., Marchiori, E. J., & Fernández-Manjón, B. (2010). Adventure games and language learning. In First international workshop on technological innovation for specialized linguistic domains: Theoretical and methodological perspectives (TISLID 10) (pp. 1–9).Google Scholar
- Elias, T. (2011). Learning analytics: Definitions processes and potential. http://learninganalytics.net/syllabus.html.
- Entertainment Software Association, (ESA). (2015). 2015 essential facts about the computer and video game industry http://www.theesa.com/article/2015-essential-facts-report-finds-nearly-half-u-s-plays-video-games/.
- Freire, M., del Blanco, Á., & Fernández-Manjón, B. (2014). Serious games as edX MOOC activities. In Proceedings of the 2014 I.E. Global Engineering Education Conference (EDUCON) (pp. 867–871).Google Scholar
- Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Educational Technology & Society, 15(3), 42–57.Google Scholar
- Hainey, T., Westera, W., Baxter, G., Connolly, T. M., Beeby, R. B., & Soflano, M. (2013). Students’ attitudes toward playing games and using games in education: Comparing Scotland and the Netherlands. Computers & Education, 69, 474–484.Google Scholar
- Hauge, J. B., Berta, R., Fiucci, G., Manjon, B. F., Padron-Napoles, C., Westra, W., & Nadolski, R. (2014). Implications of learning analytics for serious game design. In 2014 I.E. 14th International Conference on Advanced Learning Technologies (pp. 230–232). IEEE. doi:10.1109/ICALT.2014.73Google Scholar
- IMS Global Consortium. (2015a). IMS calliper analytics.Google Scholar
- IMS Global Consortium. (2015b). IMS global learning tools interoperability implementation guide v1.2 final. https://www.imsglobal.org/activity/caliperram.
- ISFE. (2014). GameTrack European digest: quarter 3 2014. http://www.isfe.eu/sites/isfe.eu/files/attachments/gametrack_european_digest_q3-14.pdf. Accessed 10 May 2016.
- Koster, R. (2004). Theory of fun for game design. Scottsdale, AZ: Paraglyph.Google Scholar
- Loh, C. S., Sheng, Y., & Ifenthaler, D. (2015). Serious games analytics: Theoretical framework. In Serious games analytics (pp. 3–29). Cham, Germany: Springer.Google Scholar
- Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. Educause Review, 46(5), 31–40.Google Scholar
- Manero, B., Torrente, J., Freire, M., & Fernández-Manjón, B. (2016). An instrument to build a gamer clustering framework according to gaming preferences and habits. Computers in Human Behavior (accepted). doi:10.1016/j.chb.2016.03.085.Google Scholar
- Michael, D., & Chen, S. (2006). Serious games: Games that educate, train, and inform. Boston, MA: Thomson.Google Scholar
- Owen, V. E., Ramirez, D., Salmon, A., & Halverson, R. (2014). Capturing learner trajectories in educational games through ADAGE (assessment data aggregator for game environments): A click-stream data framework for assessment of learning in play. American Educational Research Association Annual Meeting, pp. 1–7.Google Scholar
- Papastergiou, M., & Solomonidou, C. (2005). Gender issues in Internet access and favourite Internet activities among Greek high school pupils inside and outside school. Computers & Education, 44(4), 377–393. doi:10.1016/j.compedu.2004.04.002. (http://adageapi.org/publications.html).
- Polsani, P. (2003). Use and abuse of reusable learning objects. Journal of Digital Information, 3(4).Google Scholar
- Prinsloo, P., Slade, S., Hall, F., & Hill, B. (2013). An evaluation of policy frameworks for addressing ethical considerations in learning analytics. In International conference on learning analytics and knowledge (pp. 240–244). doi:10.1145/2460296.2460344.Google Scholar
- Saveski, G. L., Westera, W., Yuan, L., Hollins, P., Fernández-Manjón, B., Moreno-Ger, P., & Stefanov, K. (2015). What serious game studios want from ICT research: Identifying developers’ needs. In GaLA Conference 2015. Rome.Google Scholar
- Sclater, N. (2014). Code of practice for learning analytics. JISC. Available online at https://www.jisc.ac.uk/guides/code-of-practice-for-learning-analytics.
- Seif El-Nasr, M., Drachen, A., & Canossa, A. (Eds.). (2013). Game analytics – maximizing the value of player data. London, UK: Springer.Google Scholar
- Shneiderman, B. (1996). The eyes have it: A task by data type taxonomy for information visualizations. In Proceedings 1996 I.E. Symposium on Visual Languages (pp. 336–343). IEEE Computer Society Press. doi:10.1109/VL.1996.545307. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=545307
- Shute, V. J. (2011). Stealth assessment in computer-based games to support learning. Computer Games and Instruction, 55(2), 503–524.Google Scholar
- Shute, V. J., & Spector, J. M. (2008). SCORM 2.0 white paper: Stealth assessment in virtual worlds. Available online at http://scorm.com/tincanoverview/the-letsi-scorm-2-0-white-papers/.
- Shute, V. J., Ventura, M., Bauer, M., & Zapata-Rivera, D. (2009). Melding the power of serious games and embedded assessment to monitor and foster learning. In Serious games: Mechanisms and effects (pp. 295–321). New York, NY: Routledge.Google Scholar
- Siemens, G., Dawson, S., & Lynch, G. (2013). Improving the quality and productivity of the higher education sector. Policy and strategy for systems-level deployment of learning analytics. Canberra, Australia: Office of Learning and Teaching, Australian Government.Google Scholar
- Takeuchi, L. M., & Vaala, S. (2014). Level up learning: A national survey on teaching with digital games. New York, NY: The Joan Ganz Cooney Center at Sesame Workshop.Google Scholar
- Torrance, H. (2007). Assessment as learning? How the use of explicit learning objectives, assessment criteria and feedback in post-secondary education and training can come to dominate learning. Assessment in Education: Principles, Policy & Practice, 14(3), 281–294. doi:10.1080/09695940701591867.CrossRefGoogle Scholar
- Torrente, J., del Blanco, Á., Serrano-Laguna, Á., Vallejo-Pinto, J., Moreno-Ger, P., & Fernández-Manjón, B. (2014). Towards a low cost adaptation of educational games for people with disabilities. Computer Science and Information Systems, 11(1), 369–391. doi:10.2298/CSIS121209013T.CrossRefGoogle Scholar
- Torrente, J., Moreno-Ger, P., Martínez-Ortiz, I., & Fernández-Manjón, B. (2009). Integration and deployment of educational games in e-learning environments: The learning object model meets educational gaming. Educational Technology & Society, 12(4), 359–371.Google Scholar
- Van Eck, R. (2006). Digital game-based learning: It’s not just the digital natives who are restless. EDUCAUSE Review, 41(2), 16–30.Google Scholar