Learning analytics provides the education researcher with the opportunity to collect and analyse a wide range of data related to learners and their contexts for the purpose of improving learner engagement and outcomes. While much of the focus of learning analytics has been on learning management systems, game-based learning offers the possibility of gathering rich data using the programmatic features of a game engine. Any interaction by a learner with the game-based environment can be tracked, with contextual data recorded. This paper presents a model for collecting game-based learning data that shows from a practical perspective what kind of data (such as events and timings) to collect and how it can be analysed with the express purpose of improving learning experiences and outcomes. A proof of concept is presented based on the game-based learning of graph theory. Graph theory is a branch of mathematics used in many scientific disciplines, for example to model molecules, atomic structures and the evolution of species. A virtual reality-based game introduces students to the fundamentals of graph theory, for example vertices and edges, and engages them in active learning as they connect vertices according to rules presented to them. Each action is recorded in a database for analysis, including a detailed log of student progress through an exercise, recording when vertices are correctly or incorrectly connected and the varying pace of progression through an exercise. The paper discusses how the educator can quickly analyse data to identify and correct common mistakes made by students, for example those based around a misinterpretation of the rules or a difficulty with the game’s mechanics. The paper also discusses how building learning analytics into a game early in its development can be useful as a means of formative evaluation as prototypes are iteratively improved through early and frequent stakeholder engagement.
Keywords: learning analytics, game based learning, virtual reality, active learning, graph theory;
References
[1] L. Lockyer, E. Heathcote, and S. Dawson, “Informing pedagogical action: Aligning learning analytics with learning design,” American Behavioral Scientist, vol. 57, no. 10, pp. 1439–1459, 2013.
[2] G. Siemens, “Learning analytics: The emergence of a discipline,” American Behavioral Scientist, vol. 57, no. 10, pp. 1380–1400, 2013.
[3] De Gloria, F. Bellotti, and R. Berta, “Building a Comprehensive R&D Community on Serious Games,” Procedia Computer Science, vol. 15, pp. 1–3, Jan. 2012.
[4] F. Bellotti, R. Berta, and G. De, “Designing effective serious games: Opportunities and challenges for research,” International Journal of Emerging Technologies in Learning, vol. 5, no. SPECIAL ISSUE 2, pp. 22–35, 2010.
[5] D. B. Clark, B. C. Nelson, H.-Y. Chang, M. Martinez-Garza, K. Slack, and C. M. D’Angelo, “Exploring Newtonian mechanics in a conceptually-integrated digital game: Comparison of learning and affective outcomes for students in Taiwan and the United States,” Computers & Education, vol. 57, no. 3, pp. 2178–2195, Nov. 2011.
[6] D. H. Schunk, Learning Theories: An Educational Perspective. Pearson, 2012.
[7] L. W. Anderson and D. R. Krathwohl, A taxonomy for learning, teaching, and assessing: a revision of Bloom’s taxonomy of educational objectives. Longman, 2001.
[8] J. B. Biggs and K. F. Collis, Evaluating the Quality of Learning: The SOLO Taxonomy (Structure of the Observed Learning Outcome). Academic Press, 1982.
[9] M. Minović, M. Milovanović, U. Šošević, and M. Á. Conde González, “Visualisation of student learning model in serious games,” Computers in Human Behavior, vol. 47, pp. 98–107, Jun. 2015.
[10] Peterson, “Bringing ADDIE to life: Instructional design at its best,” Journal of Educational Multimedia and Hypermedia, vol. 12, no. 3, pp. 227–241, 2003.
[11] K. L. Gustafson and R. M. Branch, “What is instructional design?” Trends and issues in instructional design and technology, pp. 16–25, 2002.
[12] R. Lopes and R. Bidarra, “Adaptivity Challenges in Games and Simulations: A Survey,” IEEE Transactions on Computational Intelligence and AI in Games, vol. 3, no. 2, pp. 85–99, Jun. 2011.
[13] Rasim, A. Z. Langi, Munir, and Y. Rosmansyah, “A survey on adaptive engine technology for serious games,” in AIP Conference Proceedings, 2016, vol. 1708.
[14] M. Csikszentmihalyi, Beyond Boredom and Anxiety. Jossey-Bass Publishers, 1975.
[15] A.W. Chickering and Z. F. Gamson, “Seven Principles for Good Practice in Undergraduate Education,” AAHE Bulletin, Mar. 1987.
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