Boothe Jr., Maurice; Ochoa, Xavier; Gagnon, David; Swanson, Luke; Harpsteak, Erik
Game-Based Learning Analytics
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Gagnon, David J.
Open Game Data: An infrastructure for Learning Analytics Research
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Gagnon, David J.
Using analytics based on large audiences to develop real-time classifiers and interventions for a science exploration game
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Johansson, Annie M.; Savi, Alexander O.; Hofman, Abe D.
What Can Educational Games Tell Us About Learner Engagement?
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Lee, Yeeun; Lee, Jeongmin
Investigating Conversational Patterns with Generative AI NPCs in Role-Play for Elementary Students’ Social and Emotional Learning
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Santilario-Berthilier, Julio; Calvo-Morata, Antonio; Martinez-Ortiz, Ivan; Spaniol, Marc; Fernandez-Manjon, Baltasar
SIMVA: Serious Games Learning Analytics based on Standards and Open Code
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Ye, Lilian; Savi, Alexander O.; Hofman, Abe D.
Assessing Reliability in Game-Based Adaptive Learning Systems with A/A Tests
Game-Based Learning Analytics (GBLA) is an emerging field that combines game design principles with learning analytics to create personalized, engaging, and data-driven educational experiences. Despite significant contributions in this space, there is a lack of structured collaboration within the learning analytics community. This workshop aims to address that gap by bringing together researchers and practitioners interested in the intersection of games and learning analytics. Through this half-day workshop, we aim to formalize a community around GBLA, set foundational principles, and coordinate initial scholarly contributions while laying the groundwork to establish a special interest group.
Keynote Presentation: Open with a foundational talk by a leading expert to explore the relationship between game-based learning and learning analytics.
Community Micro-Talks: Participants will deliver brief “micro-talks” to share their prior work with an emphasis on challenges that this community could consider.
Establishing Principles of GBLA: The group will engage in a collaborative brainstorm to define and synthesize guiding principles for game-based learning analytics.
Identifying and Prioritizing Issues: The group will discuss and identify challenges in the field and prioritize them for future scholarly contributions.
Reflection and Coordination: Feedback collection and coordination of future contributions, including discussions on scholarly outputs.
Games are a rich medium with potential to facilitate and enhance students’ learning (Plass et al., 2013). Researchers in game design have coined the term “serious” or “educational” games for these games that are intentionally built to help students acquire or practice a given knowledge or skill (Loh, 2015). The development of these types of games as learning environments has evolved into an active area of research known as Game-Based Learning or GBL (Plass et al., 2020). GBL has shown that serious games utilize successful game design principles to generate personalized, engaging, and deep learning experiences (Gee, 2006) and may help students achieve better learning outcomes compared to conventional ways of learning (Mayer, 2014; Mayer, 2019).
There have been numerous games-related contributions in the field of learning analytics. In the most recent International Learning Analytics and Knowledge Conference (LAK), there were three accepted research papers directly related to learning analytics in game contexts (Mangaroska et al., 2024; Vanacore et al., 2024; Wang et al., 2024) as well as four more accepted contributions in the corresponding companion proceedings (Gamper & Schroeder, 2024; Hlosta et al., 2024; Li et al., 2024; Poquet et al., 2024). This combined with a special issue in the Journal of Learning Analytics titled "Analytics for Game Based Learning" (Kim et al., 2022) indicates a significant presence of contributions that combine learning analytics with game-based learning. Despite this, there is a lack of collaboration related to games in the learning analytics community.
There is an opportunity for a special interest group in SoLAR to facilitate connections and collaboration on the unique challenges that emerge when applying learning analytics to educational game contexts such as parsing out learning mechanics data from game mechanics data, navigating the granularity and density of game log data, and the ethics of collecting protected information from playful experiences. This prospective group will collaborate with other related groups such as the Open Game Data Initiative on projects that facilitate engaging with and analyzing the data from these types of games (eg. Gagnon et al., 2019; Gagnon et al., 2022; Liu et al., 2023; Scianna and Kim, 2024; Swanson et al., 2022). This group will also address the design of dashboards, feedback systems, pedagogical agents, narratives and more such that it facilitates the capture and analysis of learning analytics in games. There is also a chance to consider the different stakeholders that are part of the process in which game-based learning analytics are being designed (Boothe Jr. et al., in press).
Maurice Boothe Jr., New York University
Xavier Ochoa, New York University
David Gagnon, University of Wisconsin - Madison
Luke Swanson, University of Wisconsin - Madison
Erik Harpstead, Carnegie Mellon University
December 4, 2024: Call for Papers Submission Deadline
December 20, 2024: Notification of Workshop Paper Acceptance
March 3, 2025: LAK 25 conference
Boothe Jr., M., Gopalakrishnan, M., Huynh, M., Wang, Y., & Ochoa, X. (in press). Game-Based Learning Analytics: Insights from an Integrated Design Process. In Serious Games: 10th Joint International Conference, JCSG 2024, New York, NY, USA, November 7-8, 2024, Proceedings.
Gagnon, D. J., Baker, R. S., Gagnon, S., Swanson, L., Spevacek, N., Andres, J., Harpstead, E., Scianna, J., Slater, S., & San Pedro, M. O. C. Z. (2022, September 18). Exploring players’ experience of humor and snark in a grade 3-6 history practices game. https://escholarship.org/uc/item/80r6r2jp
Gagnon, D. J., Harpstead, E., & Slater, S. (2019). Comparison of Off the Shelf Data Mining Methodologies in Educational Game Analytics. EDM (Workshops), 38–43. https://ceur-ws.org/Vol-2592/paper5.pdf
Gamper, P., & Schroeder, U. (2024). Exploring auto generated solution spaces of a serious game for introductory programing courses. Companion Proceedings 14th International Conference on Learning Analytics & Knowledge, 281–283. https://www.solaresearch.org/wp-content/uploads/2024/03/LAK24_CompanionProceedings.pdf
Gee, J. P. (2006). Are video games good for learning? Nordic Journal of Digital Literacy, 1(3), 172–183.
Hlosta, M., Moser, I., Winer, A., Geri, N., Ramnarain, U., & Westhuizen, C. V. der. (2024). Learning Analytics from Virtual Reality (LAVR). Companion Proceedings 14th International Conference on Learning Analytics & Knowledge, 382–385. https://www.solaresearch.org/wp-content/uploads/2024/03/LAK24_CompanionProceedings.pdf
Kim, Y. J., Valiente, J. A. R., Ifenthaler, D., Harpstead, E., & Rowe, E. (2022). Analytics for Game-Based Learning. Journal of Learning Analytics, 9(3), Article 3. https://doi.org/10.18608/jla.2022.7929
Li, L., Feng, M., & Bang, H. J. (2024). A Data-Centric Personalized Learning Technology Solution to Accelerate Early Math Skills of Young Learners. Companion Proceedings 14th International Conference on Learning Analytics & Knowledge, 55–57. https://www.solaresearch.org/wp-content/uploads/2024/03/LAK24_CompanionProceedings.pdf
Liu, X., Slater, S., Andres, J. Ma. A. L., Swanson, L., Scianna, J., Gagnon, D., & Baker, R. S. (2023). Struggling to Detect Struggle in Students Playing a Science Exploration Game. Companion Proceedings of the Annual Symposium on Computer-Human Interaction in Play, 83–88. https://doi.org/10.1145/3573382.3616080
Loh, C. S., Sheng, Y., & Ifenthaler, D. (Eds.). (2015). Serious Games Analytics: Methodologies for Performance Measurement, Assessment, and Improvement. Springer International Publishing. http://link.springer.com/10.1007/978-3-319-05834-4
Mangaroska, K., Larssen, K., Amundsen, A., Vesin, B., & Giannakos, M. (2024). Understanding engagement through game learning analytics and design elements: Insights from a word game case study. Proceedings of the 14th Learning Analytics and Knowledge Conference, 305–315. https://doi.org/10.1145/3636555.3636885
Mayer, R. E. (Ed.). (2014). The Cambridge Handbook of Multimedia Learning (Second Edition). Cambridge University Press.
Mayer, R. E. (2019). Computer games in education. Annual Review of Psychology, 70(1), 531–549. https://doi.org/10.1146/annurev-psych-010418-102744
Plass, J. L., Mayer, R. E., & Homer, B. D. (2020). Handbook of game-based learning. MIT Press. http://ebookcentral.proquest.com/lib/nyulibrary-ebooks/detail.action?docID=6018189
Plass, J., O’Keefe, P., Homer, B., Case, J., Hayward, E., Stein, M., & Perlin, K. (2013). The Impact of Individual, Competitive, and Collaborative Mathematics Game Play on Learning, Performance, and Motivation. Journal of Educational Psychology, 105, 1050–1066. https://doi.org/10.1037/a0032688
Poquet, O., Dindar, M., Allen, L., Dever, D., & Elizabeth Cloude. (2024). Advancing Learning Analytics with Complex Dynamical Systems: Trends and Challenges in Non-Linear Modeling of Learning Data. Companion Proceedings 14th International Conference on Learning Analytics & Knowledge, 382–385. https://www.solaresearch.org/wp-content/uploads/2024/03/LAK24_CompanionProceedings.pdf
Scianna, J., & Kim, Y. J. (2024). Assessing Experimentation: Understanding Implications of Player Choices. https://repository.isls.org//handle/1/10760
Swanson, L., Gagnon, D., & Scianna, J. (2022, October 17). A Pilot Study on Teacher-Facing Real-Time Classroom Game Dashboards. arXiv.Org. https://arxiv.org/abs/2210.09427v1
Vanacore, K., Gurung, A., Sales, A., & Heffernan, N. T. (2024). The Effect of Assistance on Gamers: Assessing The Impact of On-Demand Hints & Feedback Availability on Learning for Students Who Game the System. Proceedings of the 14th Learning Analytics and Knowledge Conference, 462–472. https://doi.org/10.1145/3636555.3636904
Wang, K. D., Liu, H., DeLiema, D., Haber, N., & Salehi, S. (2024). Discovering Players’ Problem-Solving Behavioral Characteristics in a Puzzle Game through Sequence Mining. Proceedings of the 14th Learning Analytics and Knowledge Conference, 498–506. https://doi.org/10.1145/3636555.3636907