MediaWatch: Game-Based Learning for Critical Graph Literacy
Funding period: 09/2023 - Present
MediaWatch is a browser-based serious game developed at Tampere University and grounded in psychological inoculation theory. MediaWatch is designed to build resistance to data-driven misinformation by training critical graph-reading skills.
Learners work through a sequence of misleading and non-misleading graphs, identifying visual manipulation techniques, and receive one of several feedback types (corrective, elaborative, or attribution-based) on their responses. My work on this project examines how task structure, specifically graph type and feedback design, shapes the concurrent emotional, motivational, and cognitive dynamics that unfold as learners build graph literacy, extending Control-Value Theory (Pekrun, 2006) and Cognitive Load Theory into an applied media-literacy context.
This line of work is conducted in collaboration with Kristian Kiili and Manuel Ninaus, and draws on data collected by Eva Kormann and Nadine Schmerer for their theses in Austrian high-school and university contexts. Data collection has progressed across two institutional sites. The first phase took place at the University of Graz in Austria, where Eva Kormann’s thesis data informed the GALA 2024 paper and the GALA 2026 manuscript currently in preparation; that site is now complete. An ongoing phase is underway at Michigan State University with higher-education students in U.S. contexts, and the MediaWatch platform itself is being migrated to Microsoft Azure to support this U.S.-based deployment.
Two pre/post intervention studies anchor this project:
- University of Graz in Austria: A sample of 40 undergraduates completed a 2 (graph type: misleading vs. non-misleading) x 3 (feedback: control, elaborative, attribution-based) within-subjects design was used to examine how feedback type relates to affective responses, cognition, and learning outcomes. Multiple data channels were gathered in a controlled laboratory setting, including eye movements, facial expressions, and physiological indicators (e.g., heart rate, skin conductance).
- Secondary Classrooms in Styria, Austria: A sample of 125 secondary-school students completed a between-subjects version of this design, randomly assigned at the outset to a control, elaborative, or attribution-based feedback condition. Repeated surveys and logfile interactions were gathered during the interventions, which were correlated with pre/post-test assessment scores.
Present Study:
- Michigan State University: A sample of 100 undergraduate students are being recruited to engage in a between-subjects pre/post design using the same version of the design (2 x 3). Multiple data channels will be gathered to estimate cognitive, affective, and motivational processes in-situ with a larger sample.
The findings of this work will provide insight into the role of affective processes on cognition and learning outcomes with game-based learning environments
- Data management plan aligned with FAIR principles and the Academy of Finland Data Management guidelines can be found here
- Project visibility here
Publications on this Work:
- Siuko, J., Cloude, E., & Kiili, K. (2024). Improving critical graph-reading skills: The potential might lie in game-based learning. CEUR Workshop Proceedings, 3669, 79–87.
- Cloude, E. B., Kormann, E., Steiner, M., Lindstedt, A., Kiili, K., & Ninaus, M. (2024). The role of feedback type and task performance on concurrent emotions and interest during game-based learning. In F. Bellotti, M. Ninaus, & P. Dondio (Eds.), Games and Learning Alliance (Lecture Notes in Computer Science, Vol. 15348, pp. 101–111). Springer, Cham. LINK TO PDF.