1. Enhancing Stealth Assessment in Game-Based Learning Environments with Generative Zero-Shot Learning
- Author
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Henderson, Nathan, Acosta, Halim, Min, Wookhee, Mott, Bradford, Lord, Trudi, Reichsman, Frieda, Dorsey, Chad, Wiebe, Eric, and Lester, James
- Abstract
Stealth assessment in game-based learning environments has demonstrated significant promise for predicting student competencies and learning outcomes through unobtrusive data capture of student gameplay interactions. However, as machine learning techniques for student competency modeling have increased in complexity, the need for substantial data to induce such models has likewise increased. This raises scalability concerns, as capturing game interaction data is often logistically challenging yet necessary for supervised learning of student competency models. The generalizability of such models also poses significant challenges, and the performance of these models when applied to new domains or gameplay scenarios often suffers. To address these issues, we introduce a zero-shot learning approach that utilizes conditional generative modeling to generalize stealth assessment models for new domains in which prior data and competency labels may not exist. We evaluate our approach using observed student interactions with a game-based learning environment for introductory genetics. We use a conditional generative model to map latent embeddings of genetics concepts and student competencies to student gameplay patterns, enabling the generation of synthetic gameplay data associated with concepts and game levels that have not been previously introduced. Results indicate the zero-shot learning approach enhances the performance of the competency models on unseen game levels and concepts, pointing to more generalizable stealth assessment models and improved prediction of student competencies. [For the full proceedings, see ED623995.]
- Published
- 2022