In recent years, game-based learning has shown significant promise for creating engaging and effective learning experiences. Developing models that can predict whether students will struggle with mastering certain concepts could guide adaptive support to assist students with mastering those concepts. Game-based learning environments offer significant potential for unobtrusively assessing student learning without interfering with gameplay through stealth assessment. Prior work on stealth assessment has focused on a single machine learning technique such as dynamic Bayesian networks or long short-term memory networks; however, a single modeling technique often does not guarantee the best predictive performance for all concepts of interest. In this paper, we present a hybrid data-driven approach to stealth assessment for predicting students' mastery of concepts through interactions with a game-based learning environment for introductory genetics. Stealth assessment models utilize students' observed gameplay behaviors using challenge- and session-based features to predict students' learning outcomes on identified concepts. We present single-task and multi-task models for predicting students' mastery of concepts and the results suggest that the hybrid stealth assessment framework outperforms individual models and holds significant potential for predicting student competencies. [For the full proceedings, see ED607784.]