Multimodal Multi-Task Stealth Assessment for Reflection-Enriched Game-Based Learning

Game-based learning environments enable effective and engaging learning experiences that can be dynamically tailored to students. There is growing interest in the role of reflection in supporting student learning in game-based learning environments. By prompting students to periodically stop and reflect on their learning processes, it is possible to gain insight into students’ perceptions of their knowledge and problem-solving progress, which can in turn inform adaptive scaffolding to improve student learning outcomes. Given the positive relationship between student reflection and learning, we investigate the benefits of jointly modeling post-test score and reflection depth using a multimodal, multitask stealth assessment framework. Specifically, we present a gated recurrent unit-based multi-task stealth assessment framework that takes as input multimodal data streams (e.g., game trace logs, pre-test data, natural language responses to in-game reflection prompts) to jointly predict post-test scores and written reflection depth scores. Evaluation results demonstrate that the multimodal multi-task model outperforms single-task neural models that utilize subsets of the modalities, as well as non-neural baselines such as random forest regressors. Our multi-task stealth assessment framework for measuring students’ content knowledge and reflection depth during game-based learning shows significant promise for supporting student learning and improved reflection.