GLITTER: An AI-assisted Platform for Material-Grounded Asynchronous Discussion in Flipped Learning
Weirui Peng, Yinuo Yang, Zheng Zhang, Toby Jia-Jun Li
Abstract
Flipped classrooms promote active learning by having students engage with materials independently before class, allowing in-class time for collaborative problem-solving. During this pre-class phase, asynchronous online discussions help students build knowledge and clarify concepts with peers. However, it remains difficult to engage with temporally dispersed peer contributions, connect discussions with static learning materials, and prepare for in-class sessions based on their self-learning outcome. Our formative study identified cognitive challenges students encounter, including navigation barriers, reflection gaps, and contribution difficulty and anxiety. We present Glitter, an AI-assisted discussion platform for pre-class learning in flipped classrooms. Glitter helps students identify posts with shared conceptual dimensions, scaffold knowledge integration through conceptual blending, and enhance metacognition via personalized reflection reports. A lab study within subjects (n = 12) demonstrates that Glitter improves discussion engagement, sparks new ideas, supports reflection, and increases preparedness for in-class activities.
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