Cold Start Problem in Adaptive Fact Learning
Adaptive learning systems need some time to figure out how difficult individual items are for individual learners. During this 'cold start', the system can be poorly adapted to the learner. Given previously collected learning data from other learners/materials, we can try to predict individual difficulties, so that the system is better attuned to the learner from the start. We recently published a preprint showing that mitigating the cold start in this way can improve learning outcomes.
Using Adaptive Fact Learning in Education
Adaptive learning may work well in the lab, but how does it fare in the real world? By collecting data from high school and university students using adaptive learning as part of their curriculum, we can explore the value of model-based adaptive learning under uncontrolled conditions. Does it improve learning? Can we encourage effective study strategies? How can we give students and their teachers more insight into the learning progress?
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