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- W4313593684 abstract "Knowledge tracing (KT) refers to the issue of predicting learners’ knowledge states based on their learning history and is the core technology for computer-assisted adaptive learning. The latest KT research has improved prediction performance by exploring the relationship between concepts and questions. However, these models have their parameters fixed after training. Without fully or partially re-training, they can only predict all learners’ performance at all time steps using the same set of parameters, making them challenging to adapt to learners’ personal development and individual differences. To address these problems, we take inspiration from synaptic plasticity, the primary neural mechanism conferring biological brains with lifelong learning capabilities, and propose the plastic gating network (PGN) to adapt to variation in learners’ cognition over time and across individuals. At the core of the proposed model are plastic weights, which can constantly evolve after training according to the ongoing inputs and outputs. We adopt plastic weights in calculating gates and cell input in recurrent units, thus allowing the model to develop time- and individual-specific parameters that adapt to learners’ personal development and individual differences after training. Experiments on real-world datasets show that the proposed PGN outperforms baseline methods in predicting the future performance of learners. Crucially, the PGN exhibits effective adaptation to learners’ personal development and individual differences." @default.
- W4313593684 created "2023-01-06" @default.
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- W4313593684 date "2023-05-01" @default.
- W4313593684 modified "2023-10-01" @default.
- W4313593684 title "Plastic gating network: Adapting to personal development and individual differences in knowledge tracing" @default.
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- W4313593684 doi "https://doi.org/10.1016/j.ins.2023.01.011" @default.
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