Curriculum Q-Learning for Visual Vocabulary Acquisition
This addresses the problem of personalized education for vocabulary learners, though it is incremental as it builds on existing curriculum learning and reinforcement learning methods.
The paper tackles personalized curriculum learning for vocabulary acquisition using visual prompts, employing a reinforcement learning model that adapts to individual student levels, with simulation results showing the model identifies weaknesses and pushes students to their zone of proximal development.
The structure of curriculum plays a vital role in our learning process, both as children and adults. Presenting material in ascending order of difficulty that also exploits prior knowledge can have a significant impact on the rate of learning. However, the notion of difficulty and prior knowledge differs from person to person. Motivated by the need for a personalised curriculum, we present a novel method of curriculum learning for vocabulary words in the form of visual prompts. We employ a reinforcement learning model grounded in pedagogical theories that emulates the actions of a tutor. We simulate three students with different levels of vocabulary knowledge in order to evaluate the how well our model adapts to the environment. The results of the simulation reveal that through interaction, the model is able to identify the areas of weakness, as well as push students to the edge of their ZPD. We hypothesise that these methods can also be effective in training agents to learn language representations in a simulated environment where it has previously been shown that order of words and prior knowledge play an important role in the efficacy of language learning.