Previous research on motor learning has examined the effects of various training schedules on learning performance. If more practice improves learning, the practice must be tailored to the learner's skill level. One approach is to design interactive learning support systems that adapt to the learner. However, when it comes to the design of such systems, there seems to be a lack of theoretical results regarding the mechanisms of adaptation. Here, we investigate the effects of real-time personalization of practice through a Multi-Armed Bandit (MAB) algorithm. We conducted a controlled laboratory study with a simple motor task involving pointing movements where the wrist drives a cursor in a channel. We show that the MAB algorithm outperforms standard algorithms by effectively reducing movement variability and that this adaptation homogenises learners' skills. These results have theoretical implications that allow us to inform the design of interactive learning support systems.CCS Concepts: • Human-centered computing → Human computer interaction (HCI); • Computing methodologies → Reinforcement learning; • Applied computing → Computer-assisted instruction.
One of the challenges of technology-assisted motor learning is how to adapt practice to facilitate learning. Random practice has been shown to promote long-term learning. However, it does not adapt to the learner's specific learning requirements. Previous attempts to adapt learning consider the skill level of learners from past training sessions. This study investigates the effects of personalizing practice in real time, through a Curriculum Learning approach, where a curriculum of tasks is built by considering consecutive performance differences for each task. 12 participants were allocated to each of three training conditions in an experiment which required performing a steering task to drive a cursor in an arc channel. The Curriculum Learning approach was compared to two other conditions: random practice and another adaptive practice, which does not consider the learning evolution. The Curriculum Learning practice outperformed the random practice in effectively decreasing movement variability at post-test and outperformed both the random practice and the adaptive practice on transfer tests. The adaptation of Curriculum Learning practice also made learners' skills more uniform. Based on these findings, we anticipate that future research will explore the use of Curriculum Learning in interactive training tools to support motor skill learning, such as rehabilitation.
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