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A recent study on motor-skill training showed that adaptive training tools that use shape-change to adapt the training difficulty based on learners’ performance can lead to higher learning gains. However, to date, no support tools exist to help designers create adaptive learning tools. Our formative study shows that developing the adaptive learning algorithm poses a particular challenge. To address this, we built Adapt2Learn, a toolkit that auto-generates the learning algorithm for adaptive tools. Designers choose their tool’s sensors and actuators, Adapt2Learn then configures the learning algorithm and generates a microcontroller script that designers can deploy on the tool. Once uploaded, the script assesses the learner’s performance via the sensors, computes the training difficulty, and actuates the tool to adapt the difficulty. Adapt2Learn’s visualization tool then lets designers visualize their tool’s adaptation and evaluate the learning algorithm. To validate that Adapt2Learn can generate adaptation algorithms for different tools, we built several application examples that demonstrate successful deployment.
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Can Physical Tools that Adapt their Shape based on a Learner’s Performance Help in Motor Skill Training?
Adaptive tools that can change their shape to support users with motor tasks have been used in a variety of applications, such as to improve ergonomics and support muscle memory. In this paper, we investigate whether shape-adapting tools can also help in motor skill training. In contrast to static training tools that maintain task difficulty at a fixed level during training, shape-adapting tools can vary task difficulty and thus keep learners’ training at the optimal challenge point, where the task is neither too easy, nor too difficult. To investigate whether shape adaptation helps in motor skill training, we built a study prototype in the form of an adaptive basketball stand that works in three conditions: (1) static, (2) manually adaptive, and (3) auto-adaptive. For the auto-adaptive condition, the tool adapts to train learners at the optimal challenge point where the task is neither too easy nor too difficult. Results from our two user studies show that training in the auto-adaptive condition leads to statistically significant learning gains when compared to the static (F1, 11 = 1.856, p < 0.05) and manually adaptive conditions (F1, 11 = 2.386, p < 0.05).
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- Award ID(s):
- 1844406
- PAR ID:
- 10224209
- Date Published:
- Journal Name:
- ACM Conference on Tangible and Embedded Interaction
- Page Range / eLocation ID:
- 1 to 12
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3430524.3440636
