Can Physical Tools that Adapt their Shape based on a Learner’s Performance Help in Motor Skill Training?
| Autor: | Dishita G Turakhia, Yini Qi, Stefanie Mueller, Andrew Wong, Lotta-Gili Blumberg |
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| Rok vydání: | 2021 |
| Předmět: |
Point (typography)
Computer science 05 social sciences Training (meteorology) Contrast (statistics) 020207 software engineering Muscle memory 02 engineering and technology Task (project management) Variety (cybernetics) Human–computer interaction 0202 electrical engineering electronic engineering information engineering 0501 psychology and cognitive sciences Adaptation (computer science) 050107 human factors Motor skill |
| Zdroj: | TEI |
| DOI: | 10.1145/3430524.3440636 |
| Popis: | 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 |
| Databáze: | OpenAIRE |
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