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dc.contributor.authorTurakhia, Dishita G
dc.contributor.authorQi, Yini
dc.contributor.authorBlumberg, Lotta-Gili
dc.contributor.authorWong, Andrew
dc.contributor.authorMueller, Stefanie
dc.date.accessioned2022-07-18T14:05:38Z
dc.date.available2022-07-18T14:05:38Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/1721.1/143801
dc.description.abstract© 2021 Owner/Author. 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).en_US
dc.language.isoen
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.relation.isversionof10.1145/3430524.3440636en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceACMen_US
dc.titleCan Physical Tools that Adapt their Shape based on a Learner’s Performance Help in Motor Skill Training?en_US
dc.typeArticleen_US
dc.identifier.citationTurakhia, Dishita G, Qi, Yini, Blumberg, Lotta-Gili, Wong, Andrew and Mueller, Stefanie. 2021. "Can Physical Tools that Adapt their Shape based on a Learner’s Performance Help in Motor Skill Training?." TEI 2021 - Proceedings of the 15th International Conference on Tangible, Embedded, and Embodied Interaction.
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.relation.journalTEI 2021 - Proceedings of the 15th International Conference on Tangible, Embedded, and Embodied Interactionen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2022-07-18T14:01:35Z
dspace.orderedauthorsTurakhia, DG; Qi, Y; Blumberg, L-G; Wong, A; Mueller, Sen_US
dspace.date.submission2022-07-18T14:01:37Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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