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Beyond Cognitive and Affective Issues: Designing Smart Learning Environments for Psychomotor Personalized Learning

  • Olga C. SantosEmail author
Living reference work entry

Abstract

Although learning can involve cognitive, affective, and psychomotor aspects, the latter (which refers to motor skills learning) has been hardly considered when providing personalized support within smart learning environments, despite there are many activities that require learning specific motor skills, such as learning to operate (surgery), to speak with sign language, to play a musical instrument, to practice a sport technique, etc. Emerging technologies from the recently coined term of “Internet of Me,” such as wearable devices from the life-logging movement, can enrich the type of data gathered while learning by considering features related to body movements. In turn, the new big data paradigm facilitates a more efficient computing of performance indicators from the combination of the heterogeneous sources of data collected from the wearable devices. If learning environments want to be smart, they need to deploy the appropriate technological infrastructure not only to collect and process information regarding psychomotor learning but also to provide the corresponding personalized support. In this chapter, the tangibREC framework is proposed, which defines the new concept of tangible recommendations aimed to provide physical scaffolding to the recommendation process. These recommendations (identified with TORMES methodology) can be modeled and printed in 3D to physically guide learners on how to perform accurate movements in terms of the learners’ individual physical features and progress in the motor skills acquisition.

Keywords

Psychomotor learning Motor skills Tangible recommendations TORMES methodology Haptic guidance 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.aDeNu Research Group. Artificial Intelligence Department. Computer Science SchoolUNEDMadridSpain

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