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Towards Personalised Learning of Psychomotor Skills with Data Mining

Authors: Miguel Portaz; Olga C. Santos;

Towards Personalised Learning of Psychomotor Skills with Data Mining

Abstract

Data Mining (DM) currently represents a key element for improving the acquisition of knowledge and for providing proper feedback in educational environments. In this field, as well as in others, psychomotor skills represent a unique way to advance in the knowledge of human behaviours. Our research apply in two equally promising, but different domains. On one hand, we would be able to improve the learning of psychomotor skills at educational level with the use of different DM techniques. This may includes learning martial arts or supporting the acquisition of locomotor abilities. On the other hand, we would like to expand our DM research far beyond the basis of the aforementioned educational field. Thus, we can evaluate other users, such patients, with the aim of improving the re-learning of motor capabilities during recovery processes on rehabilitation, or even to detect cognitive impairments, analysing slight psychomotor alterations at early stages using DM. The latter includes gait analysis, which are currently used for screening, but not so much for predicting purposes. Although our research is still at early stages, we are following the principles set on previous researches, such those included in our intelligent Expertise Level Assessment (iELA) method.

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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