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These types of technology provide opportunities for incorporating a greater level of embodiment into mathematical learning.
The three manipulated variables were: (1) level of embodiment; (2) level of active generativity; and (3) presence of story narrative.
Also the level of embodiment achievable by using extra robotic limbs that do not have a geometry similar to human body remains to be researched.
The fourth and highest level of embodiment in learning settings requires learning environments to feature high degrees of bodily movement involving locomotion (Johnson-Glenberg et al., 2014).
The experimental results of reaching task indicate that the number of channels and type of feedback is an important factor on the level of embodiment that a supernumerary limb device can achieve in a set amount of time.
As Tran et al. (2017) state, it is still to be determined whether a higher level of embodiment according to the four-level model presented by Johnson-Glenberg et al. (2014) necessarily entails a better learning performance.
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In the remainder of this introduction, we briefly review the notion of embodied cognition, the taxonomy of levels of embodiment, and the special role of gesture in learning.
As a field, we need more methodical descriptors for the levels of embodiment in lessons.
The third and fourth levels of embodiment in the model of Johnson-Glenberg et al. (2014) encompass letting learners perform bodily movements and locomotion, respectively.
Low bodily engagement in our taxonomy is comparable to the lower two levels of embodiment defined by Johnson-Glenberg et al. (2014), i.e. watching animations or other seated interactions.
In some instances, studies rely on several different devices in order construct experimental groups aimed to assess the effects of different levels of embodiment on learners (e.g. Johnson-Glenberg et al., 2016).
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