Information Technology Reference
In-Depth Information
Chapter 3
An Embodied/Grounded Cognition Perspective
on Educational Technology
John B. Black
Teachers College, Columbia University, New York, NY 10027, USA
Students typically learn in school in ways that are disconnected to their own experi-
ence, and so they learn to know about subject areas rather than have a feel for them.
Thus, what they learn in school becomes stored in memory as abstract symbolic
knowledge that is not connected to their experience in the world, so they do not
think to apply it in their everyday life (or other contexts) when it might be appro-
priate. For example, students learn physics formulas in school that they practice
applying to problems in their physics courses, but they do not understand the impli-
cations of these formulas for how they reason about the physical dynamics of the
real world. Similarly they learn formulas for solving statistics problems in school but
that does not affect the way they reason about uncertain or statistical phenomenon
that they encounter in other contexts. The problem is that the students do not have
a body of perceptual experiences that they draw upon when learning this new sub-
ject matter for they only learn about the subject matter rather than also develop a
feel so it. Recent basic cognitive research in perceptually grounded or embodied
cognition provides a framework for understanding this distinction and for designing
educational environments that foster this deeper level of understanding.
Grounded/Embodied Cognition
Perceptually grounded or embodied cognition is an increasingly prominent area of
basic cognitive research (Barsalou, 2008). This perspective says that a full under-
standing of something involves being able to create a mental perceptual simulation
of it when retrieving the information or reasoning about it. Both behavior and neu-
roimaging experiments have shown that many phenomena that were thought to be
purely symbolic actually show perceptual effects. For example, property verification
(e.g., retrieving the fact that a horse has a mane) was thought to involve a search
from a concept node (horse) to a property node (mane) in a symbolic propositional
network and thus the time to answer and errors were determined by how many
