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This handbook is the first book ever covering the area of
Multimodal Learning Analytics (MMLA). The field of MMLA is an
emerging domain of Learning Analytics and plays an important role
in expanding the Learning Analytics goal of understanding and
improving learning in all the different environments where it
occurs. The challenge for research and practice in this field is
how to develop theories about the analysis of human behaviors
during diverse learning processes and to create useful tools that
could augment the capabilities of learners and instructors in a way
that is ethical and sustainable. Behind this area, the CrossMMLA
research community exchanges ideas on how we can analyze evidence
from multimodal and multisystem data and how we can extract meaning
from this increasingly fluid and complex data coming from different
kinds of transformative learning situations and how to best feed
back the results of these analyses to achieve positive
transformative actions on those learning processes. This handbook
also describes how MMLA uses the advances in machine learning and
affordable sensor technologies to act as a virtual observer/analyst
of learning activities. The book describes how this "virtual
nature" allows MMLA to provide new insights into learning processes
that happen across multiple contexts between stakeholders, devices
and resources. Using such technologies in combination with machine
learning, Learning Analytics researchers can now perform text,
speech, handwriting, sketches, gesture, affective, or eye-gaze
analysis, improve the accuracy of their predictions and learned
models and provide automated feedback to enable learner
self-reflection. However, with this increased complexity in data,
new challenges also arise. Conducting the data gathering,
pre-processing, analysis, annotation and sense-making, in a way
that is meaningful for learning scientists and other stakeholders
(e.g., students or teachers), still pose challenges in this
emergent field. This handbook aims to serve as a unique resource
for state of the art methods and processes. Chapter 11 of this book
is available open access under a CC BY 4.0 license at
link.springer.com.
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