Learning is a complex
phenomenon that includes intricate and complex interactions among cognitive,
motivational, affective, and social processes.
Current psychological and educational research provides a wealth of
empirical data indicating that learners of all ages have difficulty learning
complex topics in areas such as math, science, and other professional domains.
Traditionally, researchers have used either cognitive theories or
constructivist models of learning and instruction to explain different aspects
of learning. Recently, several researchers have extended these theories and
models by advancing models of metacognition and self-regulated learning (SRL)
to describe the intricate interaction of variables related to students’
learning of these complex topics and domains. These new models have been
advanced to account for the various phases (e.g., planning,
metacognitive monitoring, strategy use, and reflection) and areas (e.g.,
cognitive, affect/motivation, behavior, and context) of learning. These
emerging frameworks pose significant challenges for the design of
computer-based learning environments (CBLEs). The goal of this workshop is to
bring researchers, educators, AI researchers, and designers together to discuss
various theoretical, conceptual, empirical, and design issues related to using
computers as “MetaCognitive Tools” for enhancing student learning.
Relevant questions
include:
(1)
Can existing cognitive
and constructivistic theories and models of learning be extended into a
unifying metacognitive or SRL framework for studying the various phases (e.g.,
planning, metacognitive monitoring, strategy use, and reflection) and areas
(e.g., cognition, affect/motivation, behavior, and context) of learning with
CBLEs?
(2)
Can existing CBLEs be
used to study, detect, trace, monitor, and foster students’ metacognitive
processes and SRL?
(3)
What are the
implications of existing models and data for the design of CBLE components
necessary to detect, trace, model, and foster learners’ metacognitive processes
and SRL?
The workshop will
deal with many issues related to these broad questions, including (but not
limited to):
(1)
Empirical studies of
learners’ metacognitive processing and SRL of complex topics and domains
in school and professional domains. How do they account for the complex
interaction between the various phases and areas of learning?
(2)
How effective are existing CBLEs’ in detecting, tracing, and
monitoring learners’ metacognitive and self-regulatory behaviors during
learning? What are implications for the design for MetaCognitive tools to
support learning? Which of these components and/or aspects of metacognition and
SRL can and should be modeled and why?
(3)
How can recent advances in AIED be used to design
Metacognitive tools to detect, trace, model, and foster students’
metacognitive and self-regulatory behaviors during learning? Can existing
computational and AI techniques be used model the several phases (e.g.,
planning, metacognitive monitoring, strategy use, and reflection) and areas
(e.g., cognitive, affective, motivational, contextual) of metacognition and
SRL?
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