The knowledge-learning-instruction (KLI) framework

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15 Οκτ 2013 (πριν από 3 χρόνια και 9 μήνες)

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The Knowledge
-
Learning
-
Instruction (KLI) Framework:

Bridging the Science
-
Practice Chasm to Enhance Robust Student Learning

By
Ken Koedinger

and his
LearnLab
colleagues


Although the last 25 years of progress in the learning sciences ha
s discovered much

about human
thinking, learning and problem solving, there remains substantial disagreement about the most
effective ways to apply th
ose discoveries

to education
al practice. One fundamental problem is
that any such application requires a coherent and consistent overarching framework that can
adequately represent what is known about human learning, while still being able to guide and
constrain instructional design
, implementation, and assessment.
Our efforts

to formulate such a
framework have resulted in the
Knowledge
-
Learning
-
Instruction (KLI) framework
, which
was
published this summer in Cognitive Science.

KLI promotes the emergence of
instructional principles
of high potential for generality,
while explicitly identifying constraints of a
nd opportunities for
detailed analysis of the
knowledge students may acquire in courses
. Drawing on research
across domains of s
cience,
math, and language learning
, KLI suggests that optimal
Instructional

choices depend on which of
many possible
L
earning
processes are needed to achieve which of many possible
K
nowledge
acquisition goals.
The exploration of this three
-
way
“KLI
dependency


requires a specification
of different kinds of knowledge, learning, and instruction. For instance, the
framework specifies
three broad categories of learning processes: 1) memory and fluency building, 2) inducti
on and
refinement, and 3)

underst
anding

and sense making
. Cognitive psychology and cognitive
neuroscience
have substantially advanced our understanding of
memory and fluency building

processes
, through experimen
tal results, modeling, and in pursuing instruction implications (e.g.,
spaced practice and the testing effect).
They

have
made less progress on the last two. In contrast,
educational researchers have made most progress on
understanding and sense making
, but have
paid little attention to the first two. Interestingly,
machine learning
research has focused
largely

on indu
ction and refinement (e.g., statistical classification algorithms).
A

challenge for learning
sciences is bringing these disparate views together.

To pursue an example
,

educational psychologists have produced educational
recommendations, like the
worked example

effect
, that are at odds with recommendations of
cognitive psychologists, like the
testing effect
.
While these two opposing
, albeit research based,
recommendations

suggest incompatible instructional advice, the

KLI
dependency

suggests a way
out

of the dilemma
:

C
ognitive psychologists have focused on kinds of knowledge

(i.e., facts)

for
which memory is the primary learning process whereas educational psychologists have focused
on kinds of knowledge

(i.e.
,
general proce
dures)
for which induction is primary. Testing best
enhances memory of facts, but examples best enhance induction of

general procedures.

If optimal instructional decisions are highly dependent on domain
-
specific knowledge
characteristics, a practical science of learning must make parallel progress on both across
-
domain learning theories
and

within
-
domain knowledge theor
ies. Th
is challenge
may seem
daunting
,

but

a
tremendous research opportunity

is emerging as
educational technologies

are
increasingly supplying

Big Data
.
Teams integrating
machine l
earning and cognitive science

are

produc
ing

data
-
driven learner model developments
, from
fine grain models of learning transfer
through models of
metacognition and motivation

to
models of classroom social interaction

and
learning by dialogue
.