Promoting Deep Learning
“A person with a brain full of knowledge is not a teacher … until he or
she can convey that knowledge to another person.”
http://teachpsych.org/resources/e-books/eit2008/eit08-09.pdf
Purpose
Discuss ways to promote deep learning, not
ways to create deep learners.
Deep or surface learning????
Agenda
Deep vs. surface learning
How do we promote surface learning?
How do we promote deep learning?
Deep learning activities
Deep vs. Surface Learning
Approaches to learning, not attributes of individuals
Deep Learning (LT Memory)
Involves analyzing new information,
relating this information to prior
experiences, and leads to understanding
and long-term retention of the new
information for use in problem solving
in unfamiliar contexts.
Surface Learning (ST Memory)
Involves accepting new information as
isolated facts, and leads to short-term
retention of the information for use as
“cookbook” solutions to problems that
are not fully understood.
Characteristics
Looking for meaning
Relying on rote learning
Relates previous knowledge to new
knowledge
New knowledge and previous
knowledge are unrelated
Focuses on the concepts needed to solve
a problem (memorizes for
understanding)
Focuses on reproducing solutions to
similar looking problems (memorizes
for regurgitation)
How do we promote
surface learning?
Promoting Surface Learning
Rushing to cover too much material
Presenting concepts as unrelated facts
Assessing more complex concepts without
knowledge of foundational principles
Assess rote learning
How do we promote
deep learning?
Promoting Deep Learning
Allow time for reflection
New material requires linkage of multiple
concepts and previous experiences
Identify appropriate material for deep learning
Teachers model attributes – demonstrate
cognitive aspects of solving problems
What else must be considered for deep learning?
Forming a Long-Term Memory
Working Memory
Attention
Emotion
&
Motivation
Sensation
• Makes sense
• Relates to past experiences
• “Aha, I see it!”
• Has meaning
• New learning is relevant
• “I understand!”
• Emotions increase attention
• Enhanced experiences
• “This is way cool!”
• Visual
• Verbal
• Hands-on
Retention by Instructional Method
Retention
After 24 hours
Verbal
Processing
Verbal and
Visual
Processing
Lecture
5%
Reading
10%
Audiovisual
20%
Demonstration
30%
Discussion Group
50%
Practice by Doing
75%
Teach Others / Immediate Use of Learning
90%
Doing
Deep Learning Activities
Bloom’s Level
Terms
Increasing Complexity
Habits of Mind (Creativity)
Deep Learning
CREATE
design
compose
imagine
EVALUATE
critique
judge
appraise
ANALYZE
compare
distinguish
examine
APPLY
practice
calculate
execute
UNDERSTAND
explain
discuss
outline
REMEMBER
define
recall
recognize
Your Activities
Deep Learning Activities
Consider the topic “inference about a
population mean” in a beginning stats course.
Using the previous chart, how could you
address each level in class?
For instance…
Deep Learning Activities
Bloom’s Level
CREATE
Habits of Mind (Creativity)
Deep Learning
Math Knowledge and Application
EVALUATE
ANALYZE
APPLY
design
compose
imagine
Activities for Lesson
Locate a data set of interest
and provide insight on the
population mean.
How do we
critique
Address how sample size and
judge
assess
deep
levels of confidence impact a
appraise
confidence interval.
learning
in
compare
Interpret a confidence interval
distinguish
in terms of the original
activities
at
examine
problem.
practice
Createof
a confidence interval
each
level
calculate
given the appropriate
execute
information.
thinking?
explain
UNDERSTAND
REMEMBER
Terms
Create a confidence interval
given the appropriate
information.
Locate a data set of interest
and provide insight on the
population mean.
Find a point estimator and
sample variance.
Address how sample size and
levels of confidence impact a
confidence interval.
discuss
outline
Expand from point estimates
to an interval of plausible
values.
Interpret a confidence interval
in terms of the original
problem.
define
recall
recognize
Find a point estimator and
sample variance.
Expand from point estimates
to an interval of plausible
values.
Key Ingredients
Relate learning to prior experiences
- Topic makes sense
Exhibit emotion and motivation
- Enhance experiences
Ensure relevance
- Topic has meaning
Questions
References
Engineering Subject Centre Guide: Learning and Teaching Theory for
Engineering Academics www.engsc.uk/er/theory/learning.asp
K. Crawford and A. Fekete (1997), “What do exam results really
measure?” Proceedings of the 2nd Australasian Conference on Computer
Science Education: 185-190
“Assessing and Developing Metacognitive Skills” The Teaching Professor.
(December 2009)
“Why Students Struggle in Math Course” The Teaching Professor.
(February 2010)
R. Carter (2009). The Human Brain Book.
D. Sousa (2006). How the Brain Learns.
C. Heath and D. Heath (2008). Made to Stick.