RELATING RESEARCH ON LEARNING TO EDUCATIONAL METHODS IN
UNDERGRADUATE ENGINEERING EDUCATION
A Capstone Experience Manuscript
Presented by
Gabrielle Mehlman
Completion Date:
May 2010
Approved By:
__________________________________
Professor James Rinderle, Mechanical and Industrial Engineering
__________________________________
Professor Donald Fisher, Mechanical and Industrial Engineering
ii
ABSTRACT
Title: Relating Research on Learning to Educational Methods in Undergraduate
Engineering
Author: Gabrielle Mehlman, Mechanical Engineering
CE Type: Independent Capstone Thesis
Approved By: James Rinderle, Mechanical and Industrial Engineering
Approved By: Donald Fisher, Mechanical and Industrial Engineering
Related research in the fields of education, neuroscience, and psychology has presented
various theories on how people learn. There is currently a disconnect between the
theories on how people learn and the way undergraduate engineering courses are
traditionally taught. Engineering Education and Mind, Brain, and Education (MBE)
Science are two emerging disciplines relevant to undergraduate engineering education.
Engineering Education literature has yet to address how courses could be structured to
account for discoveries about learning and MBE Science has yet to discuss in depth
college level learners.
By relating research from the two fields, as well as from education, neuroscience, and
psychology, suggestions can be made for teaching undergraduate engineers. These
suggestions are related to guided practice in class, lesson length, assessments, students‘
prior knowledge, metacognitive reflection, the student-teacher relationship and learning
environment, the variation of problems, and using teaching styles that address different
learning styles. Engineering educators have developed several specific teaching methods,
such as deductive, inquiry-based, case-based, problem/project based, and just in time
teaching (JiTT), which have been assessed to varying degrees, and can be evaluated for
their ability to address the way people learn.
In conclusion, the disconnect between how people learn and how people teach may be
due to a lack of awareness about how people learn or the fact that instructors are experts
while students are novices. The inductive teaching method better aligns with how people
learn than deductive methods, but there are several barriers to the introduction of these
methods. Since revolutionary change may not be possible immediately, several
techniques that can be incorporated in any teaching method and that take into account
students‘ learning processes are proposed, and further research on the effectiveness of
these recommendations is suggested.
iii
ABSTRACT
Title: Relating Research on Learning to Educational Methods in Undergraduate
Engineering
Author: Gabrielle Mehlman, Mechanical Engineering
CE Type: Independent Capstone Thesis
Approved By: James Rinderle, Mechanical and Industrial Engineering
Approved By: Donald Fisher, Mechanical and Industrial Engineering
Related research in the fields of education, neuroscience, and psychology has presented
various theories on how people learn. There is currently a disconnect between the
theories on how people learn and the way undergraduate engineering courses are
traditionally taught. Engineering Education and Mind, Brain, and Education (MBE)
Science are two emerging disciplines relevant to undergraduate engineering education.
Engineering Education literature has yet to address how courses could be structured to
account for discoveries about learning and MBE Science has yet to discuss in depth
college level learners.
By relating research from the two fields, as well as from education, neuroscience, and
psychology, suggestions can be made for teaching undergraduate engineers. These
suggestions are related to guided practice in class, lesson length, assessments, students‘
prior knowledge, metacognitive reflection, the student-teacher relationship and learning
environment, the variation of problems, and using teaching styles that address different
learning styles. Engineering educators have developed several specific teaching methods,
such as deductive, inquiry-based, case-based, problem/project based, and just in time
teaching (JiTT), which have been assessed to varying degrees, and can be evaluated for
their ability to address the way people learn.
In conclusion, the disconnect between how people learn and how people teach may be
due to a lack of awareness about how people learn or the fact that instructors are experts
while students are novices. The inductive teaching method better aligns with how people
learn than deductive methods, but there are several barriers to the introduction of these
methods. Since revolutionary change may not be possible immediately, several
techniques that can be incorporated in any teaching method and that take into account
students‘ learning processes are proposed, and further research on the effectiveness of
these recommendations is suggested.
iv
TABLE OF CONTENTS CHAPTER 1. Introduction ..........................................................................................1
1.1. Motivation ................................................................................................1
1.2. Background: Levels of Learning Constructs .............................................3
CHAPTER 2. How People Learn ................................................................................7
2.1. Overview ..................................................................................................7
2.2. The Brain: Literally ..................................................................................8
2.3. Memory and Storage .............................................................................. 10
2.4. Uniqueness of Each Brain ....................................................................... 12
2.5. The Importance of Prior Knowledge ....................................................... 15
2.6. Timing of Learning................................................................................. 19
2.7. Effect of Emotional States ...................................................................... 21
2.8. Mastery of Skills Versus Concepts ......................................................... 23
2.9. Summary ................................................................................................ 25
CHAPTER 3. Implications for Teaching ................................................................... 27
3.1. Overview ................................................................................................ 27
3.2. Addressing Memory and Storage, Synaptic Adhesion, and Timing ......... 28
3.2.1. Guided Practice .............................................................................. 28
3.2.2. Lesson Length ................................................................................ 29
3.2.3. Assessment ..................................................................................... 30
3.3. Connecting to Prior Knowledge and Skill-Sets ....................................... 32
3.4. Establishing Motivation .......................................................................... 34
3.5. Metacognitive Reflection ........................................................................ 35
3.6. Student-Teacher Relationship and Creating a Positive Learning
Environment ........................................................................................... 36
3.7. Varied Problems ..................................................................................... 37
3.8. Teaching Styles ...................................................................................... 38
3.9. The Scientific Method Approach ............................................................ 40
3.10. Summary of Teaching Implications ........................................................ 42
CHAPTER 4. Teaching Methods in Relation to Learning.......................................... 44
4.1. Overview ................................................................................................ 44
4.2. Deductive Methods ................................................................................. 45
v
4.3. Inductive Methods .................................................................................. 45
4.3.1. General Definition .......................................................................... 45
4.3.2. Inquiry Based Instruction ................................................................ 46
4.3.3. Case-Based Instruction ................................................................... 48
4.3.4. Problem and/or Project-based Instruction ........................................ 51
4.3.5. Just-in-time Teaching...................................................................... 53
4.3.6. Inductive Methods Comparison and Barriers ................................... 54
4.4. Active and Cooperative Learning ........................................................... 56
CHAPTER 5. Conclusions and Future Research ....................................................... 59
5.1. Conclusions ............................................................................................ 59
5.2. Future Research ...................................................................................... 62
Works Cited .................................................................................................................. 64
vi
LIST OF FIGURES
Figure 1. Bloom‘s Taxonomy summary (Source:
http://ewe.springbranchisd.com/acostaf/DDI/Blooms%20Taxonomy%20for%20Thi
nking.jpg) ................................................................................................................4
Figure 2. Neurons (Source: Jensen 2005) Figure 3.Synapse (Source: Jensen 2005) ........9
Figure 4. The diagram illustrates the theory of temporary and permanent memories.
Information gathered from our senses lasts only a few seconds in immediate
memory. Information in working memory usually endures for minutes or hours, but
can be retained for days if necessary. The long-term storage sites (also called
permanent memory) store information for years. (Source: Sousa 2008) .................. 10
Figure 5. The eight intelligences describe the different types of competencies that we all
possess in varying degrees and that we use in our daily lives (Gardner 1993). ........ 13
Figure 6. The degree of retention varies during a learning episode. We remember best
that which comes first (prime-time-1) and last (prime-time-2). We remember least
that which comes just past the middle. (Sousa 2008) .............................................. 20
Figure 7: Variety of chemicals that influence emotional states (Source: Jensen 2005) .... 21
Figure 8: Strategies to facilitate reconciliation (Source: Hewson & Hewson 1983) ........ 33
Figure 9: Dimensions of Learning and Teaching Styles (Source: Felder 1988) ............... 39
Figure 10. Illustration for inquiry teaching example (Source: How Students Learn:
Science) ................................................................................................................. 47
Figure 11. Summary of suggestions for any teaching method ......................................... 61
LIST OF TABLES
Table 1. Summary of Felder‘s Learning Styles (Source: Felder 1988) ............................ 14
Table 2: Summary of main points from Chapter 2: How People Learn .......................... 26
Table 3. Step in the scientific method and related benefit to learning ............................. 41
Table 4. Correlation between learning process discoveries and teaching implications .... 43
Table 5. Means and Standard Deviations for the 74 students (Source: Raju 1999) .......... 50
Table 6. Instructional Demands Imposed by Inductive Teaching Methods. (Source: Felder
2007) ..................................................................................................................... 54
Chapter 1. Introduction 1
CHAPTER 1. INTRODUCTION
1.1. MOTIVATION
Many core engineering courses, such as statics, dynamics, and thermodynamics,
have been taught using the same lecture format for as many as thirty to seventy years
despite growing evidence that these are not the most effective methods (Dichter 2001).
The teaching methods have not changed while ―the circumstances facing practicing
engineers today are considerably different from those of the past‖ (Felder 1988). The
National Science Foundation led an initiative in 1993 to improve engineering education
which motivated the creation of countless articles, books and conferences focused on the
improvement of engineering education. As a result, engineering education at this point
began to develop as an independent field of research. The Journal of Engineering
Education, created by the American Society for Engineering Education with the purpose
of advancing ―rigorous scholarship in engineering education‖ and catalyzing ―the
formation of a vibrant global community of scholars and practitioners dedicated to
advancing engineering education through education research,‖ had mostly focused on
course and curricular changes, addressing instructional technologies, integrated curricula,
student teams, reordering or adding of topics to the curricula, mentoring, and issues of
retention (Borrego 2007). The journal‘s earliest origins are traced to the World‘s
Engineering Congress in 1893, but the journal was repositioned in 1993 as a ―scholarly
professional journal‖, and articles began to reflect ―increasing sophistication in the use of
scientific and pedagogical protocols‖ (Lohman 2005). It is a main source of articles on
the improvement of engineering education, but has yet to address what has been
discovered about the way people learn.
Chapter 1. Introduction 2
In another part of the academic world, the field of Mind, Brain and Education
(MBE) Science, ―the intersection of neuroscience, education and psychology,‖ has
simultaneously been emerging over the past decade (Tokuhama-Espinosa 2010). While
learning has been addressed in each of the three disciplines (neuroscience, education and
psychology), MBE Science emphasizes the implications these findings have on teaching.
As of 2010, when Tokuhama-Espinosa published The New Science of Teaching and
Learning; Using the Best of Mind, Brain, and Education Science in the Classroom,
college students and adults were reported as the least represented in MBE Science
literature, and policy issues have been focused on above curriculum practice.
The purpose of this thesis is to relate research about how people learn from the
fields of engineering education and MBE Science, as well as psychology, neuroscience,
and education, and connect this research to what it indicates for engineering
undergraduate education. In this chapter, the motivation for the thesis is conveyed and
several constructs which will be used to define the goals of engineering education will be
defined. Chapter 2 will connect and relate the research on how people learn engineering
concepts and skills. The third chapter will discuss implications these findings have for
teaching undergraduate engineering. The fourth chapter will address different teaching
methods which have been proposed for or used in undergraduate engineering, and relate
their effectiveness to the research on learning. The last chapter provides conclusive
statements regarding the most effective methods and suggestions as to how our
understandings of the brain and how people learn can be utilized to improve the full
range of teaching methods.
Chapter 1. Introduction 3
In summary, this thesis is an addition to the MBE Science literature in the realm
of college age engineering learning. The presentation of this material will make a case for
the importance, and relative ease of implementation, of teaching undergraduate
engineering students with the brain in mind.
1.2. BACKGROUND: LEVELS OF LEARNING CONSTRUCTS
The goals of engineering education will be discussed in terms of several
constructs from the fields of psychology and education which define levels of
understanding. Engineers generally work to develop, improve and optimize products,
devices and/or processes. These problems are most often ill defined problems; if they
were well defined, they would have been solved already. In order to solve ill defined
problems, a deep, as opposed to surface, understanding of a concept or skill is required.
This section will discuss different ways this deep understanding is defined and discussed
in the literature.
One of the most widely known constructs of learning was initiated by Benjamin
Bloom, beginning in 1948. He, along with several other educators, set out to classify the
objectives and goals of educators (Forehand 2005). They divided the objectives into three
domains; affective (social/behavioral), psychomotor (physical or motor skills), and
cognitive (knowledge based); and advised that education be focused on all three aspects
in order to move toward a more holistic approach. Eight years later, in 1956, Bloom et al.
published The Taxonomy of Educational Objectives, the Classification of Educational
Goals, Handbook 1: Cognitive Domain which outlined six hierarchical levels of thinking
in only the cognitive domain. This hierarchy is known as ―Bloom‘s Taxonomy‖ and
―while … other educational taxonomies … have been developed, it is Bloom‘s
Chapter 1. Introduction 4
Taxonomy which remains, even after nearly fifty years, the de facto standard‖ (Forehand
2005).
The first three levels of the taxonomy are knowledge, comprehension, and
application. The last three levels, analysis, synthesis, and evaluation, are considered to be
the levels of higher-order of thinking. As it is a hierarchy, an individual must graduate
from one level to the next, i.e. cannot apply information before knowing and
comprehending it. Below, in Figure 1, is a summary of each level.
Figure 1. Bloom‘s Taxonomy summary (Source:
http://ewe.springbranchisd.com/acostaf/DDI/Blooms%20Taxonomy%20for%20Thinking.jpg)
A goal of undergraduate engineering education is for students to reach these levels of
higher order thinking; evaluation, synthesis, and analysis; at which they will be able to
solve ill defined problems. In many cases, the ability to apply a basic concept to a slightly
Chapter 1. Introduction 5
different situation can be difficult, especially toward the beginning of an undergraduate‘s
career (Sousa 2008).
Another construct encountered in the literature was established by Jean Piaget in
1958. This construct is concerned with human intellectual development over a lifetime, as
opposed to the state of a learner at a point in time. It classifies four stages; sensory-motor,
preoperational, concrete thought and formal thought (Karplus 2003). The latter two are
the stages of logical operations, and are relevant to the desired level of learning in
engineering education. Concrete thought is characterized by the usage ―of direct
experience, concrete objects and familiar actions‖ and ―needs reference to familiar
actions, objects, and observable properties‖. Formal thought is characterized by the
ability to ―reason with concepts, relationships, abstract properties, axioms, and theories‖
and to use ―symbols to express ideas‖ (Karplus 2003). Formal thinkers may be better
equipped to transfer ideas to various contexts due to the abstract nature of their
understanding.
In How People Learn: Brain, Mind, Experience and School, a distinction is made
between a novice and an expert (Bransford 2000). In many ways, this distinction is
similar to the distinction between a concrete and formal thinker. The following
characteristics of experts are expressed in How People Learn:
1. Experts notice features and meaningful patterns of information that are not noticed by novices.
2. Experts have acquired a great deal of content knowledge that is organized in ways that reflect a deep
understanding of their subject matter.
3. Experts‘ knowledge cannot be reduced to sets of isolated facts or propositions but, instead, reflects
contexts of applicability: that is, the knowledge is
―conditionalized‖ on a set of circumstances.
Chapter 1. Introduction 6
4. Experts are able to flexibly retrieve important aspects of their knowledge with little attention effort.
5. Though experts know their disciplines thoroughly, this does not guarantee that they are able to teach others.
6. Experts have varying levels of flexibility in their approach to new situations.
While a new graduate of an undergraduate engineering program may not be expected to
be an expert in his or her field, it is the hope that certain characteristics of one have begun
to appear.
These three constructs− Bloom‘s taxonomy, Piaget‘s concrete vs. formal thinking,
and How People Learn‘s novice vs. expert level− are the main three constructs through
which learning will be evaluated. In general, ―successful‖ learning will be attributed to
reasoning at the upper three levels of Bloom‘s Taxonomy, at a formal thought level, and
at an expert level. The three different constructs have fairly consistent descriptions of
successful, deep learning, and therefore will be used at different points relatively
interchangeably.
Chapter 2. How People Learn 7
CHAPTER 2. HOW PEOPLE LEARN
2.1. OVERVIEW
Over the past 15 years, discoveries in neuroscience have provided scientists with
a deeper understanding of the brain and how it acquires and retains knowledge (Sousa
2008). Similarly, educational scientists have been developing theories for decades
through experimentation and observation about how people most effectively learn
(Tokuhama-Espinosa 2010). Many, but not all, of these theories have found varying
degrees of support from recent discoveries in neuroscience. Neuroscientists have made
several discoveries about the composition and operations of the brain. Scientists from
both fields have developed theories regarding storage in the brain, the uniqueness of each
brain, the importance of preconceptions, the timing of learning, the effect of emotional
states and the difference between mastering concepts and skills. In the following chapter
the discoveries from each field will be explored and compared to determine the state of
the field of how people learn.
Three main sources were utilized to learn about how the brain learns: The New
Science of Teaching and Learning: Using the Best of Mind, Brain, and Education Science
in the Classroom by Tracey Tokuhama-Espinosa, How the Brain Learns Mathematics by
David Sousa and Teaching with the Brain in Mind (2nd
Edition) by Erik Jensen. The first
of these is an extensive addition to the MBE Science field. In the first half of this book,
Tokuhama-Espinosa classifies information on the brain as that which is currently known,
probably true, still just intelligent speculation, and definitively ‗neuromyths‘ and
misunderstandings. In the second half, she discusses how this information can and should
be used in classrooms and educational policy. This book provides a thorough summary of
Chapter 2. How People Learn 8
the ―state of the art‖ in Mind, Brain, and Education Science, and distinguishes between
that which should be stated as fact and that which should be regarded as speculation or
myth. The second book, How the Brain Learns Mathematics, is one of several related
books written by David Sousa; the others including How the Brain Learns, How the
Brain Learns to Read, How the Gifted Brain Learns and How the Special Needs Brain
Learns. The Mathematics volume was chosen for its relevance to engineering; many of
the functions which apply to learning math and solving word problems are similar to
those in engineering. Teaching with the Brain in Mind provides a simplified explanation
of the brains inner functions and detailed chapters on the effects of emotional states.
These three sources are the framework for the information provided in this thesis
regarding the brain and how knowledge is acquired within it. The sources from education
and psychology are more varied, and will be introduced as needed.
2.2. THE BRAIN: LITERALLY
Neuroscience has made several discoveries about the tissue which makes up the
brain and how the brain works and learns. The brain is made up of water (78 percent), fat
(10 percent), and protein (8 percent), and it consists of neuron and glial cells (Jensen
2005). The neuron cells are a key element in the learning process. Each neuron has a cell
body; a tail-like extension, called the axon, associated with information output; and
branchlike input extensions called dendrites (Figure 2).
Chapter 2. How People Learn 9
Figure 2. Neurons (Source: Jensen 2005) Figure 3.Synapse (Source: Jensen 2005)
Two neurons are considered connected when the axon of one is near the dendrite
of another. The juncture between two connected neurons is called the synapse (Figure 3).
Learning occurs when the synapse can be bridged; the cells change ―their receptivity to
messages based on previous stimulation‖ (Jensen 2005). Protein strands hold the axon
and dendrite in close proximity during a process called synaptic adhesion. The strength of
this bond generally determines the degree to which the concept or skill was learned and
will be able to be recalled in the future (Jensen 2005).
Learning in the brain is similar to the notion of ―survival of the fittest‖:
…whatever is first, whatever activities are more frequent,
and whatever actions are more coherent will ―win‖ the
competition for network wiring and signal the brain to
allocate space and resources to that set of behaviors.
(Jensen 2005)
This means that what is used and needed will be retained and also gain space in the brain.
For example, it has been found that ―areas of the auditory cortex increased in size with
specific auditory trainings over time‖ (Jensen 2005). This also means that unused or
unimportant synapses are eliminated. For example, certain cultures may be unable to
pronounce certain sounds because the synapse controlling that function was eliminated at
a young age (Jensen 2005). Therefore, an action or concept is more likely to be learned
Chapter 2. How People Learn 10
and retained if it is repeated and deemed important, yet there are other factors which
affect memory such as the chemical state of the brain and individual aptitude.
2.3. MEMORY AND STORAGE
Neurologists have determined that memory can be divided into two stages of short
term and two major types of long term storage. The two stages of short term memory are
the immediate memory, which lasts seconds, and the working memory, which lasts
minutes to days (Figure 3). The two major types of long-term storage are declarative and
nondeclarative (Sousa 2008).
The immediate memory stores items such as a phone numbers, which someone
may remember for a few seconds but will forget several minutes later. The working
memory stores item for minutes to days. When a person has difficulty recalling a word or
number and remembers it randomly later on, it is due to the working memory continuing
to search for it. The working memory also accounts for our ability to ―multitask.‖
Figure 4. The diagram illustrates the theory of temporary and permanent memories. Information gathered from our senses lasts only a few seconds in immediate memory. Information in working memory usually endures for minutes or hours, but can be retained for days if necessary. The long-term storage sites (also called permanent memory) store information for years. (Source: Sousa 2008)
Chapter 2. How People Learn 11
An idea that proceeds from the immediate memory to the working memory may
then be stored in long term memory, declarative or nondeclarative as appropriate to the
nature of the idea. Declarative memory stores episodic memories, the memories from our
own lives, and semantic memories, the memory of facts, faces, objects and so on.
Nondeclarative memory stores procedural memories, which can be either motor skills,
such as those used to drive a car, or cognitive skills, such as problem solving (Sousa
2008). The differences between these types of long term memories will be explored in
further depth in Section 2.8: Concepts vs. Skills.
The process of storing an idea in long-term memory is essentially the act of
learning. As explained previously, the strength of the synapse created in long term
memory determines the degree to which it was learned. It is intuitive: an idea well
learned can immediately be recalled because it is conditioned to respond whereas
something not as well learned may require time or prodding to recall. The synapse in
most cases may only be activated through the original context and may need to be
connected with other ideas in order to be recalled in a dissimilar context.
The difference between Piaget‘s theories of concrete or formal thought may be
due to the number of connections associated with an idea or thought, as opposed to the
strength of the connection. When a person is using formal reasoning patterns, he or she
does not need reference to familiar actions, objects and observable properties (Karplus
1977), perhaps because the idea is well connected, and can be accessed through other
contexts and prompts; there is more than one path through which to access the
information. Therefore, a greater number of connections may lead to the ability to think
of the concept more abstractly.
Chapter 2. How People Learn 12
2.4. UNIQUENESS OF EACH BRAIN
The number of neurons in a human brain can range from 30 to 50 billion neurons
(Jensen quoting Shankle 2008). Not only do the number of neurons vary by the billions,
but the brain‘s lay-out and thickness in different areas vary as well. Since the human
brain has the largest area of uncommitted cortex, humans have extraordinary flexibility
for learning (Jensen 2008). This area is organized by the experience of the individual
and is considered highly plastic (Tokuhama-Espinosa 2010). Combined with the innate
structure of an individual‘s brain, use and disuse define the physical structure of the
brain, since use increases the connection between ideas and disuse leads to atrophy. In
this way, practice, repetition and rehearsal may not make an idea correct, but they will
make it permanent (Sousa 2008, Tokuhama-Espinosa 2010). Each learner has a
completely unique brain, and therefore will learn slightly differently.
Educators have developed theories regarding the uniqueness of each brain for
over twenty years. In 1983, Howard Gardner proposed his theory of multiple
intelligences, which changed the view of intelligence from a binary question of intelligent
or not, to a question of which type of intelligent. His theory still has yet to be supported
by neurological evidence, but has been observed anecdotally and is still used by
educators and students to explain the individual nature of teaching and learning. He
classifies eight different categories: intrapersonal, interpersonal, naturalist,
logical/mathematical, linguistic, musical, spatial, and bodily/kinesthetic. The essence of
each intelligence is illustrated in the figure below.
Chapter 2. How People Learn 13
Figure 5. The eight intelligences describe the different types of competencies that we all possess in varying degrees and that we use in our daily lives (Gardner 1993).
This theory asserts that a person will learn best when material is presented within the
context of his or her preferred intelligence (Gardner 1993). This is consistent with the
issue of prior knowledge discussed in the next section, Section 2.5.
It has been similarly proposed by several educational researchers that people have
different learning styles and that an individual has a preferred learning style relating to
their prior experiences, personality, and brain physiology (Felder 1988). At present, there
is no neurological evidence to support this theory either.
Richard Felder‘s theory of learning styles was developed specifically with
engineering education in mind (1988). Felder contends that there are a total of 32
Chapter 2. How People Learn 14
different learning styles arising from combinations of five binary categories (25= 32). The
learning categories are summarized in the table below.
Table 1. Summary of Felder‘s Learning Styles (Source: Felder 1988)
Learning Style
Category Possible types Description
Perception Sensing Observation, gather data through the senses
Intuitive Indirect perception by way of the unconscious—
speculation, imagination, hunches
Input Visual
Remember best what is seen; pictures, diagrams, flow
charts, time lines, films, demonstrations
Auditory Remember much of what they hear and more of what
they hear and say; discussion, explaining to others
Organization Inductive
Reasoning progression that proceeds from particulars to
generalities; one infers principles
Deductive Reasoning progression that proceeds from generalities
to particulars; one deduces consequences
Processing Active
(experimentation)
Doing something in the external world with the
information—discussion, explaining, testing
Reflective
(observation)
Examining and manipulating the information
introspectively
Understanding Sequential Follow linear reasoning processes
Global Make intuitive leaps, may be unable to explain how
solution came to be
Felder‘s original article, Learning and Teaching Styles in Engineering Education,
provides more in depth descriptions of each learning style, and can be found on his
website (http://www4.ncsu.edu/unity/lockers/users/f/felder/public/Learning_Styles.html).
Felder concludes that most college age engineering students are sensing, visual,
inductive, and active learners, and lean toward sequential learning over global learning
(Felder 1988). Most engineering instructors teach in a way which lends itself to intuitive,
auditory, deductive, reflective, and sequential learners, meaning that of the five
Chapter 2. How People Learn 15
categories that college age engineering students tend to belong to only one is addressed
directly by instructors (Felder 1988). Felder further argues that
Mismatches exist between common learning styles of
engineering students and traditional teaching styles of
engineering professors. In consequence, students become
bored and inattentive in class, do poorly on tests, get
discouraged about the courses, the curriculum, and
themselves, and in some cases change to other curricula or
drop out of school.
The multiple intelligence and learning style theories are similar in that they are
consistent with the idea that each brain is unique, but multiple intelligence theory
contends that learners have a preference for the context in which content is presented,
whereas the learning style theory suggests that there is a preference for the process
through which this content is presented (Denig 2004). While theories of multiple
intelligences and learning styles are consistent with the fact that each brain is unique,
there has not been enough physical evidence to conclude that different people literally
use different processing strategies. The subject and stage of learning generally affect the
process strategy used by an individual, so due to the inconsistency of learning preferences
these theories are not yet completely supported by neurological studies (Sousa 2008,
Tokuhama-Espinosa 2010).
2.5. THE IMPORTANCE OF PRIOR KNOWLEDGE
As has already been explained, each brain is unique, and therefore it cannot be
assumed that a student is a ―blank slate‖ when first entering a classroom. Educational
psychologists began to understand this as early as 1968, when educational psychologist,
Ausebel, stated that ―The most important single factor influencing learning is what the
Chapter 2. How People Learn 16
learner already knows. Ascertain this and teach him accordingly‖ (Hewson & Hewson
1983). Recent neurological studies have solidified and reinforced this belief.
In Teaching with the Brain in Mind, Eric Jensen writes that the importance of
prior knowledge is ―not a mythical concoction. It consists of real, physical brain matter
(synapses, neurons, and related, connected networks).‖ A single association of prior
knowledge may have as many as 10,000 connections to other ideas (Jensen 2005). The
brain literally builds upon prior knowledge. According to research on the brain, ―Finding
out what students already know and asking them to make connections to another, more
accurate model is how the real learning process begins‖ (Jensen 2005). It lends itself to a
metaphor of a house; the prior knowledge is the foundation, and without strong links to
the foundation, the house will not stay up (Tokuhama-Espinosa 2010).
Research in education has found similar results; that students‘ prior experiences,
skills, and knowledge greatly affected their ability to learn new material (Clement 1981,
Gardner 1993). Gardner‘s theory of multiple intelligences asserts that students learn best
within the frame of their individual intelligence, which is nearly equivalent to the
statement that students learn best within the context of prior knowledge or skills. John
Clement, a physics education researcher, found in 1980 that many entering physics
students ―have a stable, alternative view of the relationship between force and
acceleration‖ which is ―highly resistant to change … and makes a full understanding of
Newton‘s first and second laws very difficult.‖ He suggests ―new concepts must displace
or be remolded from stable concepts that the student has constructed over many years‖
(Clement 1981). This is very relevant to the constructivist theory, which is a theory about
knowledge and learning that has implications for teaching.
Chapter 2. How People Learn 17
This theory was originated by Jean Piaget‘s research on young children. It is
described in Constructivism: Theory, Perspectives, and Practice as the following:
…the theory describes knowledge not as truths to be
transmitted or discovered, but as emergent, developmental,
nonobjective, viable constructed explanations by humans
engaged in meaning-making… Learning from this
perspective is viewed as a self-regulatory process of
struggling with the conflict between existing personal
models of the world and discrepant new insights… (Fosnot
2005)
The constructivist theory suggests that ―individuals create their own new understandings,
based upon the interaction of what they already know and believe, and the phenomena or
ideas with which they come into contact,‖ (Richardson 1997). It furthermore contends
that ―it is the learner alone who makes the connections in any meaningful way…
Learners construct further knowledge by modifying that which they have already‖
(Fosnot 2005). Constructivist theory is considered ―still just intelligent speculation‖ in
terms of neuroscientific studies, because while it is already clear that neurons build upon
one another, ―student experiences are so individual that there is no way to measure this
construction‖ (Tokuhama-Espinosa 2010).
Another psychological theory, the theory of mental models, describes how
humans interact with the world, and also aligns with the discovery that prior knowledge
is important in learning. This theory argues that ―human beings understand the world by
constructing working models of it in their minds‖ and furthermore ―since these models
are incomplete, they are simpler than the entities they represent‖ (Johnson-Laird 1983).
For example, most humans use or mimic time, but do not have a direct explanation of it
(Johnson-Laird 1983). This issue of mental models is an especially important construct in
relation to engineering learning, since the students‘ mental models will affect their
Chapter 2. How People Learn 18
understanding of how the world works. For example, students may have observed that a
bowling ball dropped in air will hit the ground before a piece of paper and might
therefore have constructed a mental model in which heavy objects fall faster than light
objects, a model not consistent with Newton‘s law of gravitation. Students must
recognize and alter their mental models to match science content.
Neurological and educational research indicates that prior knowledge is also
highly resistant to change (Jensen 2005, Clement 1993). As previously stated, each
neuron may be connected to as many as 10,000 other neurons; that‘s up to 10,000
connections to re-direct or fix in order to unlearn or relearn an idea (Sousa 2008). The
longer an idea has existed in a person or the greater number of times it has been used, the
stronger and deeper the connections are, and the more difficult the idea is to change. Prior
knowledge can be compared to a tree with deep roots, and the older the tree, the deeper
the roots are and the harder it will be to relocate or remove (Jensen 2005). A student may
misunderstand a concept due to deep-rooted competing, conflicting or unreliable prior
knowledge, which can be very difficult to remove or change unless the student can
recognize his or her own thinking. This process is called metacognitive reflection, and is
addressed in further detail in Section 3.3. In topics such as physics, students may have
created elaborate belief systems to explain how things move which are in direct
opposition to the true nature of how they act (Clement 1981). The issues of prior
knowledge have several implications for teaching explored in greater detail in Chapter 3:
Implications for Teaching.
Chapter 2. How People Learn 19
2.6. TIMING OF LEARNING
The quality of learning is affected by the amount of time spent per session
learning as well as the number of sessions and distribution over time. In relation to the
amount of time per session, ―learning improves with short sessions and rest intervals
versus constant exposure to new material‖ (Jensen 2005). Rest intervals allow the learner
to set new information in the brain and solidify connections. Jensen finds that adults can
generally receive direct instruction for up to 15-20 minutes, which aligns with Sousa‘s
account that an adult can process an item in the working memory for about 10-20 minutes
before losing interest (Sousa 2008). Essentially, the working memory reaches capacity
after about 20 minutes of thinking about one concept or idea at which time the individual
must break from this thought through reflection or distraction. The capacity of the
working memory is directly correlated to the attention span during learning.
Another documented effect of the capacity of the working memory during a single
learning episode is the primacy-recency effect, also known as the serial position effect
(Sousa 2008). This term is used for the phenomena that learners remember best that
which is taught at the beginning, second best that which comes at the end, and least of all
the material taught in the middle (Sousa 2008). Below is a figure illustrating this
phenomenon during a 40-minute lesson.
Chapter 2. How People Learn 20
Figure 6. The degree of retention varies during a learning episode. We remember best that which comes first (prime-time-1) and last (prime-time-2). We remember least that which comes just past the middle. (Sousa 2008)
Downtime occurs right around 20 minutes, which is when the working memory would
reach capacity, and then not be able to retain information regarding that same thought
again for 5-10 minutes.
A concept or skill can be learned over several periods that are close together,
called massed practice, or several periods spread over a greater amount of time, called
distributed practice (Sousa 2008). Cramming for an exam is an example of massed
practice. Independently, massed practice often leads to a loss of information since the
material has no further significance after the assessment or examination. In effect,
students extend their working memory for several days when they cram, and the
information does not progress to long-term memory (Tokuhama-Espinosa 2010). Massed
practice followed by distributed practice is the most effective path to retention. Over
time, connections are solidified and meaning is created. This also has direct and obvious
implications for teaching and assessment explored in Chapter 3: Implications for
Teaching.
Chapter 2. How People Learn 21
2.7. EFFECT OF EMOTIONAL STATES
―Here‘s what we know: Emotions drive attention, create meaning, and have their
own memory pathways‖ (Sousa 2008 quoting LeDoux, 1994). Learning and recall ability
is greatly affected by the ―whole-body state‖ of the learner; ―Even with the learning
stored properly, only the right ‗state activations‘ (meaning the right neuronal assemblies)
and the appropriate chemical mix will retrieve the learning‖ (Jensen 2005). Personal and
interpersonal forces affect the whole-body state of an individual.
The personal forces are mostly due to the well being of the individual; the
chemical balance within the brain greatly affects one‘s ability to recall and retain. This
balance is determined by nutrition, sleep, stress level, happiness as well as motivation and
confidence. The figure below illustrates the three main chemicals that influence a
person‘s emotions, and by what they are affected.
Figure 7: Variety of chemicals that influence emotional states (Source: Jensen 2005)
Chapter 2. How People Learn 22
Certain chemicals, such as cortisol, can actually increase retention in small doses. ―Many
studies (Shors, Weiss, & Thompson, 1992) show that a brief period of stress enhances
hippocampal learning (the source of our explicit memories)‖ (Jensen 2005). Therefore, a
healthy amount of stress can improve a student‘s learning. For example, if an instructor
informs the class that a particular idea will be on the next test, student stress levels may
increase at the mention of an upcoming assessment, but they undoubtedly will experience
a heightened attention and more explicit memory of this point in the lesson. They may
remark that they remember the idea because it was pointed out that it will be on the next
test. If this had not been pointed out, the idea may not have been remembered as
explicitly. The clearer memory is caused by the increase in cortisol due to stress.
Increased levels of stress over longer periods of time, called distress, are
detrimental to cognition skills (Jensen 2005). ―Distress has been shown to kill brain cells
(Sapolsky, 1992), to reduce the number of new brain cells produced (Gould, McEwen,
Tanapat, Galea, & Fuchs, 1997), and to damage the hippocampus.‖ Thus, if a course is
consistently beyond the students‘ capabilities, it might be counterproductive to learning
processes.
Stress can also be caused by perceived threats. This could be a due to an
interpersonal interaction; positive or negative, they are fundamental to the learning
process. ―The brain depends on interactions from others to make sense of social
situations‖ (Tokuhama-Espinosa 2010). Positive interactions, in the form of instructor
support or feedback, can greatly enhance the learning process of students (Jensen 2005,
Tokuhama-Espinosa 2010). Tokuhama-Espinosa reports that
Chapter 2. How People Learn 23
The increased self-esteem and enhanced self-efficacy that
come from support by authority figures not only release
endorphins in the learner‘s brain but also suppress stress
hormones that can impede learning.
Feedback, good or bad, ―shows the teacher cares enough about [the students‘] progress to
take the time to comment on their work and show them specific steps to improve‖
(Tokuhama-Espinosa 2010). ―Feedback‖ does not mean a numerical grade, but a
commentary on the process or progress of the student. Positive feedback can also increase
confidence, and therefore lift expectations for future capabilities.
Conversely, low expectations can cause students to underperform. A study of top
female performers in mathematics found that by informing female subjects that a
mathematics test often shows gender differences the female subjects underperformed
their male counterparts, whereas the female subjects who were told that there was no
gender disparity performed equally (Sousa 2008). This is called the ―stereotype affect‖
and suggests that students ―at-risk‖, including women and other minorities in the
engineering field, may especially benefit from encouragement (Felder 2001).
2.8. MASTERY OF SKILLS VERSUS CONCEPTS
As previously mentioned, long-term memories are stored in the brain as either
declarative, explicit memory or non-declarative, implicit memory (Sousa 2008).
Reiterating, declarative memory consists of episodic (the memories from our own lives)
and semantic (the memory of facts, faces, and objects) memory. Nondeclarative memory
consists solely of procedural memory (the memory of motor or cognitive skills). In the
engineering discipline, declarative and nondeclarative memories are of equal importance,
since both are required to solve ill-defined problems.
Chapter 2. How People Learn 24
―Procedural and cognitive skill acquisition involve some different brain processes
and memory sites from cognitive concept learning,‖ but exactly how they differ is not
completely clear as of yet (Sousa 2008). It is known that learning new skills literally
reorganizes the brain mass, and therefore takes time and effort, whereas content learning
may not have the same reorganizational effect (Sousa 2008).
Since the types of long-term memory involve different processes and sites, Sousa
asks ―If they are learned differently, should they be taught differently?‖ From research
across the field, it seems the answer is no; ―neither context nor cognition can be
understood in isolation; they form an integrated system in which the cognitive skill in
question becomes part of the context‖ (Ceci & Roazzi, 1994). ―It can be argued that
scientific content (such as concepts and theories), scientific thinking skills, and
understanding the nature of science are inseparable‖ (Leonard 2000). This statement adds
to the argument that the process scientists follow to discover content and knowledge is
integral to the understanding of the content and knowledge itself. While this is a slightly
different argument, it can nonetheless be concluded from the concepts of the brain that
have been explored that all learning is done within a context, and linked to that context.
The goal is therefore to be able to learn the skill or concept to an extent that it can
be transferred beyond the initial context (Jensen 2005). Studies have shown that students
with particular street smarts could utilize a certain algebraic skill in the context of
handling money, but could not apply same skill in a school setting (Bransford 2000)
―Both a higher degree of detail and a higher density of relationships, including those
between new information and prior knowledge of a subject, make knowledge more useful
and more transferable‖ (Perrenet 2000). Therefore, the ability to transfer is improved by
Chapter 2. How People Learn 25
the number of connections to prior knowledge that a person can make to the information
or skill.
2.9. SUMMARY
Discoveries regarding the composition and functioning of the brain, memory and
storage, uniqueness, the importance of prior knowledge, the timing of learning, the effect
of emotional states and the mastery of concepts versus skills have been discussed.. The
following table outlines the major points of this chapter and serves as a reference and a
summary of these points. Each of these findings could have many implications for how
lessons should be taught and structured. Several of these implications have been
identified throughout the literature and will be discussed in the following chapter,
Implications for Teaching.
Chapter 2. How People Learn 26
Table 2: Summary of main points from Chapter 2: How People Learn
Topic Major Points
Brain - Learning related to physical synaptic connections - Competitive nature
Memory and Storage - Short term
Immediate (seconds)
Working (minutes to days)- capacity of 15-20 minutes on one idea
- Long term
Declarative- episodic and semantic memories
Nondeclarative- procedural memories
Uniqueness - Number of neurons: 30-50 billion - Defined by experience and need - Educational theories
multiple intelligences
learning styles
Prior Knowledge - Physical matter - Deep rooted, resistant to change - Psychology theories
Constructivist theory
Mental models
Timing of Learning - Improves with short sessions and rest intervals
Working memory capacity of 15-20 minutes on one topic - Primacy-recency effect - Distributed vs. massed practice
Emotional States - Learning and recall ability affected by chemicals in the brain (emotions)
- Healthy amount of stress (cortisol) increases retention - Distress is detrimental to learning and retention - Feedback and support increase positive emotions and learning - Stereotype effect
Skills vs. Concepts - Involve different processes and memory sites, but are understood as an integrated system
- Goal of skill is to be able to transfer
Improves with connections made to different contexts
Chapter 3. Implications for Teaching 27
CHAPTER 3. IMPLICATIONS FOR TEACHING
3.1. OVERVIEW
―The search for meaning is innate in human nature‖ (Tokuhama-Espinosa 2010).
If this is the case, then teaching should be able to incorporate innate aspects of learning.
Several suggestions for teaching can be deduced from the present understanding of the
brain and learning discussed in Chapter 2. This chapter will discuss these suggestions,
which include that learning by college-age students is improved when students are
provided the following: guided practice, confirmation of relevance, time for
metacognitive reflection, consideration of prior knowledge and skill sets, appropriate
lesson length and assessment strategies, a regard for the student-teacher relationship and
the learning environment, problems varying in difficulty and context, and information
through teaching styles which address different learning preferences. Guided practice,
lesson length, and assessment all are primarily concerned with synaptic adhesion,
memory and storage, and timing, and will thus be grouped together. Furthermore, many
of the elements about how people learn can be addressed by structuring lessons in a
sequence similar to that of the scientific method, which will be explained in Section 3.7.
Each suggestion addresses one or more of the facts about the learning process
outlined in Chapter 2, but it is not implied that the implications for teaching are limited to
these suggestions. A summary of the presented suggestions in relation to the specific
discoveries on learning is provided in Section 3.10. at the conclusion of this chapter.
Chapter 3. Implications for Teaching 28
3.2. ADDRESSING MEMORY AND STORAGE, SYNAPTIC ADHESION, AND TIMING
3.2.1. GUIDED PRACTICE
Since ―practice makes permanent‖ and prior knowledge can be very resistant to
change (Sousa 2008), students‘ first interaction with a new concept or skill will greatly
affect the quality of retention. If initially learned incorrectly, a concept or skill will be
difficult to correct, even more-so if the student fails to recognize the error. As such,
guided practice during students‘ first interaction with a new concept or thinking operation
can be effective to ensure it is retained correctly, instead of solidified incorrectly (Sousa
2008). It has been found anecdotally that ―students are more willing to attempt and to
attend to learning a new skill when they are introduced to it and provided guidance in
applying it‖ (Tokuhama-Espinosa 2010).
There are several reasons that repetition is essential to the learning process. As
stated previously, the synapse created between neurons is the main mechanism of
learning. This synaptic bond is strengthened through repetition. Reiterating, it has been
found that the brain is plastic in the sense that it will reform to the needs and usage of the
individual. Therefore, repeated practice will in fact shape the brain and determine the
areas which are most developed. It is undesirable for incorrect practice to become
imbedded in the physiology of the brain. This is related to the formation of bad habits;
just as overeating and alcoholism can become physiological addictions through practice,
the brain becomes accustomed to certain approaches or techniques to solving problems.
In many college-level courses, guided practice may not be possible on an
individual level, but could be achieved by immediate feedback on a problem that must be
solved in class (Leonard 2000). It is important to ensure that students earnestly attempt
Chapter 3. Implications for Teaching 29
the problem and are provided with relevant feedback, such that they are actively
practicing the skill as opposed to observing it being practiced. When students are shown
examples, they do not engage with the material and are not actively practicing the
concept or skill themselves.
Guided practice can also be facilitated by asking students to discuss problems and
solutions with their neighbors, and then leading a discussion with the entire class
providing their results. The true answer should eventually be identified to dispel any false
conclusions; science is not determined by the majority (How Students Learn: Science
2005, Karplus 1977). Furthermore, motivation to participate and some amount of
background information should be provided beforehand, such that time can be and is used
effectively; students may not actively participate if they do not see the reason to do so
and feel they are capable.
Guided practice is especially important in subject matter about which students
may have solidified preconceptions, such as statics and dynamics, which deal with
objects in the real world. In the first few examples in a classroom setting, students will
come into conflict with their existing mental models, and will either correct them or not.
By receiving immediate feedback, students are more likely to dispel misconceptions, as
opposed to retaining conflicting beliefs.
3.2.2. LESSON LENGTH
Students may benefit from lessons of 15-20 minutes since the single subject
working memory of an adult tends to reach capacity after this duration. This does not
imply that all university class sessions should be cut from 50 minutes to 15, but instead
that the lesson itself could be divided into shorter sections. After 15-20 minutes
Chapter 3. Implications for Teaching 30
approaching a single topic from one direction, the instructor could either adjust the
approach to the topic, e.g. by switching from one method described in Chapter 4.
Teaching Methods, or could change topics altogether.
This suggestion is also consistent with the primacy-recency effect (Section 1.5:
Timing of Learning). During prime-time 1, the students are engaged in the first concept.
When ―down-time‖ begins, a new concept or approach is introduced. This may induce
another prime-time learning period, or perhaps increase stress levels due to a sudden
change and therefore improve attention span. Either way, a sudden change in approach or
topic is hopefully drawing students out of the down-time period, so they are more likely
to learn and retain new material.
3.2.3. ASSESSMENT
Assessment includes but is not limited to summative exams; it can also include
weekly homework or in-class assessments. ―The key to the formation of new reasoning
patterns is an individual‘s responding to his or her inadequacy in using the present
reasoning patterns to cope with a demand‖ (Karplus 1977). The instructor is responsible
for creating this demand. For example, if the goal is for students to be able to transfer a
concept or skill to unfamiliar contexts, the assessments require they achieve or attempt
this objective by addressing problems in novel contexts. Students must be forced to
practice at the level at which they are expected to become proficient.
The type, difficulty, format, and nature of an assessment will determine the
backwash, or effect the assessment tasks have on the students‘ learning (Biggs 2007). ―If
[the activities an assessment format usually elicits] match your objectives, the backwash
Chapter 3. Implications for Teaching 31
is positive, but if they do not, the backwash will encourage students to use surface
approaches to learning‖ (Biggs 2007). Correspondingly, it is argued that
the approach to learning is not a fixed characteristic of an
individual… all learners are capable of using both deep and
surface approaches, and it is their perception of the
demands of a task that largely determines which approach
they actually use. (Dichter 2001)
Therefore, it is important that the assessments (exams, homework, projects) create
positive backwash and demand a deep approach to learning.
A deep approach should be demanded throughout the course, since more practice
of a given skill leads to an increased likelihood for retention. As a note, students should
not be confronted for the first time with a new demand on a major summative assessment
(Felder 1994). Students may enter a state of distress if confronted with a need for a new
skill for the first time on an exam, which may negatively affect their emotional state for
learning and ability to recall. They should be exposed to the thinking and reasoning that
will be expected of them before they are evaluated on it.
Retention can also be improved by administering cumulative assessments. This
will require students to recall information beyond the time it can be temporarily stored.
Distributed practice is essentially forced upon the students, since they must use the
information several times over a longer period of time. Students are also more motivated
to apply a deep learning approach, since they are aware that they will need the
information in the future. Lastly, cumulative assessments force students to comprehend
the new material, revisit previous material and then connect the two (Sousa 2008). This
connection corresponds with a deeper and broader understanding of the material. In many
cases, cumulative assessments correspond well with science courses since concepts tend
Chapter 3. Implications for Teaching 32
to build upon previously-learned concepts.
3.3. CONNECTING TO PRIOR KNOWLEDGE AND SKILL-SETS
To take advantage of students‘ prior knowledge and skill-sets, it is effective to
determine or gauge the cognitive and conceptual level of the students upon entrance to
the course. New material and skills can then be placed within this context. By connecting
to prior knowledge, students are more capable to make sense of the new material.
In the article, The Effect of Instruction using Students’ Prior Knowledge and
Conceptual Change Strategies on Science Learning (1983), Hewson & Hewson state that
learning is the process of reconciliation of a new concept with existing knowledge. In
order for this reconciliation to occur, the students must find the new concept to be
intelligible, plausible, and fruitful. By intelligible, it is meant that the student ―can
construct a coherent representation of [the new concept]‖ independently. Once
understanding is accomplished, the student must also believe that the concept is true, or
believe that it is plausible. For example, a student may understand that ―gravity is
constant,‖ but he or she may nonetheless continue to believe that in a vacuum a bowling
ball will fall at a faster rate than a piece of paper. This means he or she has not reconciled
what has been learned with their previous experience and beliefs. Lastly, students must
believe that the information is fruitful, or ―provides explanatory and predictive power.‖
This aspect of Hewson & Hewson‘s theory will be explained in further detail in the
section below, Section 3.4: Establishing Motivation.
Four different strategies are suggested to facilitate reconciliation: integration of a
new concept with existing (correct) concepts; differentiation between existing concepts to
a more clearly defined, but closely related concept; exchange of an existing concept for a
Chapter 3. Implications for Teaching 33
new more plausible and fruitful one; or conceptual bridging which establishes ―an
appropriate context in which important abstract concepts can be lined with meaningful
common experiences‖ (Hewson & Hewson1983). These approaches are summarized in
Figure 8 below.
Figure 8: Strategies to facilitate reconciliation (Source: Hewson & Hewson 1983)
Neurological findings have also supported the theory that it is important to link
new learning with prior knowledge. ―Brain scans have shown that when new learning is
readily comprehensible (sense) and can be connected to past experience (meaning), there
is substantially more cerebral activity followed by dramatically improved retention‖
(Sousa 2008 quoting Maquire, Frith & Morris, 1999). The importance is that the new
STRATEGIES TOFACILIATATE
RECONCILIATION
Differentiation
• Between existing and new
Integration
• Of new concept with existing (correct) concept
Exchange
• Existing concept for new more plausible/fruitful
Conceptual bridging
• Establish appropriate context for abstract concepts
Chapter 3. Implications for Teaching 34
information is relevant to past experience and understanding.
3.4. ESTABLISHING MOTIVATION
Due to the fact that learning is ―competitive,‖ ideas that are deemed important are
most likely to be retained by students. This is commonly regarded as a quintessential
aspect of a lesson (Sousa 2008, Tokuhama-Espinosa 2010). ―If teachers cannot answer
the ‗Why do we need to know this‘ question in a way that is meaningful to the students,
then we need to rethink why we are teaching that item at all‖ (Sousa 2008). Therefore, it
is effective for instructors to be conscious of and stress the reasons for teaching a concept
or idea.
Hewson & Hewson‘s argument that a lesson must be fruitful (―provides
explanatory and predictive power‖) is directly related to student motivation (1983). When
students appreciate the usefulness and purpose of learning a concept or skill they are
more likely to want to learn it.
Studies on the physical areas of the brain or the
neurotransmitters involved in motivation indicate how
different areas of the brain are actually activated when a
learner has high or low motivation for a task (Depue &
Collins 1999), meaning that motivation is not only a
psychological state of mind but also a physical need.
(Tokuhama-Espinosa 2010)
Felder argues that students can be motivated by relating the material to what is
still to come in the same course, to material in other courses, and to the students‘ personal
experiences. This will especially help inductive and global learners. Inductive learners
benefit from seeing ―the phenomena before they can understand and appreciate the
underlying theory‖ and global learners benefit grasping the big picture or end goal of the
lesson. All learners will benefit from the ability to place a concept within a broader frame
Chapter 3. Implications for Teaching 35
and to make more connections to other ideas and beliefs. Therefore, it is effective for
instructors to have a clear understanding of why a lesson is being taught in terms that will
make sense to and motivate the students.
3.5. METACOGNITIVE REFLECTION
Metacognitive reflection is defined as ―engaging students in reflecting on what they
did to carry out a thinking operation they have just completed‖ in order to ―make thinking
visible‖ (Beyer 1997). In many cases, people reason through a problem with little or no
comprehension of how they reached a solution. Recognition of this process enables
learners to improve upon their methods (Beyer 1997). ―Before we can repair or
strengthen something that is broken or is not working as well as it should be, we need to
be aware of exactly how it presently functions‖ (Beyer 1997).
When students are aware of and understand their current approach, they are more
likely to recognize areas and means for improvement. Practicing the skill of reflection in
itself can be beneficial for the students. Improvement of metacognitive skills tends to
result in ―better conceptions of learning, greater awareness of cognitive strategies, more
complex and integrated knowledge structures, and more accessible and usable
knowledge‖ (Billing 2007). The latter two outcomes also lead to a higher potential of
transferability to other contexts, since both imply a greater quantity and quality of
connections to a new idea or skill. Metacognitive reflection leads to improved
transferability because it leads students to isolate the process from the specific context.
This process can then be applied to other contexts.
One method of metacognitive reflection asks students to share with their peers
their learning process or strategy for solving a problem. By doing so, students can reflect
Chapter 3. Implications for Teaching 36
on their own process, as well as recognize steps they may have missed or overlooked.
Another strategy involves requesting students to write down the steps they followed in
order to solve a certain problem. The key element to metacognitive reflection is that
students reflect on an approach already taken, and link this reflection to future possible
approaches. The main implication for teaching is that reflection should be structured into
the course, either as class time or as part of the homework. Practice in metacognitive
reflection will help students improve their approaches to problems as well as the
likelihood for transfer of these approaches.
3.6. STUDENT-TEACHER RELATIONSHIP AND CREATING A POSITIVE LEARNING
ENVIRONMENT
The student-teacher relationship exists on two levels: the class and the individual
level. In general, the instructor will increase motivation and learning by demonstrating an
interest and investment in the students‘ learning and a confidence in their abilities. This is
confirmed by the finding that students‘ learn better when they feel supported and
motivated by their instructor.
Personal interactions between the students and the instructor also increase
students‘ accountability to the instructor, and therefore increases motivation to come to
class, participate, and do assignments well. The most important aspect of a positive
learning environment is that the students feel ‗safe‘ to respond and interact with the
course. This is naturally improved when the students feel supported by their instructor. It
may be useful for instructors to facilitate a ―healthy‖ amount of stress at times so that the
students remain engaged. This can be facilitated by creating a sense of urgency, stressing
the importance of learning, reminding students of an upcoming exam, or calling on
Chapter 3. Implications for Teaching 37
students at random. The latter of these should be used with caution, because in extreme
cases it can cause anxiety as opposed to attentiveness.
3.7. VARIED PROBLEMS
Introducing the students to problems which vary in required skill level and
context will improve the students‘ ability to transfer problem solving skills from one
particular context to another (Billing 2007). David Billing‘s article, Teaching for transfer
of core/key skills in higher education: Cognitive skills, a result of a survey of mostly
cognitive science literature on the transferability of skills, states that ―teaching should use
multiple examples, varied training contexts, and guidance on problem solving methods,
including explicit task analysis‖ in order to facilitate the transfer of cognitive skills
(2007).
Problem difficulty is varied by requiring a surface understanding in some
problems and a deeper or closer to expert understanding in others. If a deeper level of
understanding is the objective, then students will benefit from problems which require
such an understanding. Students may benefit from also being provided more basic
problems toward the introduction of a concept in order to allow them to establish their
understanding and make initial connections. Many courses naturally follow this sequence,
but it is important that problems eventually demand a deep approach to learning.
It is also important to vary the context of the problems to increase potential for
transfer. If students are learning a general concept, but are presented with examples in
one context, it is likely they will only associate the concept with that one context. If
examples in other contexts are provided, then students are more likely to recognize the
generality and applicability of the overarching concept. This is especially important in
Chapter 3. Implications for Teaching 38
engineering where a concept may be learned in one course and will be needed in an
entirely different course. For example, differential equations may be learned in a math
course, but must be utilized in an electrical engineering or controls course. Students also
benefit from some problems that directly relate to previous or real-world experience.
These examples often act as anchoring examples, since they fit into the students
established working model. Therefore, a variation in the context of problems given to
students, including some examples that relate to prior experience, will enhance students
understanding and ability to transfer.
3.8. TEACHING STYLES
In Section 1.3. Uniqueness of Each Brain, Felder‘s theory of learning styles was
introduced. Felder further proposes that for each learning genre there is an associated
teaching approach, which is illustrated in Figure 8 below. Highlighted are the typical
preferred learning and teaching styles of undergraduate engineering students and
instructors.
Chapter 3. Implications for Teaching 39
Figure 9: Dimensions of Learning and Teaching Styles (Source: Felder 1988)
A variation in teaching style throughout the course can account for all different
learning styles, although ―most college-age engineering students are sensing, visual,
inductive, and active learners, and lean toward sequential learning over global learning‖
(Felder 1988). Traditional lectures address intuitive, auditory, deductive, reflective and
sequential learners (Felder 1988). Therefore, it is important to explore approaches which
align with the typical college age student. Specific methods are described in Chapter 4:
Teaching Methods in Relation to Learning.
While there are identified typical learning styles, teaching with several different
styles may ensure that any type of student learns the material. Variation in teaching style
will also help students develop their abilities to learn through all modes, creating more
well-rounded students who are capable of calling upon different approaches to learning.
Using one method of teaching can be detrimental to students and the class. Students
Chapter 3. Implications for Teaching 40
taught through one method may only develop one set of learning skills, and those who
learn well through this teaching style will flourish while those who don‘t may fail and/or
become discouraged. Utilizing a variation of teaching styles is effective in both
accommodating different students, as well as challenging students to attempt different
approaches.
3.9. THE SCIENTIFIC METHOD APPROACH
Many educational researchers have found that structuring a lesson in the same
fashion as the scientific method can be very effective (Felder 1988, Lawson 2000,
Dichter 2001). This approach helps to address different learning styles, it positively
correlates with the current understanding of how the brain learns and it aligns with
constructivist theory.
The scientific method generally consists of the following steps:
1. Define the question
2. Gather information and resources
3. Construct hypothesis
4. Perform experiment and collect data
5. Analyze results
6. Interpret data and draw conclusions that serve as a starting point for new hypothesis
7. Retest
This is translated to a teaching method by the following the directions; in parentheses
after each step is the learning style which is benefitted by the given step:
Follow the scientific method in presenting theoretical
material. Provide concrete examples of the phenomena the
theory describes or predicts (sensing/inductive); then
develop the theory or formulate the model
(intuitive/inductive/sequential); show how the theory or
model can be validated and deduce its consequences
Chapter 3. Implications for Teaching 41
(deductive/sequential); and present applications
(sensing/deductive/sequential). (Felder 1988)
Each step positively affects the learning process. In the figure below, the step in the
scientific method is provided with an explanation of how it benefits learning.
Table 3. Step in the scientific method and related benefit to learning
Step in Scientific Method Benefit to Learning
1. Define the question 1. Students recognize need for new information
2. Gather information and resources 2. Fit learning into prior experience and context
3. Construct hypothesis 3. Determine preconceptions
4. Perform experiment/solve problem and collect data
4. New ideas constructed through experience
5. Analyze results 5. Connect with prior conceptions
6. Interpret data, draw conclusions new hypothesis
6. Make intelligible (sense)
7. Retest 7. Plausibility, and different contexts transfer
Constructivist theorist, Lawson, argues that these steps are the natural process
humans take to acquire knowledge; ―knowledge acquisition involves the generation and
test of ideas and takes the form of several If/and/Therefore arguments‖ (Lawson 2000).
The If/and/Therefore process is illustrated in the following example involving a gas tank
which is not lighting, and the process an individual would take to diagnose the problem.
The individual might begin with the belief that the wind blew the lighter out, and thus:
If … the wind had blown out (hypothesis)
And …a second match is used to relight the barbecue (test
condition).
Then …the barbecue should relight (expected result).
But …when the second match was used, the barbecue still
did not relight (observed result). (Lawson 2000)
Chapter 3. Implications for Teaching 42
This process is analogous to the steps of the scientific method: there is a question, a
hypothesis, testing, result, analysis, and a conclusion which acts as a new hypothesis.
Teaching in the same sequence that humans naturally acquire knowledge presumably
improves student learning.
3.10. SUMMARY OF TEACHING IMPLICATIONS
Several suggestions for teaching have been described that derive from discoveries
about learning. The correlation, weak or strong, between each of the implications for
teaching and the discoveries about the way people learn is outlined in Table 4.
Chapter 3. Implications for Teaching 43
Table 4. Correlation between learning process discoveries and teaching implications
F A C T S A B O U T L E A R N I N G P R O C E S S
Synaptic
Connection
Memory
and
Storage
Unique-
ness
Prior
Knowledge
Timing of
Learning
Emotional
States
Concepts vs.
Skills
IM
PL
IC
AT
IO
NS
F
OR
T
EA
CH
IN
G
Guided
Practice
Relevance
Metacognitive
Reflection
Acknowledge-
ment of Prior
Knowledge
Lesson length
Assessment
Student-teacher
relationship
Learning
environment
Varied
problems
Teaching styles
Scientific
Method
Approach
Strong correlation
Weak correlation
As is shown, the scientific method approach correlated with five of the seven discoveries
about learning, whereas each other suggestion correlated with two to three of them.
Therefore, this approach is suggested as a single approach to take in to account what has
been found about the way people learn.
Chapter 4. Teaching Methods in Relation to Learning 44
CHAPTER 4. TEACHING METHODS IN RELATION TO LEARNING
4.1. OVERVIEW
Approaches to teaching are generally classified into two general categories:
deductive and inductive. A deductive teaching method presents the theory, and then
provides specific examples of the application of this theory. Inductive teaching methods
begin with examples, from which a conclusion or theory is constructed. The approaches
do not necessarily equate with engagement; it is possible for students to be active and
engaged with either deductive or inductive instruction.
The deductive teaching method is fundamentally equivalent to a traditional lecture
approach, and will be addressed in the following section, but will not be examined as
deeply as the inductive methods. Under the umbrella of the inductive approach are
several specific methods which are described and analyzed in the Inductive Methods
section. The deductive and inductive methods will be discussed in terms of how people
learn, and will be analyzed in relation to these fundamental findings.
Experience with the di