RELATING RESEARCH ON LEARNING TO EDUCATIONAL … · The inductive teaching method better aligns with how people learn than deductive methods, but there are several barriers to the

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.


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.


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


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


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.


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


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).


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


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)


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.


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.


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


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


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


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.


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


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.


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.


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.


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)


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


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.


―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


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.


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.


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


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


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


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


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


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


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


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


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


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


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.


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


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


(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)


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.


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

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RELATING RESEARCH ON LEARNING TO EDUCATIONAL … · The inductive teaching method better aligns with how people learn than deductive methods, but there are several barriers to the