ASEE Prism, 6(4), 18-23 (December 1996).
Students have different learning styles--characteristic
strengths and preferences in the ways they take in and process
information. Some students tend to focus on facts, data, and
algorithms; others are more comfortable with theories and mathematical
models. Some respond strongly to visual forms of information,
like pictures, diagrams, and schematics; others get more from
verbal forms--written and spoken explanations. Some prefer to
learn actively and interactively; others function more introspectively
Functioning effectively in any professional capacity, however,
requires working well in all learning style modes. For example,
competent engineers and scientists must be observant, methodical,
and careful (characteristics of the sensing style in one
of the learning style models to be described) as well as innovative,
curious, and inclined to go beyond facts to interpretation and
theory (characteristics of the intuitive style in that
model). Similarly, they must develop both visual and verbal
skills. Information routinely comes in both forms, and much of
it will be lost to someone who cannot function well in both of
If professors teach exclusively in a manner that favors their
students' less preferred learning style modes, the students' discomfort
level may be great enough to interfere with their learning. On
the other hand, if professors teach exclusively in their students'
preferred modes, the students may not develop the mental dexterity
they need to reach their potential for achievement in school and
An objective of education should thus be to help students build
their skills in both their preferred and less preferred modes
of learning. Learning style models that categorize these
modes provide good frameworks for designing instruction with the
desired breadth. The goal is to make sure that the learning needs
of students in each model category are met at least part of the
time. This is referred to as "teaching around the cycle."
FOUR LEARNING STYLE MODELS
Before looking at some examples of teaching around the cycle,
let's examine four learning style models that have been used effectively
in engineering education.
The Myers-Briggs Type Indicator (MBTI)
This model classifies students according to their preferences on scales derived from psychologist Carl Jung's theory of psychological types. Students may be:
The MBTI type preferences can be combined to form 16 different
learning style types. For example, one student may be an ESTJ
(extravert, sensor, thinker, perceiver) and another may be an
INFJ (introvert, intuitor, feeler, judger).
Engineering professors usually orient their courses toward introverts
(by presenting lectures and requiring individual assignments rather
than emphasizing active class involvement and cooperative learning),
intuitors (by focusing on engineering science rather than design
and operations), thinkers (by stressing abstract analysis and
neglecting interpersonal considerations), and judgers (by concentrating
on following the syllabus and meeting assignment deadlines rather
than on exploring ideas and solving problems creatively).
Kolb's Learning Style Model
This model classifies students as having a preference for 1) concrete experience or abstract conceptualization (how they take information in), and 2) active experimentation or reflective observation (how they internalize information). The four types of learners in this classification scheme are
Herrmann Brain Dominance Instrument (HBDI)
This method classifies students in terms of their relative preferences for thinking in four different modes based on the task-specialized functioning of the physical brain. The four modes or quadrants in this classification scheme are
Engineering professors on the average are strongly Quadrant A
dominant and would like their students to be that way as well,
according to Edward and Monika Lumsdaine (see references). Most
engineering instruction consequently focuses on left-brain Quadrant
A analysis and Quadrant B methods and procedures associated with
that analysis, neglecting important skills associated with quadrant
C (teamwork, communications) and quadrant D (creative problem
solving, systems thinking, synthesis, and design). This imbalance
is a disservice to all students, but particularly to the 20-40%
of entering engineering students with strong preferences for C
and D quadrant thinking.
Felder-Silverman Learning Style Model
This model classifies students as:
For the past few decades, most engineering instruction has been
heavily biased toward intuitive, verbal, deductive, reflective,
and sequential learners. However, relatively few engineering
students fall into all five of these categories. Thus most engineering
students receive an education that is mismatched to their learning
styles. This could hurt their performance and their attitudes
toward their courses and toward engineering as a curriculum and
career. In the section "Teaching to All Types" I
suggest some instructional methods for addressing the learning
needs of the full spectrum of learning styles.
LEARNING STYLES IN ACTION
Here are some ways that engineering educators have applied learning
style models to provide students with an education that addresses
both their learning strengths and weaknesses.
Applications of the Myers-Briggs Type Indicator
During the 1980s, thousands of engineering students and hundreds of engineering professors took the MBTI as part of a research study conducted by a consortium of eight engineering schools and the Center for Applications of Pyschological Type. The study examined the effects of psychological type differences on the education and career development of engineering students. Educators have used the results to design methods for improved teaching and advising.
For example, Charles Yokomoto, an electrical engineering professor
at Indiana University-Purdue University at Indianapolis, uses
the MBTI as a diagnostic tool for students having academic difficulties.
He administers the instrument to them, gives them the results,
and describes the characteristics of their type. If the descriptions
seem accurate to the students Yokomoto helps them devise remedial
approaches that not only capitalize on their strengths but also
use their weaker modes when doing so is the more appropriate learning
approach. Letting the students assess the accuracy of the descriptions
is essential. Like all other assessment instruments, the MBTI
provides clues, not infallible labels. The student is the ultimate
judge of his or her behavior patterns.
Working with an ISTJ (introvert, sensor, thinker, judger) student
who was failing the introductory course in electrical circuits,
Yokomoto speculated and confirmed that the student relied too
heavily on memorization and drill (traits of ISTJs) as approaches
to problem solving. The professor persuaded his student to add
strategies based more on a fundamental understanding of the concepts.
The student's performance began to improve: by his senior year
he was earning A's, and he subsequently received a master's degree
in electrical engineering.
In another case, Yokomoto found that an ENTJ (extrovert, intuitor,
thinker, judger) student jumped directly into mathematical derivation
on every homework and test problem (behavior consistent with extroverted
intuition) rather than using routine procedures for routine problems.
The resulting demands on the student's time caused problems with
assignment completion and test performance. Once the student
realized what he was doing, he began to apply his analytical talents
when needed rather than using them indiscriminately and inefficiently.
As a result, his performance improved.
(For more information about this work, contact Charles Yokomoto, email@example.com.)
Applications of the Kolb Model
Julie Sharp, an associate professor of technical communications in the chemical engineering department at Vanderbilt University, has administered the Kolb Learning Style Inventory to her technical communication classes and senior chemical engineering laboratory course for the past six years. In the communication class, she gives the students a handout describing ways to communicate effectively to the four different learning types. The students then prepare and give 10-minute presentations designed to appeal to all types. In the laboratory course, the students keep journals in which they describe conflicts and accomplishments within their lab groups, relating them to the group members' learning styles. Sharp has found that teaching students about learning styles helps them learn the course material because they become aware of their thinking processes. More importantly, she says, it helps them develop interpersonal skills that are critical to success in any professional career.
(For more information, contact Julie Sharp, firstname.lastname@example.org.)
In 1989 the College of Engineering and Technology at Brigham Young
University initiated a faculty training program based on Kolb
learning styles. About one-third of the engineering faculty members,
all volunteers, were trained in the concepts of the Kolb model
and methods of teaching to each Kolb type. The volunteers implemented
the approach in their courses, reviewed videotapes of their teaching,
and discussed their successes and problems in focus groups. The
benefits of the program have been significant. Many faculty members--including
some who did not participate in the original training --have redesigned
their courses in an attempt to reach the full spectrum of learning
styles. They do so by using a variety of teaching methods such
as group problem solving, brainstorming activities, design projects,
and writing exercises in addition to formal lecturing. Additionally,
discussions about teaching have become a regular part of department
faculty meetings; the general level of interest and concern about
teaching has increased throughout the engineering college; and
several faculty members have become involved in the "scholarship
of teaching," presenting and publishing peer-reviewed papers
related to engineering education.
(For more information, contact John Harb, email@example.com, or Ronald Terry, firstname.lastname@example.org.)
Applications of the Herrmann Brain Dominance Instrument
In the early 1990s, Edward Lumsdaine and Jennifer Voitle, then of the University of Toledo's engineering college, studied the HBDI types of the college's students and faculty members. They found that many engineering students and professors were left-brain thinkers--logical, analytical, verbal, and sequential. Their data also indicated a strong attrition rate among right-brain thinkers, with many of them dropping out despite earning top grades in analytical courses. "A dominant reason for their choosing other majors is the inhospitable learning climate in engineering, which does not accommodate their thinking preferences, even though voices in industry are increasingly demanding engineers with precisely those thinking skills," Lumsdaine and Voitle claimed in a 1993 paper on their research.
The authors reviewed the existing mechanical engineering curriculum,
found it skewed toward left-brained thinking skills, and set out
to provide a better balance by introducing more creativity, design,
innovation, and teamwork into selected courses. One course, "Introduction
to Computing," originally consisted of 20 percent quadrant
A activities (structured programming) and 80 percent quadrant
B activities ("following the rules" in canned, routine
programs). The redesigned version involved approximately 20 percent
each for quadrants A and B and 30 percent each for quadrants C
and D (student experiments, question formulation, design, modeling,
and optimization). Students worked in teams formed by the professors
to provide balance in HBDI types. Student performance levels
and attitudes to the course improved considerably because of these
These results and results of similar studies led Edward and Monika
Lumsdaine to conclude in a 1995 Journal of Engineering Education
article that the HBDI can serve several important functions.
These include helping students gain insight into their learning
styles and formulate successful learning strategies; helping instructors
understand students' questions, comments, and answers in the context
of their thinking preferences; helping instructors and students
form whole-brain teams for optimum problem solving; and assessing
the influence of curriculum changes on individual and collective
student thinking skills.
(For more information, contact Edward Lumsdaine, email@example.com)
Applications of the Felder-Silverman Model
Along with Barbara Soloman, the coordinator for advising in the First-Year College at North Carolina State University, I am developing an Index of Learning Styles (ILS) that classifies students on four of the five Felder-Silverman dimensions (all but inductive/deductive). The ILS is in a beta version, and some professors are already testing it with their students.
For example, Peter Rosati, a civil engineering professor at the
University of Western Ontario, has used the ILS to assess the
learning styles of engineering faculty members and first-year
and fourth-year engineering students at his university. Rosati
found that faculty members were significantly more reflective,
intuitive, and sequential than the students. The results suggest
that professors could improve engineering instruction by increasing
the use of methods oriented toward active learners (participatory
activities, team projects), sensing learners (guided practice,
real-world applications of fundamental material), and global learners
(providing the big picture, showing connections to related material
in other courses and to the students' experience).
(For more information, contact Peter Rosati, firstname.lastname@example.org.)
At the University of Michigan, Susan Montgomery, an assistant
professor of chemical engineering, is developing multimedia instructional
modules that address the spectrum of Felder-Silverman preferences.
To do this, she assessed her students' learning styles with the
ILS and surveyed them to determine the attitudes of the different
types toward different features of instructional modules. She
reports that sensing and visual learners rated demonstrations
highly; sensing learners liked having access to derivations of
equations (which they may not have grasped as fully as the intuitors
when the instructor first presented the equations in class); and
active, sensing, and visual learners preferred movies more than
their reflective, intuitive, and verbal counterparts did.
(For more information, contact Susan Montgomery, email@example.com.)
In another style-based approach to software instruction,
Curtis Carver and Richard Howard, assistant professors at the
U.S. Military Academy, have developed a hypermedia package for
a computer science course on information systems. The package,
which is distributed on the World Wide Web, is based on the Felder-Silverman
model. Every lesson starts with a list of objectives and is followed
by several different presentations of the lesson material, each
geared toward a different learning style. For example, students
can learn how to install a hard drive by going through a Harvard
Graphics slide show, which is mostly text and appeals to verbal
and sequential learners. Alternatively, they can learn the same thing by
viewing embedded pictures, animations, and movies, which would
appeal to visual and global learners.
The hypermedia package allows students to assess their learning
styles using an online version of the ILS. The Web interface
then provides them the option of having the material presented
in a manner compatible with their style preferences, structuring
the lesson so that the preferred media elements come first. Students
who prefer to organize the presentations themselves without following
a particular sequence may do so also.
(The hypermedia package can be accessed at http://www.eecs.usma.edu/cs383/tools.htm. For more information, contact Curtis Carver, firstname.lastname@example.org, or Richard Howard, email@example.com.)
At North Carolina State University, I've used the Felder-Silverman
model to design the instruction in a longitudinal study of engineering
education. I taught five sequential chemical engineering courses
in a way that would appeal to a range of learning styles. I presented
course material inductively, moving from facts and familiar phenomena
to theories and mathematical models rather than always using the
"fundamentals, then applications" approach. I used
realistic examples of engineering processes to illustrate basic
principles and occasionally provided opportunities for laboratory
and plant visits. I stressed active learning experiences in class,
reducing the time I spent lecturing. In homework assignments
I routinely augmented traditional formula substitution problems
with open-ended questions and problem formulation exercises.
I used extensive cooperative learning, and tried to get the students
to teach one another rather than rely on me exclusively. So far,
the results of my study suggest that teaching to the full spectrum
of learning styles improves students' learning, satisfaction with
their instruction, and self-confidence.
(For more information and references to papers on the longitudinal study, contact Richard Felder, firstname.lastname@example.org.)
TEACHING TO ALL TYPES
Here are some strategies to ensure that your courses present information that appeals to a range of learning styles. These suggestions are based on the Felder-Silverman model.
A learning style model is useful if balancing instruction on each
of the model dimensions meets the learning needs of essentially
all students in a class. The four models I've discussed in this
article satisfy this criterion. Which model educators choose
is almost immaterial, since the instructional approaches that
teach around the cycle for each of the models are essentially
identical. Whether educators are designing a course or curriculum,
writing a textbook, developing instructional software, forming
cooperative learning teams, or helping students develop interpersonal,
leadership, and communication skills, they will benefit from using
any of these models as the basis of their efforts.
For more information on each of the learning style models discussed
in this article, check the following sources.
Myers-Briggs Type Indicator
G. Lawrence, People Types and Tiger Stripes, 3rd Edition. Gainesville, FL, Center for Applications of Psychological Type, 1994.
M.H. McCaulley, "The MBTI and Individual Pathways in Engineering Design." Engr. Education, 80, 537-542 (1990).
M.H. McCaulley, G.P. Macdaid, and J.G. Granade. "ASEE-MBTI
Engineering Consortium: Report of the First Five Years."
Presented at the 1985 ASEE Annual Conference, June 1985.
Kolb Learning Style Model
D.A. Kolb, Experiential Learning: Experience as the Source of Learning and Development. Englewood Cliffs, NJ, Prentice-Hall, 1984.
B. McCarthy, The 4MAT System: Teaching to Learning Styles with Right/Left Mode Techniques. Barrington, IL, EXCEL, Inc., 1987.
J.E. Stice, "Using Kolb's Learning Cycle to Improve Student Learning." Engr. Education, 77, 291-296 (1987).
J.N. Harb, S.O. Durrant, and R.E. Terry. "Use of the Kolb
Learning Cycle and the 4MAT System in Engineering Education."
J. Engr. Education, 82(2), 70-77 (1993).
Herrmann Brain Dominance Model
N. Herrmann, The Creative Brain. Lake Lure, NC, Brain Books, 1990.
M. Lumsdaine and E. Lumsdaine. "Thinking Preferences of Engineering
Students: Implications for Curriculum Restructuring." J.
Engr. Education, 84(2), 193-204 (1995).
Felder-Silverman Learning Style Model
R.M. Felder and L.K. Silverman. "Learning Styles and Teaching Styles in Engineering Education." Engr. Education, 78 (7), 674-681 (1988).
R.M. Felder, "Reaching the Second Tier:
Learning and Teaching Styles in College Science Education," J. Coll. Sci. Teaching,
23(5), 286--290 (1993).