using felder’s index of learning styles in the classroom kay c dee, glen a. livesay department of...
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Using Felder’s Index of Learning Styles
in the Classroom
Kay C Dee, Glen A. Livesay
Department of Biomedical EngineeringTulane University, New Orleans, LA 70118 USA
Who, What, Why?
We are not here to tell you “how you should teach.”
The Dark Side of Teaching Well
You’llNEVER gettenure!!
Of course, this varies with institution and priorities.
“You prep for classes your way, Harris, I’ll prep for classes my way.”
Overview
Broad Questions:
What are some of the different ways that students take in and process information?
Which learning styles are favored by:• many students?• the teaching style of many professors?
What can we do to reach a full spectrum of learning styles?
Outline
I What is Felder’s Index of Learning Styles (ILS)? Where did it come from?
II What has the ILS told us about learning styles so far?
III Let’s be fair - are there concerns or critiques associated with the ILS?
IV How can we use learning style information to make informed teaching style choices?
V Does using ILS information in the classroom actually make a difference?
Learning Styles
• perception, acquisition, processing, and retention of information
• both cognitive and affective behaviors
• individuality
• maximal learning when instruction capitalizes on an individual’s learning style preferences
- the matching hypothesis
There are several definitions of “learning style.”
Generally, these definitions include aspects of:
Felder’s Index of Learning Styles
• Relatively short questionnaire
• Specifically formulated with engineering students in mind
• Does not require professional scoring and interpretation
• Collected data/publications available [1]
• Dimensions well-suited for discussions of teaching as well as learning
Active Reflective
Sensor IntuitorVisual Verbal
Sequential Global
Index of Learning Styles: Overview
ILS Domains
VisualVisual Verbal Verbal
• Pictures
• Diagrams
• Flow charts
• Plots
• Spoken words
• Written words
• Formulas
“Show me the systems you’re talking about.”
“Explain what’s going on inside the systems.”
ILS Domains
ActiveActive Reflective Reflective
• Tends to process information while doing something active
• Likes group work
• May start tasks prematurely
• Tends to process information introspectively
• Likes independent work
• May never get around to starting tasks
“Let’s just try it out.” “Let’s make sure we’ve thought this through.”
ILS Domains
SensorSensor Intuitor Intuitor
• Focuses on sensory input - what is seen, heard, touched, etc.
• Prefers concrete information: facts and data
• Focuses on ideas, possibilities, theories
• Prefers more abstract information: theory and models
“How does this class relate to the real world?”
“All we did were plug-and-chug assignments.”
ILS Domains
SequentialSequential Global Global
• Can function with partial understanding
• Makes steady progress
• Good at detailed analysis
• Needs to see the big picture
• May start slow and then make conceptual leaps
• Good at creative synthesis
“I need to focus on one part of the project and get it done - then I can move onward.”
“I need to see how this all fits together before I can start the project.”
What We’re NOT Saying
We don’t mean to “put people in boxes.”
What We’re NOT Saying
We don’t mean to “put people in boxes.”
Most people, however, have somepreferences (mild, moderate, or strong).
Everyone learns both actively and reflectively, both visually and verbally, etc.
Origins of ILS Domains
Sensor - Intuitor
• Carl Jung’s theory of psychological types:sensing and intuition modes of perception
• Myers-Briggs Type Indicator:sensors and intuitors as problem solvers
• Kolb’s experiential learning model:concrete experience and abstract conceptualization
Origins of ILS Domains
Active - Reflective
• Myers-Briggs Type Indicator: extrovert and introvert
• Kolb’s experiential learning model: active experimentation and reflective observation
Kolb’s Cycle
ConcreteExperience
Makingsomething new
ReflectiveObservation
Internalizingexperience
AbstractConceptualization
Developingconcepts
ActiveExperimentation
Doing it
Doing
Feeling
Watching
Thinking
Kolb’s Learning Model
ConcreteExperience
ReflectiveObservation
AbstractConceptualization
ActiveExperimentation
Diverger
AssimilatorConverger
Accommodator
Kolb’s Learning Model
ConcreteExperience
ReflectiveObservation
AbstractConceptualization
ActiveExperimentation
Diverger
AssimilatorConverger
Accommodator
Kolb’s and Felder’s Models
ConcreteExperience
AbstractConceptualization
Diverger
AssimilatorConverger
Accommodator
ReflectiveActive
Kolb’s and Felder’s Models
Diverger
AssimilatorConverger
Accommodator
ReflectiveActive
Sensor
Intuitor
Active Reflective
Sensor IntuitorVisual Verbal
Sequential Global
Index of Learning Styles: Overview
Faculty Learning Styles
73
38 36
27
0
10
20
30
40
50
60
70
80
90
100
Visual Active Sensor Global
Preferred Learning Style
Perc
en
t of
Pop
ula
tion
n=568 [2]
(national)83
2517
75 n=12(Tulane BMEN)
Learning Styles - Tulane
0
10
20
30
40
50
60
70
80
90
100
Visual Active Sensor Global
Preferred Learning Style
Perc
en
t of
Pop
ula
tion
83
2517
75
n=12(BMEN faculty)
88
6260
52
n=255 [3]
(ENGR students)
Learning Styles of Other Engineers
Tulane, Engr (n=255) [3]
0
10
20
30
40
50
60
70
80
90
100
Visual Active Sensor Global
Preferred Learning Style
Perc
en
t of
Pop
ula
tion
88
62 60
52
80
69
86
U Western Ontario, Engr (n=858) [4]
U Michigan, Chem Engr (n=143) [5]
Ryerson Univ, Elec Engr (n=87) [6]
69 67
53
59 57
72
3328 28
Learning Styles and Gender
0
10
20
30
40
50
60
70
80
90
100
Visual Active Sensor Global
Preferred Learning Style
Perc
en
t of
Pop
ula
tion
89
72
58
35
Males, Engr(n = 692)
69
59 61
25
Females, Engr(n = 135)
University of Western Ontario [7]
Learning Styles and Gender
Males, Engr(n = 692)
Females, Engr(n = 135, 16.3%)
61
48
0
10
20
30
40
50
60
70
80
90
100
Visual Active Sensor GlobalPreferred Learning Style
Perc
en
t of
Pop
ula
tion
89
35
69
25
9184
72
59 56
78
5861
50
67
University of Western Ontario
Males, Engr(n = 129)
Females, Engr(n = 63, 32.8%)
Tulane University
Index of Learning Styles: Critiques
Concerns which have been noted regarding the use of the Index of Learning Styles:
• Doesn’t predict academic performance. [8]
• The matching hypothesis - just a hypothesis - is difficult to prove. [9,10]
• Lacks statistical validation. [8]
• Bunch’a hooey. [11]
Predicting Academic Performance
We found little or no correlation between SAT score and cumulative GPA at the end of the sophomore year.
R2 = 0.16
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
800 900 1000 1100 1200 1300 1400 1500 1600
SAT score
GP
A
Tulane sophomores in Statics, all disciplines, n=98 [3]
StrongMod
MildMild
ModStrong
1500+ (4)
1400-1499 (21)
1300-1399 (40)
1200-1299 (24)
1100-1199 (5)
Below 1100 (4)
0.00
0.10
0.20
0.30
0.40
0.50
0.60
SAT Score
SensorIntuitor
Pe
rce
nt
of
Po
pu
lati
on
Intuitors Outperformed Sensors on SAT
Tulane sophomores, Statics group, n=98 [3]
ILS = Academic Performance? No.
Some concerns regarding the ILS appear to arise from a misapplication of the inventory:
• It was not developed to enable predictions of academic performance.
• It was not developed as a selection tool to determine ‘who should be an engineer’.
Activities or tests which engage only one learning style may not illustrate the true potential or abilities of a group of students.
Testing the Matching Hypothesis
B = f (P, E)Behavior-person-environment paradigmleads to the idea of optimizing the instructional environment for optimal learning.
Testing the matching hypothesis is difficult -there are many learning style schemes to test, not all easily comparable to each other.
Meta-analyses [9,10] have claimed that a majority of published studies support the matching hypothesis.
Statistical Validation
Validity(Accuracy)
item total correlation (ITC)
coefficient
Reliability(Precision)
Statistical Analysis
SPSS was used to:
• Calculate reliability coefficients for each learning style domain.
- a larger value implies a more internally consistent construct.
• Perform item and factor analyses to determine which items were most strongly correlated with each other and how many factors were present within each domain.
- removing poorly correlated items increases
Active-Reflective
Sensor-Intuitor
Visual-Verbal
Sequential-Global
ILS Domain
0.564 0.718 0.596 0.544
n=248 n=246 n=242 n=244
Reliability () of ILS Domains
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Alp
ha
attitude[12]
achievement
Reliability () of Core ILS Domains
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Active-Reflective
Sensor-Intuitor
Visual-Verbal
Sequential-Global
ILS Domain
Alp
ha
0.582 0.744 0.679 0.622
n=249 n=247 n=248 n=248
achievement
attitude[12]
Measures of Reliability
is commonly used for estimating reliability (mean of split halves). Challenges:
- low number of questions (11 per domain)- mutually exclusive (dichotomous) questions- no ‘right’ answer to questions
Test-retest reliability is what is estimating: to what degree will people obtain the same ILS scores if they take the test again? Challenges:
- requires multiple administrations - if too long between, people may change- if too short between, people may remember
test
Test-Retests Are Correlated Over Time
0
0.3
0.4
0.5
0.6
0.7
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0.9
Four(n=24)
Seven(n=40)
Twelve(n=26)
Sixteen(n=24)
Months Between Test - Retest
Corr
ela
tion
Coeffi
cie
nt
Betw
een
Test
- R
ete
st
Active-ReflectiveSensor-IntuitorVisual-VerbalSequential-Global
§ § §
Population includessame students
§NOT significant (p>0.05)
0
2
4
6
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12
14
1009050 60 70 800 10 20 30 40
Specific Answers Correlated Over Time
% Students Repeating Original Answerson a Given Question in Retest
Nu
mb
er
of
Qu
esti
on
s*
Test-Retest Data(16 month interval, n=24)*Out of 44 questions.
More ‘Repeatable’ Questions
Test-Retest Data(16 month interval, n=24)
37) I am more likely to be considered: a) outgoing b) reserved
41) The idea of doing homework in groups, with one grade for the entire group: a) appeals to me b) does not appeal to me
43) I tend to pictures places I have been: a) easily and fairly b) with difficulty and without accurately much detail
Greater than 90% of students answered test-retest identically on these questions
Less ‘Repeatable’ Questions
Test-Retest Data(16 month interval, n=24)
50% or less of students answered test-retest identically on these questions
17) When I start a homework problem, I am more likely to: a) start working on the b) try to fully understand the solution immediately problem first
36) When I am learning a new subject, I prefer to: a) stay focused on the b) try to make connections between
subject, learning as that subject and related subjects much about it as I can
44) When solving problems in a group, I would be more likely to: a) think of steps in b) think of possible consequences or the solution process applications in a range of areas
16) When I’m analyzing a story or a novel: a) I think of the incidents b) I know the themes and must and put them together go back to find the incidents
Validation Study Summary
The ILS satisfies general guidelines for reliability across all domains.
• between 0.54 and 0.72 with all questions. increased in all domains with “core” questions,
especially visual/verbal, sequential/global.
Test-retest scores in all domains were significantly correlated over various intervals.
• correlation was highest at shortest interval, and generally reduced with longer intervals.
Recommendations
We believe Felder’s ILS to be a useful, appropriate, statistically-acceptable tool for characterizing learning preferences and discussing teaching methods.
There is (as always) some room for improvement. We encourage others to test new questions, work on statistical validation - especially when the ILS is administered to large numbers of students at one time - and share their findings.
Additional Comments on Validity
The nature of the ILS - to force choices for a set of individual questions - necessarily spreads out responses.
- Increases in variance are directly related to lower values for .
Guidelines for statistical validity developed for tests of achievement (e.g., minimum of 0.7) should not be blindly applied to the ILS.
“An instrument is valid if it measures what it is intended to measure”.
Dimensions of Teaching and Learning [13]
Preferred Learning Style
CorrespondingTeaching Style
VisualVerbal Input Presentation
VisualVerbal
ActiveReflective Processing
StudentParticipation
ActivePassive
SensorIntuitor Perception Content
ConcreteAbstract
SequentialGlobal Understanding Perspective
SequentialGlobal
The “Traditional” Lecture Format
The traditional engineering lecture format (teaching style) tends to be (almost exclusively):
INTUITIVE
SEQUENTIAL
PASSIVE
VERBAL
Learning Styles and “Traditional” Lectures
The traditional lecture format does match some students’ preferred learning styles, however, the majority of students tend to prefer:
VISUAL, ACTIVE, and SENSING approaches
In fact, the teaching style utilized in the traditional lecture does not necessarily match the preferred learning styles of professors!
Teach to a Student’s Style, or Against? [14]
The matching hypothesis: teaching to a student’s learning style provides the best opportunity for learning.
- a student functioning in their preferred modes is focused on learning and not on overcoming a barrier.
However, should we teach to the strengths of the student, or work to help them develop in their areas of weakness (less preferred modes)?
- students will need to be able to function in different modalities at different times, e.g. both actively and reflectively, both visually and verbally, etc.
Teach to Many Preferred Styles [14]
With the diversity of learning styles in the classroom, do we teach to a single, preferred learning style? If so, which one?
- teaching to a single, preferred learning style (or using a single style to teach) will benefit those FEW students who prefer that chosen style.
The best solution is likely to utilize a variety of instructional styles and modes of delivery in a course.
- enable ALL of the students to function in their preferred modes some of the time, and also encourage
development in less-preferred modes.
Teaching Styles - Reaching Styles
Good news:Traditional lecturing does address several categories of learning styles.
VERBAL, (REFLECTIVE), SEQUENTIAL, and INTUITOR
Better news:Engaging multiple learning styles does NOT require complete restructuring of a course, or eliminating traditional lectures.
Still better news:Teaching methods that address styles short-changed by traditional methods (e.g. VISUAL, ACTIVE, GLOBAL, and SENSOR) often accommodate multiple styles.
For example: [15]
• Motivate theoretical material with prior presentation of phenomena that the theory will help explain, and problems the theory will be used to solve (SENSOR, GLOBAL).
• Balance concrete information (SENSOR) with conceptual information (INTUITOR) in all courses.
Teaching Styles - Reaching Styles
Teaching Styles - Reaching Styles
• Complement oral and written explanation of concepts (VERBAL) with extensive use of sketches, plots, etc. and physical demonstrations where possible (VISUAL).
• Illustrate abstract concepts with at least some numerical examples (SENSOR), in addition to traditional algebraic examples (INTUITOR).
• Use physical analogies and demonstrations to improve students’ grasp of magnitudes of calculated quantities (SENSOR, GLOBAL).
• Demonstrate the logical flow of individual course topics (SEQUENTIAL), and also highlight connections to other material in the course and other courses, in other disciplines, and in everyday experience (GLOBAL).
Teaching Styles - Reaching Styles
• Provide time in class for students to think about material being presented (REFLECTIVE) and for active participation (ACTIVE).
- pause during lecture to allow time for thinking and formulation of questions (reflective).
- assign 1-minute papers, where students write the most important point of the lecture and the most pressing
unanswered question (active and reflective).- assign brief, group problem-solving exercises where students work with neighbors (active and reflective).
• Encourage or mandate cooperation on homework, or through team projects (ACTIVE).
Teaching Styles - Reaching Styles
Teach the Cycle!Concrete
Experience
ReflectiveObservation
AbstractConceptualization
ActiveExperimentation
(Kolb’s Cycle, that is.)
Meta-Active Learning
What types of reasons might professors give for not using these ideas (for example: active learning exercises) in their courses?
2 minutes, Go!
Fears - Active Learning
TIME-CONSUMINGLOSE CONTROL OF THE CLASS
UNPREDICTABILITY
“How will I cover the syllabus?”
“What do I want to cover?”
“What do I want the students to be able to do?”
TOO MUCH EFFORT
Active Learning: Benefits
• Students cannot be ‘passive vessels’ - they must be engaged with the material
• Clearly informs instructor what studentsunderstand and what they don’t
• Shifts focus from professor to material(“sage on the stage” to “guide on the side”)
• Increases and personalizes student-professor interactions
Active Learning: Potential Drawbacks
• Students cannot be ‘passive vessels’ - they must be engaged with the material
• Clearly informs instructor what studentsunderstand and what they don’t
• Shifts focus from professor to material
The Whole Enchilada - DOES IT WORK?
A longitudinal study of chemical engineering students has shown that courses designed to accommodate a spectrum of learning styles:
• increased students’ confidence in their academic preparation [17]
• raised overall academic performance [16] (even in subsequent courses taught “traditionally” by other instructors [17])
• increased student retention [16]
• increased graduation rate [17]
Data from Tulane BMEN
We have modified several junior-level courses to address the sensing and active learning preferences of our students.
New lab components were made possible through a National Science Foundation “Course, Curriculum and Laboratory Improvement” grant.
A team of biomedical and psychology faculty designed an assessment questionnaire to be used as part of the evaluation plan.
TUBA Model
Tulane University Biomedical Assessment (TUBA) model
Five constructs:1. My perception of what happened in the course2. Laboratory or “laboratory-like” experiences3. How my skills and abilities were enhanced in
the course4. My assessment of the course5. The instructor
Administered to 134 students in Spring 2001, 113 students in Fall 2001, and 77 students in Spring 2002.
TUBA Model
53 “fill-in-the-blank” questions using the scale1. strongly disagree2. disagree3. neutral or undecided4. agree5. strongly agree
Example:1. This course included a number of “hands-on” projects or exercises. _____
Statistical validation for TUBA model was conducted using data from the three administrations. [18]
Assessing the Impact
Three courses were assessed in Spring 2001 and Spring 2002.
Longitudinal results were assessed by performing independent t-tests on each item.
The number of items which demonstrated significant (p < 0.05) improvement were summed and reported.
No items showed ‘negative improvement’ from Spring 2001 to Spring 2002.
Results
BMEN 340 - Spring 2001 and Spring 2002
ConstructItems
ImprovedTotal Items
Perception of course 5 8
Laboratory-like experiences
5 6
Skill and ability enhancement
5 16
Assessment of course 3 11
Instructor 5 12
What Happened?
BMEN 340 incorporated no hands-on activities in 2001 but added three laboratories in 2002, teaching students a new skill set (cell culture experiments).
Students in 2002 expressed higher ratings of their:• teamwork skills• interest in conducting research or working in
the area• confidence in their abilities• instructor’s knowledge of the material
QUIZ
I What is Felder’s Index of Learning Styles (ILS)? Where did it come from?
II What has the ILS told us about learning styles so far?
III Let’s be fair - are there concerns or critiques associated with the ILS?
IV How can we use learning style information to make informed teaching style choices?
V Does using ILS information in the classroom actually make a difference?
Wrap-up and Summary
I What is Felder’s Index of Learning Styles (ILS)? Where did it come from?
Active Reflective
Sensor IntuitorVisual Verbal
Sequential Global
* Similar to stages of Kolb’s cycle.
*
*
Wrap-up and Summary
II What has the ILS told us about learning styles so far?
Students tend, in general to prefer visual, active, and sensing learning styles.
Not all populations of students (or faculty) are the same.
Wrap-up and Summary
III Let’s be fair - are there concerns or critiques associated with the ILS?
Yes.
BUT: We believe Felder’s ILS to be a useful, appropriate, statistically-acceptable tool for characterizing learning preferences and discussing teaching methods. Nothing more, nothing less.
Wrap-up and Summary
IV How can we use learning style information to make informed teaching style choices?
There are many ways.
Start small. Try an approach more than once before giving up. Tell students what you are doing and why.
If you try only two things:• show pictures or models (visual)• provide short times to think and short times
to interact (reflective / active)
Wrap-up and Summary
V Does using ILS information in the classroom actually make a difference?
Yes.
Thank you.
Acknowledgements
We thank:
• The Rose-Hulman Institute of Technology, for the opportunity to present this material.
• The students who participated in our studies, for their time and good will.
• Rich Felder, for mentoring and inspiration.
• The National Science Foundation for support provided by awards DUE-0088333, BES-9983931, and BES-0093969.
Time Into Lecture When Information Was Presented(minutes)
100
70
20
Perc
en
t In
form
ati
on
Reta
ined
0 10 50
Lecture
Lecture withactive breaks
R. Brent, R. Felder, J. Stice, National Effective Teaching Institute, 1998.
Active Learning and Info Retention