Presentation on theme: "Using Felder’s Index of Learning Styles in the Classroom Kay C Dee, Glen A. Livesay Department of Biomedical Engineering Tulane University, New Orleans,"— Presentation transcript:
Using Felder’s Index of Learning Styles in the Classroom Kay C Dee, Glen A. Livesay Department of Biomedical Engineering Tulane University, New Orleans, LA 70118 USA
We are not here to tell you “how you should teach.”
The Dark Side of Teaching Well You’ll NEVER get tenure!! 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  Dimensions well-suited for discussions of teaching as well as learning
ActiveReflective SensorIntuitor VisualVerbal SequentialGlobal Index of Learning Styles: Overview
ILS Domains Visual 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 Active 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 Sensor 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 Sequential 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 some preferences (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 Concrete Experience Making something new Reflective Observation Internalizing experience Abstract Conceptualization Developing concepts Active Experimentation Doing it Doing Feeling Watching Thinking
Kolb’s Learning Model Concrete Experience Reflective Observation Abstract Conceptualization Active Experimentation Diverger Assimilator Converger Accommodator
Kolb’s Learning Model Concrete Experience Reflective Observation Abstract Conceptualization Active Experimentation Diverger AssimilatorConverger Accommodator
Learning Styles of Other Engineers Tulane, Engr (n=255)  0 10 20 30 40 50 60 70 80 90 100 VisualActiveSensorGlobal Preferred Learning Style Percent of Population 88 62 60 52 80 69 86 U Western Ontario, Engr (n=858)  U Michigan, Chem Engr (n=143)  Ryerson Univ, Elec Engr (n=87)  69 67 53 59 57 72 33 28
Learning Styles and Gender 0 10 20 30 40 50 60 70 80 90 100 VisualActiveSensorGlobal Preferred Learning Style Percent of Population 89 72 58 35 Males, Engr (n = 692) 69 59 61 25 Females, Engr (n = 135) University of Western Ontario 
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 VisualActiveSensorGlobal Preferred Learning Style Percent of Population 89 35 69 25 91 84 72 59 56 78 58 61 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.  The matching hypothesis - just a hypothesis - is difficult to prove. [9,10] Lacks statistical validation.  Bunch’a hooey. 
Predicting Academic Performance We found little or no correlation between SAT score and cumulative GPA at the end of the sophomore year. R 2 = 0.16 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 8009001000110012001300140015001600 SAT score GPA Tulane sophomores in Statics, all disciplines, n=98 
SAT Score Sensor Intuitor Percent of Population Intuitors Outperformed Sensors on SAT Tulane sophomores, Statics group, n=98 
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 paradigm leads 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 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 0.5640.7180.5960.544 n=248n=246n=242n=244 Reliability () of ILS Domains 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Alpha attitude  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 Alpha 0.5820.7440.6790.622 n=249n=247n=248 achievement attitude 
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 0.8 0.9 Four (n=24) Seven (n=40) Twelve (n=26) Sixteen (n=24) Months Between Test - Retest Correlation Coefficient Between Test - Retest Active-Reflective Sensor-Intuitor Visual-Verbal Sequential-Global §§§ Population includes same students § NOT significant (p>0.05)
Specific Answers Correlated Over Time % Students Repeating Original Answers on a Given Question in Retest Number of Questions* 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  Preferred Learning Style Corresponding Teaching Style Visual Verbal InputPresentation Visual Verbal Active Reflective Processing Student Participation Active Passive Sensor Intuitor PerceptionContent Concrete Abstract Sequential Global UnderstandingPerspective Sequential Global
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?  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  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:  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
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 Reflective Observation Abstract Conceptualization Active Experimentation (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 -CONSUMING LOSE 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 students understand 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 students understand 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  raised overall academic performance  (even in subsequent courses taught “traditionally” by other instructors  ) increased student retention  increased graduation rate 
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 course 2. Laboratory or “laboratory-like” experiences 3. How my skills and abilities were enhanced in the course 4.My assessment of the course 5.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 scale 1. strongly disagree 2. disagree 3. neutral or undecided 4. agree 5. 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. 
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 Construct Items Improved Total Items Perception of course58 Laboratory-like experiences 56 Skill and ability enhancement 516 Assessment of course311 Instructor512
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? ActiveReflective SensorIntuitor VisualVerbal 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.
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 Percent Information Retained 01050 Lecture Lecture with active breaks R. Brent, R. Felder, J. Stice, National Effective Teaching Institute, 1998. Active Learning and Info Retention