Using Felder’s Index of Learning Styles in the Classroom

1. 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

2. Who, What, Why?

3. We are not here to tell you “how you should teach.”

4. You’ll NEVER get tenure!! The Dark Side of Teaching Well Of course, this varies with institution and priorities.

5. “You prep for classes your way, Harris, I’ll prep for classes my way.”

6. 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?

7. 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? IIILet’s be fair - are there concerns or critiques associated with the ILS? IVHow can we use learning style information to make informed teaching style choices? VDoes using ILS information in the classroom actually make a difference?

8. Learning Styles There are several definitions of “learning style.” Generally, these definitions include aspects of: • 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

9. 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

10. Visual Verbal Sensor Intuitor Sequential Global Active Reflective Index of Learning Styles: Overview

11. 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.”

12. 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 make sure we’ve thought this through.” “Let’s just try it out.”

13. 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.”

14. 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.”

15. What We’re NOT Saying We don’t mean to “put people in boxes.”

16. Everyone learns both actively and reflectively, both visually and verbally, etc. What We’re NOT Saying We don’t mean to “put people in boxes.” Most people, however, have some preferences (mild, moderate, or strong).

17. 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

18. Origins of ILS Domains • Active - Reflective • Myers-Briggs Type Indicator: • extrovert and introvert • Kolb’s experiential learning model: • active experimentation and reflective observation

19. Internalizing experience Feeling Making something new Reflective Observation Active Experimentation Watching Doing Developing concepts Abstract Conceptualization Doing it Thinking Kolb’s Cycle Concrete Experience

20. Diverger Accommodator Assimilator Converger Kolb’s Learning Model Concrete Experience Reflective Observation Active Experimentation Abstract Conceptualization

21. Kolb’s Learning Model Concrete Experience Accommodator Diverger Active Experimentation Reflective Observation Converger Assimilator Abstract Conceptualization

22. Kolb’s and Felder’s Models Concrete Experience Accommodator Diverger Active Reflective Converger Assimilator Abstract Conceptualization

23. Kolb’s and Felder’s Models Sensor Accommodator Diverger Active Reflective Converger Assimilator Intuitor

24. Visual Verbal Sensor Intuitor Sequential Global Active Reflective Index of Learning Styles: Overview

25. 83 n=12 (Tulane BMEN) 75 73 38 36 27 25 17 Faculty Learning Styles 100 n=568 [2] (national) 90 80 70 60 Percent of Population 50 40 30 20 10 0 Visual Active Sensor Global Preferred Learning Style

26. 88 83 75 n=255 [3] (ENGR students) 62 60 52 25 17 Learning Styles - Tulane 100 n=12 (BMEN faculty) 90 80 70 60 Percent of Population 50 40 30 20 10 0 Visual Active Sensor Global Preferred Learning Style

27. 86 80 72 69 69 67 59 57 53 33 28 28 U Michigan, Chem Engr (n=143)[5] Ryerson Univ, Elec Engr(n=87)[6] U Western Ontario, Engr (n=858)[4] Learning Styles of Other Engineers 100 88 90 80 70 62 60 60 52 Percent of Population 50 40 30 20 10 0 Visual Active Sensor Global Preferred Learning Style Tulane, Engr(n=255)[3]

28. Learning Styles and Gender Males, Engr (n = 692) 100 89 90 Females, Engr (n = 135) 80 72 69 70 61 University of Western Ontario [7] 59 58 60 Percent of Population 50 40 35 30 25 20 10 0 Visual Active Sensor Global Preferred Learning Style

29. Learning Styles and Gender 100 91 89 90 84 78 80 72 69 67 70 61 61 59 58 56 60 Percent of Population 50 48 50 40 35 25 30 20 10 0 Visual Active Sensor Global Preferred Learning Style University of Western Ontario Tulane University Males, Engr (n = 692) Females, Engr (n = 135, 16.3%) Males, Engr (n = 129) Females, Engr (n = 63, 32.8%)

30. 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]

31. 2 R = 0.16 Predicting Academic Performance We found little or no correlation between SAT score and cumulative GPA at the end of the sophomore year. 4.0 3.5 3.0 2.5 GPA 2.0 1.5 1.0 Tulane sophomores in Statics, all disciplines, n=98 [3] 0.5 0.0 800 900 1000 1100 1200 1300 1400 1500 1600 SAT score

32. Percent of Population SAT Score Sensor Intuitor Intuitors Outperformed Sensors on SAT Tulane sophomores, Statics group, n=98[3]

33. 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.

34. 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.

35. a coefficient item total correlation (ITC) Statistical Validation Reliability (Precision) Validity (Accuracy)

36. Statistical Analysis • SPSS was used to: • Calculate a reliability coefficients for each learning style domain. • a larger a 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 a.

37. 0.564 0.718 0.596 0.544 Active- Reflective Sensor- Intuitor Visual- Verbal Sequential- Global n=248 n=246 n=242 n=244 ILS Domain Reliability (a) of ILS Domains 0.8 achievement 0.7 0.6 attitude[12] 0.5 Alpha 0.4 0.3 0.2 0.1 0

38. Reliability (a) of Core ILS Domains 0.582 0.744 0.679 0.622 0.8 achievement 0.7 0.6 attitude[12] 0.5 Alpha 0.4 0.3 0.2 0.1 0 Active- Reflective Sensor- Intuitor Visual- Verbal Sequential- Global n=249 n=247 n=248 n=248 ILS Domain

39. 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 a 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

40. Active-Reflective Sensor-Intuitor Visual-Verbal Sequential-Global  NOT significant (p>0.05) § Population includes same students Test-Retests Are Correlated Over Time 0.9 0.8 0.7 0.6 Correlation Coefficient Between Test - Retest 0.5   0.4 0.3 0 Four (n=24) Seven (n=40) Twelve (n=26) Sixteen (n=24) § § § Months Between Test - Retest

41. Specific Answers Correlated Over Time Number of Questions* % Students Repeating Original Answers on a Given Question in Retest Test-Retest Data (16 month interval, n=24) *Out of 44 questions.

42. More ‘Repeatable’ Questions Greater than 90% of students answered test-retest identically on these questions 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 Test-Retest Data (16 month interval, n=24)

43. Less ‘Repeatable’ Questions 50% or less of students answered test-retest identically on these questions 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 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 Test-Retest Data (16 month interval, n=24)

44. Validation Study Summary • The ILS satisfies general guidelines for a reliability across all domains. • a between 0.54 and 0.72 with all questions. • a 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.

45. 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.

46. 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 a. • Guidelines for statistical validity developed for tests of achievement (e.g., a 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”.

47. Dimensions of Teaching and Learning [13] Preferred Learning Style Corresponding Teaching Style Visual Verbal Visual Verbal Input Presentation Active Reflective Active Passive Student Participation Processing Sensor Intuitor Concrete Abstract Perception Content Sequential Global Sequential Global Understanding Perspective

48. The “Traditional” Lecture Format The traditional engineering lecture format (teaching style) tends to be (almost exclusively): VERBAL PASSIVE SEQUENTIAL INTUITIVE

49. 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!

50. 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.