Vary your teaching techniques with Professor Felder

In this article:

• We talk for the first time in this series about Professor Felder, an expert in education.
• We discover his approach to teaching based on different learning styles.
• Thanks to him, we propose new criteria to enrich your courses.

Who is Richard Felder?

Like Eric Mazur, Richard M. Felder is a scientist who pivoted toward education engineering. As he reveals in an interview (1), he has spent 15 “very conventional” years teaching and doing research in chemical engineering, before realising that the terrible results of many of his otherwise very smart undergraduate students implied that something was wrong.

Reflecting on the fact that no one had ever taught him how to teach, he started looking for an answer in the cognitive and educational psychology literature. In the following years he became more and more involved with educators (he even married one!) and their scientific discoveries, until he developed a more successful approach to his lectures.

Alternate learning styles

Avec la psychologue Linda Silverman, Felder a eu l’intuition que différents élèves pouvaient avoir des styles d’apprentissage différents. Ensemble, ils ont conçu tout un test autour de cette idée. Les dimensions des styles d’apprentissage de Felder-Silverman peuvent être résumées par cinq questions différentes :

Together with psychologist Linda Silverman, Felder had the intuition that students display different learning styles. In fact, he designed a whole test around this idea.

Felder-Silverman’s dimensions of learning styles may be summarised in five different questions:

1. Do you prefer to perceive information through sensory (physical sensations) or intuitive (memories and ideas) means?
2. Is sensory information more effectively perceived through visual (pictures, diagrams, demonstrations) or verbal (sounds, written and spoken words) encoding?
3. Are you more comfortable with an inductive approach, where facts are given first and underlying theory is inferred? Or do you prefer being deductive, when principles are given first and consequences and applications are deduced?
4. Is the information better processed actively, through peer discussion and physical engagement, or —reflectively_, through a rather individual introspection?
5. Is your understanding process rather sequential, based on a logical progression of small incremental steps, detail after detail, or global, involving large jumps and favouring big concepts?

Our examples of combinations to enrich your courses

In these terms, one can imagine many possible combinations: some anchored in practical applications, careful but slow; others quicker and theoretical but potentially careless.

A striking observation is that most traditional college classes cover a limited variety of styles: a common teaching style is indeed intuitive, verbal, deductive, reflective, and sequential. Sensing, visual, inductive, active, and global approaches are confined to laboratory classes, that seldom bring sufficient skills development or rarely cover a large portion of the concepts offered by the curriculum.

As we have attempted to demonstrate in a previous article, introducing new challenging ingredients in the lectures needn’t involve major transformations in one’s teaching style. A better balance of styles could be achieved by implementing some of the following solutions, again suggested by Prof. Felder (2). These examples were designed to be applied in the STEM sector, but nothing stops you from adapting them to your own discipline.

• When exposing students to theoretical content, first present phenomena which the theory will help to explain (sensing, inductive, global). Give the students some real-case problems, to see how far they can go without the new tools you are about to provide.
• Information should be provided in a balanced mix of concrete (sensing) and conceptual (intuitive) description, in all courses. For example, when explaining the concepts of torque and angular motion, get students to apply pressure on a door in different ways and let them interpret the outcome of their experiments.
• Algebraic problems (intuitive) should sometimes be matched with numerical examples (sensing).
• Magnitudes of calculated physical quantities should be illustrated by concrete analogies (sensing, global). Introduce the concept of density by discussing the difference between the same volume of water and mercury, for example.
• Both personal time (reflective) and active student participation (active) should be allowed in class when new material is being presented. “One-minute papers” are ideal to close a lecture, letting students reflect by themselves and write the most important point made in the lecture, as well as the most important unanswered question. Group tasks are also very effective when students are asked to answer a variety of questions. This is one of the typical strategies used by Prof. Mazur.
• Encourage team-based cooperation on homework (active).
• The course topics are inevitably presented following a logical flow (sequential), but it doesn’t mean that one can’t establish connections between the current and other material in the same course, or with other courses and disciplines (global). One of Felder’s examples is that cell metabolism is related to energy release by glucose oxidation, which in turn is related to nuclear processes, electron orbit decay, fire combustion, etc.

A few points to bear in mind

However, a few caveats are warranted (3):

• Learning style dimensions are continua, not exclusive categories. A student’s preference for a given style goes from negligible to strong.
• Learning style profiles express tendencies and are far from being infallible behavioural predictors. In fact, the evidence of targeted education methods for specific individuals is still very controversial. According to the current state of research, personality profiles are to be considered neuromyths instead of established methods (4, 5). We must warn that the blind faith in personality profiles leads to pseudoscientific interpretations and may reinforce undue biases against individuals.
• Learning style preferences are not reliable indicators of learning strengths and weaknesses.
• Declared comfort in learning style preferences may be influenced by previous educational experiences.
• Before scaring the teachers about a possible unbearable additional workload, Felder also clarifies that the point is not to adopt a personalised teaching style for every student. Rather, the point is to be aware that more than one method is available to them, and to expose a cohort of students to the best possible variety of approaches. Getting students out of their comfort zone to strengthen their less developed abilities will eventually turn them into successful scholars.

As most potential misinterpretations are ruled out, there are, however, elements to keep in mind when considering different teaching methods. One of the techniques to improve learning is to vary the types of encoding of information. In this sense, Felder’s advice retains its full scientific value.

To conclude, as Felder discusses in a recently edited book (5), technology can certainly be handy in scaling these methods to a large class. While a personal relationship with an instructor probably won’t ever be replaced by technology, multimedia online presentations allow students to repeat lessons as often as they wish. He also admits that response systems with immediate feedback liberate students from their condition of passive observers: together with your personal touch, Wooclap can bring to life many of the listed suggestions.

Sources

1. https://www.engr.ncsu.edu/wp-content/uploads/drive/1jK7Aw77LPVnJ9JiZcVccr6Mv2ELYCR2v/2002-LiberatoInterview(JSciEd).pdf
2. Felder, R. M. & Spurlin, J. (2005). Applications, reliability and validity of the Index of Learning Styles. International Journal of Engineering Education, 21, 103-112.
3. Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning Styles: Concepts and Evidence. Psychological Science in the Public Interest, 9(3), 105–119. https://doi.org/10.1111/j.1539-6053.2009.01038.x
4. Shaw, R.-S. (2019). The Learning Performance of Different Knowledge Map Construction Methods and Learning Styles Moderation for Programming Language Learning. Journal of Educational Computing Research, 56(8), 1407–1429. https://doi.org/10.1177/0735633117744345
5. Felder, R. M. (1993). Reaching the Second Tier: Learning and Teaching Styles in College Science Education. Journal of College Science Teaching, 22(5), 286–290
6. Felder, R. M., & Brent, R. (2016). Teaching and learning STEM: A practical guide.