Teaching methods and strategies are of great importance in our modern world of fast-paced information flow. The traditional classroom lecture model where a professor talks for 90 minutes, the class takes notes, does exercises and problems in class, and reviews for quizzes and exams has been proven effective only to a certain extent. New models of teaching and learning are causing much change in the way educators teach and students learn, how assessments are done, and what type of activities are chosen.
Effective pedagogy in education stems from careful curriculum and course planning, outcomes-based assessments (Spady, 1994), teaching skills, student engagement, faculty support, and several other areas. Teaching techniques in delivering complex course material are also crucial in many subjects, especially highly-technical ones.
On the student learning side, several models of learning according to learning styles, multiple intelligences, and other learning-related aspects have been implemented in many course curricula worldwide, but their actual effects on learning and scholastic achievement are not so clear due to a lack of more robust, controlled experimental research. On the bright side, neuroimaging studies have found specific brain loci where we can map out individual and group similarities and differences in learning abilities and modalities.
This article will discuss Bloom’s Taxonomy, the Feynman method/Feynman technique, Wieman’s Scientific Teaching Method, the Pomodoro Technique, Learning Styles, the Ten Multiple Intelligences, neuroimaging and learning, other teaching techniques, and future teaching and learning trends.
Bloom’s Taxonomy of Educational Objectives was formulated in the 1950s by educational psychologist Benjamin Bloom (Lasley, 2016). Essentially, this is a framework for organizing learning objectives and activities into different levels of complexity. This framework tracks student abilities and development as learning progresses from simple memorization to synthesis and conceptual mastery of the subject matter.
It has five basic levels which are (adapted from Yale Poorvu Center for Teaching and Learning, 2021):
Revised in 2002, several more revisions have been made or proposed, including the use of verbs for nouns in the taxonomy planning of lessons (e.g, analyzing instead of analysis) (Krathwohl, 2002).
Krathwohl’s revisions also included the setting of an assessment table vis-à-vis Bloom’s learning outcomes. Four dimensions of knowledge, namely, factual knowledge, conceptual knowledge, procedural Knowledge, and metacognitive knowledge are mapped against the six Bloom’s taxonomy learning outcomes: 1. Remember 2. Understand 3. Apply 4. Analyze 5. Evaluate 6. Create (Krathwohl, 2002). The table is filled up with the planned activities corresponding to each of the learning objectives. This setup enables teachers to map the progress of each individual student, and to design alternative activities and assessment methods for other students, especially those with learning disabilities.
It is more strategic and effective to set learning OBJECTIVES instead of learning GOALS; the former refer to specific target knowledge, skills, and abilities that students must learn or develop. Learning goals, on the other hand, are the overall outcomes, whereas learning objectives are measurable/quantifiable.
A good formula one can use is:
Learning activities and their corresponding student assessments must be aligned with the set learning objectives.
For example, a wide learning goal would be for students to develop a broader knowledge of evolution throughout geological time (Britannica, 2020). Specific learning goals may include learning the timelines of the development of life on Earth from the Pre-Cambrian through the various eras, along with their respective time scales and representative fossil types.
The Feynman method (or Feynman technique) is a teaching and learning technique espoused by Richard Feynman, who won the 1965 Nobel Prize in Physics for his work in quantum electrodynamics (NobelPrize.org, 2022). This technique aims to enable one to explain what he/she knows in a very simple way. It is a very effective method for teaching a complex subject matter—ideas are distilled to their main essence.
The technique involves four steps (Cam, 2020):
One should try this with one’s own subject matter or expertise, and see how deeply one understands it. Simple and clear explanations are a hallmark of deep understanding—the ability to explain technical concepts to others in a simple way is a great skill for teachers and should be part of one’s teaching methods and strategies.
Passive listening to lectures has always been the norm in university lectures. A better method of active learning called the Weiman method of scientific teaching has been proven more effective.
Carl Wieman, who won the Nobel Prize in Physics 2001, has long been a proponent of this method. In active learning, students perform activities that require them to be actively processing and applying information, completing exercises, and solving problems with fellow students (Weiman, 2014).
The approach involves several steps, each of which generates information about student learning before, during, and after discussions among themselves, with the instructor only listening in and gauging student progress, taking notes of mistakes in directions and conceptual knowledge.
Before this, a series of questions is asked, and each student answers using a ‘clicker’ device, anonymously recording their answers. After the discussion activity, they answer the same questions but this time, armed with more knowledge and information. They tend to get the correct answer the second time because of all the investment they had in discussing the problems and attempting to solve them. It is only at the end does the instructor explain the answers, emphasizing where the students got them wrong (Wieman, 2015).
And apparently, it works. In two introductory physics classes (N = 267 and N = 271) using controlled conditions, comparisons of the learning of a specific set of topics and objectives were done. One class was taught using 3 hours of traditional lecture by an experienced and highly-rated instructor. The other class received 3 hours of instruction given by a trained but inexperienced instructor (using the Wieman method). Results showed increased student attendance, higher engagement, and more than twice the learning in the latter, taught using research-based instruction (Deslauriers, et al, 2011).
Source: Deslauriers, et al, 2007
The Pomodoro technique (Cirillo, 2006) consists of doing a specific task with 25 minutes of intense concentration, followed by a 5-minute break. After 20 cycles, a 20-minute break is taken. The main idea is that the period of intense concentration should be devoted solely to the single task at hand, with no distractions. Use a timer to time the steps exactly (Cirillo used a tomato-shaped clock and called it the Pomodoro, Italian for “tomato”).
This technique eliminates distractions and is a preferred method for some, including medical students who have to memorize a lot of factual information in a short period of time.
“Learning styles” refers to the preference of different people to process information in different ways and, thus, they learn more effectively when they receive instruction in a way that matches their preferences.
One of the most popular learning style inventories is called VARK, for its visual, aural, verbal [reading/writing], and kinesthetic aspects.
Visual learners, for example, learn best if a concept is presented graphically, through images; kinesthetic learners learn more effectively when the touch or otherwise involve bodily movements in learning. Matching instruction with individuals’ learning style[s] was the new pedagogic idea that changed the education landscape.
A 2009 paper by a group of cognitive psychologists claimed that there was a lack of empirical evidence supporting the concept of learning styles-based instruction (Pashler et al., 2009). Thereafter, a meta-review examined the literature from 2009-2013 to determine if there were more studies that could test the matching of learning styles hypothesis, and to identify interaction effects. Correlational and experimental research on learning styles showed that the more methodologically-sound studies tended to refute the hypothesis. Learning styles instruction enjoy broad acceptance in practice. However, the majority of research evidence suggests (up to 2013) that learning styles have no benefit to student learning (Cuevas, 2015).
This just shows that there are very few studies that have used a reproducible experimental method to specifically test for these interaction effects, or of “meshing” (the matching of learning styles with effective teaching methods). More robust and controlled studies are still needed, with direct effect measurements of matching learning styles with instruction. Also, isolating overall student performance causative effects is always tricky as this problem is multifactorial, and there are so many different factors that affect student learning.
Another thing to consider is that the VARK model need not be highly unimodal, meaning that students are not just mainly one of the VARK types, but may have two or three modes of learning styles.
A study involving first year undergraduate medical students (n=91) found no visual (V) unimodal learners and bimodals were AK (33%, most common) and AR (16.5%). The most common trimodal preference was ARK (8.9%), and no quadrimodal person (having all four, VARK) was found. No significant differences between males and females were found in the distribution of unimodal and multimodal preferences (Prithishkumar and Michael, 2014).
Source: Prithishkumar and Michael, 2014, (n=91)
An important point is that disciplines do affect the way the subject matter is taught—surgery is highly tactile, and involves a lot of kinesthetic aspects; law is highly case-study and reading-based, so visual/ auditory modes may be more appropriate. Thus “learning styles” are also affected by the nature of the discipline. Teachers can adjust their curricula to maximize learning for all students.
Howard Gardner posited that a single measure of intelligence, known as “g” is not the only form of intelligence—in fact, humans have seven to 10 intelligences, known as “Multiple Intelligences” (MIs) (Gardner, 1983). The list started off with the first seven intelligences (domains of individual differences) in the following list, but has recently been amended, making it 10 MIs currently. He formulated his theory from brain lesion research and other studies. These are as follows (primary brain regions adapted from Shearer, 2018):
MI is dynamic and different for each individual. It is noteworthy to emphasize that multiple intelligence types are NOT learning styles (Gardner, 2013).
According to Gardner, a “Style is a hypothesis of how an individual approaches the range of materials. If an individual has a “reflective style,” he is hypothesized to be reflective about the full range of materials. We cannot assume that reflectiveness in writing necessarily signals reflectiveness in one’s interaction with others (Strauss, 2013). “ On being a “visual” learner or an “auditory” learner, spatial information and reading are sensed first with the eyes, but they have entirely different cognitive faculties.
The multiple intelligences concept is not concerned with how stimuli enter the brain, but with the processes and structures that process and act on the incoming sensory information. It is however, important to consider learning styles and MI together when designing a teaching and learning platform for different students.
The Intelligence quotient or IQ, as traditionally tested for and measured by many standardized admission and aptitude tests, actually test only one of the multiple intelligences—the logical-mathematical intelligence (Gardner, 1983). The additional intelligences are possibly masked in differently-skilled and differently-abled students as they are not explicitly tested expect in special circumstances like music or sports.
The differences in MI between genders and the grades-in-school of Mexican elementary schoolchildren (n = 161) were analyzed through a self-administered questionnaire, and results showed that the students’ mean averages in the eight categories of MI were similar in both genders; only intrapersonal intelligence showed a significant differences in gender (males had higher intrapersonal differences than females). (González-Treviño, et al, 2020).
In a middle school study in Israel (n= 158 seventh-graders), it was found that in excellent classes, 80.9% of students had logical intelligence, while in ordinary classes only 48.4% of students have logical intelligence. Excellent classes had a higher percentage with two or three dominant intelligences than ordinary classes, and it was noted that these include all types of intelligences, such as spatial, musical, kinesics, and others, not just logical and verbal intelligences. The dominant intelligences predicting student educational achievement is only the logical-mathematical intelligence domain (Yavich and Rotnitsky, 2020).
Neuroimaging utilizes fMRI (functional magnetic resonance imaging), which measures brain activity by determining the levels of oxygenated blood, or high brain activities, in certain brain areas when given a specific stimulus or task. Specifically, the main technique is known as Blood Oxygen Level Dependent (BOLD) functional magnetic resonance imaging (fMRI) (Glover, 2011). BOLD-fMRI has been used to assess the concept of multiple brain networks and the separate domains related to intelligence.
Network Neuroscience Theory proposes that general intelligence or g originates from individual differences in the system-wide topology and dynamics of the human brain (Barbey, 2018). In particular, in the journal Trends in Cognitive Science, “[R]ecent discoveries in network neuroscience motivate a new perspective about the role of global network dynamics in general intelligence—breaking away from standard theories that account for individual differences in g on the basis of a single brain region, network, or the overlap among specific networks. Accumulating evidence instead suggests that network flexibility and dynamics are crucial for the diverse range of mental abilities underlying general intelligence” (Barbey, 2018).
Knowing which brain regions are activated in intelligence domains is not enough. Further studies on how these neurological regions can be exploited to maximize learning, including methodologies and types of teaching techniques and activities, are warranted.
Mnemonics are great for memorizing long, complex lists by using memorable and easy words to replace the list, using the first letter in common. PEMDAS (Please Excuse My Dear Aunt Sally) is a great mnemonic for remembering the order of mathematical operations—Parentheses, Exponents, Multiplication / Division, then Addition / Subtraction (from left to right).
The memory palace method or method of loci is a memorization strategy that utilizes visualizations of familiar spatial environments to enhance recall. Functional neuroimaging of superior memorizers showed that they do not have exceptional intellectual ability or remarkable structural brain differences. Instead, they found that these individuals use a spatial learning strategy, engaging the hippocampus which is critical to spatial memory (Maguire, et al, 2003).
Flash cards are also extremely useful; questions are printed on one side, with the answer at the back. Figuring out the answers quickly and repeating this with a set of information is very helpful to retention and memorization. This is popular among pharmaceutical students who have to memorize thousands of drugs, and medical students who memorize much information, anatomy and physiology included.
Coupled with good instructional design, the future looks bright for these kinds of technologies.
Dizon and Tang, 2017
Dizon and Tang, 2017
In modern classrooms, laptops are replacing pen and paper for taking lecture notes, but some people, particularly of older generations, still find it more helpful to memorize when they write things down. Writing down information several times forges neural pathways related to kinesthetic and visual learning, enabling retention.
In modern classrooms, laptops are replacing pen and paper for taking lecture notes, but some people, particularly of older generations, still find it more helpful to memorize when they write things down. Writing down information several times forges neural pathways related to kinesthetic and visual learning.
The emergence of Intelligent Tutoring Systems (ITS) signals the start of more personalized adaptive learning environments for students. Artificial Intelligence (AI) bots or servers continuously learn where students succeed and fail via deep learning and machine learning; they then adapt their teaching pedagogy to the students’ level and devise ways to help increase their understanding and knowledge, and eventually, retention and mastery. Some basic psychometric perspectives for knowledge assessment are found in the article by Minn (Minn, 2022).
Guo, et al, 2021
The COVID-19 pandemic and current realities of working and studying from home have made distance learning and hybrid learning the de facto mode of instruction, and this will continue in the foreseeable future. In addition, teaching methods and strategies for adult learners will be more needed.
There are many more future trends. For example, miniaturization of electronics devices have always been the trend and it would not be surprising to find teaching and learning to be molded by these new technologies. Additionally, technology that creates direct neural links to the brain is not far away and is actively being developed. This would make most teaching strategies less important and perhaps moot as wireless connectivity to the Internet and knowledge bases blend seamlessly with neural links. The concept of pure learning would finally be at the forefront, regardless of approach.