Teachers employ a vast array of instructional methods, but one universal element is the use of examples. No teaching approach eschews examples. On the contrary, guides for effective teaching embrace the value of using good examples (e.g., Rosenshine, 2012). Given their importance, teachers should design and use examples carefully and assess their impact on student learning. Little guidance, however, exists to help teachers accomplish these tasks. In this article, I synthesize the research on examples in the hopes of providing that guidance.
I define an example as “any specific instance, illustration, demonstration or activity that is representative of a concept” (Chew, 2007, p. 74). This intentionally broad definition covers exemplars that illustrate concepts, as well as analogies, stories, learning activities, worked problems, and problem sets. We typically think of examples as prototypes for a concept, such as Bach as an example of Baroque music, but they can take other forms, such as metaphors (e.g., the heart is like a pump). Teaching activities designed get students to think about a concept can be examples. In a literature course, students may learn about foreshadowing by identifying examples in a literary work. Worked problems and problems sets can also serve as examples of a concept, especially in STEM disciplines.
If we understand what examples contribute to learning, we can use them more effectively and diagnose the problems when examples fail. Learning from examples is a complex process with errors possible at multiple points (Chew, 2007). First, the teacher presents a new, more advanced concept, which students often find abstract and vague. Then the teacher presents an example, which should ground the abstract concept in a concrete reality that students can grasp. Now students have to generalize from the example back to the abstract concept. They need to map the elements of the example onto the characteristics of the concept such that they can now recognize and analyze new examples of the concept. For instance, in my introductory psychology class, I teach the concept of conformity, a form of social influence. Conformity, as I define it, is when people change their behavior to match the behavior of others. After presenting the definition, I might give this example: You start a new job and you notice that the other employees wear a certain brand of polo shirt. You hurry out and buy those shirts. Ideally, when students map relevant parts of the example onto the definition of conformity, their understanding deepens, and they start to recognize other examples of conformity.
How might an example fail? If the example is too complex, ambiguous, or confusing, it will fail. Good examples are simpler than the concepts they represent. Unfortunately, the curse of expertise works against teachers’ abilities to judge the effectiveness of explanations and examples. Experts typically underestimate how quickly and easily novices will learn concepts (Fisher & Keil, 2016). Next, students may confuse knowing the example with understanding the concept. Chi (Chi et al., 1989; Chiu & Chi, 2014) found that higher-achieving students explain to themselves how examples relate to concepts without prompting. Struggling students, however, tend to simply record the example without self-explanation or reflection. Finally, students may never generalize from the concrete example to a general, abstract understanding. They may think that conformity relates only to workplace clothing decisions.
Most examples break into two parts: the surface component and the structural component (Chew, 2007). The surface component refers to the contextual parts of the example that are not relevant to its representation of the concept. These include such elements as storyline, wording, objects, and numbers that can be changed without altering the nature of the example. In my conformity example, the surface components include the workplace setting and polo shirts. The example could describe a school setting with brands of shoes and still be about conformity. The structural component embodies the essential elements of the concept. Changing the structural component changes the example. If the manager tells all new employees to wear polo shirts, the example is no longer about conformity but about obedience. Typically, the surface component is more familiar to students than the structural component, and students may confuse the surface structure with the concept. Here are some examples of conformity with different surface components but the same structural component.
The surface component determines the level of student familiarity and interest. The more familiar and interesting the example is to students, the more likely its effectiveness for learning. Teachers need to select and adapt examples cognizant of the interests of the students they teach.
What must teachers keep in mind when designing or selecting examples? The following factors merit consideration (Chew, 2007).
Typically, teachers explain a concept and then provide one or two examples. The rest is up to the student. Research has uncovered ways to increase the effectiveness of examples. For example, teachers can use questions as part of formative assessments to induce student self-reflection on examples (Lee & Hutchinson, 1998). As part of a review, a teacher could provide a novel example of conformity and have students in pairs determine what concept it represents. Students can classify collections of examples of related concepts, such as conformity, compliance, and obedience. Teachers can present a variety of examples with different surface components, such as the ones for conformity, to teach students to differentiate the structural components from the surface components, which improves learning (Butler et al., 2017; Paas & van Merriënboer, 1994).
For STEM classes, teachers can scaffold a series of problems that go from simple to complex. If the teacher works an example problem, then students can first solve a problem with similar surface components, then a problem with somewhat different surface components, and finally one with markedly different surface components (Hampton & Chew, 2010). Another way of scaffolding learning “fades” examples (Atkinson et al., 2003). The teacher first provides a worked example for students to study and follows with an example that is almost solved, but students have to complete the last step. Students solve a sequence of examples with less and less of the problem solved for them. For other ways to use worked examples, see Paas and van Gog (2006), Renkl (2014), and van Gog et al. (2010).
Examples should be designed and used with a learning goal in mind. Chew and Cerbin (2020) described nine cognitive challenges that teachers must address successfully for students to learn. Examples can be designed to address at least four of those challenges: student mental mindset, metacognition, ineffective learning strategies, and transfer. Examples can change student mindsets by showing the relevance and importance of course concepts. The examples of conformity aim to show students the relevance of conformity to their everyday experiences. Hopefully, that changes attitudes about the importance of course content. Metacognitive awareness results from the feedback examples provide about students’ level of understanding of a concept. If an example doesn’t make sense, then the student doesn’t fully understand the concept. Teachers can use examples in ways that promote student learning and model effective learning strategies (Weinstein et al., 2019). Examples can be used for retrieval practice. Teachers can mix up the order of examples and space them out over several days. All these strategies improve learning. Finally, examples powerfully show the applicability of concepts beyond the classroom and give students practice in applying them in novel situations.
Teachers understand the value of good examples for student learning but may not be designing and using examples optimally. This essay provides a research-based framework for designing and using examples that can help teachers maximize the impact of examples on learning.
Butler, A. C., Black-Maier, A. C., Raley, N. D., & Marsh, E. J. (2017). Retrieving and applying knowledge to different examples promotes transfer of learning. Journal of Experimental Psychology: Applied, 23(4), 433–446. https://doi.org/10.1037/xap0000142
Chew, S. L. (2007). Designing effective examples and problems for teaching statistics. In D. S. Dunn, R. A. Smith, & B. Beins (Eds.), Best practices for teaching statistics and research methods in the behavioral sciences (pp. 73–91). Erlbaum.
Chew, S. L., & Cerbin, W. J. (2020). The cognitive challenges of effective teaching. The Journal of Economic Education, https://doi.org/10.1080/00220485.2020.1845266
Chi, M. T. H., Bassok, M., Lewis. M. W., Reimann, P., & Glaser, R. (1989). Self-explanation: How students study and use examples in learning to solve problems. Cognitive Science, 13(2), 145–182. https://doi.org/10.1016/0364-0213(89)90002-5
Chiu, J. L., & Chi, M. T. H. (2014). Supporting self-explanation in the classroom. In V. A. Benassi, C. E. Overson, & C. M. Hakala (Eds.), Applying science of learning in education: Infusing psychological science in the curriculum (pp. 91–103). Society for the Teaching of Psychology. https://teachpsych.org/ebooks/asle2014/index.php
Fisher, M., & Keil, F. C. (2016). The curse of expertise: When more knowledge leads to miscalibrated explanatory insight. Cognitive science, 40(5), 1251–1269. https://doi.org/10.1111/cogs.12280
Hampton, A. G., & Chew, S. L. (2010, January). Designed sequences of examples facilitate learning of statistical concepts [Poster presentation]. National Institute for the Teaching of Psychology, St. Pete Beach, FL.
Lee, A. Y., & Hutchison, L. (1998). Improving learning from examples through reflection. Journal of Experimental Psychology: Applied, 4(3), 187–210. https://doi.org/10.1037/1076-898X.4.3.187
Paas, F., & van Gog, T. (2006). Optimising worked example instruction: Different ways to increase germane cognitive load. Learning and Instruction, 16(2), 87–91. https://doi.org/10.1016/j.learninstruc.2006.02.004
Paas, F. G. W. C., & van Merriënboer, J. J. G. (1994). Variability of worked examples and transfer of geometrical problem-solving skills: A cognitive-load approach. Journal of Educational Psychology, 86(1), 122–133. https://doi.org/10.1037/0022-06184.108.40.206
Renkl, A. (2014). Learning from worked examples: How to prepare students for meaningful problem solving. In V. A. Benassi, C. E. Overson, & C. M. Hakala (Eds.), Applying science of learning in education: Infusing psychological science into the curriculum (pp. 118–130). Society for the Teaching of Psychology. https://teachpsych.org/ebooks/asle2014/index.php
Rosenshine, B. (2012). Principles of instruction: Research-based strategies that all teachers should know. American Educator, 36(1), 12–19, 39. https://files.eric.ed.gov/fulltext/EJ971753.pdf
Sweller, J., van Merriënboer, J.J.G. & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 31, 261–292. https://doi.org/10.1007/s10648-019-09465-5
Weinstein, Y., Sumeracki, M., & Caviglioli, O. (2019). Understanding how we learn: A visual guide. Routledge.
Stephen L. Chew, PhD, is a professor of psychology at Samford University. Trained as a cognitive psychologist, his primary research area is the cognitive basis of effective teaching and learning. He is the creator of a groundbreaking series of YouTube videos for students on how to study effectively in college, which have been viewed over three million times and are in wide use from high schools to professional schools. Author contact: email@example.com.