Small Teaching: Everyday Lessons from the Science of Learning

by James M. Lang

As Benedict Carey put it in How We Learn, “The brain does not store facts, ideas, and experiences like a computer does, as a file that is clicked open, always displaying the identical image. It embeds them in networks of perceptions, facts, and thoughts” (Carey 2014a p. 20). An easy way to illustrate this notion of networked knowledge is to point to the difference between an expert in a subject (such as you) and a novice learner (such as your student). When your student encounters facts in your discipline for the first time, she picks them up as fragmented, isolated units, almost like dates of historical events scattered randomly across a timeline.

If I pointed to any one of those dates, a novice learner who has memorized the facts could tell me a single thing about it: 1865, End of the American Civil War. But now imagine that I pointed to this date and asked an American historian to tell me about it. He would have a huge network of other information he could provide to me about that date, and he could also connect that date to other relevant dates on the timeline. That date links in his mind to dozens or hundreds of other facts. And that, according to one basic understanding of human knowing, is what constitutes knowledge: the web of connections we have between the things we know.

According to How Learning Works: Seven Research-Based Principles for Smart Teaching, an important difference between the knowledge of experts and novices is “the number or density of connections among the concepts, facts, and skills they know” (Ambrose, Bridges, DiPietro, Lovett, and Norman 2010, p. 49). Experts have dense weaves of connections betw...

Density of connections affects both knowledge and comprehension. When new facts are woven into a dense network of connections, they are implanted there more firmly and are more likely to be activated in multiple contexts. And because they are tied to lots of other facts and information, the expert c...

predictive activities prepare your mind for learning by driving you to seek connections that will help you make an accurate prediction.

[Predictive activities] reshape our mental networks by embedding unfamiliar concepts…into questions we at least partly comprehend…Even if the question is not entirely clear and its solution unknown, a guess will in itself begin to link the questions to possible answers. And those networks light up like Christmas lights when we hear the concepts again.

In other words, when you are forced to make a prediction or give an answer to a question about which you do not have sufficient information, you are compelled to search around for any possible information you might have that could relate to the subject matter and help you make a plausible prediction. That search activates prior knowledge you have about the subject matter and prepares your brain to slot the answer, when you receive it, into a more richly connected network of facts.

Roediger and his coauthors in Make It Stick describe it this way: “Unsuccessful attempts to solve a problem encourage deep processing of the answer when it is later supplied, creating fertile ground for its encoding, in a way that simply reading the answer cannot” (Brown, Roediger, and McDaniel 2014, p. 88). The ground is fertile because the learner's brain has now activated several connections between the question and other possible contexts, and when the answer arrives in the soil it takes hold more quickly and firmly because of the link between the answer and those other contexts.

Illusions of fluency represent one of the foremost challenges we face in helping students learn subject matter deeply. Anyone who has taught for more than a year or two has encountered the befuddled student who comes to office hours after an exam or assignment and explains that he studied the material for many hours and thought he had it down cold. Such a student is laboring under the illusion of fluency, possibly because he engaged in common study strategies like reading the textbook or reviewing his notes over and over again.

The literature on human learning repeatedly reveals, however, that those strategies prove effective primarily for short-term learning. If we want long-term learning from our students, we have to teach them (and advise them to study for themselves) with more active learning strategies like the ones described in these chapters. Students who take pretests or make predictions are forced to confront the depth of their knowledge, and that confrontation—when it reveals gaps or weaknesses—might spur them to better or more determined learning.

I made earlier in this chapter that wrong predictions do not seem to harm future learning: Learners do have to receive fairly immediate feedback on the accuracy of their predictions or pretest answers if we don't want those wrong answers to leave a deeper impression than the correct ones. In all of the aforementioned experiments, the learners were given that immediate feedback: in the case of the first experiment, it came within 8 seconds; in the language tutorial, the film continued immediately after students wrote their explanation, which helps explain why the wrong answers they originally gave did not stick.

No doubt wrong answers can stick or lead to confusion if they are left uncorrected. So although prediction activities must have quick follow-up responses, I have seen no specific formula for how immediately the feedback has to arrive. It seems likely that the sooner it arrives the better—if not in the same class session as the prediction activity, then at least by the next class period.

We can think about predictive activities as a cognitive version of something we normally ask of learners who are attempting to master a skill: namely, requiring them to engage that skill before they are prepared to complete it successfully. We can all likely draw from our experiences in remembering attempts to master skills of one sort or another, and we know full well that however much one might read in advance about throwing a football or painting a portrait or giving a speech, the real learning happens after we have thrown ourselves into the situation and made that first (unsuccessful) attempt.

When I took a class to become licensed in scuba diving, we spent the first half of every session in a classroom taking notes on some skill we would have to practice in the pool. I typically jumped in the pool for the second half of class thinking I had that skill mastered; within a few minutes my fluency illusions were dispelled, and I floundered around for a while, doing it completely wrong until the instructor swam over and gave me some (immediate) feedback, at which point the real learning began.

I don't spend the entire semester lecturing to my freshman composition students about all of the writing techniques they will need to write a perfect academic essay and then give them one final assignment to show me how they have mastered those skills. I assign essays from the beginning of the semester, even though some of what they need to write great academic essays won't be covered for another 4 or 8 or 12 weeks. Asking students to make predictions before learning new material just represents another version of this common teaching approach.

The opening minutes of a class might include the opportunity for students to make predictions about what will be covered in that class period; prediction activities in the final minutes of class will help prepare them for the work they will be completing prior to the next class session.

For both retention and comprehension, you can follow the lead of Elizabeth Bjork and her colleagues by giving your students pretests on course material they are about to learn.

They have to reason with a formula rather than just repeat a formula.

Prediction–Exposure–Feedback Even without formal testing or the use of clickers, you can always ask students to make informal, in-class predictions about any course material to which they are about to be exposed. This could happen in almost any discipline, in any type of class. Scientists know full well how prediction plays a role in the scientific method—in the form of the hypothesis—and likely already ask students to engage in predictive activities in their use of laboratory experiments and reports. But outside of the laboratory, and in other disciplines, instructors can still follow this same basic approach. If students have done some advanced reading from the textbook, which you plan to cover in more depth in lecture that day, you might begin that lecture by asking them to apply some concept they read about in the textbook to an example with which you will begin the day's class session.

Or you can always ask students to make predictions about new content based on their knowledge from earlier in the semester, from their previous courses, or from their own general knowledge. How Learning Works gave two quick examples of this: “Before asking students to read an article from the 1970s, you might ask them what was going on historically at the time that might have informed the author's perspective. Or when presenting students with a design problem, you might ask them how a famous designer, whose work they know, might have approached the problem.” In these kinds of questions, again, you are requiring students “not only to draw on prior knowledge but also to use it to reason about new knowledge” (Ambrose, Bridges, DiPietro, Lovett, and Norman 2010, p. 33). Ideally, you will both ask for the prediction and give them the opportunity to explain why they made it; doing so will require them to examine their thinking and might help them recognize fluency illusions. Even more ideally, after you give them the answer you might ask them to explain why their predictions did or did not hold true.

Prediction, in other words, forces them to marshal what they know thus far about the novel to anticipate what comes next.

Asking students to make predictions requires a very small investment of time, which makes predicting an ideal small teaching activity. The following principles can help guide the creation of prediction activities in your classroom.

Remember that you don't want wrong predictions hanging around in students' heads for very long; the more immediate the correction, the better.

Induce Reflection As Daniel Willingham argued, “Memory is the residue of thought” (p. 54). In other words, we remember what we spend a little time thinking about.

Prediction provides an excellent spur for thought, in that you can ask students to think about why they made their prediction, what actually happened (if the prediction leads to direct observ...

With prediction we move beyond the foundational act of memorization into more complex cognitive territories. But that doesn't mean you can't still make use of quick strategies and brief windows of time to incorporate prediction into your courses. These reliable prediction activities give you some easy starting points.

Prior to first content exposure, ask students to write down what they already know about that subject matter or to speculate about what they will be learning.

When presenting cases, problems, examples, or histories, stop before the conclusion and ask students to predict the outcome.

In this chapter we considered the mechanics of learning and prediction from a more cognitive perspective, but we shouldn't discount the role that attention and emotion play in this process. Predictions make us curious—I wonder whether I will be right?—and curiosity is an emotion that has been recently demonstrated to boost memory when it is heightened prior to exposure to new material.

“The brain's reward system seemed to prepare the hippocampus for learning.” Curiosity and anticipation of an answer, taken together, led the brains of these subjects to snap to attention and form deeper and longer memories (Yuhas 2014).

Chapter 3 Interleaving

The learning principle that helps explain this improvement in my language acquisition skills is called interleaving, and it involves two related activities that promote high levels of long-term retention: (a) spacing out learning sessions over time; and (b) mixing up your practice of skills you are seeking to develop.

The contrast between these two methods is usually described in the literature as massed versus spaced (or sometimes distributed) learning. In massed learning, students focus entirely on one skill or set of material until they have mastered it; in distributed practice, students space out their learning sessions over time. At the end of the study periods for both groups in this experiment, the students were given a vocabulary test on the words; both groups averaged about 16 of 20 words correct.

The researchers then returned to the classroom a week later, without any prior warning to the students, and tested them on the vocabulary again. This time the results diverged sharply: the massed practice students remembered around 11 of the vocabulary words, whereas the spaced practice students remembered around 15. Remember that both groups had the same total learning time and completed the same tasks; only the spacing of their learning activities differed.

The theory that explains the power of spaced learning stems at least in part from what we have learned about the importance of retrieval practice. One of the challenges to our memories is the ability to pull desired information from our long-term memories when we need it. The more times we practice drawing specific skills or information from our long-term memory, the better we get at it. When we engage in massed learning exercises, focusing on one set of content repeatedly, we never have to access the learned material from the deeper recesses of our long-term memory. By contrast, if we use spaced learning to allow some time for the forgetting of learned material to set in, we are forced to draw material from our longer-term memory when we return to it. Spacing out learning thus forces us to engage at least partially in memory retrieval.

That forced cycle of forgetting and retrieving is only half the explanation for the power of spaced learning. As the authors of Make It Stick explain, the time that intervenes between spaced learning sessions also allows our minds to better organize and solidify what we are studying: Embedding new learning in long-term memory requires a process of consolidation, in which memory traces (the brain's representations of new learning) are strengthened, given meaning, and connected to prior knowledge—a process that unfolds over hours and may take several days. Rapid-fire practice leans on short-term memory. Durable learning, however, requires time for mental rehearsal… Hence, spaced practice works better. The increased effort to retrieve the learning after a little forgetting has the effect of retriggering consolidation, further strengthening memory. (Brown, Roediger, and McDaniel 2014, p. 49)

Our brains need time to undertake the processes of encoding, consolidating, and organizing newly learned material, and the gaps between spaced learning sessions allow it that time.

we should help students space out their learning both in how we design our courses and in how we encourage them to study. However, we can help our students even further if we consider spaced learning as one aspect of interleaving, a broader approach to helping our students learn.

Interleaving refers to the practice of spending some time learning one thing and then pausing to concentrate on learning a second thing before having quite mastered that first thing, and then returning to the first thing, and then moving onto a third thing, and then returning to the second thing, and so forth. In short, it involves the process of both spacing and mixing learning activities—the spacing happening by virtue of the mixing.

An interleaved approach would look quite different.

According to all of the research we have on interleaving, though, they are going to know it much better than the students in the massed example at the end of the semester—and, more importantly, after they have left the course.

As you are reading this, you are perhaps thinking to yourself that this all sounds very messy and might even provoke frustration from you and your students since it would be much neater and cleaner to march your way through the concepts in order. Indeed it would, and the research on interleaving confirms what you suspect: learners often find it frustrating. I experienced this frustration myself when I began using that Strengthen Skills tab in my language-learning program and found that I didn't know nearly as much as I thought I did. Research also tells us that massed practice works very effectively for short-term learning, which is why students like it and why they can often perform well on exams when they engage in massed learning exercises like cramming.

Spiraling can feel frustrating to the learner because you are, in a sense, going around in circles. However, you are also moving upward with each spiral, adding new layers of learning every time you push back through the material.

mixing your study or practice as well as spacing it. The authors of the study present this brief explanation for why they believe the Mixers so definitively outperformed the Blockers:

“a significant advantage of interleaving and variation,” argued the authors of Make It Stick, “is that they help us learn better how to assess context and discriminate between problems, selecting and applying the correct solution from a range of possibilities” (Brown, Roediger, and McDaniel 2014, p. 53).