My best friend and I host a pop culture podcast together. Awards season is always an exciting time for our show, as it allows us to make predictions about who will take home the top prizes. We make a point of trying to see as many Academy Awards nominees as possible before the big event.

We often lament the fact that many of the organizations charged with bestowing entertainment awards have historically favored white men, including the Academy of Motion Picture Arts and Sciences. Keeping this in mind, we frequently discuss on our show who will win versus who should win. We find that sharing our picks publicly on the podcast and airing our personal opinions adds some emotional investment before the broadcast takes place.

There have been many upsets in Oscar history, but who could forget the controversial 2017 mix‐up of La La Land and Moonlight? The crowd watched in disbelief as presenter Faye Dunaway misspoke and announced La La Land as the Best Picture and then moments later corrected her error to proclaim that Moonlight was the true victor.

On the podcast, I had predicted that La La Land, a musical about old Hollywood starring two white Hollywood A‐listers, would take home the Oscar based on the bias of the Academy. Nevertheless, I had argued that Moonlight, which features an all‐black cast and explores queer themes, was the more deserving film of the two. In a way, both of my predictions came to fruition when Moonlight defied the odds. The reason this mix‐up is such a memorable moment in Oscar history for me is because I remember being rocked one way and then another. As I watched the drama unfold, I felt like I had a stake in the game because I had made a prediction that Moonlight should win but assumed it would likely be snubbed. I was intellectually and emotionally invested, and those investments lodged the results more firmly in my memory.

The experience of predicting the outcomes of awards shows reflects a basic principle that cognitive researchers have emphasized for decades. Making predictions about material you hope to master will increase your ability to remember and understand it. One of the reasons that happens is because making a prediction activates your current knowledge about a subject, warming up your brain and preparing it to learn new information in the subject area. Successfully selecting the outcome of an entertainment award show requires knowing or remembering a bit about each of the nominees. How many awards have they won or lost in the past? Did they win or lose based on the historical bias of the voting body? Did they perform better or worse than expected? Acknowledging the obvious fact that answering these questions will not guarantee a perfect prediction record, the more I can recall about each nominee, the more informed I will be in predicting a categorical winner.

Making predictions also stirs up emotions that can help motivate and drive learning. Watching a series of Academy Award nominated films feeds my curiosity about who will take home the prize. I am thereby less likely to check social media or fold laundry while I watch a nominated film, and instead pay close attention. I also feel pleasure in making correct predictions, even when they do not correspond with my own personal preferences. Emotions have the power to focus our attention and provide cognitive fuel. My emotional attachment to making a prediction about Best Actor or Best Director helps it to secure a place in my long‐term memory.

The power of prediction extends far beyond my enthusiastic ruminations on a pop culture podcast. Predicting offers a direct route to help our students acquire knowledge and comprehension without great fanfare or time commitment. In this chapter, we view predicting as a natural and advantageous inclination in the Small Teaching classroom that takes nothing but six minutes or less out of your day.

WHAT'S THE THEORY?

Testing out our abilities before we are ready is a form of prediction, but sometimes it can be a dangerous one. Last summer, I decided to take up skateboarding. I watched a series of YouTube videos and even a MasterClass by skate legend Tony Hawk. I hopped on my board with great confidence, cruising the neighborhood until I came to a steep downhill and promptly broke my arm. Learning by listening, reading, and watching a pro skateboarder, prior to getting on the board was only so helpful. At a certain point, I had to get on the board and try it for myself. I found that Hawk's instruction was well and good but unresponsive to my individual needs. I had failed to master how to slow down or stop. It wasn't until I took a serious fall that I discovered what I needed. When my arm healed, I sought a skilled local skateboarder for hands‐on instruction. That was the point at which my real learning began. I don't recommend breaking your arm as any form of pretest, but I can say my initial failure left me eager to learn from an experienced instructor and fill in my knowledge gaps.

As teachers, we facilitate this type of learning in our classrooms every day, asking students to try out cognitive skills they have yet to master. I don't spend the whole year lecturing about active reading practices and then test them on Moby Dick come June. I assign short readings from the start of the semester, even though some of the skills they need won't be covered until mid‐way through the school year. Furthermore, I pair students strategically and encourage them to gather context from one another.

Prediction supports learning in three distinct ways. First, prediction improves retention and comprehension of learned material. Consider the best practices of modeling and simulation that are emphasized in the Next Generation Science Standards (NGSS), a set of research‐based, up‐to‐date K–12 science standards introduced by the National Research Council in 2013. The NGSS standards encourage students to formulate scientific questions and make predictions before planning and carrying out investigations. The standards rely on a cyclical design process, which naturally encourages students to make predictions.

During my four‐year stint as a fourth‐grade STEM teacher, I put the NGSS standards to the test in the wake of Hurricane Harvey. I tasked students with researching residential architecture and designing a hurricane‐proof house. They were given a budget and the cost of materials. (A paper towel tube was priced at $400, a square foot of aluminum foil at $2,000, etc.) Each group built its model to scale with the understanding that it would have to withstand 30 seconds of wind from a leaf blower and the torrential downpour of a gallon of water.

On hurricane simulation day, we made predictions about which designs would weather the storm free of damage and why. Students cheered and jeered as they watched the other groups attentively in order to take note of their strongest features. After the first simulation, I asked the groups to go back and make alterations. “Rede‐sign your failures,” I told them. Nearly every group was able to apply what it had learned from its predictions in the first trial in order to improve the stability of its own house model for a second trial.

Predictions are not limited to the science classroom; they can be found across the curriculum in K–8 education, particularly when it comes to literacy. The second distinct way in which prediction supports learning is by forcing students to make personal connections and form attachments to their own approaches or theories.

A 2020 study of fourth‐grade reading instruction demonstrated how prediction improved student learning in literacy instruction. Students who engaged in literature circles showed huge gains, as opposed to students in text‐centered classrooms (Birsen and Kaya‐Tosun, 2020). In the study, literature circles provided a collaborative approach to reading comprehension and elevated student voices and personal experiences. By contrast, the text‐centered approach fostered a more independent process limited to discussing content directly from the book.

Researchers found that literature circles encouraged students to work as a team, with each member taking on a designated role to propel active reading and discussion. Literature circle roles often include jobs like connector, questioner, and illustrator in order to weave a web of context around reading and practice higher‐order thinking skills, including prediction. On the contrary, text‐centered instruction in the study relied on retelling the story and identifying its main ideas. Text‐centered responses thereby focused on the simple summarization of a book, whereas reader‐centered responses moved beyond summarization in order to forge personal connections with the content of a book (Birsen and Kaya‐Tosun, 2020).

Researchers saw a steady decrease in text‐centered responses and a continuous increase in the reader‐centered responses of literature circles. “In other words, it can be said that students develop a personal response to the book with the literature circles, make more connections with the events in the book and show a desire to participate in the story” (Birsen and Kaya‐Tosun, 2020). Students who engaged in literature circles participated in active reading, analyzing passages for foreshadowing, making predictions, and assessing their correctness among peers. A student in a literature circle might even be asked to take on the explicit role of “predictor” and make a prediction about what would unfold in the next chapter. By making a public prediction, the stakes become more emotional and the readers become more engaged.

An important distinction between effective and ineffective predictive activities is that effective predicting warrants immediate feedback. Lingering misinformation sometimes leaves a deep impression; it seems likely that the sooner correct information arrives, the better—if not during the same class session as the prediction activity, then at least by the following class. The correction does not necessarily have to come from a teacher. At times, peer learning can be a more powerful method for encouraging students to take risks.

Finally, making predictions requires a fair amount of trust and willingness to fail. It takes a particular classroom culture for predictive activities to reap their full potential. This is the third distinct manner in which prediction supports deep learning.

Darcy Schwartz is an art educator for whom I have particular admiration in my home city of Worcester. She serves over 300 students per week at her community studio, which she designed to make art affordable and accessible for all local youth. Schwartz is very open minded when it comes to her teaching practice, but she has one steadfast rule: “We don't have erasers on our pencils here because I don't want anyone to feel like if you're just sketching something or drawing something that you need to erase it to make it perfect,” she told me. “It is already perfect because if you just keep sketching, eventually it can become what you want it to be” (2019). Schwartz's attitude conveys confidence and assurance. The absence of erasers does not prevent her from helping students to hone their skills, it simply signals the value of problem solving and risk‐taking in her studio.

Your students are capable of making predictions before they learn new material by following these basic models and the ones I introduce next, which will not cost you more than six minutes a day.

IN SHORT

• Ask students to make predictions before they carry out investigations and simulations of a problem.
• Assign students specific reading roles designed to strengthen emotional investment, including an official “predictor.” By sharing predictions publicly, learners will develop a stake in the game.
• Provide immediate feedback. If a prediction contains illogical conclusions or blatant misinformation, don't let it linger.
• Foster an environment where learners feel safe to take risks. Stoke curiosity with frequent pauses to make predictions.
• Use predictive activities to inform heterogeneous groups of students. Strategically pair novices with experts.

MODELS

The ideal moment for predictive activities is at the beginning of a learning experience. The opening of class is a perfect chance for students to make predictions based on your learning objective and agenda. Bell‐ringers and do nows have become commonplace over the last two decades, so much so that I think we sometimes forget the power they have to boost learning by way of prediction. Once in a while, I catch myself including bell‐ringers and do nows in my lesson plans simply because they are requirements on the teacher evaluation rubric, without regarding their true value. Consider the following models for leveraging the opening of class, the opening of the semester, and the opening of the school year as opportunities to make predictions.

PRINCIPLES

Making predictions requires little to no preparation, but leaves an enormous impact—an ideal activity for the Small Teaching classroom. The following principles can help guide you to create six‐minute prediction activities for your classroom.

SMALL TEACHING QUICK TIPS: PREDICTING

We have already discussed the merits of retrieval and memorization as a foundational practice. Meaningful prediction relies on the collection of prior knowledge and the ability to formulate complex connections. That doesn't mean negating quick strategies to incorporate prediction into your classroom. These reliable prediction activities will set you on the right track:

• Provide students with six minutes to make a prediction from the moment they walk through the door. The task should require no direct instruction for students to get started. That way, you can take attendance while setting a productive tone and activating prior knowledge and curiosity via prediction.
• Begin the course, unit, or class with a short pretest that correlates directly to your final assessment. You do not need to grade pretests. Use them as a sample set to help inform your instruction and condense your curriculum.
• Survey the class anonymously using an anticipation guide made up of simple agree‐or‐disagree statements. Share the results and correct misconceptions.
• Encourage students to make predictions together about a new unit of study based on a list of key terms.
• Pause before the conclusion of a problem, an experiment, or an entire lesson and ask students to make a prediction. Close class with an exit ticket asking students to predict what material will be covered the next day. Close the loop by correcting any misconceptions that arise.

CONCLUSION

In the opening of this chapter, I acknowledged my personal experience making public predictions about the Academy Awards on a pop culture podcast. I found the act of predicting drew me into films emotionally and urged me to pay closer attention. The mechanics of learning and prediction can likewise be considered from a cognitive perspective. Emotion and attention play a valuable role. Predictions tap into our egos, making us ask the question, “Will I be right?” Predictions also provoke curiosity, stimulating our brains to stay focused. Asking our students to make predictions is often the shortest path to curiosity.

Feeling compelled to inquire about a new topic is a pivotal step to maximizing learning outcomes. In fact, a 2019 Turkish study examined student questions as a means for increasing meaningful learning and motivation (Huner, 2021). In the study, Huner cites the positive correlation between curiosity and question asking. He draws on the observations of researcher A.R. Zolfagharia stating, “curiosity causes children to ask 125 questions daily while it triggers an average of 6 questions among adults with a logical worldview” (Huner, 2021). What better time to capitalize on prediction than when our students are young and curious? Consider how you can use the opening or closing moments of your class to prime your students to spin the silk and weave the web of deep and lasting learning in six minutes or less.