To Make Science Engaging, We Need a Sesame Street for Adults
This article is part of the magazine, "The Future of Science In America: The Election Issue," co-published by LeapsMag, the Aspen Institute Science & Society Program, and GOOD.
In the mid-1960s, a documentary producer in New York City wondered if the addictive jingles, clever visuals, slogans, and repetition of television ads—the ones that were captivating young children of the time—could be harnessed for good. Over the course of three months, she interviewed educators, psychologists, and artists, and the result was a bonanza of ideas.
Perhaps a new TV show could teach children letters and numbers in short animated sequences? Perhaps adults and children could read together with puppets providing comic relief and prompting interaction from the audience? And because it would be broadcast through a device already in almost every home, perhaps this show could reach across socioeconomic divides and close an early education gap?
Soon after Joan Ganz Cooney shared her landmark report, "The Potential Uses of Television in Preschool Education," in 1966, she was prototyping show ideas, attracting funding from The Carnegie Corporation, The Ford Foundation, and The Corporation for Public Broadcasting, and co-founding the Children's Television Workshop with psychologist Lloyd Morrisett. And then, on November 10, 1969, informal learning was transformed forever with the premiere of Sesame Street on public television.
For its first season, Sesame Street won three Emmy Awards and a Peabody Award. Its star, Big Bird, landed on the cover of Time Magazine, which called the show "TV's gift to children." Fifty years later, it's hard to imagine an approach to informal preschool learning that isn't Sesame Street.
And that approach can be boiled down to one word: Entertainment.
Despite decades of evidence from Sesame Street—one of the most studied television shows of all time—and more research from social science, psychology, and media communications, we haven't yet taken Ganz Cooney's concepts to heart in educating adults. Adults have news programs and documentaries and educational YouTube channels, but no Sesame Street. So why don't we? Here's how we can design a new kind of television to make science engaging and accessible for a public that is all too often intimidated by it.
We have to start from the realization that America is a nation of high-school graduates. By the end of high school, students have decided to abandon science because they think it's too difficult, and as a nation, we've made it acceptable for any one of us to say "I'm not good at science" and offload thinking to the ones who might be. So, is it surprising that a large number of Americans are likely to believe in conspiracy theories like the 25% that believe the release of COVID-19 was planned, the one in ten who believe the Moon landing was a hoax, or the 30–40% that think the condensation trails of planes are actually nefarious chemtrails? If we're meeting people where they are, the aim can't be to get the audience from an A to an A+, but from an F to a D, and without judgment of where they are starting from.
There's also a natural compulsion for a well-meaning educator to fill a literacy gap with a barrage of information, but this is what I call "factsplaining," and we know it doesn't work. And worse, it can backfire. In one study from 2014, parents were provided with factual information about vaccine safety, and it was the group that was already the most averse to vaccines that uniquely became even more averse.
Why? Our social identities and cognitive biases are stubborn gatekeepers when it comes to processing new information. We filter ideas through pre-existing beliefs—our values, our religions, our political ideologies. Incongruent ideas are rejected. Congruent ideas, no matter how absurd, are allowed through. We hear what we want to hear, and then our brains justify the input by creating narratives that preserve our identities. Even when we have all the facts, we can use them to support any worldview.
But social science has revealed many mechanisms for hijacking these processes through narrative storytelling, and this can form the foundation of a new kind of educational television.
Could new television series establish the baseline narratives for novel science like gene editing, quantum computing, or artificial intelligence?
As media creators, we can reject factsplaining and instead construct entertaining narratives that disrupt cognitive processes. Two-decade-old research tells us when people are immersed in entertaining fiction narratives, they loosen their defenses, opening a path for new information, editing attitudes, and inspiring new behavior. Where news about hot-button issues like climate change or vaccination might trigger resistance or a backfire effect, fiction can be crafted to be absorbing and, as a result, persuasive.
But the narratives can't be stuffed with information. They must be simplified. If this feels like the opposite of what an educator should be doing, it is possible to reduce the complexity of information, without oversimplification, through "exemplification," a framing device to tell the stories of individuals in specific circumstances that can speak to the greater issue without needing to explain it all. It's a technique you've seen used in biopics. The Discovery Channel true-crime miniseries Manhunt: Unabomber does many things well from a science storytelling perspective, including exemplifying the virtues of the scientific method through a character who argues for a new field of science, forensic linguistics, to catch one of the most notorious domestic terrorists in U.S. history.
We must also appeal to the audience's curiosity. We know curiosity is such a strong driver of human behavior that it can even counteract the biases put up by one's political ideology around subjects like climate change. If we treat science information like a product—and we should—advertising research tells us we can maximize curiosity though a Goldilocks effect. If the information is too complex, your show might as well be a PowerPoint presentation. If it's too simple, it's Sesame Street. There's a sweet spot for creating intrigue about new information when there's a moderate cognitive gap.
The science of "identification" tells us that the more the main character is endearing to a viewer, the more likely the viewer will adopt the character's worldview and journey of change. This insight further provides incentives to craft characters reflective of our audiences. If we accept our biases for what they are, we can understand why the messenger becomes more important than the message, because, without an appropriate messenger, the message becomes faint and ineffective. And research confirms that the stereotype-busting doctor-skeptic Dana Scully of The X-Files, a popular science-fiction series, was an inspiration for a generation of women who pursued science careers.
With these directions, we can start making a new kind of television. But is television itself still the right delivery medium? Americans do spend six hours per day—a quarter of their lives—watching video. And even with the rise of social media and apps, science-themed television shows remain popular, with four out of five adults reporting that they watch shows about science at least sometimes. CBS's The Big Bang Theory was the most-watched show on television in the 2017–2018 season, and Cartoon Network's Rick & Morty is the most popular comedy series among millennials. And medical and forensic dramas continue to be broadcast staples. So yes, it's as true today as it was in the 1980s when George Gerbner, the "cultivation theory" researcher who studied the long-term impacts of television images, wrote, "a single episode on primetime television can reach more people than all science and technology promotional efforts put together."
We know from cultivation theory that media images can shape our views of scientists. Quick, picture a scientist! Was it an old, white man with wild hair in a lab coat? If most Americans don't encounter research science firsthand, it's media that dictates how we perceive science and scientists. Characters like Sheldon Cooper and Rick Sanchez become the model. But we can correct that by representing professionals more accurately on-screen and writing characters more like Dana Scully.
Could new television series establish the baseline narratives for novel science like gene editing, quantum computing, or artificial intelligence? Or could new series counter the misinfodemics surrounding COVID-19 and vaccines through more compelling, corrective narratives? Social science has given us a blueprint suggesting they could. Binge-watching a show like the surreal NBC sitcom The Good Place doesn't replace a Ph.D. in philosophy, but its use of humor plants the seed of continued interest in a new subject. The goal of persuasive entertainment isn't to replace formal education, but it can inspire, shift attitudes, increase confidence in the knowledge of complex issues, and otherwise prime viewers for continued learning.
[Editor's Note: To read other articles in this special magazine issue, visit the beautifully designed e-reader version.]
The Friday Five covers five stories in research that you may have missed this week. There are plenty of controversies and troubling ethical issues in science – and we get into many of them in our online magazine – but this news roundup focuses on new scientific theories and progress to give you a therapeutic dose of inspiration headed into the weekend.
Listen on Apple | Listen on Spotify | Listen on Stitcher | Listen on Amazon | Listen on Google
Here are the stories covered this week:
- The eyes are the windows to the soul - and biological aging?
- What bean genes mean for health and the planet
- This breathing practice could lower levels of tau proteins
- AI beats humans at assessing heart health
- Should you get a nature prescription?
Two-and-a-half year-old Huckleberry, a blue merle Australian shepherd, pulls hard at her leash; her yelps can be heard by skiers and boarders high above on the chairlift that carries them over the ski patrol hut to the top of the mountain. Huckleberry is an avalanche rescue dog — or avy dog, for short. She lives and works with her owner and handler, a ski patroller at Breckenridge Ski Resort in Colorado. As she watches the trainer play a game of hide-and-seek with six-month-old Lume, a golden retriever and avy dog-in-training, Huckleberry continues to strain on her leash; she loves the game. Hide-and-seek is one of the key training methods for teaching avy dogs the rescue skills they need to find someone caught in an avalanche — skier, snowmobiler, hiker, climber.
Lume’s owner waves a T-shirt in front of the puppy. While another patroller holds him back, Lume’s owner runs away and hides. About a minute later — after a lot of barking — Lume is released and commanded to “search.” He springs free, running around the hut to find his owner who reacts with a great amount of excitement and fanfare. Lume’s scent training will continue for the rest of the ski season (Breckenridge plans operating through May or as long as weather permits) and through the off-season. “We make this game progressively harder by not allowing the dog watch the victim run away,” explains Dave Leffler, Breckenridge's ski patroller and head of the avy dog program, who has owned, trained and raised many of them. Eventually, the trainers “dig an open hole in the snow to duck out of sight and gradually turn the hole into a cave where the dog has to dig to get the victim,” explains Leffler.
By the time he is three, Lume, like Huckleberry, will be a fully trained avy pup and will join seven other avy dogs on Breckenridge ski patrol team. Some of the team members, both human and canine, are also certified to work with Colorado Rapid Avalanche Deployment, a coordinated response team that works with the Summit County Sheriff’s office for avalanche emergencies outside of the ski slopes’ boundaries.
There have been 19 avalanche deaths in the U.S. this season, according to avalanche.org, which tracks slides; eight in Colorado. During the entirety of last season there were 17. Avalanche season runs from November through June, but avalanches can occur year-round.
High tech and high stakes
Complementing avy dogs’ ability to smell people buried in a slide, avalanche detection, rescue and recovery is becoming increasingly high tech. There are transceivers, signal locators, ground scanners and drones, which are considered “games changers” by many in avalanche rescue and recovery
For a person buried in an avalanche, the chance of survival plummets after 20 minutes, so every moment counts.
A drone can provide thermal imaging of objects caught in a slide; what looks like a rock from far away might be a human with a heat signature. Transceivers, also known as beacons, send a signal from an avalanche victim to a companion. Signal locators, like RECCO reflectors which are often sewn directly into gear, can echo back a radar signal sent by a detector; most ski resorts have RECCO detector units.
Research suggests that Ground Penetrating Radar (GPR), an electromagnetic tool used by geophysicists to pull images from inside the ground, could be used to locate an avalanche victim. A new study from the Department of Energy’s Sandia National Laboratories suggests that a computer program developed to pinpoint the source of a chemical or biological terrorist attack could also be used to find someone submerged in an avalanche. The search algorithm allows for small robots (described as cockroach-sized) to “swarm” a search area. Researchers say that this distributed optimization algorithm can help find avalanche victims four times faster than current search mechanisms. For a person buried in an avalanche, the chance of survival plummets after 20 minutes, so every moment counts.
An avy dog in training is picking up scent
Sarah McLear
While rescue gear has been evolving, predicting when a slab will fall remains an emerging science — kind of where weather forecasting science was in the 1980s. Avalanche forecasting still relies on documenting avalanches by going out and looking,” says Ethan Greene, director of the Colorado Avalanche Information Center (CAIC). “So if there's a big snowstorm, and as you might remember, most avalanches happened during snowstorms, we could have 10,000 avalanches that release and we document 50,” says Greene. “Avalanche forecasting is essentially pattern recognition,” he adds--and understanding the layering structure of snow.
However, determining where the hazards lie can be tricky. While a dense layer of snow over a softer, weaker layer may be a recipe for an avalanche, there’s so much variability in snowpack that no one formula can predict the trigger. Further, observing and measuring snow at a single point may not be representative of all nearby slopes. Finally, there’s not enough historical data to help avalanche scientists create better prediction models.
That, however, may be changing.
Last year, an international group of researchers created computer simulations of snow cover using 16 years of meteorological data to forecast avalanche hazards, publishing their research in Cold Regions Science and Technology. They believe their models, which categorize different kinds of avalanches, can support forecasting and determine whether the avalanche is natural (caused by temperature changes, wind, additional snowfall) or artificial (triggered by a human or animal).
With smell receptors ranging from 800 million for an average dog, to 4 billion for scent hounds, canines remain key to finding people caught in slides.
With data from two sites in British Columbia and one in Switzerland, researchers built computer simulations of five different avalanche types. “In terms of real time avalanche forecasting, this has potential to fill in a lot of data gaps, where we don't have field observations of what the snow looks like,” says Simon Horton, a postdoctoral fellow with the Simon Fraser University Centre for Natural Hazards Research and a forecaster with Avalanche Canada, who participated in the study. While complex models that simulate snowpack layers have been around for a few decades, they weren’t easy to apply until recently. “It's been difficult to find out how to apply that to actual decision-making and improving safety,” says Horton. If you can derive avalanche problem types from simulated snowpack properties, he says, you’ll learn “a lot about how you want to manage that risk.”
The five categories include “new snow,” which is unstable and slides down the slope, “wet snow,” when rain or heat makes it liquidly, as well as “wind-drifted snow,” “persistent weak layers” and “old snow.” “That's when there's some type of deeply buried weak layer in the snow that releases without any real change in the weather,” Horton explains. “These ones tend to cause the most accidents.” One step by a person on that structurally weak layer of snow will cause a slide. Horton is hopeful that computer simulations of avalanche types can be used by scientists in different snow climates to help predict hazard levels.
Greene is doubtful. “If you have six slopes that are lined up next to each other, and you're going to try to predict which one avalanches and the exact dimensions and what time, that's going to be really hard to do. And I think it's going to be a long time before we're able to do that,” says Greene.
What both researchers do agree on, though, is that what avalanche prediction really needs is better imagery through satellite detection. “Just being able to count the number of avalanches that are out there will have a huge impact on what we do,” Greene says. “[Satellites] will change what we do, dramatically.” In a 2022 paper, scientists at the University of Aberdeen in England used satellites to study two deadly Himalayan avalanches. The imaging helped them determine that sediment from a 2016 ice avalanche plus subsequent snow avalanches contributed to the 2021 avalanche that caused a flash flood, killing over 200 people. The researchers say that understanding the avalanches characteristics through satellite imagery can inform them how one such event increases the magnitude of another in the same area.
Avy dogs trainers hide in dug-out holes in the snow, teaching the dogs to find buried victims
Sarah McLear
Lifesaving combo: human tech and Mother Nature’s gear
Even as avalanche forecasting evolves, dogs with their built-in rescue mechanisms will remain invaluable. With smell receptors ranging from 800 million for an average dog, to 4 billion for scent hounds, canines remain key to finding people caught in slides. (Humans in comparison, have a meager 12 million.) A new study published in the Journal of Neuroscience revealed that in dogs smell and vision are connected in the brain, which has not been found in other animals. “They can detect the smell of their owner's fingerprints on a glass slide six weeks after they touched it,” says Nicholas Dodman, professor emeritus at Cummings School of Veterinary Medicine at Tufts University. “And they can track from a boat where a box filled with meat was buried in the water, 100 feet below,” says Dodman, who is also co-founder and president of the Center for Canine Behavior Studies.
Another recent study from Queens College in Belfast, United Kingdom, further confirms that dogs can smell when humans are stressed. They can also detect the smell of a person’s breath and the smell of the skin cells of a deceased person.
The emerging avalanche-predicting human-made tech and the incredible nature-made tech of dogs’ olfactory talents is the lifesaving “equipment” that Leffler believes in. Even when human-made technology develops further, it will be most efficient when used together with the millions of dogs’ smell receptors, Leffler believes. “It is a combination of technology and the avalanche dog that will always be effective in finding an avalanche victim.”