But when Fludd was asked why her employees were so effective at processing more information than the average collector, she didn’t have any great answers. She couldn’t explain why her workers seemed to absorb so much more. So after the conference, Chase hired the consulting firm Mitchell Madison Group to examine her methods.
“How did you figure out that it’s better to call women in the morning?” a consultant named Traci Entel asked her when Fludd was back in the office.
“Do you want me to show you my calendar?” said Fludd. The consultants weren’t certain why she needed a calendar to explain her methods, but sure, they said, let’s see the calendar. They expected Fludd to pull out a datebook or journal. Instead, she dropped a binder onto the table. Then she wheeled over a cart containing several more binders just like it.
“Okay,” Fludd said, leafing through pages filled with numbers and scribbled notes. She found the sheet she was looking for. “One day, I came up with this idea that it would be easier to collect from younger people, because I figured they’re more eager to keep a good credit score,” she said.
Fludd explained that coming up with such theories was common on her team. Employees would gather during lunch breaks or after work to kick around ideas. Typically, these ideas didn’t make much sense—at least, at first. In fact, the ideas were often somewhat nonsensical, such as the suggestion that an irresponsible young person who is already behind on her debts, for some reason, would suddenly care deeply about improving her credit score. But that was okay. The point wasn’t to suggest a good idea. It was to generate an idea, any idea at all, and then test it.
Fludd looked at her calendar. “So the next day, we started calling people between the ages of twenty-one and thirty-seven.” At the end of the shift, employees reported no noticeable change in how much they had convinced people to pay. So the following morning, Fludd changed one variable: She told her employees to call people between the ages of twenty-six and thirty-one. The collection rate improved slightly. The next day, they called a subset of that group, cardholders between twenty-six and thirty-one with balances between $3,000 and $6,000. Collection rates declined. The next day: Cardholders with balances between $5,000 and $8,000. That led to the highest collection rates of the week. In the evenings, before everyone left, managers gathered to review the day’s results and speculate on why certain efforts had succeeded or failed. They printed out logs and circled which calls had gone particularly well. That was Fludd’s “calendar”: the printouts from each day with annotations and employees’ comments as well as notes suggesting why certain tactics had worked so well.
With further testing, Fludd determined that her original theory regarding young people was a dud. That, in itself, wasn’t surprising. Most of the theories were duds initially. Employees had all kinds of hunches that didn’t bear up under testing. But as each experiment unfolded, workers became increasingly sensitive to patterns they hadn’t noticed before. They listened more closely. They tracked how debtors would respond to various questions. And eventually, a valuable insight would emerge—like, say, it’s better to call people’s homes between 9:15 and 11:50 in the morning because the wife will pick up and women are more likely to pay a family’s debts. Sometimes, the debt collectors would develop instincts they couldn’t exactly put into words but learned to heed nonetheless.
Then someone would propose a new theory or experiment and the process would start all over again. “When you track every call and keep notes and talk about what just happened with the person in the next cubicle, you start paying attention differently,” Fludd told me. “You learn to pick up on things.”
To the consultants, this was an example of someone using the scientific method to isolate and test variables. “Charlotte’s peers would generally change multiple things at once,” wrote Niko Cantor, one of the consultants, in a review of his findings. “Charlotte would only change one thing at a time. Therefore she understood the causality better.”
But something else was going on, as well. It wasn’t just that Fludd was isolating variables. Rather, by coming up with hypotheses and testing them, Fludd’s team was heightening their sensitivity to the information flowing past. In a sense, they were adding an element of disfluency to their work, performing operations on the “data” generated during each conversation until lessons were easier to absorb. The spreadsheets and memos that they received each morning, the data that appeared on their screens, the noises they heard in the background of phone calls—that became material for coming up with new theories and running various experiments. Each phone call contained tons of information that most collectors never registered. But Fludd’s employees noticed it, because they were looking for clues to prove or disprove theories. They were interacting with the data embodied in each conversation, turning it into something they could use.
This is how learning occurs. Information gets absorbed almost without our noticing because we’re so engrossed with it. Fludd took the torrent of data arriving each day and gave her team a method for placing it into folders that made it easier to understand. She helped her employees do something with all those memos they received and the conversations they were having—and, as a result, it was easier for them to learn.
III.
Nancy Johnson became a teacher in Cincinnati because she didn’t know what else to do with her life. It had taken her seven years to make it through college, and after graduating, she’d become a flight attendant, married a pilot, and then decided to settle down. In 1996, she started substituting in Cincinnati’s public schools, hoping it would lead to a full-time job. She went from classroom to classroom, guiding classes on everything from English to biology, until she finally got a permanent offer as a fourth-grade teacher. On her first day, the principal saw her and said, “So you’re Ms. Johnson.” He later admitted he had gotten a number of applications with the same last name and wasn’t fully certain which one he had hired.
A few years later, in response to the federal government’s No Child Left Behind law, Cincinnati began tracking students’ performances in reading and math via standardized exams. Johnson was soon drowning in reports. Each week, she received memos on students’ attendance and their progress in vocabulary, math proficiency, reading, writing, literature comprehension, and something called “cognitive manipulation,” as well as reviews of her classroom’s proficiency, her teaching aptitude, and the school’s overall scores. There was so much information that the city had hired a team of data visualization experts to design the weekly memos the district delivered via the Internet dashboards. The graphics team was talented. The charts Johnson received were easy to read, and the Internet sites contained clear summaries and color-coded trend lines.
But in those first few years, Johnson hardly looked at any of it. She was supposed to use all that information in designing her curricula, but it made her head hurt. “There were lots of memos and statistics, and I knew I was supposed to be incorporating them into my classroom, but it all just kind of washed over me,” she said. “It felt like there was this gap between all those numbers and what I needed to know to become a better teacher.”
Her fourth-grade kids were mostly poor, and many were from single-parent families. She was a good teacher, but her class still fared badly on assessment exams. In 2007, the year before Cincinnati’s Elementary Initiative began, her students scored an average of 38 percent proficiency on the state’s reading test.
Then, in 2008, the Elementary Initiative was launched. As part of that reform, Johnson’s principal mandated that all teachers had to spend at least two afternoons a month in the school’s new data room. Around a conference table, teachers were forced to participate in exercises that made data collection and statistical tabulation even more time consuming. At the start of the semester, Johnson and her colleagues were told that as part of the EI, they had to create an index card for every student in their class. Then, every other Wednesday, Johnson would go into the data room and transcribe the past two week’s test
scores onto each student’s card, and then group all the cards into color-coded piles—red, yellow, or green—based on whether students were underperforming, meeting expectations, or exceeding their peers. As the semester progressed, she also began grouping cards based on who was improving or falling behind over time.
It was intensely boring. And, frankly, it seemed redundant because all this information was already available on the students’ online dashboards. Moreover, many of the people in that room had been teaching for decades. They didn’t feel like they needed piles of cards to tell them what was going on in their own classrooms. But an order was an order, and so they went into the data room every other week. “The rule was that everyone had to actually handle the cards, physically move them around,” Johnson said. “Everyone hated it, at least at first.”
Then one day a third-grade teacher had an idea. Since he had to spend so much time transcribing test scores, he decided to also note on each student’s index cards which specific questions they had gotten wrong on that week’s assessment exam. He convinced another third-grade teacher to do the same. Next, they combined their cards and made piles by grouping students who had made similar mistakes. When they were done, the piles showed a pattern: A large number of students in one class had done well on pronoun use but had stumbled at fractions; a large number of students in the other classroom had scored the opposite way. The teachers traded curricula. Both classes’ scores went up.
The following week, someone else suggested dividing cards from multiple classes into piles based on where students lived. Teachers started giving everyone from the same neighborhoods similar reading assignments. Test scores ticked up. Students were doing their homework together on the bus rides home.
Johnson began putting her students into work groups based on the piles of cards she was making in the data room. Handling the index cards, she found, gave her a more granular sense of each student’s strengths and weaknesses. She found herself going into the data room a couple of times a week and putting students’ cards into smaller and smaller piles, experimenting with arranging them in different ways. She had felt, before, like she knew her class pretty well. But this was a far deeper level of understanding. “When there are twenty-five students and just one teacher, it’s easy to stop seeing them as individuals,” she said. “I had always thought of them as a class. The data room made me focus on particular kids. It forced me to look at them one by one and ask myself, what does this kid need?”
Midway through the year, some of Johnson’s colleagues noticed that a small group of students in each class were struggling on math questions. It wasn’t a big enough trend that any one teacher would have noticed on their own, but inside the data room, the pattern became clear. That’s how the school-wide Hot Pencil Drills started. Soon, students such as eight-year-old Dante were spending each morning filling out multiplication tables as fast as they could, and then speed-walking to the main office to have the fastest test takers’ names read over the PA system. Within twelve weeks, the school’s math scores were up by 9 percent.
Eight months after the Elementary Initiative was launched, Johnson’s class sat for their yearly assessment exam. By that point, she was visiting the data room all the time. She and her colleagues had created dozens of piles of index cards. They had tested various lesson plans and were tracking results on long strips of paper torn from rolls and taped to the walls. Columns of numbers and scribbled notes filled the data room.
The test results came back six weeks later. Johnson’s students scored an average of 72 percent, almost double her class’s result the previous year. The school’s overall scores had more than doubled. In 2009, Johnson became a teacher coach, traveling to other schools in Cincinnati to help instructors learn to use their own data rooms. In 2010, she was selected by her peers as Cincinnati’s Educator of the Year.
IV.
Delia Morris was a high school freshman when Cincinnati launched the Elementary Initiative, and so she was too old to benefit from the reforms occurring at places such as South Avondale. And by the time city officials began expanding the program, it seemed too late for her in other respects. Delia’s father was fired that year from his job as a security guard at a local grocery store. Then he got into a fight with their landlord. Not long after, Delia came home to find an orange sticker and a padlock on the apartment’s front door and everything she and her seven siblings owned stuffed into black garbage bags in the hall. The family was able to stay with people from their church for a while, and then crowded into the apartments of family friends, but from that point on, they moved every few months.
Delia was a good kid and a hard worker. Her teachers had noticed she was unusually smart—gifted enough, they felt, to make it out of Cincinnati’s bad neighborhoods and into college. But that didn’t mean escape was guaranteed. Every year there were a handful of students who seemed destined for something better until poverty pulled them back down. Delia’s teachers were hopeful but not naïve. They knew that even for gifted students, a better life was sometimes out of reach. Delia knew that, as well. She worried that even a whiff of homelessness would change how her teachers perceived her, so she didn’t tell anyone what was going on at home. “Going to school was the best part of each day,” she told me. “I didn’t want to ruin that.”
When Delia started her sophomore year at Western Hills High in 2009, the city began expanding its education reforms to high schools. However, some early results among older students proved disappointing. Teachers complained that innovations such as the data rooms were a start but not a solution. Older students were already too hardened, their teachers said; their timelines for intervention were too short. To change kids’ lives, they argued, schools needed to help students get better at making the kinds of decisions that offered few opportunities for experimentation. They needed to help teenagers decide between going to college or getting a job; whether to terminate a pregnancy or get married; how to pick among family members when everyone needs your help.
So the school district shifted its focus for high school students. Alongside the Elementary Initiative, the district began creating engineering classes within Western Hills High and other schools in partnership with local universities and the National Science Foundation. The goal was “a multidisciplinary approach to education that encourages students to leverage the technology they use in their daily lives to solve real world problems,” a summary of the program read. Ninety percent of students at Western Hills lived below the poverty line. Their classrooms had peeling linoleum floors and cracked chalkboards. “Leveraging technology” was not what most students worried about. Delia signed up for an engineering course taught by Deon Edwards, whose introductory remarks reflected the reality that surrounded all of them.
“We’re going to learn how to think like scientists,” he told his class. “We’re going to leave your parents and friends behind and learn to make choices with clear eyes, without the baggage everyone wants to put on you. And if any of you didn’t have anything to eat this morning, I keep energy bars in my desk and you should help yourself. There’s nothing wrong with saying you’re hungry.”
The real focus of Mr. Edwards’s class was a system for decision making known as “the engineering design process,” which forced students to define their dilemmas, collect data, brainstorm solutions, debate alternative approaches, and conduct iterative experiments. “The engineering design process is a series of steps that engineers follow when they are trying to solve a problem and design a solution for something; it is a methodical approach to problem solving,” one teacher’s manual explained. The engineering design process was built around the idea that many problems that seem overwhelming at first can be broken into smaller pieces, and then solutions tested, again and again, until an insight emerges. The process asked students to define precisely the dilemma they wanted to solve, then to conduct research and come up with multiple solutions, and then conduct tests, measure results, and repeat the procedure until an answer was found.
It told them to make problems more manageable until they fit into scaffolds and mental folders that were easier to carry around.
The class’s first big assignment was to design an electric car. For weeks, students in Mr. Edwards’s class arranged themselves into teams and followed flowcharts detailing each engineering design process step. The classroom had few materials to work with. But that was okay, because the real point of the exercise was to learn how to squeeze information from your environment, no matter where it comes from. Soon students were visiting car dealerships, going to mechanics’ shops, and raiding aluminum cans from recycling bins to make battery-testing kits from instructions they had found online. “My first job is to teach them to slow down a little bit,” Deon Edwards told me. “These are kids who solve problems all day long. They deal with missing parents and violent boyfriends and classmates on drugs. Everything they experience says they have to choose quickly. I just want to show them that if you have a system for making choices, you can afford to slow down and think.”
Smarter Faster Better: The Secrets of Being Productive in Life and Business Page 25