segunda-feira, setembro 07, 2020

"Her real job"

Um texto dedicado ao meu parceiro das conversas oxigenadoras e à sua preocupação com a preparação das pessoas para a Industria 4.0.

Segundo ele, e julgo que tem razão, quase ninguém se preocupa com a preparação das pessoas para a Industria 4.0, o foco está todo na tecnologia.
"While Grosso understands that part of her official job is to transmit a body of content from her head to those of the students, she thinks her real job—her most important job—is to help students become capable of thinking in a complex and uncertain world. To her that means embracing the messiness of the world and not attempting to simplify it for students as if students can’t deal with messiness.
That means helping them learn both how to build models (rather than handing them prebuilt ones) and how to build better ones together. She introduces them to the ladder of inference, a framework from business and education theorist Chris Argyris, which describes how humans reason, starting with selecting which data to take into account and then making increasingly specific inferences about the selected data—up the rungs of a metaphoric ladder to reach a conclusion on the subject of their thinking, at the top of that ladder. Grosso creates an exercise by which she writes different fragments of a story on a number of paper fish that she hides around the classroom. For example, the story may be about why she arrived at school grumpy one morning, and one fish may say “woke up late” while another may say “forgot marked tests at home,” and so on. Student groups go on a fishing expedition to find and collect the fish, and then attempt to come to a conclusion based on the fish that their group happened to find.
Since different groups find different data-laden fish, the groups come to different conclusions. Instead of judging which conclusion is “right,” they explore how collecting different data means that each group might come to a different conclusion. Grosso highlights that although we can never collect all the data ourselves, we make our model more robust by being curious and asking questions of others who may have access to data that we don’t. By rejecting the need for one “right” answer, Grosso’s students become more confident. They gain the confidence to share their thinking, because if their answer is different from others’, it might just be because they collected different data or interpreted the data differently, not because their answer is “wrong.” The process also encourages students to make and think about connections—between what they and other students know—so that they can integrate multiple insights.
Beth Grosso’s approach underlies the agenda I propose here for educators to help preserve American democratic capitalism and enhance its ability to sustainably deliver broadening and rising prosperity. The job of educators should be to prepare students for a complex adaptive system, not to make them capable of operating only a narrow part of a complicated machine. We need to equip our youth for a world that isn’t about perfecting a machine but rather about achieving a balance—an endless journey of transitory improvements rather than definitive solutions. That is the only way we will produce the citizens that we need and the business executives and political leaders to pilot productively. Currently, the formal education system produces overconfident reductionists who don’t see that they are operating in a complex adaptive system and are altogether too sure of the quality and usability of their piece-part solutions. The purpose of education needs to shift, as Beth Grosso illustrates, toward producing sophisticated yet humble model integrators. To do so, educators must do four things.
Temper the Inclination to Teach Certainty
At present, the formal education system predominantly teaches certainty; that is, that there is one right answer and many wrong ones
Lembro-me da perplexidade da minha amiga Marina, que na altura estudava Biologia na universidade, ao perceber que tinha saído um artigo numa revista científica que desclassificava o que ela tinha aprendido numa aula na semana anterior.

Trechos retirados de "When More Is Not Better" de Roger Martin prossegue.

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