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CHO(HAN)Haejoang
Field Trips Anywhere
CHO(HAN)Haejoang

중국의 AI 교육 광풍 소식

조한 2019.08.04 02:42 조회수 : 93

수학 시간에 AI tutor 제도로 교육혁명을 일으키고 있다는 뉴스다.

한국 학원에서 오래 전에 이미 만들었던 것일수도 있는데 

여튼 AI의 힘을 많이 빌리는 방향으로 가고 있다.

그러나 여전히 입시를 목표로 할 경우, 그런 adaptive learning 은 사실상 큰 효과를 내기 힘들 것.

personalized learning, 독학자로 키우는 것이 관건이라고 서양학자들은 앞다투어 말하고 있다.

China has started a grand experiment in AI education. It could reshape how the world learns. - MIT Technology Review

 

AI tutor, machine learning

Zhou Yi was terrible at math. He risked never getting into college. Then a company called Squirrel AI came to his middle school in Hangzhou, China, promising personalized tutoring. He had tried tutoring services before, but this one was different: instead of a human teacher, an AI algorithm would curate his lessons. The 13-year-old decided to give it a try. By the end of the semester, his test scores had risen from 50% to 62.5%. Two years later, he scored an 85% on his final middle school exam. “I used to think math was terrifying,” he says. “But through tutoring, I realized it really isn’t that hard. It helped me take the first step down a different path.”

adaptive learning, personalized learning

Squirrel’s approach may yield great results on traditional education, but it doesn’t prepare students to be flexible in a changing world, the experts I spoke to say. “There’s a difference between adaptive learning and personalized learning,” says Chris Dede, a professor at Harvard University in the Technology, Innovation, and Education Program. Squirrel is doing adaptive learning, which is about “understanding exactly what students know and don’t know.” But it pays no attention to what they want to know or how they learn best. Personalized learning takes their interests and needs into account to “orchestrate the motivation and time for each student so they are able to make progress.”

 

Pace, Path and Destination

Jutta Treviranus, a professor at the Ontario College of Art and Design University who pioneered personalized learning to improve inclusivity in education, breaks it down further. “Personalized learning has a number of levels,” she says: she calls them pace, path, and destination.

If the pace of learning is personalized, students with different abilities are allowed different amounts of time to learn the same material. If the path is personalized, students might be given different motivations to reach the same objectives (“Here’s why statistics is relevant to your love of baseball”) and offered the material in different formats (e.g., video versus text). If the destination is personalized, students can choose, for instance, whether to learn with a vocational school or a university in mind.

“We need students to understand their own learning. We need them to determine what they want to learn, and we need them to learn to learn,” Treviranus says. “Squirrel AI doesn’t address those things at all. It only makes it more efficient to bring all of the students to the same standardized place.”

That doesn’t mean that adaptive learning systems won’t have any place in the 21st-century classroom. David Dockterman, a colleague of Dede’s, believes their strength in training people on structured knowledge is still valuable. But it would be a mistake to make them the predominant “teacher” in the classroom: “The kinds of rote activities—knowledge retrieval, skill acquisition—that are more readily teachable with a smart tutor are also the things that are more readily accomplished by a smart machine,” he says.

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