The Tech Elite’s Quest to Reinvent School in Its Own Image
A Day in the Life
Like a true startup, Khan Lab School constantly changes its schedule to accommodate evolving workflow and logistical demands. Different age-groups follow different self-paced lesson plans, but here’s an example of a day at the Lab School.
9–9:15 am: Morning Meeting
A daily all-school meeting where students learn about things like current events, view the work of their fellow classmates, and focus on relationships.
9:15–9:45 Advisory
Students break out into cohorts sorted by age. They attend one-on-one meetings with advisers to set personal goals. (One ambitious 12-year-old hopes to launch a small-scale NGO.) Some days include “Goal Studio” time to work on these independent passion projects.
9:45–10:45 Literacy Lab, Part 1
Teachers cover all the essentials, from developing main ideas to composing blog posts.
10:45–11 Morning Break
11–11:30 Literacy Lab, Part 2
Instructors use digital tools like Lexia and LightSail to assess students’ reading levels and work with individuals on problem areas.
11:30–12 Inner Wellness
Students improve their mental well-being by practicing mindfulness.
12–12:45 pm Lunch
12:45–1 Afternoon Meeting
Another schoolwide gathering for announcements and updates.
1–2:30 Math/Computer Science Lab
Using videos from Khan Academy, students practice skills at their math level. Younger students receive more direct instruction, while older students might work on a collaborative engineering project.
2:30–3 Outer Wellness
Students participate in physical fitness activities, including gardening and playing sports like field hockey, soccer, and Ultimate Frisbee.
3–4 Cleanup, Read Aloud, Flexible Pick Up/Recess
4–6 Studio Time/Pick Up
During this optional period, students work on their own without direct supervision, though the staff is available for help.
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In the news:
Google just open-sourced TensorFlow, its AI engine.
*linear algebra computational graph engine with automatic gradient calculation
I really wonder how this will fit into the established deep learning software ecosystem - it has clear advantages over any single one of the large players (Theano, Torch, Caffee), but lacks the established community of any of them. As a researcher in the field, it's really frustrating that there is no standardisation and you essentially have to know a ton of software frameworks to effectively keep up with research, and I highly doubt Google entering the fray will change this.
https://xkcd.com/927/