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Participants from all over the globe have just completed the first two stages of the inaugural batch of the Deep Learner web-based workshop. Instead of just a bunch of pre-recorded lectures, Deep Learner is designed with Content + Community, and the cutting-edge learning priniciples in mind.


Here’s a short video explaining this particular challenge and why we need to make distinctions between

1. Intensity vs. Consistency, &
2. Timely vs. Timeless matter.

The Lack of Time

Aside from the universal issue of the lack of time, there is a particular reason why therapists are time-poor: We give a lot of our time to others. Meanwhile, we not only neglect to partition and protect time not only for self-care, but also time to foster our own development.

And here is one of the early practical tips participants from Deep Learner were given to deal with the arrow of time.

Protect Deep Learning

We not only need to make a distinction between Shallow Work vs Deep Work, we also need to protect and automate for deep learning to happen on a consistent basis. This video is an excerpt from the Deep Learner web-based workshop for Psychotherapists. For more, go to darylchow.com/courses

Protect Deep Learning

(Note: We cover 2 other practical tips in Modules 1 out of 8: The Arrow of Time: 1. Protect Attention and 2. Protect Play).

A lot of sketchnoting in this workshop. Not perfect, but it does the job.

Time is precious (even when—or more so when we are in lockdown). We can squander it as consumers, or we can create something special as creators to better serve others.

I hope you take the time.

Life’s most persistent and urgent question is: What are you doing for others?

~Dr. Martin Luther King, Jr.

p/s: The 2nd batch of Deep Learner will kick-off in June 2020. Email us to be on the waitlist: info@darylchow.com

2 Responses

  1. June 29, 2020

    […] we looked at the value of protect time and space for deep learning. In today’s blog, we take you under the hood of Module 1.4. Create Play. (click to watch the […]

  2. January 7, 2022

    […] See Protect Deep Learning […]

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