Teaching Yourself to Learn (Again) : Cognitive Theory for Professional Development

By Marissa Utterberg

Elevator Pitch

My skills are obsolete! Legacy Python has a death clock! Whether you’re alarmed or excited, our industries are evolving. How can we level up without getting another degree? This talk will focus on 6 principles of expertise. Learn from “How People Learn” while you learn about Computer Vision.


This talk begins with a discussion of why we need to be lifelong learners.

No bootcamp, university, or other time-boxed education program can prepare us for everything - especially in industries that are immersed in disruption and development. So what is the value of an education? And how can we keep up (or level up) without necessarily going back to school?

Using research from the field of cognitive science, the remainder of the talk covers theories and actionable strategies for how to approach learning needs.

The 6 principles of expertise, published by the Committee on Developments in the Science of Learning, are defined…

  • Experts notice features and meaningful patterns of information that are not noticed by novices.
  • Experts have acquired a great deal of content knowledge that is organized in ways that reflect a deep understanding of their subject matter.
  • Experts’ knowledge cannot be reduced to sets of isolated facts or propositions but, instead, reflects contexts of applicability: that is, the knowledge is “contextualized” on a set of circumstances.
  • Experts are able to flexibly retrieve important aspects of their knowledge with little attentional effort.
  • Though experts know their disciplines thoroughly, this does not guarantee that they are able to teach others.
  • Experts have varying levels of flexibility in their approach to new situations.

…and described, using examples from machine learning and hardware to compare and contrast novice-like learning with expert-like learning. In breaking down what cognitive scientists have described about expertise, we identify key attitudes and strategies for effective professional development.

Other topics include:

Participants will leave this talk with research-driven perspectives on expertise - and a few starter scripts - that will empower their own growth.


I’m a Data Scientist, Community Engineer, and former math teacher with a master’s degree in Educational Research & Assessment. As a self-taught career changer, I drew on my experience teaching others and crafted my own educational program for a successful entry into the rapidly evolving field of data science. I feel very strongly that professional development is a crucial skill in tech fields because our tooling evolves fairly drastically throughout our careers. My hope in this talk is to provide inspiring insights and actionable strategies that participants can use to motivate and improve their own learning.