A reader sent me an email that asked The Tech Edvocate to explain the difference between deep learning and deeper learning, and I decided to write a blog responding to their query. The short answer is that deep learning is all about tech and machine learning, and deeper learning is all about cognition and critical and higher order thinking skills. Let’s examine both concepts.

Deep Learning is the pyramid of concepts that give computers and machines the ability to learn complex ideas by developing them from simpler ones. Deep learning is an offshoot of machine learning, just as machine learning is an offshoot of artificial intelligence. Deep learning uses huge neural networks to create complex algorithms that take machine learning to a more organic and productive level. Deep learning devices are able to discover and learn their own features, which is akin to training themselves. The term “deep” is used to denote how “deep” the algorithms can go in terms of mapping and labeling “higher” ideas and features from “lower” ones.

Deeper Learning is a critical and higher order thinking model that is made up of the following 6 competencies: master core academic content, think critically and solve complex problems, communicate effectively, work collaboratively, learn how to learn, develop academic mindsets. Deeper learning is a manifestation of the backlash from standardized testing critics. It was developed to help students develop skill sets that would enable them to learn from, thrive, and adapt in real-world environments. I would be remiss if I did not point out that proponents of deeper learning are not breaking new ground. Many would say that deeper learning simply describes what exemplary teachers have been doing since the beginning of time. Someone just decided to give it a name.

Well, that’s my short explanation of the difference between deep learning and deeper learning. Are you someone who considers themselves a deep or deeper learning guru? If so, what would you add to my post? What would you change?