Problem-Based Learning

Deep vs. Surface Learning highlights a divergence of opinion around what it means to know something. Deep Learning is associated with intrinsically motivated forms of engagement, characterized by making meaningful connections between new and previous understandings. Surface Learning tends to be associated with extrinsic motivations and is focused on the memorization and recall of information for formulaic responses. (Note: not to be confused with Deep Learning.)

Inquiry-Based Learning was designed to interrupt assumptions of context-free knowledge, passive learning, and smooth paths to understanding. It focuses on pursuing authentic interests, posing researchable questions, and participating in knowledge production. Several types of Inquiry-Based Learning have been described, varying on matters of teacher role, learner autonomy, and flexibility of focus.

At its root, Active Learning is a critique of and response to “passive learning” – that is, delivery-based teaching methods that position the learner as a passive recipient of external knowledge. Active Learning thus encompasses a range of learning formats in which students are active, ranging from simply experiencing what is being learned (e.g., riding a bike) to more formally structured approaches.

Deep Learning refers to iterative machine learning algorithms in which each layer of non-linear processing uses output from the previous layer to form a hierarchy of concepts. Deep Learning algorithms have been useful for speech recognition, computer vision, social network filtering. generating mathematical proofs, playing sophisticated games, modeling complex natural systems. (Note: not to be confused with Deep vs. Surface Learning.)