Deep learning- a term originally coined to articulate the concept of ‘going beyond the surface’ or incorporating new learning into your existing knowledge frameworks (Marton and Saljo, 1976) has undergone a resurgence in recent times. This has occurred both in terms of machine learning theory and the human version- but setting aside the engineering connotations for the purposes of this post- let’s consider the L&T applications of deep learning that we can harness in our classrooms.
Recently, discussion of ‘deep active learning’ has emerged as a marriage of deep, and active, learning theories (Matsushita, 2015). The concepts are well aligned, as deep learning requires students to reflect on their learning and apply it to real life experiences, making connections, analysing and evaluating concepts. Active learning similarly requires student involvement in their learning, with students actively exploring concepts, evaluating, making connections and synthesising information via applied tasks. Considering the two processes simultaneously (‘deep active learning’) results in activities that allow students to participate in knowledge building by applying the learning to authentic contexts, evaluating that process and then reflecting on ‘what it means’ in terms of their own knowledge frameworks.