Our discussion today centered around student learning patterns in the online environment. The focus of Nancy's PhD work examines this question. And as a professor who is always struggling with how to get the message across to students this is a question of central importance to me.
As my sabbatical proceeds I have been thinking more and more about how learning happens. It seems clear that there is a role for abstraction in the learning process, whether we are learning with our hands, our eyes, or our ears. As I've written in previous posts I think it is important that students understand the "roadmap" of their learning process. This goes beyond the question of whether assignments and assessments reflect learning objectives. In fact, in my experience, as long as students trust the learning experience and understand the learning environment you can push the envelope with difficult exams or even tangentially related assignments and still carry the confidence of the students.
How does this relate to the question of deep learning? For one thing, deep learning is based on a hierarchy of concepts. Higher-level or "large" concepts are constructed from lower-level or "small "concepts. In the online environment, with limited contact between professor and students, It is essential to establish the relationship between higher-level concepts and lower-level concepts. Somehow, in a seven week course, this must occur very early in the course and it must be reinforced from week to week. To me, this is laying out a roadmap that instructs students in why they are learning what they are learning. The more we can reiterate the roadmap the more we encourage metacognition.
But how do we accomplish this? This comes to the second point regarding deep learning. In deep learning, learning representations (objects) are images. Images represent abstractions of varying levels. The online environment allows us to present a great variety (more than we could in a normal classroom) of images and sounds. When used frequently but judiciously objects reinforce the roadmap of learning. Consequently, students examining images can learn from them at many levels.
The image may convey an unarticulated aesthetic. It may present a whole array of aesthetic sensations interpretable only to the learner. I discussed this last week when I referred to Aztec aesthetics and my struggle to understand them. This points to the reality that deep learning through abstractions is a deeply human process that transcends cultural boundaries.
In a community of learners there are many different styles of learning and perception. One interpretation of this fact is the idea of "multiple intelligences." So, a learning environment that is rich with images can serve the multiple intelligences of the targeted learners. A learning environment rich with images conveys many layers and connections between abstractions that may only be barely articulated. These abstractions communicate with learners, perhaps unconsciously, through the learners' personal aesthetic.
In the vocabulary of deep learning, this phenomenon may be framed as semi-supervised learning or even unsupervised learning. This does not imply a lack of planning for the course. Rather, it is a highly choreographed process in which students are presented with tightly focused images, text, and sounds that elicit a variety of learning sensations. Whether "semi-supervised" or "unsupervised," especially with seasoned adult learners like ones in my online program, this kind of learning engenders confidence and trust in the process. This I think, is the core to a successful learning environment that is characterized by metacognition among the learners.
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