Archive for September 14, 2018

Applying diSessa’s Knowledge in Pieces Framework to Understanding the Notional Machine

In Lauren Margulieux’s blog where she summarizes papers from learning sciences and educational psychology, she takes on Andy diSessa’s 1993 paper “Toward an epistemology of physics” where diSessa applies his “knowledge in pieces” framework to how students develop an understanding of physics.  (See blog post here.)

The idea is that humans assemble their understanding of complex phenomenon out of knowledge of physical experiences, p-prims. Quoting Lauren:

Elements: P-prims are knowledge structures that are minimal abstractions of common phenomena and typically involve only a few simple parts, e.g., an observed phenomenon, like a person hitting a pen and that pen rolling across the table, and an explanation, like when people hit things, they move. P-prims are both phenomenological, meaning that they are interpretations of reality, and primitive, meaning that are (1) based on often rudimentary self-explanations and (2) an atomic-level mental structure that is only separated into parts by excessive force.

Cognitive Mechanism: P-prims are only activated when the learner recognizes similarities between a p-prim and the current phenomena. Recognition is impacted by many different features, such as cuing, frequency of activation, suppression, salience, and reinforcement. Because activation of p-prims depends on contextual features of phenomena, novices often fail to recognize relevant p-prims unless the contextual features align.

I find diSessa’s framework fascinating, and I’ve always wondered how we could apply it to students learning the notional machine (see blog post here on notional machine). My guess is that students use p-prims to develop their mental model of how the computer works, because — what else could they use? In the end, isn’t all our understanding grounded in physical experiences?  But using p-prims will likely lead to misconceptions since the notional machine is not based in the physical world.

Maybe this is a source of common misconceptions in learning computing.  The list of misconceptions that students have about variables, loops, scope, conditionals, and data structures is long and surprisingly consistent — across languages, over time.  What could possibly be the common source of all those misconceptions?  Maybe it’s physical reality.  Maybe students generally apply the same p-prims when trying to understand computing, and that’s why the same misconceptions arise. It’s sort of like using a metaphor to understand something in computing, but then realizing that the metaphor itself is leading to misconceptions.  And the metaphor that’s getting in our way is the use of physical world primitives for understanding the computational world.

Colleen Lewis, as a student of diSessa’s, uses the Knowledge in Pieces framework in her work.  In her terrific ICER 2012 paper, she does a detailed analysis of students’ debugging to identify misconceptions that they have about state. State is an interesting concept to study from a KiP perspective. It’s a common issue in CS, but less common in Physics. It’s not clear to me how students connect computational state to state in the real world.  Is it state like water being frozen or liquid, or state like being painted blue?  Do they get that state is malleable?

This is a rich space to explore in computing education. What are the p-prims for understanding the notional machine? How do students use the physical world to understand the computational one?

Read more of Lauren’s post here: Article Summary: diSessa (1993) Knowledge in Pieces Framework

September 14, 2018 at 7:00 am 4 comments


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