Article Summary: diSessa (1993) Knowledge in Pieces Framework

Motivation

diSessa’s motivation was to understand students’ intuitions of mechanisms in physics and how those intuitions affect formal learning of physics (the actual title of this paper is “Toward an Epistemology of Physics”). The Knowledge in Pieces framework used in this paper, and which will be the focus of this summary, has since been used across many topics, such as learning recursion in computing (Chao et al., 2018). The framework is based on empirical work, but as the paper is 126 pages long, those parts of the paper, and many others, are excluded from this summary.

Knowledge in Pieces (KiP)

The KiP framework is intended to describe learning of complex topics in a way that accounts for learners preconceptions (i.e., intuitions and informal self-explanations) of the topic. In addition, KiP explores the misconceptions that arise as knowledge develops. diSessa discusses four issues related to “theory building about any knowledge system” (p. 111) and how they relate to phenomenological primitives (p-prims), the building blocks of KiP:

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.

Development: Understanding of complex concepts develops from an unstructured and disjointed collection of p-prims. Through instruction and deliberate experience, p-prims become organized so that highly useful ones become central to a concept and more obscure ones develop few connections within the developing knowledge structure. P-prims that are based on incorrect or incomplete explanations can lead to misconceptions as learners negotiate relationships among p-prims. Ultimately, p-prims are subsumed into more formal knowledge structures that are based on principles in the field, such as physics laws, that are applied more consistently than recognition-activated p-prims.

Systematicity: The systematicity of knowledge development depends on p-prims’ mutual use (recognition of one p-prim activates recognition of another), common attributes (similar vocabulary among p-prims allow for relationships among them), top-down coherence (early salient p-prims align with later p-prims), mutual plausibility (phenomenological syllogisms), completeness (a set of p-prims should explain different expressions of the same concept), and abstraction (the breadth of phenomena that a p-prim can explain intuitively).

Later work on the KiP framework has expanded the elements that contribute to development of knowledge structures to include p-prims, coordination classes, epistemological beliefs, and meta-representational beliefs (Hammer et al., 2005 as cited in Chao et al., 2018). Most notably, Taber and Garcia-Franco (2010, as cited in Chao et al., 2018) identified knowledge structures that are similar to p-prims but are not reliant on physical experiences.

Why this is important

As much as I don’t want to write a summary that says, “you should really go read the paper,” the paper has much more to offer than this limited summary. Especially from a methodological perspective, diSessa discusses how to identify p-prims and common difficulties that you would face, such as that learners have a hard time articulating p-prims because they are not strongly related to vocabulary. By describing his work in physics in detail, he demonstrates how to apply the KiP framework, including to other fields. Researchers in other fields find the KiP framework useful for exploring how learners develop knowledge about complex concepts based on preconceptions.

Chao, J., Feldon, D. F., & Cohoon, J. P. (2018). Dynamic mental model construction: A Knowledge in Pieces-based explanation for computing students’ erratic performance on recursion. The Journal of the Learning Environment, 27(3), 431-473.

diSessa, A. A. (1993). Toward an epistemology of physics. Cognition and Instruction, 10(2&3), 105-225.

Hammer, D., Elby, A., Scherr, R. E., & Redish, E. F. (2005). Resources, framing, and transfer. In J. P. Mestre (Ed.), Transfer of learning from a modern multidisciplinary perspective (pp. 89–120). Greenwich, CT: Information Age.

Taber, K. S., & Garcia-Franco, A. (2010). Learning processes in chemistry: Drawing upon cognitive resources to learn about the particulate structure of matter. The Journal of the Learning Sciences, 19(1), 99–142.

For more information about the article summary series or more article summary posts, visit the article summary series introduction.

2 thoughts on “Article Summary: diSessa (1993) Knowledge in Pieces Framework

  1. Pingback: Article Summary: Series Introduction | Lauren Margulieux

  2. Pingback: Applying diSessa’s Knowledge in Pieces Framework to Understanding the Notional Machine | Computing Education Research Blog

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