Fostering Strategy #2: Instructor teaches distinctive forms or patterns that are important in the discipline

(most recent update 24jan2018) (return to workshop front page)

Contributors: Pamela Marshall, Sally Wu


The instructor or instructional materials explicitly points out the attributes of distinctive clusters of visual features that recur frequently on visualizations within the field of study, and are considered by practitioners in the field to be meaningful or significant. We are going to call these meaningful, recurring clusters "patterns." Instruction aims to help students "see" domain-relevant relations/interactions/structures.

  • What constitutes "meaningful" varies from field to field. Two common sources of meaningfulness are causality and consequences.
  • There seem to be two main ways in which the meaningful patterns are "pointed out." The two approaches may be combined in instruction around the same concept or phenomenon.
    • Multiple separate cases or instances are shown.
    • A sketch or cartoon or concept-based-visualization is shown, perhaps with the characteristic features pointed out. Spatial language, gestures, and labels may be used to point out the features.


  • Patterns on geological maps: e.g. plunging syncline, fault, basin
  • Patterns on topographic maps: e.g. V-shaped stream-cut valleys versus U-shaped glacial-carved valleys
  • Patterns on medical images characteristic of specific diseases
  • In bioinformatics, students learn to look for a motif with a dataset (e.g. DNA sequence)
  • In algebra, students learn to recognize distinctive patterns in graphs (Kellman Massey & Son, 2010)

Affordances of this strategy/what it is good for:

  • This strategy helps establish the whole idea that looking for patterns is an important part of looking at data. Learners who came up through an NGSS-infused curriculum may already have this habit of mind from the NGSS Cross-Cutting Concept of "Patterns". But learners without this background may still think of maps and graphs as tools for looking up values, such as "What is the altitude of Denver?" and "What was the temperature at 3pm?"
  • Once the learner is easily able to recognize typical patterns, anomalies and outliers become more salient. Occurrence of normal patterns and occurrence of anomalies, can both be important in interpreting visualizations .
  • This strategy helps inculcate learners into a field or discipline, equipping them with the "professional vision" (Goodwin, 1994) that they will need as they move into a specialization.
  • This strategy strengthens learners' perceptual capabilities (e.g. to recognize boundaries, changes in color, contrast, objects).
  • This strategy may deepen learners' understanding of the world as comprising themes with variations, in which both the themes and the variations are important.
  • Once a suite of important patterns of a domain have been learned, instruction can build upon this knowledge to have learners tackle higher-order problems involving synthesis, evaluation, or analysis (e.g. combine medical imaging with other data for diagnosis in medicine, or use a geologic map to develop a geologic history.)

Potential pitfalls & challenges:

  • Assessment and diagnosis of learner's difficulties can be difficult; does a struggling learner not know the pattern, or not see what the instructor is seeing, or see what the instructor is seeing but not recognize it as a match to a known pattern?
  • Learners can misapply patterns or use patterns in the wrong context.
  • It can take a long time for learners to be able to use patterns effectively.
  • Learners (or experts) may perceive patterns that are not actually there (can see patterns in noise).

Emergent insights:

  • The idea behind this teaching strategy is that the visually-available world presents us with visual stimuli that resemble things we have seen before. To take advantage of this regularity in the world, our minds have evolved the ability to compare what we are seeing at this moment with what we will call a "library" of "patterns." In some domains of STEM education, this pattern-matching ability is leveraged with the instructional aim of helping students stock their "library" with "patterns" that have significance in the domain under study.
  • In addition to stocking one's library with domain-relevant patterns, the learner also needs to learn to retrieve the appropriate pattern when confronted with a novel stimulus (a new visualization in the domain for which the library is being stocked.) This is more difficult than it seems at first glance, because the novel stimulus is seldom or never an exact match with the pattern in the library. The learner needs to learn to judge when the match is close enough to justify declaring that a new instance of the pattern has been found.
  • Making this judgement well requires:
    • knowing which visual aspects of a pattern are essential, which tend to vary from case to case, and what is the range of common variation,
    • understanding how the visual aspects of a pattern are associated with the meaning or significance,
    • weighing the consequence of a false positive versus a false negative in declaring a match.
  • This aspect of learning about visualizations is a form of perceptual learning (Kellman & Massey, 2013). As such, it is closely allied with perceptual learning around stimuli that are NOT visualizations/representations, such as learning to identify kinds of leaves in botany or kinds of minerals in geology (Nosofsky et at. 2017). Research on perceptual learning has found that it is important to give learners practice with the full range of variation that experts would classify as falling with the pattern. There is a tendency to show novices only the good cases, whereas they need to see marginal cases as well.
  • Didactic instruction and rote learning can work for part of the teaching/learning sequence where the instructor is pointing out and explaining the different patterns and why they are important. However, for the part of the teaching/learning sequence where the learner is developing good judgment and skill at recognizing the pattern amid the visual complexity of the real world, an instructional design that involves cycles of learner tries with feedback will be required.

Researchable questions:

  • Do different types of patterns require different instructional approaches and supports, or can we find some approaches that are generalizable?
  • How do humans store their "library" of patterns? And if we better understood how humans store their library of patterns, how could we use that information to design instruction that adds new patterns to the library more efficiently and accurately?
  • How do humans tap into their mental library of patterns when it is time to see if a novel stimulus aligns with any already-known patterns? In particular, how does the human pattern-matching system cope with the (typical) situation where the novel stimulus to be evaluated for match/no-match has some similarities with the exemplar in the library but is not an exact match? If we better understood this process, how could we use that information to design instruction to help students hone their pattern-matching competency?
  • When done by experts, pattern matching can feel and appear to be instantaneous and effortless. The domain-accepted answer materializes in the expert's consciousness with no awareness that a sorting or matching process has occurred. Are there exercises that can be done in a professional development context to help instructors who are content domain experts become more aware of how they are doing pattern matching? And if so, how can this new-found awareness be translated into better instructional practices?
  • Does building a library of patterns and improving one's pattern-matching skill in one domain make it easier to do the same in another domain? (analogy: learning a 3rd language is easier after you have learned a 2nd language)
  • We know that humans vary in their ability to isolate exact pattern matches in complex visual fields, as tested by the Hidden Figures test or Embedded Figures test. Do these tests have any value in predicting who is likely to excel at vocations that require superior visual pattern-matching ability (perhaps this might include botanist, radiologist)?
  • How does the complexity of the to-be-learned pattern impact the dosage of instruction? (And how do we quantify "complexity" to be able to even address this question?)

References & Credits:

  • Atit, K., Weisberg, S. M., Newcombe, N. S., & Shipley, T. F. (2016). Learning to interpret topographic maps: Understanding layered spatial information. Cognitive Research: Principles and Implications, 1(2). doi: 10.1186/s41235-016-0002-y
  • Biederman, I., & Shiffrar, M. M. (1987). Sexing day-old chicks: a case study and expert systems analysis of a difficult perceptual learning task. Journal of Experimental Psychology: Learning, Memory and Cognition, 13, 640-645.
  • Goodwin, C. (1994). Professional Vision. American Anthropologist, 96, 606-633.
  • Kastens, K. A., Shipley, T. F., Boone, A., & Straccia, F. (2016). What geoscience experts and novices look at, and what they see, when viewing data visualizations. Journal of Astronomy & Earth Science Education, 3(1), 27-58.
  • Kellman, P. J. (2013). Adaptive and perceptual learning technologies in medical education and training. Military Medicine, 178(10 Suppl), 98–106.
  • Kellman, P. J., & Massey, C. M. (2013). Perceptual learning, cognition, and expertise. Psychology of Learning and Motivation, 58, 117-165.
  • Kellman, P. J., & Massey, C., & Son, J. (2010). Perceptual Learning Modules in Mathematics: Enhancing Students' Pattern Recognition, Structure Extraction, and Fluency. Topics in Cognitive Science, 2. 285 - 305.
  • Nosofsky, R.M., Sanders, C.A., Gerdom, A., Douglas, B.J., McDaniel, M.A. (2017). On Learning Natural-Science Categories That Violate the Family-Resemblance Principle, Psychological Science, Vol 28, Issue 1, pp. 104 - 114