Recent research shows that prior knowledge, or what you already know, is a key factor in performance, including decision making. Recent findings by decision scientists Saurabh Bhargava, George Loewenstein, and Justin Sydnor, for example, illustrate the difficulty of making good healthcare plan decisions without adequate and accurate prior knowledge. A majority of the people in the study chose plans that cost more (in many cases, a lot more) than they needed to. In other words, they could have paid less for the same coverage. This happened not only the first year of implementation, but the following year as well. A lack of prior knowledge needed to analyze the plans led to poor results.
Prior knowledge means what we know, stored in memory. When people have inadequate or incorrect prior knowledge, it is difficult to learn, understand, or make decisions. This is the problem we saw in the health plan research.
In this article, I’m discussing how prior knowledge is a part of our memory system, how it impacts performance, and the implications for learning practitioners.
We can talk about memory at three levels: Sensory memory very briefly holds sensory information, such as what we see and hear. Working memory then processes what we deem as important. And long-term memory stores what we know (prior knowledge).
Figure 1: Three levels of memory
Prior knowledge affects what we take in from the environment in sensory memory and how we give meaning to what we are processing. People who know more (have more prior knowledge) find it easier to learn because what they know helps them understand new information. An experienced optician, for example, knows how different eyeglass lens materials work for different prescriptions. When new lens materials are available, she can add the new lens material to her understanding and readily apply this knowledge when filling new glasses prescriptions.
Complete, well-organized, and accurate prior knowledge helps us:
- Select what is important
- Understand new information
- Make connections between what we are learning and what we know
- Solve problems
When we know very little, we miss things that are important; we process new information in a vacuum and cannot make connections. Therefore, building complete, accurate, and usable prior knowledge is one of the most important outcomes of learning, as it’s what we use to do our work, solve problems, and continue to learn.
Prior knowledge is stored in long-term memory in schemas, which are organized knowledge structures. Schemas organize information with related information (Figure 2). For example, you may use a specific process to buy a car. That’s your car-buying schema. What you know about how to build instruction is stored in schemas so you can recall needed information and use it to do tasks and solve problems.
Figure 2: Long-term memory and schema (from Shank, P. (2018). Manage Memory for Deeper Learning, Amazon)
Schemas aren’t fixed. They can be—and often are—changed, added to, rearranged, and the like, as a result of learning. When you learn something new about how to build instruction, for example, it can add to your existing schema or change it. One example of radically changing existing schema is when learning practitioners realize that creating learning to meet the needs of various learning styles should be removed from their practice. Something similar happened to my grocery shopping schema when I changed my diet to lower my blood sugar. No more excursions though any of the snack aisles.
Figure 3 shows multiple components of prior knowledge. Prior knowledge components toward the left side of Figure 3 are a necessary foundation but not enough for application. As we add knowledge components toward the right side, application becomes more possible.
Figure 3: Components of prior knowledge with examples (adapted from Hailikari, T., Katajavuori, N., & Lindblom-Ylanne, S., 2008, from Shank, P. (2018). Manage Memory for Deeper Learning).
Many widely discussed research-based learning tactics are about the need to design for prior knowledge. For example, we recommend different learning strategies for those with less prior knowledge and those with more prior knowledge. To teach problem-solving, for example, we use examples for people with less knowledge of the topic and problems for people with more. We recommend practice remembering to make prior knowledge easier to retrieve from schemas.
Of course, people start learning to do a job with limited knowledge and that’s expected. What we know about the importance of prior knowledge, however, makes it obvious that we not only need to provide foundational knowledge to those with less, but also increase the knowledge of those who know more. The benefits of expertise are largely a result of having a flexible, accurate, and deep knowledge base.
Explore memory and learning
Schemas and prior learning are not the only ways to use memory as an ally in designing learning experiences. Register for The eLearning Guild’s Science of Learning Summit, an online conference taking place May 15–16, 2019. Patti Shank will present “Designing for Memory.” Other learning leaders will dig into learning experience design, cognitive load, and affective learning, and explore current research pertaining to learning.
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