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Is Google making knowledge obsolete?

Our CEO Andrew Smith Lewis recently spoke at SXSWedu about how having information in the palm of our hand is changing what, how, and why we learn. This is an important topic for those of us who work at Cerego - after all, we work every day to help people build knowledge and quantify what they know. In fact, as Andrew argued, knowledge might be more important than ever. Creativity, deep understanding and expertise rely on it. In this post, we’ll take a deeper dive into exactly how knowledge supports our learning, understanding, reasoning and creativity, and explore some of the scientific evidence behind the value of knowledge. Fair warning - it’s a topic we’re passionate about, so it’s a bit longer than our usual posts!

 Why the knowledge rich get richer

Advocates for education reform hold that developing the four C’s - creativity, critical thinking, collaboration and communication - are now more essential than developing core knowledge (Mishra & Mehta, 2017). The focus on learner proficiency in the four C’s is well-founded. In the last 3 decades the US has shifted from an industrial to a service economy, fueled by technology and innovation. In 2016, around 80% of the value in the S&P500 was represented by intangible, idea-based assets, such as brands, patents, software, and research (Council on Competitiveness, 2016). The workforce needed to work with these products and services consists of capable thinkers, communicators, and collaborators. Yet, the majority of them will also have a smartphone available for ‘just in time’ access to information, upward of around 92% of 18-29 year olds (Pew Research Center, 2017). This is where the question of what knowledge-building is worth comes in (we can Google most things after all). Can we outsource our knowledge and still realize the full potential of our minds? Has knowledge become obsolete?

In fact, the opposite is true. Far from being obsolete, knowledge is critical to cognition for at least three reasons:

  • Prior knowledge helps us take in new information
  • Prior knowledge allows us to think and reason more effectively
  • Prior knowledge leads us to retain more of what we learn

knowledge-quote-1@2x.png

As we’ll see, knowledge benefits every stage of the learning process: “as you first take in new information (either via listening or reading), as you think about this information, and as the material is stored in memory,” (Willingham, 2006). 

 

KNOWLEDGE HELPS Take in new information

Rather than being blank slates, developmental psychologists have long held that people construct understanding out of what they already know and believe (Piaget, 1952; Vygotsky, 1978). Jean Piaget used the term schema to refer to the conceptual building blocks that people use as a basis for cognition. Cognitive scientists describe comprehension in terms of an automatic retrieval process that brings in both episodic and semantic knowledge from our long-term memory (Cook & Gueraud, 2005). We retrieve much more than just facts from memory: our perception of new information is shaped by our prior knowledge, including our expectations and biases (Dror et al, 2005). This is referred to as top down processing. In short, our prior knowledge is always running in the background, and having more of it is a distinct advantage. 

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Adult education typically requires ‘reading to learn’ and listening to lectures. At a basic level, our knowledge allows us to read or listen with fluency (without interruptions to our comprehension). Perfetti and Hart (2002) argue that “skill in reading comprehension resets to a considerable extent on knowledge of words.” For example, Kaakinen and colleagues (2003) compared eye tracking of study participants as they read a text about four common diseases (e.g, the flu) and four less common diseases (e.g., typhus). They found that reading about unfamiliar diseases was slower, primarily because participants more frequently reread previous sentences than while reading about the familiar diseases.

Aside from the mastery of one’s native language, every discipline has its own terms and definitions that must be mastered in order to operate within it. The learning of scientific terms is often a target of criticism for encouraging the 'bulimic learning' of facts that are shallowly memorized out of any rich context (Zorek, Sprague, & Popovich, 2010). This is a valid criticism of poor instructional practice, yet ignores the fact that science vocabulary and concept knowledge are themselves critical to instruction (Harmon, Hendrick & Wood, 2005). As Kendeou and van den Broek (2007) point out, “there is a well-documented advantage in comprehension of [scientific] texts for readers with high knowledge ... over readers with low knowledge.” This type of foundational knowledge is not only critical to understanding, but also allows communication, collaboration, and creativity using a common language (that’s three of the four C's).

 

KNOWLEDGE allows US to THINK AND REASON more Effectively

The popular Bloom’s Taxonomy places remembering and understanding as foundational learning objectives, prerequisites that must be mastered sequentially in order for learners to achieve more complex levels of reasoning (Krathwohl, 2002). The learning science literature supports the notion that knowledge helps us think and problem solve more easily. So much so in fact, that it suggests that the brilliance of experts across fields may be largely due to their fluent access to domain knowledge, rather than a superior intellect.

Problem solving takes cognitive resources. Working memory is the term used to describe the set of processes that temporarily hold active memories or information “online” in our attention, so that it may be used in service of cognition (Cowan et al, 2005). This system is notoriously limited in capacity; 7 plus or minus 2 items being the widely known 'magic' number for what we may hold online at any given time (Miller, 1965). Without any prior knowledge, simply understanding a problem can consume most of our working memory capacity (Willingham, 2006). Internalized knowledge frees up our cognitive resources to make inferences, create strategies, and monitor our own approach to problem solving (use metacognition).

Chunking information into units of meaning is a way to expand the amount of information we can maintain. For example, a list of nine letters F-B-I-I-R-S-C-I-A may be more easily learned as three familiar chunks, FBI, IRS, and CIA (Halford, Cowan, & Andrews, 2007). People tend to chunk information with which they have deep familiarity, which may lead to a greater speed and capacity of information processing. “Experts are able to fluently access relevant knowledge because their understanding of subject matter allows them to quickly identify what is relevant. Hence, their attention is not overtaxed by complex events" (National Research Council, 2000).

Subject matter experts may filter new information through their existing knowledge, allowing them to spot patterns, relationships or discrepancies that a non-expert would never notice. Chi, Feltovich and Glaser (1981) had physics novices and experts group different physics problems into categories. They found that novices tended to sort problems by similarities in subject matter (simple machines for example), whereas experts sorted by the physical laws or method needed to solve the problem itself. This integration of problem-solving methods into the knowledge itself was further demonstrated when experts and novices were asked to construct a schema for an incline plane (Chi, Glaser, & Rees, 1982).

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In a pioneering study, DeGroot (1965) demonstrated the value of background knowledge when studying chess masters versus novices. All players were asked to think aloud as they were shown examples of board situations and asked what move they would make. DeGroot hypothesized that the masters would have better ability to think through possible moves and anticipate countermoves. Surprisingly, it was found that non-experts were equivalently thoughtful when making moves. DeGroot concluded that the knowledge that the chess masters had acquired had made them better able to recognize strategic implications of each move. In other words, they didn’t think more effectively, they just thought of higher quality options due to their deeper knowledge.

The National Research Council (2000) summarized several key principles of experts knowledge. Among them are that:

  • Experts notice features and meaningful patterns of information that are not noticed by novices
  • Experts have acquired a great deal of content knowledge that is organized in ways that reflect a deep understanding of their subject matter
  • Experts are able to flexibly retrieve important aspects of their knowledge with little attentional effort.

If it were possible to outsource all domain knowledge to Google, we would expect that to come at a cost to both the speed and quality of our thinking, as well as our potential to become masters of a field.

 

Knowledge Leads us to retain more of what we learn

Simply put, it is easier to remember new information when it's related to a familiar topic. Learning occurs more readily when we can attach new information to an existing knowledge framework, or schema.

For example, Beier and Ackerman (2005) found that domain knowledge of health topics significantly predicted retention of both lecture and homework content in a sample of adults. Hambrick (2003) found that the ability to remember basketball news was directly related to prior basketball knowledge. Anderston (1981) reported that individuals were more likely to remember new facts about famous people than unknown people. Some researchers believe that prior knowledge is so essential to learning that it accounts for much of what we commonly attribute to individual aptitude (Willingham, 2006).

Dochy (1994) summarizes why prior knowledge aids in new learning:

  • Relevant schemas, or knowledge structures in long-term memory are already available for new learning to to fit into.
  • The elaboration of existing memories leads to multiple retrieval pathways in the cognitive representation
  • Our attention is directed selectively at familiar topics, leading to deeper processing.
  • The load on working memory is reduced by prior knowledge, allowing new information to be processed more efficiently (encoding effort is reduced).

Prior knowledge may also influence learning outcomes by increasing motivation for learning. There is evidence that readers with greater background knowledge adopt better strategies for reading and express more interest in their topic (McNamara & Kintsch, 1996). Armed with a foundation, it becomes easier, more engaging, and more satisfying to deepen our knowledge on an existing topic than to begin anew.

 

KNOWLEDGE IN A CHANGING WORLD

The importance of achieving fluency over the core knowledge and skills of one’s career discipline has not diminished in our technologically connected society. Kereluik and colleagues (2013) reviewed the value of knowledge in the 21st Century, concluding that everything has changed and nothing has changed. “The world of the future will continue to depend on specialized knowledge (or domain knowledge), and high-level cognitive skills (such as creativity and critical thinking). These skills ... are required for successful learning and achievement in any time.” Innovation will come through people that have deep knowledge of more than one discipline, and are able to form new connections between the two (cross-disciplinary knowledge). The need to keep knowledge in our heads remains just as vital as it ever was, even though the types of knowledge 'worth knowing' may vary across contexts.

As Mishra and Mehta (2017) point out, ‘content neutral’ creative thinking is a myth. “What is ironic is that those who are emphasizing the four C’s (creativity, collaboration, critical thinking, communication - or what we are calling meta-knowledge) do not realize that being successful in each of these requires disciplinary and cross-disciplinary knowledge. It is a mistake to think that creativity or collaboration or communication can happen in a vacuum. What will one be creative about?

And we agree: The question for the future of education isn’t whether we should build knowledge - it’s how.

_________

References

Anderson, J.R. (1981). Effects of prior knowledge on memory for new information. Memory & Cognition, 9, 237-246.

Beier, M. E., & Ackerman, P. L. (2005). Age, ability, and the role of prior knowledge on the acquisition of new domain knowledge: promising results in a real-world learning environment. Psychology and aging, 20(2), 341.

Chi, M.T.H., Feltovich, P.J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121-152.

Chi, M.T.H., Glaser, R., & Rees, E. (1982). Expertise in problem solving. In R.J. Sternberg (Ed.), Advances in the psychology of human intelligence, Vol. 1, (pp. 7 - 35). Hillsdale, NJ: Erlbaum.

Cook, A. E., & Guéraud, S. (2005). What have we been missing? The role of general world knowledge in discourse processing. Discourse Processes, 39(2-3), 265-278.

Council on Competitiveness (2016).  Work: Thriving in a turbulent, technological and transformed global economy.  Washington, D.C.

Cowan, N., Elliot, E. M., Saults, J. S., Morey, C. C., Mattox, S., Hismjatullina, A., et al. (2005). On the capacity of attention: Its estimation and its role in working memory and cognitive aptitudes. Cognitive Psychology, 51, 42-100.

DeGroot. A.D. (1965). Thought and choice in chess. Mouton, The Hague.

Dochy, F. J. R. C. (1994). Prior knowledge and learning. In T. Husen, & T. N. Postlethwaite (Eds.), The International Encyclopedia of Education (2nd ed.) (pp. 4698–4702). Oxford / New York: Pergamon Press.

Dror, I. E., Peron, A. E., Hind, S. L., & Charlton, D. (2005). When emotions get the better of us: the effect of contextual top‐down processing on matching fingerprints. Applied Cognitive Psychology, 19(6), 799-809.

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Harmon, J. M., Hedrick, W. B., & Wood, K. D. (2005). Research on vocabulary instruction in the content areas: Implications for struggling readers. Reading & Writing Quarterly, 21(3), 261-280.

Kaakinen, J. K., Hyönä, J., & Keenan, J. M. (2003). How prior knowledge, WMC, and relevance of information affect eye fixations in expository text. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29(3), 447.

Kereluik, K., Mishra, P., Fahnoe, C., & Terry, L. (2013). What knowledge is of most worth: Teacher knowledge for 21st century learning. Journal of Digital Learning in Teacher Education, 29(4), 127-140.

Krathwohl, D.R. (2002). A revision of Bloom’s Taxonomy: An overview. Theory into Practice, 41 (4), 212-218.

McNamara, D. S., & Kintsch, W. (1996). Learning from texts: Effects of prior knowledge and text coherence. Discourse processes, 22(3), 247-288.

Miller, G.A. (1965). The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review, 63 (2), 81-97.

Mishra, P., & Mehta, R. (2017). What We Educators Get Wrong About 21st-Century Learning: Results of a Survey. Journal of Digital Learning in Teacher Education, 33(1), 6-19.

National Research Council. (2000). How people learn: Brain, mind, experience, and school: Expanded edition. National Academies Press.

Perfetti, C. A., & Hart, L. (2002). The lexical quality hypothesis. Precursors of Functional Literacy, 11, 67-86.

Pew Research Center (2017, January 12). Mobile fact sheet. Retrieved from http://www.pewinternet.org/fact-sheet/mobile/

Piaget, J. (1952). The origins of intelligence in children (Vol. 8, No. 5). New York: International Universities Press.

Vygotsky, L. S. (1978). Mind in society: The development of higher mental process. Cambridge, MA: Harvard University Press.

Willingham, D. T. (2006). How knowledge helps: It speeds and strengthens reading comprehension, learning-and thinking. American Educator, 30(1), 30.

Zorek, J. A., Sprague, J. E., & Popovich, N. G. (2010). Bulimic learning. American Journal of Pharmaceutical Education, 74(8), 157.

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