Some Differences Between Experts and Novices

As learning professionals, it is important that we understand that novice learners and expert learners demand/require and need a different approach to instruction. You cannot develop learning events for novices using the same instructional strategies as you do for someone who has a high level of knowledge in a content area.

In fact, Ruth Clark points to research that indicates that”many of the instructional methods that are effective for novices either have no effect or, in some cases, depress the learning of learners with more expertise.” She goes on to say that “Training designed for learners with greater prior knowledge requires different instruction methods than training designed for novice learners.” [1]

So, let’s look at some of the differences..

In comparing the knowledge structure of experts with the knowledge structure of novices, differences have been observed in both the nature of their knowledge and their problem-solving strategies. For experts, the knowledge structure represents phenomena in the domain in relation to higher-order principles. In other words, experts represent problems at deep structural levels in terms of basic principles within a domain; novices represent problems in terms of surface or superficial characteristics.

For example, in physics, an expert arranges knowledge around higher-order principles like Newton’s laws of force while a novice organizes knowledge around the behavior of individual objects on inclined planes.[2] A novice learning professional focuses on determining which delivery method is best for instruction while an expert considers whether or not the situation describe by the manager can actually be solved by a formal learning intervention. A novice salesperson focuses on “making the sale” an expert develops a relationship with the client.

Additionally, in the minds of experts, knowledge is organized in the form of a problem schema that includes procedures for solving relevant problems and content knowledge. This means that when an expert views a problem, they are able to see both the problem set and possible procedures for solving the problem, a novice views domain knowledge and problem solving knowledge separately.

So when confronted with a problem, the expert applies her knowledge problem schema by working forward from the information given to solve the problem. They tend to work from the known to the unknown. The expert has solved many similar problems and recalls schemas easily. The novice, on the other hand, tends to work backwards. They begin with the unknown in the problem and try to use trial and error or incomplete schemas to solve it.

Another difference is that novices often have inefficient use of short-term and long-term memory because knowledge is not stored in a single bundle or schema but, rather, is spread out in different related but not grouped domain areas. An expert is more efficient in searching her memory because large portions of content are “bundled” or “chunked” for easy retrieval.

In a pattern recognition task with Chess, novices and experts are asked to recall the placement of pieces on a board which is shown to them in a mid-game configuration, the Chess master has access to over 50,000 configurations of Chess pieces on a board and remembers the mid-game position of pieces within the context of one of those configurations (they match the pattern to an existing pattern they already know).[3]

A novice attempts to memorize and hold in short term memory the position of each individual piece and eventually this is unsuccessful because of the number of pieces that need to be memorized. In other words, the expert groks the Chess board and pieces, the novice tries to memorize.

Finally, novices tend to lack awareness of errors and omissions and the need to continually check solutions and assumptions. Experts, use strong self-monitoring skills that include testing and fine-tuning solutions and challenging assumptions.[4]

This content is based on the book The Gamification of Learning and Instruction: Game-based Methods and Strategies for Training and Education


[1] Clark, R., Nguyen, F. & Sweller, J. (2006) Efficiency in Learning: Evidence-based guidelines to manage cognitive load. Pfeiffer. Page 247. Chapter 7 and 7 of “The Gamification of Learning and Instruction.

[2] Gredler, M. E. (1997) Learning and Instruction: Theory into Practice. 3rd Ed. Prentice Hall: Upper Saddle River, NJ.

[3] Gredler, M. E. (1997) Learning and Instruction: Theory into Practice. 3rd Ed. Prentice Hall: Upper Saddle River, NJ.

[4] Gredler, M. E. (1997) Learning and Instruction: Theory into Practice. 3rd Ed. Prentice Hall: Upper Saddle River, NJ.

Posted in: Design, Education

Leave a Comment (0) ↓
Karl Kapp
  • About
  • Contact