Worked-example effect
Learning effect predicted by cognitive load theory / From Wikipedia, the free encyclopedia
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The worked-example effect is a learning effect predicted by cognitive load theory.[1][full citation needed] Specifically, it refers to improved learning observed when worked examples are used as part of instruction, compared to other instructional techniques such as problem-solving[2][page needed] and discovery learning.[3][full citation needed] According to Sweller: "The worked example effect is the best known and most widely studied of the cognitive load effects".[4]: 165
Worked examples improve learning by reducing cognitive load during skill acquisition, and "is one of the earliest and probably the best known cognitive load reducing technique".[5]: 3 In particular, worked examples provide instructions to reduce extraneous cognitive load and increase germane cognitive load for the learner initially when few schemas are available. Intrinsic cognitive load is a third type of cognitive load that provides a base load that is irreducible. Extraneous load is reduced by scaffolding of worked examples at the beginning of skill acquisition. Finally, worked examples can also increase germane load when prompts for self-explanations are used.[5]: 3
Renkl suggests that worked examples are best used in "sequences of faded examples for certain problem types in order to foster understanding in skill acquisition," and that prompts, help system, and/or training be used to facilitate the learners' self-explanations.[2][page needed] This view is supported by experimental findings comparing a faded worked-example procedure and a well-supported problem-solving approach.[6]
"However, it is important to note that studying [worked examples] loses its effectiveness with increasing expertise",[2][page needed] an effect known as the expertise reversal effect.[7]: 259 Further limitations of the classical worked-example method include "focusing on one single correct solution and on algorithmic skill domains".[2][page needed] Addressing such restrictions in multimedia learning environments remains an area of active research.[2]