Expertise reversal effect
[1][2] The primary recommendation that stems from the expertise reversal effect is that instructional design methods need to be adjusted as learners acquire more knowledge in a specific domain.
"[2] Instructional techniques that assist learners to create long term memory schema are more effective for novices or low-knowledge individuals, who approach a learning situation or task without these knowledge structures to rely on.
[1][3] Slava Kalyuga, one of the leading researchers in this area, writes, "instructional guidance, which may be essential for novices, may have negative consequences for more experienced learners.
[6] The goal of any learning task is to construct integrated mental representations of the relevant information, which requires considerable working memory resources.
Low-knowledge learners lack schema-based knowledge in the target domain and so this guidance comes from instructional supports, which help reduce the cognitive load associated with novel tasks.
If the instruction fails to provide guidance, low-knowledge learners often resort to inefficient problem-solving strategies that overwhelm working memory and increase cognitive load.
[13][14] Second, expertise reversal effects have been found in studies outside of the cognitive load paradigm, indicating that alternative explanations remain viable.
[3] Interactions between levels of knowledge and the worked-example effect: Worked examples provide a problem statement followed by a step-by-step demonstration of how to solve it.
By creating breaks in the instructional material (for example, dividing animations into several videos), segmentation reduces cognitive load by giving the learner time to process and reflect on the information.
[21] Studies addressing the expertise reversal effect have found worked examples, particularly those which "tailor fading of worked examples to individual students' growing expertise levels",[22] to be effective in improving learning results (Atkinson et al. 2003; Renkl et al. 2002, 2004; Renkl and Atkinson 2007).
The advent of intelligent instructional software such as Cognitive Tutor, which can trace student learning and assess knowledge acquisition, provides a platform within which adaptive fading can be applied.
