Rey, Günter Daniel; Buchwald, Florian:
The expertise reversal effect: Cognitive load and motivational explanations
In: Journal of Experimental Psychology: Applied, Jg. 17 (2011), Heft 1, S. 33 - 48
2011Artikel/Aufsatz in Zeitschrift
The expertise reversal effect: Cognitive load and motivational explanations
Rey, Günter Daniel; Buchwald, FlorianLSF


The expertise reversal effect occurs when a learner's expertise moderates design principles such as the redundancy principle (i.e., redundant information should be excluded rather than included) derived from the cognitive load theory. Although this effect is supported by numerous experiments, indicating an overall large effect size, a variety of explanations have been proposed. The present experiment tested a cognitive load and a motivational explanation with 104 students, who reported a lack of experience in the presented instructional contents. They spent about 30 min with the instructional material to learn fundamental concepts about the gradient descent (a mathematical optimization algorithm), and with a retention and transfer test used as dependent measures. Each learner was randomly assigned to one cell of a 2 (either novices or experts introduced to through the instructional design presented previously) × 2 (either with or without additional text explaining the animations) between-subjects factorial design. The expertise reversal effect concerning the redundancy principle was replicated. Novices receiving additional text scored higher on retention and transfer than did novices without additional text, while this result was reversed for experts. Results suggest that this effect can be explained by the learner's cognitive load differences rather than overall motivation differences. Furthermore, a partial overlap was found between the motivational subdimension, “probability of success,” and a cognitive load measure. On the practical side, instructional designers should consider the learner's level of expertise and their cognitive load when applying design principles. Further implications for adaptive learning environments are discussed.